1
|
Chen Z, Zeng Y, Ma P, Xu Q, Zeng L, Song X, Yu F. Integrated GMPS and RAMP3 as a signature to predict prognosis and immune heterogeneity in hepatocellular carcinoma. Gene 2024:148958. [PMID: 39312983 DOI: 10.1016/j.gene.2024.148958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Revised: 06/14/2024] [Accepted: 09/18/2024] [Indexed: 09/25/2024]
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is a highly fatal malignant worldwide. As different expression levels of specific genes can lead to different HCC outcomes, we aimed to develop a gene signature capable of predicting HCC prognosis. METHODS In this study, transcriptomic sequencing and relevant clinical data were extracted from public platforms. The guanine monophosphate synthase (GMPS)|receptor activity-modifying protein 3 (RAMP3) gene pair was developed based on the relative values of gene expression levels. Nomograms were developed using R software. Immune status was assessed through single-sample gene set enrichment analysis. GMPS knockdown was achieved through siRNA transfection. Quantitative reverse transcription PCR, apoptosis assays, and cell proliferation were performed to verify the function of GMPS|RAMP3 in HCC cells. RESULTS Here, a gene pair containing GMPS and RAMP3 was successfully constructed. We demonstrated that the GMPS|RAMP3 gene pair was an independent predictor with strong prognostic prediction power, based on which a nomogram was established. Functional analysis revealed that the enrichment of cell cycle-related pathways and immune status differed considerably between the two groups, with cell cycle-related genes highly expressed in the high GMPS|RAMP3 value group. Finally, cell experiments indicated that GMPS knockdown significantly repressed proliferation, promoted apoptosis, and enhanced the sensitivity of HCC cells to gemcitabine. CONCLUSIONS The gene pair GMPS|RAMP3 is a novel prognostic predictor of HCC, providing a promising approach to the treatment and assessment of immune heterogeneity in HCC.
Collapse
Affiliation(s)
- Zhuoyan Chen
- Department of Gastroenterology, Dongyang People's Hospital, 60 Wuningxi Road, Jinhua, China
| | - Yuan Zeng
- Department of Gastroenterology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Peipei Ma
- Department of Gastroenterology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Qian Xu
- Department of Gastroenterology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Liuwei Zeng
- Department of Gastroenterology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Xian Song
- Department of Gastroenterology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Fujun Yu
- Department of Gastroenterology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
| |
Collapse
|
2
|
Sarkar S, Saha SA, Swarnakar A, Chakrabarty A, Dey A, Sarkar P, Banerjee S, Mitra P. The molecular prognostic score, a classifier for risk stratification of high-grade serous ovarian cancer. J Ovarian Res 2024; 17:159. [PMID: 39095849 PMCID: PMC11296390 DOI: 10.1186/s13048-024-01482-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Accepted: 07/19/2024] [Indexed: 08/04/2024] Open
Abstract
BACKGROUND The clinicopathological parameters such as residual tumor, grade, the International Federation of Gynecology and Obstetrics (FIGO) score are often used to predict the survival of ovarian cancer patients, but the 5-year survival of high grade serous ovarian cancer (HGSOC) still remains around 30%. Hence, the relentless pursuit of enhanced prognostic tools for HGSOC, this study introduces an unprecedented gene expression-based molecular prognostic score (mPS). Derived from a novel 20-gene signature through Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression, the mPS stands out for its predictive prowess. RESULTS Validation across diverse datasets, including training and test sets (n = 491 each) and a large HGSOC patient cohort from the Ovarian Tumor Tissue Analysis (OTTA) consortium (n = 7542), consistently shows an area-under-curve (AUC) around 0.7 for predicting 5-year overall survival. The mPS's impact on prognosis resonates profoundly, yielding an adjusted hazard-ratio (HR) of 6.1 (95% CI: 3.65-10.3; p < 0.001), overshadowing conventional parameters-FIGO score, residual disease, and age. Molecular insights gleaned from mPS stratification uncover intriguing pathways, with focal-adhesion, Wnt, and Notch signaling upregulated, and antigen processing and presentation downregulated (p < 0.001) in high-risk HGSOC cohorts. CONCLUSION Positioned as a robust prognostic marker, the 20-gene signature-derived mPS emerges as a potential game-changer in clinical settings. Beyond its role in predicting overall survival, its implications extend to guiding alternative therapies, especially targeting Wnt/Notch signaling pathways and immune evasion-a promising avenue for improving outcomes in high-risk HGSOC patients.
Collapse
Affiliation(s)
- Siddik Sarkar
- Cancer Biology & Inflammatory Disorder, Translational Research Unit of Excellence (TRUE), CSIR-Indian Institute of Chemical Biology, Kolkata, WB, 700032, India.
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, UP, 201002, India.
| | - Sarbar Ali Saha
- Cancer Biology & Inflammatory Disorder, Translational Research Unit of Excellence (TRUE), CSIR-Indian Institute of Chemical Biology, Kolkata, WB, 700032, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, UP, 201002, India
| | - Abhishek Swarnakar
- Cancer Biology & Inflammatory Disorder, Translational Research Unit of Excellence (TRUE), CSIR-Indian Institute of Chemical Biology, Kolkata, WB, 700032, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, UP, 201002, India
| | - Arnab Chakrabarty
- Cancer Biology & Inflammatory Disorder, Translational Research Unit of Excellence (TRUE), CSIR-Indian Institute of Chemical Biology, Kolkata, WB, 700032, India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, UP, 201002, India
| | - Avipsa Dey
- Cancer Biology & Inflammatory Disorder, Translational Research Unit of Excellence (TRUE), CSIR-Indian Institute of Chemical Biology, Kolkata, WB, 700032, India
| | - Poulomi Sarkar
- Cancer Biology & Inflammatory Disorder, Translational Research Unit of Excellence (TRUE), CSIR-Indian Institute of Chemical Biology, Kolkata, WB, 700032, India
| | - Sarthak Banerjee
- Cancer Biology & Inflammatory Disorder, Translational Research Unit of Excellence (TRUE), CSIR-Indian Institute of Chemical Biology, Kolkata, WB, 700032, India
| | - Pralay Mitra
- Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, WB, 721302, India
| |
Collapse
|
3
|
Liao LS, Xiao ZJ, Wang JL, Liu TJ, Huang FD, Zhong YP, Zhang X, Chen KH, Du RL, Dong MY. A Four Amino Acid Metabolism-Associated Genes (AMGs) Signature for Predicting Overall Survival Outcomes and Immunotherapeutic Efficacy in Hepatocellular Carcinoma. Biochem Genet 2024; 62:1577-1602. [PMID: 37658254 DOI: 10.1007/s10528-023-10502-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 08/10/2023] [Indexed: 09/03/2023]
Abstract
Metabolites are important indicators of cancer and mutations in genes involved in amino acid metabolism may influence tumorigenesis. Immunotherapy is an effective cancer treatment option; however, its relationship with amino acid metabolism has not been reported. In this study, RNA-seq data for 371 liver cancer patients were acquired from TCGA and used as the training set. Data for 231 liver cancer patients were obtained from ICGC and used as the validation set to establish a gene signature for predicting liver cancer overall survival outcomes and immunotherapeutic responses. Four reliable groups based on 132 amino acid metabolism-related DEGs were obtained by consistent clustering of 371 HCC patients and a four-gene signature for prediction of liver cancer survival outcomes was developed. Our data show that in different clinical groups, the overall survival outcomes in the high-risk group were markedly low relative to the low-risk group. Univariate and multivariate analyses revealed that the characteristics of the 4-gene signature were independent prognostic factors for liver cancer. The ROC curve revealed that the risk characteristic is an efficient predictor for 1-, 2-, and 3-year HCC survival outcomes. The GSVA and KEGG pathway analyses revealed that high-risk score tumors were associated with all aspects of the degree of malignancy in liver cancer. There were more mutant genes and greater immune infiltrations in the high-risk groups. Assessment of the three immunotherapeutic cohorts established that low-risk score patients significantly benefited from immunotherapy. Then, we established a prognostic nomogram based on the TCGA cohort. In conclusion, the 4-gene signature is a reliable diagnostic marker and predictor for immunotherapeutic efficacy.
Collapse
Affiliation(s)
- Lu-Sheng Liao
- The Key Laboratory of Molecular Pathology (For Hepatobiliary Diseases) of Guangxi, Affiliated Hospital of Youjiang Medical University for Nationalities, No. 98, Chengxiang Road, Youjiang District, Baise, 533000, Guangxi, China
- School of Medical Laboratory Medicine, Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China
- Modern Industrial College of Biomedicine and Great Health, Youjiang Medical University for Nationalities, Baise, China
| | - Zi-Jun Xiao
- Modern Industrial College of Biomedicine and Great Health, Youjiang Medical University for Nationalities, Baise, China
| | - Jun-Li Wang
- Department of Reproductive Medicine, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China
| | - Ting-Jun Liu
- Modern Industrial College of Biomedicine and Great Health, Youjiang Medical University for Nationalities, Baise, China
| | - Feng-Die Huang
- Modern Industrial College of Biomedicine and Great Health, Youjiang Medical University for Nationalities, Baise, China
| | - Yan-Ping Zhong
- Modern Industrial College of Biomedicine and Great Health, Youjiang Medical University for Nationalities, Baise, China
| | - Xin Zhang
- Modern Industrial College of Biomedicine and Great Health, Youjiang Medical University for Nationalities, Baise, China
| | - Ke-Heng Chen
- Modern Industrial College of Biomedicine and Great Health, Youjiang Medical University for Nationalities, Baise, China
| | - Run-Lei Du
- Hubei Key Laboratory of Cell Homeostasis, College of Life Sciences, Wuhan University, Wuhan, 430072, Hubei, China.
| | - Ming-You Dong
- The Key Laboratory of Molecular Pathology (For Hepatobiliary Diseases) of Guangxi, Affiliated Hospital of Youjiang Medical University for Nationalities, No. 98, Chengxiang Road, Youjiang District, Baise, 533000, Guangxi, China.
- School of Medical Laboratory Medicine, Youjiang Medical University for Nationalities, Baise, 533000, Guangxi, China.
- Modern Industrial College of Biomedicine and Great Health, Youjiang Medical University for Nationalities, Baise, China.
| |
Collapse
|
4
|
Wang X, Cui X, Wang W, Sun J, Wang Y, Han W, Xie X, Zhu Z, Zhang X, Yu L, Liu D. Deciphering essential druggable genes reveals potential immune-inflammatory axis in hepatocellular carcinoma. Comput Biol Med 2023; 167:107625. [PMID: 37918266 DOI: 10.1016/j.compbiomed.2023.107625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 09/30/2023] [Accepted: 10/23/2023] [Indexed: 11/04/2023]
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) is a malignant tumor with a high mortality rate and poor prognosis in patients. Its pathogenesis is a complex process of multi-factors and multi-steps. However, the etiology and exact molecular mechanism are not completely clear. METHODS Here, we constructed a specific-expressed network based on RNA sequencing data. Gene and miRNA expression profiles and clinical evidence were integrated to detect hepatocellular carcinoma survival modules. Finally, we attempted to identify potential key biomarkers and drug targets by integrating drug sensitivity analysis and immune infiltration analysis. RESULTS A total of 42 prognostic modules for hepatocellular carcinoma were detected. The prognostic modules were significantly enriched with known cancer-related molecules and 12.93 % molecules of prognostic modules had been found were the targets of small molecule drug. In addition, we found that 38 of 42 (90.48 %) essential genes were associated with the proportions of at least one of the 7 immune cell types. CONCLUSION We integrated clinical prognosis information, RNA sequencing data, and drug activity data to explore risk modules of hepatocellular carcinoma. Through drug sensitivity analysis and immune infiltration analysis, we assessed the value of hub genes in the modules as potential biomarkers and drug targets for hepatocellular carcinoma. The protocol provides new insight into parsing the molecular mechanism and theoretical basis of hepatocellular carcinoma.
Collapse
Affiliation(s)
- Xiaoren Wang
- Department of Infectious Disease, The Second Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Xudong Cui
- Department of Infectious Disease, The Fourth Hospital of Harbin Medical University, Harbin, China; NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, China
| | - Wencan Wang
- Guangzhou National Laboratory, Guangzhou, China
| | - Jia Sun
- Department of Infectious Disease, The Fourth Hospital of Harbin Medical University, Harbin, China; NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, China
| | - Yan Wang
- Department of Infectious Disease, The Fourth Hospital of Harbin Medical University, Harbin, China; NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, China
| | - Wanru Han
- Department of Infectious Disease, The Fourth Hospital of Harbin Medical University, Harbin, China; NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, China
| | - Xiaotong Xie
- Department of Infectious Disease, The Fourth Hospital of Harbin Medical University, Harbin, China; NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, China
| | - Zhu Zhu
- Department of Infectious Disease, The Fourth Hospital of Harbin Medical University, Harbin, China; NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, China
| | - Xijun Zhang
- E.N.T. Department, The Fourth Hospital of Harbin Medical University, Harbin, China
| | - Lei Yu
- Department of Infectious Disease, The Fourth Hospital of Harbin Medical University, Harbin, China; NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, China.
| | - Dabin Liu
- Department of Infectious Disease, The Fourth Hospital of Harbin Medical University, Harbin, China; NHC Key Laboratory of Molecular Probes and Targeted Diagnosis and Therapy, Harbin Medical University, Harbin, China.
| |
Collapse
|
5
|
Yu X, Chen P, Yi W, Ruan W, Xiong X. Identification of cell senescence molecular subtypes in prediction of the prognosis and immunotherapy of hepatitis B virus-related hepatocellular carcinoma. Front Immunol 2022; 13:1029872. [PMID: 36275676 PMCID: PMC9582940 DOI: 10.3389/fimmu.2022.1029872] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 09/20/2022] [Indexed: 01/10/2023] Open
Abstract
Hepatitis B virus (HBV)-infected hepatocellular carcinoma (HCC) has a high incidence and fatality rate worldwide, being among the most prevalent cancers. The growing body of data indicating cellular senescence (CS) to be a critical factor in hepatocarcinogenesis. The predictive value of CS in HBV-related HCC and its role in the immune microenvironment are unknown. To determine the cellular senescence profile of HBV-related HCC and its role in shaping the immune microenvironment, this study employed a rigorous evaluation of multiple datasets encompassing 793 HBV-related HCC samples. Two novel distinct CS subtypes were first identified by nonnegative matrix factorization, and we found that the senescence-activated subgroup had the worst prognosis and correlated with cancer progression. C1 and C2 were identified as the senescence-suppressed and senescence-activated subgroups. The immune microenvironment indicated that C2 exhibited a relatively low immune status, higher tumor purity, and lower immune scores and estimated scores, while the C1 subgroup possessed a better prognosis. The CS score signature based on five genes (CENPA, EZH2, G6PD, HDAC1, and PRPF19) was established using univariate Cox regression and the lasso method. ICGC-LIRI and GSE14520 cohorts were used to validate the reliability of the CS scoring system. In addition, we examined the association between the risk score and hallmark pathways through gene set variation analysis and gene set enrichment analysis. The results revealed a high CS score to be associated with the activation of cell senescence-related pathways. The CS score and other clinical features were combined to generate a CS dynamic nomogram with a better predictive capacity for OS at 1, 2, and 3 years than other clinical parameters. Our study demonstrated that cellular senescence patterns play a non-negligible role in shaping the characteristics of the immune microenvironment and profoundly affecting tumor prognosis. The results of this study will help predict patient prognosis more accurately and may assist in development of personalized immunotherapy for HBV-related HCC patients.
Collapse
Affiliation(s)
- Xue Yu
- School of Medicine, Jianghan University, Wuhan, China
- Department of Integrated Chinese and Western Medicine, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China
- *Correspondence: Xiaoli Xiong,
| | - Peng Chen
- Department of Respiratory Medicine, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China
- *Correspondence: Xiaoli Xiong,
| | - Wei Yi
- Department of Integrated Chinese and Western Medicine, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China
| | - Wen Ruan
- School of Medicine, Jianghan University, Wuhan, China
| | - Xiaoli Xiong
- Department of Integrated Chinese and Western Medicine, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China
- *Correspondence: Xiaoli Xiong,
| |
Collapse
|
6
|
The Systematic Analyses of RING Finger Gene Signature for Predicting the Prognosis of Patients with Hepatocellular Carcinoma. JOURNAL OF ONCOLOGY 2022; 2022:2466006. [PMID: 36199791 PMCID: PMC9529411 DOI: 10.1155/2022/2466006] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 09/08/2022] [Indexed: 12/24/2022]
Abstract
RING finger (RNF) proteins are frequently dysregulated in human malignancies and are tightly associated with tumorigenesis. However, the expression profiles of RNF genes in hepatocellular carcinoma (HCC) and their relations with prognosis remain undetermined. Here, we aimed at constructing a prognostic model according to RNF genes for forecasting the outcomes of HCC patients using the data from The Cancer Genome Atlas (TCGA) program. We collected HCC datasets to validate the values of our model in predicting prognosis of HCC patients from International Cancer Genome Consortium (ICGC) platform. Then, functional experiments were carried out to explore the roles of the representative RNF in HCC progression. A total of 107 differentially expressed RNFs were obtained between TCGA-HCC tumor and normal tissues. After comprehensive evaluation, a prognostic signature composed of 11 RNFs (RNF220, RNF25, TRIM25, BMI1, RNF216P1, RNF115, RNF2, TRAIP, RNF157, RNF145, and RNF19B) was constructed based on TCGA cohort. Then, the Kaplan-Meier (KM) curves and the receiver operating characteristic curve (ROC) were employed to evaluate predictive power of the prognostic model in testing cohort (TCGA) and validation cohort (ICGC). The KM and ROC curves illustrated the good predictive power in testing and validation cohort. The areas under the ROC curve are 0.77 and 0.76 in these two cohorts, respectively. Among the prognostic signature genes, BMI1 was selected as a representative for functional study. We found that BMI1 protein level was significantly upregulated in HCC tissues. Moreover, the inhibitor of BMI1, PTC-209, displayed an excellent anti-HCC effect in vitro. Enrichment analysis of BMI1 downstream targets showed that BMI1 might be involved in tumor immunotherapy. Together, our overall analyses revealed that the 11-RNFs prognostic signature might provide us latent chances for evaluating HCC prognosis and developing novel HCC therapy.
Collapse
|
7
|
Identification of multi-omics biomarkers and construction of the novel prognostic model for hepatocellular carcinoma. Sci Rep 2022; 12:12084. [PMID: 35840618 PMCID: PMC9287549 DOI: 10.1038/s41598-022-16341-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 07/08/2022] [Indexed: 12/05/2022] Open
Abstract
Genome changes play a crucial role in carcinogenesis, and many biomarkers can be used as effective prognostic indicators in various tumors. Although previous studies have constructed many predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance is unsatisfactory. Because multi-omics data can more comprehensively reflect the biological phenomenon of disease, we hope to build a more accurate predictive model by multi-omics analysis. We use the TCGA to identify crucial biomarkers and construct prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. The performances of predictive models were evaluated and validated through survival analysis, Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Multiple mRNAs, lncRNAs, miRNAs, CNV genes, and SNPs were significantly associated with the prognosis of HCC. We constructed five single-omic models, and the mRNA and lncRNA models showed good performance with c-indexes over 0.70. The multi-omics model presented a robust predictive ability with a c-index over 0.77. This study identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC. In addition, we constructed multiple single-omic models and an integrated multi-omics model that may provide practical and reliable guides for prognosis assessment.
Collapse
|
8
|
Liu J, Lu J, Li W, Mao W, Lu Y. Machine Learning Screens Potential Drugs Targeting a Prognostic Gene Signature Associated With Proliferation in Hepatocellular Carcinoma. Front Genet 2022; 13:900380. [PMID: 35836576 PMCID: PMC9273781 DOI: 10.3389/fgene.2022.900380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 06/13/2022] [Indexed: 02/05/2023] Open
Abstract
Background: This study aimed to screen potential drugs targeting a new prognostic gene signature associated with proliferation in hepatocellular carcinoma (HCC). Methods: CRISPR Library and TCGA datasets were used to explore differentially expressed genes (DEGs) related to the proliferation of HCC cells. Differential gene expression analysis, univariate COX regression analysis, random forest algorithm and multiple combinatorial screening were used to construct a prognostic gene signature. Then the predictive power of the gene signature was validated in the TCGA and ICGC datasets. Furthermore, potential drugs targeting this gene signature were screened. Results: A total of 640 DEGs related to HCC proliferation were identified. Using univariate Cox analysis and random forest algorithm, 10 hub genes were screened. Subsequently, using multiplex combinatorial screening, five hub genes (FARSB, NOP58, CCT4, DHX37 and YARS) were identified. Taking the median risk score as a cutoff value, HCC patients were divided into high- and low-risk groups. Kaplan-Meier analysis performed in the training set showed that the overall survival of the high-risk group was worse than that of the low-risk group (p < 0.001). The ROC curve showed a good predictive efficiency of the risk score (AUC > 0.699). The risk score was related to gene mutation, cancer cell stemness and immune function changes. Prediction of immunotherapy suggetsted the IC50s of immune checkpoint inhibitors including A-443654, ABT-888, AG-014699, ATRA, AUY-922, and AZ-628 in the high-risk group were lower than those in the low-risk group, while the IC50s of AMG-706, A-770041, AICAR, AKT inhibitor VIII, Axitinib, and AZD-0530 in the high-risk group were higher than those in the low-risk group. Drug sensitivity analysis indicated that FARSB was positively correlated with Hydroxyurea, Vorinostat, Nelarabine, and Lomustine, while negatively correlated with JNJ-42756493. DHX37 was positively correlated with Raltitrexed, Cytarabine, Cisplatin, Tiotepa, and Triethylene Melamine. YARS was positively correlated with Axitinib, Fluphenazine and Megestrol acetate. NOP58 was positively correlated with Vorinostat and 6-thioguanine. CCT4 was positively correlated with Nerabine. Conclusion: The five-gene signature associated with proliferation can be used for survival prediction and risk stratification for HCC patients. Potential drugs targeting this gene signature deserve further attention in the treatment of HCC.
Collapse
Affiliation(s)
- Jun Liu
- Department of Clinical Laboratory, Yue Bei People’s Hospital, Shantou University Medical College, Shaoguan, China
- Medical Research Center, Yue Bei People’s Hospital, Shantou University Medical College, Shaoguan, China
| | - Jianjun Lu
- Department of Medical Affairs, First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Wenli Li
- Reproductive Medicine Center, Yue Bei People’s Hospital, Shantou University Medical College, Shaoguan, China
| | - Wenjie Mao
- Emergency Department, Yue Bei People’s Hospital, Shantou University Medical College, Shaoguan, China
| | - Yamin Lu
- Department of Clinical Laboratory, Yue Bei People’s Hospital, Shantou University Medical College, Shaoguan, China
| |
Collapse
|
9
|
Qiu T, Chen Y, Meng L, Xu T, Zhang H. Identification of a metabolism-related gene signature predicting overall survival for bladder cancer. Genomics 2022; 114:110402. [PMID: 35714826 DOI: 10.1016/j.ygeno.2022.110402] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 05/24/2022] [Accepted: 06/09/2022] [Indexed: 11/16/2022]
Abstract
Reprogramming of metabolism is becoming a novel hallmark of cancer. This study aims to perform bioinformatics analysis of metabolism-related genes in bladder cancer, and to construct a signature of metabolism-related genes for predicting the prognosis. A total of 373 differentially expressed metabolism-related genes were identified from TCGA database. Taking survival time and clinical information into consideration, we constructed a risk score to predict clinical prognosis. Low-risk patients had a better prognosis than high-risk patients. Multivariate analysis showed that risk score was an independent prognostic indicator in bladder cancer. ROC curve also proved that risk score had better ability to predict prognosis than other individual indicators. Nomogram also showed a clinical net benefit to evaluate the prognosis of bladder cancer patients. GSEA revealed several metabolism-related pathways that were differentially enriched in the high-risk and low-risk groups, which might help to explain the underlying mechanisms. This signature was confirmed to be an effective prognostic biomarker in bladder cancer.
Collapse
Affiliation(s)
- Tianzhu Qiu
- Department of Oncology, First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, Jiangsu, China
| | - Yi Chen
- Department of Oncology, First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, Jiangsu, China
| | - Lijuan Meng
- Department of Geriatric Oncology, First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, Jiangsu, China
| | - Tongpeng Xu
- Department of Oncology, First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, Jiangsu, China.
| | - Hao Zhang
- Department of Oncology, First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, Jiangsu, China.
| |
Collapse
|
10
|
Zhang W, Chen K, Tian W, Zhang Q, Sun L, Wang Y, Liu M, Zhang Q. A Novel and Robust Prognostic Model for Hepatocellular Carcinoma Based on Enhancer RNAs-Regulated Genes. Front Oncol 2022; 12:849242. [PMID: 35646665 PMCID: PMC9133429 DOI: 10.3389/fonc.2022.849242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 04/01/2022] [Indexed: 11/13/2022] Open
Abstract
Evidence has demonstrated that enhancer RNAs (eRNAs) play a vital role in the progression and prognosis of cancers, but few studies have focused on the prognostic ability of eRNA-regulated genes (eRGs) for hepatocellular carcinoma (HCC). Using gene expression profiles of HCC patients from the TCGA-LIHC and eRNA expression profiles from the enhancer RNA in cancers (eRic) data portal, we developed a novel and robust prognostic signature composed of 10 eRGs based on Lasso-penalized Cox regression analysis. According to the signature, HCC patients were stratified into high- and low-risk groups, which have been shown to have significant differences in tumor immune microenvironment, immune checkpoints, HLA-related genes, DNA damage repair-related genes, Gene-set variation analysis (GSVA), and the lower half-maximal inhibitory concentration (IC50) of Sorafenib. The prognostic nomogram combining the signature, age, and TNM stage had good predictive ability in the training set (TCGA-LIHC) with the concordance index (C-index) of 0.73 and the AUCs for 1-, 3-, and 5-year OS of 0.82, 0.77, 0.74, respectively. In external validation set (GSE14520), the nomogram also performed well with the C-index of 0.71 and the AUCs for 1-, 3-, and 5-year OS of 0.74, 0.77, 0.74, respectively. In addition, an important eRG (AKR1C3) was validated using two HCC cell lines (Huh7 and MHCC-LM3) in vitro, and the results demonstrated the overexpression of AKR1C3 is related to cell proliferation, migration, and invasion in HCC. Altogether, our eRGs signature and nomogram can predict prognosis accurately and conveniently, facilitate individualized treatment, and improve prognosis for HCC patients.
Collapse
Affiliation(s)
- Wei Zhang
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Kegong Chen
- Department of Cardio-Thoracic Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.,Department of Ultrasound, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Wei Tian
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Qi Zhang
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Lin Sun
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Yupeng Wang
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Meina Liu
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| | - Qiuju Zhang
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin, China
| |
Collapse
|
11
|
Cui Y, Jiang N. Identification of a seven-gene signature predicting clinical outcome of liver cancer based on tumor mutational burden. Hum Cell 2022; 35:1192-1206. [PMID: 35622212 DOI: 10.1007/s13577-022-00708-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 04/20/2022] [Indexed: 12/13/2022]
Abstract
The total number of somatic mutations may affect the prognosis of cancer, so we applied bioinformatics methods to investigate the association between the TMB (tumor mutational burden)-related differentially expressed genes (DEGs) and the prognosis of hepatocellular carcinoma (HCC). We calculated the TMB value of the patients with HCC in TCGA database and identified the differentially expressed genes between the high-TMB and low-TMB patients. We performed functional enrichment analysis and LASSO Cox regression analysis of the DEGs, and seven genes were screened to establish a risk score model. A nomogram based on the risk scores was drawn to assess the predictive outcomes compared to the actual outcomes. The expression level of the seven genes was verified in cancer cell lines. Moreover, we explored the difference in immune cells infiltration and immune checkpoints between the high-risk and low-risk groups. The results showed that the DEGs between the high-TMB and low-TMB patients were enriched in extracellular matrix organization. A seven-gene risk score model (PAGE1, CHGA, OGN, MMP7, TRIM55, MAGEA6, and MAGEA12) was established for predicting HCC prognosis. Patients with lower risk scores had longer survival time and lower mortality rate. The nomogram based on risk scores and TNM staging showed good performance and reliability in predicting the clinical outcomes. Significant differences in cell infiltration and checkpoints were found between the high-risk and low-risk groups. Our study demonstrated a seven-gene signature and a nomogram based on the risk score model to predict the prognosis of HCC. Some of the newly identified DEGs may be potential biomarkers or therapeutic targets.
Collapse
Affiliation(s)
- Yunlong Cui
- Department of Hepatobiliary Surgery, Tianjin Medical University Cancer Institute and Hospital, 300060, Tianjin, People's Republic of China
| | - Ning Jiang
- Tianjin Key Laboratory of Exercise Physiology and Sports Medicine, Tianjin University of Sport, 301617, Tianjin, People's Republic of China.
| |
Collapse
|
12
|
Zhao X, Liu W, Liu B, Zeng Q, Cui Z, Wang Y, Cao J, Gao Q, Zhao C, Dou J. Exploring the underlying molecular mechanism of liver cancer cells under hypoxia based on RNA sequencing. BMC Genom Data 2022; 23:38. [PMID: 35590240 PMCID: PMC9121577 DOI: 10.1186/s12863-022-01055-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 05/06/2022] [Indexed: 12/18/2022] Open
Abstract
Background The aim of our study was to use the differentially expressed mRNAs (DEmRNAs) and differentially expressed miRNAs (DEmiRNAs) to illustrate the underlying mechanism of hypoxia in liver cancer. Methods In this study, a cell model of hypoxia was established, and autophagy activity was measured with western blotting and transmission electron microscopy. The effect of hypoxia conditions on the invasion of liver cancer cell was evaluated. RNA sequencing was used to identify DEmRNAs and DEmiRNAs to explore the mechanism of hypoxia in liver cancer cells. Results We found that autophagy activation was triggered by hypoxia stress and hypoxia might promote liver cancer cell invasion. In addition, a total of 407 shared DEmRNAs and 57 shared DEmiRNAs were identified in both HCCLM3 hypoxia group and SMMC-7721 hypoxia group compared with control group. Furthermore, 278 DEmRNAs and 24 DEmiRNAs were identified as cancer hypoxia-specific DEmRNAs and DEmiRNAs. Finally, we obtained 19 DEmiRNAs with high degree based on the DEmiRNA-DEmRNA interaction network. Among them, hsa-miR-483-5p, hsa-miR-4739, hsa-miR-214-3p and hsa-miR-296-5p may be potential gene signatures related to liver cancer hypoxia. Conclusions Our study may help to understand the potential molecular mechanism of hypoxia in liver cancer. Supplementary Information The online version contains supplementary material available at 10.1186/s12863-022-01055-9.
Collapse
Affiliation(s)
- Xin Zhao
- Department of Hepatobiliary Surgery, The Third Hospital of Hebei Medical University, No.139 Ziqiang Road, Shijiazhuang City, 050051, Hebei Province, China
| | - Wenpeng Liu
- Department of Hepatobiliary Surgery, The Third Hospital of Hebei Medical University, No.139 Ziqiang Road, Shijiazhuang City, 050051, Hebei Province, China
| | - Baowang Liu
- Department of Hepatobiliary Surgery, The Third Hospital of Hebei Medical University, No.139 Ziqiang Road, Shijiazhuang City, 050051, Hebei Province, China
| | - Qiang Zeng
- Department of Hepatobiliary Surgery, The Third Hospital of Hebei Medical University, No.139 Ziqiang Road, Shijiazhuang City, 050051, Hebei Province, China
| | - Ziqiang Cui
- Department of Hepatobiliary Surgery, The Third Hospital of Hebei Medical University, No.139 Ziqiang Road, Shijiazhuang City, 050051, Hebei Province, China
| | - Yang Wang
- Department of Hepatobiliary Surgery, The Third Hospital of Hebei Medical University, No.139 Ziqiang Road, Shijiazhuang City, 050051, Hebei Province, China
| | - Jinglin Cao
- Department of Hepatobiliary Surgery, The Third Hospital of Hebei Medical University, No.139 Ziqiang Road, Shijiazhuang City, 050051, Hebei Province, China
| | - Qingjun Gao
- Department of Hepatobiliary Surgery, The Third Hospital of Hebei Medical University, No.139 Ziqiang Road, Shijiazhuang City, 050051, Hebei Province, China
| | - Caiyan Zhao
- Department of Infectious Disease, The Third Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jian Dou
- Department of Hepatobiliary Surgery, The Third Hospital of Hebei Medical University, No.139 Ziqiang Road, Shijiazhuang City, 050051, Hebei Province, China.
| |
Collapse
|
13
|
Chen Y, Huang M, Zhu J, Xu L, Cheng W, Lu X, Yan F. Identification of a DNA Damage Response and Repair-Related Gene-Pair Signature for Prognosis Stratification Analysis in Hepatocellular Carcinoma. Front Pharmacol 2022; 13:857060. [PMID: 35496321 PMCID: PMC9038539 DOI: 10.3389/fphar.2022.857060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 02/24/2022] [Indexed: 12/12/2022] Open
Abstract
Background: Nowadays, although the cause of hepatocellular carcinoma (HCC) mortality and recurrence remains at a high level, the 5-year survival rate is still very low. The DNA damage response and repair (DDR) pathway may affect HCC patients’ survival by influencing tumor development and therapeutic response. It is necessary to identify a prognostic DDR-related gene signature to predict the outcome of patients. Methods: Level 3 mRNA expression and clinical information were extracted from the TCGA website. The GSE14520 datasets, ICGC-LIRI datasets, and a Chinese HCC cohort were served as validation sets. Univariate Cox regression analysis and LASSO-penalized Cox regression analysis were performed to construct the DDR-related gene pair (DRGP) signature. Kaplan–Meier survival curves and time-dependent receiver operating characteristic (ROC) analysis curves were calculated to determine the predictive ability of this prognostic model. Then, a prognostic nomogram was established to help clinical management. We investigated the difference in biological processes between HRisk and LRisk by conducting several enrichment analyses. The TIDE algorithm and R package “pRRophetic” were applied to estimate the immunotherapeutic and chemotherapeutic response. Results: We constructed the prognostic signature based on 23 DDR-related gene pairs. The patients in the training datasets were divided into HRisk and LRisk groups at median cut-off. The HRisk group had significantly poorer OS than the LRisk group, and the signature was an independent prognostic indicator in HCC. Furthermore, a nomogram of the riskscore combined with TNM stage was constructed and detected by the calibration curve and decision curve. The LRisk group was associated with higher expression of HBV oncoproteins and metabolism pathways, while DDR-relevant pathways and cell cycle process were enriched in the HRisk group. Moreover, patients in the LRisk group may be more beneficial from immunotherapy. We also found that TP53 gene was more frequently mutated in the HRisk group. As for chemotherapeutic drugs commonly used in HCC, the HRisk group was highly sensitive to 5-fluorouracil, while the LRisk group presented with a significantly higher response to gefitinib and gemcitabine. Conclusion: Overall, we developed a novel DDR-related gene pair signature and nomogram to assist in predicting survival outcomes and clinical treatment of HCC patients. It also helps understand the underlying mechanisms of different DDR patterns in HCC.
Collapse
Affiliation(s)
- Yi Chen
- State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Mengjia Huang
- State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Junkai Zhu
- State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Li Xu
- State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Wenxuan Cheng
- State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Xiaofan Lu
- State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| | - Fangrong Yan
- State Key Laboratory of Natural Medicines, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing, China
| |
Collapse
|
14
|
Zhu M, Zeng Q, Fan T, Lei Y, Wang F, Zheng S, Wang X, Zeng H, Tan F, Sun N, Xue Q, He J. Clinical Significance and Immunometabolism Landscapes of a Novel Recurrence-Associated Lipid Metabolism Signature In Early-Stage Lung Adenocarcinoma: A Comprehensive Analysis. Front Immunol 2022; 13:783495. [PMID: 35222371 PMCID: PMC8867215 DOI: 10.3389/fimmu.2022.783495] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 01/21/2022] [Indexed: 12/24/2022] Open
Abstract
Background The early-stage lung adenocarcinoma (LUAD) rate has increased with heightened public awareness and lung cancer screening implementation. Lipid metabolism abnormalities are associated with lung cancer initiation and progression. However, the comprehensive features and clinical significance of the immunometabolism landscape and lipid metabolism-related genes (LMRGs) in cancer recurrence for early-stage LUAD remain obscure. Methods LMRGs were extracted from Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Samples from The Cancer Genome Atlas (TCGA) were used as training cohort, and samples from four Gene Expression Omnibus (GEO) datasets were used as validation cohorts. The LUAD recurrence-associated LMRG molecular pattern and signature was constructed through unsupervised consensus clustering, time-dependent receiver operating characteristic (ROC), and least absolute shrinkage and selection operator (LASSO) analyses. Kaplan-Meier, ROC, and multivariate Cox regression analyses and prognostic meta-analysis were used to test the suitability and stability of the signature. We used Gene Ontology (GO), KEGG pathway, immune cell infiltration, chemotherapy response analyses, gene set variation analysis (GSVA), and GSEA to explore molecular mechanisms and immune landscapes related to the signature and the potential of the signature to predict immunotherapy or chemotherapy response. Results First, two LMRG molecular patterns were established, which showed diverse prognoses and immune infiltration statuses. Then, a 12-gene signature was identified, and a risk model was built. The signature remained an independent prognostic parameter in multivariate Cox regression and prognostic meta-analysis. In addition, this signature stratified patients into high- and low-risk groups with significantly different recurrence rates and was well validated in different clinical subgroups and several independent validation cohorts. The results of GO and KEGG analyses and GSEA showed that there were differences in multiple lipid metabolism, immune response, and drug metabolism pathways between the high- and low-risk groups. Further analyses revealed that the signature-based risk model was related to distinct immune cell proportions, immune checkpoint parameters, and immunotherapy and chemotherapy response, consistent with the GO, KEGG, and GSEA results. Conclusions This is the first lipid metabolism-based signature for predicting recurrence, and it could provide vital guidance to achieve optimized antitumor for immunotherapy or chemotherapy for early-stage LUAD.
Collapse
Affiliation(s)
- Mingchuang Zhu
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Qingpeng Zeng
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Tao Fan
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yuanyuan Lei
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Feng Wang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Sufei Zheng
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xinfeng Wang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hui Zeng
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Fengwei Tan
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Nan Sun
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qi Xue
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jie He
- Department of Oncology, Renmin Hospital of Wuhan University, Wuhan, China
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
- *Correspondence: Jie He,
| |
Collapse
|
15
|
Peng L, Ye R, Zhu X, Xie Y, Zhong B, Liu Y, Li H, Xie B. LINC02273 Promotes Hepatocellular Carcinoma Progression via Retaining β-Catenin in the Nucleus to Augment Wnt Signaling. BIOMED RESEARCH INTERNATIONAL 2022; 2022:9631036. [PMID: 35132378 PMCID: PMC8817111 DOI: 10.1155/2022/9631036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 11/15/2021] [Accepted: 12/16/2021] [Indexed: 12/12/2022]
Abstract
Hepatocellular carcinoma (HCC) is a lethal malignancy whereas the molecular mechanisms remain poorly understood. Recently, long noncoding RNAs (lncRNA) have been shown to regulate HCC progression. However, the involved lncRNAs remain to be fully explored. Here, we showed the expression pattern and biological function of a recently identified lncRNA, LINC02273, in HCC. LINC02273 played a critical role in HCC progression via stabilizing β-catenin. Knockdown of LINC02237 remarkably inhibited the proliferation, stemness, migration, and invasion abilities, whereas it increased the apoptosis of HCC cells. Overall, we characterized the functions of LINC02273 in HCC and its potential as a novel HCC targeting candidate.
Collapse
Affiliation(s)
- Liang Peng
- Medical College, Soochow University, Suzhou 215006, China
- The Second People's Hospital of Jingdezhen, Jingdezhen 333000, China
| | - Rong Ye
- Department of General Surgery III, The First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China
| | - Xiansen Zhu
- Department of Pathology, The First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China
| | - Yuankang Xie
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China
| | - Baiyin Zhong
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China
| | - Yao Liu
- Department of Gastroenterology, The First Affiliated Hospital of Gannan Medical College, Ganzhou 341000, China
- Ganzhou Key Laboratory of Gastrointestinal Carcinomas, The First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China
| | - Heping Li
- Department of Medical Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou 510080, China
| | - Binhui Xie
- Department of Hepatobiliary Surgery, The First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China
| |
Collapse
|
16
|
Fang X, Duan SF, Hu ZY, Wang JJ, Qiu L, Wang F, Chen XL. Inhibition of Matrix Metalloproteinase-8 Protects Against Sepsis Serum Mediated Leukocyte Adhesion. Front Med (Lausanne) 2022; 9:814890. [PMID: 35145983 PMCID: PMC8821815 DOI: 10.3389/fmed.2022.814890] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 01/03/2022] [Indexed: 12/29/2022] Open
Abstract
Purpose Leukocyte adhesion to vascular and matrix Metalloproteinase-8 (MMP8) expression is increased in sepsis and associated with poor prognosis in sepsis patients. This study aimed to investigate the role of MMP8 in sepsis serum mediated leukocyte adhesion. Methods Bioinformatics analysis of GSE64457 and GSE65682 was performed to evaluate the role of MMP8 in the progression of sepsis. Expression of MMP8 in blood samples from patients with sepsis was detected by qRT-PCR and ELISA. Human umbilical vein endothelial cells (HUVECs) were treated with sepsis serum, control serum, and MMP8 inhibitor. Expression of vascular cell adhesion molecule-1 (VCAM-1) and intercellular cell adhesion molecule-1 (ICAM-1) were detected by qRT-PCR and ELISA, respectively. The protein expression of total p38, phosphorylated-p38, ERK1/2, and p-ERK1/2 was detected by Western blotting. Peripheral blood mononuclear cells (PBMCs) and polymorphonuclear neutrophils (PMNs) were incubated with the treated HUVECs to calculate leukocyte adhesion. Results Four hundred and twenty-nine differentially expressed genes (DEGs) and seven hub genes between sepsis patients and healthy controls were identified. GO function analysis of DEGs and hub genes indicated that the DEGs and hub genes were mainly enriched in neutrophil activation. MMP8 was selected as a key gene with an unfavorable prognosis in sepsis patients. The mRNA and protein expression of MMP8 in blood from sepsis patients were significantly higher than controls. Leukocyte adhesion and mRNA and protein expression of VCAM-1 and ICAM-1 were significantly increased in the sepsis serum group compared to that in the control group, as was the protein expression of p-p38 and p-ERK1/2. However, the MMP8 inhibitor suppressed the leukocyte adhesion promoted by sepsis serum by decreasing the expression of VCAM-1, ICAM-1, p-p38, and p-ERK1/2. Conclusion Our study indicated that MMP8 acts as a key gene in the development of sepsis, and sepsis serum promotes leukocyte adhesion to HUVECs via MMP8, which suggest that MMP8 might be a potential therapeutic target for sepsis.
Collapse
|
17
|
Guo DZ, Huang A, Wang YP, Cao Y, Fan J, Yang XR, Zhou J. Development of an Eight-gene Prognostic Model for Overall Survival Prediction in Patients with Hepatocellular Carcinoma. J Clin Transl Hepatol 2021; 9:898-908. [PMID: 34966653 PMCID: PMC8666363 DOI: 10.14218/jcth.2020.00152] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 03/27/2021] [Accepted: 04/11/2021] [Indexed: 01/02/2023] Open
Abstract
BACKGROUND AND AIMS The overall survival (OS) of hepatocellular carcinoma (HCC) remains dismal. Bioinformatic analysis of transcriptome data could identify patients with poor OS and may facilitate clinical decision. This study aimed to develop a prognostic gene model for HCC. METHODS GSE14520 was retrieved as a training set to identify differential expressed genes (DEGs) between tumor and adjacent liver tissues in HCC patients with different OS. A DEG-based prognostic model was then constructed and the TCGA-LIHC and ICGC-LIRI datasets were used to validate the model. The area under the receiver operating characteristic curve (AUC) and hazard ratio (HR) of the model for OS were calculated. A model-based nomogram was established and verified. RESULTS In the training set, differential expression analysis identified 80 genes dysregulated in oxidation-reduction and metabolism regulation. After univariate Cox and LASSO regression, eight genes (LPCAT1, DHRS1, SORBS2, ALDH5A1, SULT1C2, SPP1, HEY1 and GOLM1) were selected to build the prognostic model. The AUC for 1-, 3- and 5-year OS were 0.779, 0.736, 0.754 in training set and 0.693, 0.689, 0.693 in the TCGA-LIHC validation set, respectively. The AUC for 1- and 3-year OS were 0.767 and 0.705 in the ICGC-LIRI validation set. Multivariate analysis confirmed the model was an independent prognostic factor (training set: HR=4.422, p<0.001; TCGA-LIHC validation set: HR=2.561, p<0.001; ICGC-LIRI validation set: HR=3.931, p<0.001). Furthermore, a nomogram combining the model and AJCC stage was established and validated, showing increased OS predictive efficacy compared with the prognostic model (p=0.035) or AJCC stage (p<0.001). CONCLUSIONS Our eight-gene prognostic model and the related nomogram represent as reliable prognostic tools for OS prediction in HCC patients.
Collapse
Affiliation(s)
- De-Zhen Guo
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University; Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education; Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Ao Huang
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University; Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education; Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yu-Peng Wang
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University; Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education; Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Ya Cao
- Cancer Research Institute, Xiangya School of Medicine, Central South University; Key Laboratory of Carcinogenesis and Cancer Invasion, Ministry of Education, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Jia Fan
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University; Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education; Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, China
- Institute of Biomedical Sciences, Fudan University, Shanghai, China
- State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, China
| | - Xin-Rong Yang
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University; Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education; Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, China
- Correspondence to: Jian Zhou and Xin-Rong Yang, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 136 Yi Xue Yuan Road, Shanghai 200032, China. ORCID: https://orcid.org/0000-0002-2118-1117 (JZ), https://orcid.org/0000-0002-2716-9338 (XRY). Tel: +86-21-64041990, Fax: +86-21-64037181, E-mail: (JZ) or (XRY)
| | - Jian Zhou
- Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University; Key Laboratory of Carcinogenesis and Cancer Invasion (Fudan University), Ministry of Education; Shanghai Key Laboratory of Organ Transplantation, Zhongshan Hospital, Fudan University, Shanghai, China
- Institute of Biomedical Sciences, Fudan University, Shanghai, China
- State Key Laboratory of Genetic Engineering, Fudan University, Shanghai, China
- Correspondence to: Jian Zhou and Xin-Rong Yang, Liver Cancer Institute, Zhongshan Hospital, Fudan University, 136 Yi Xue Yuan Road, Shanghai 200032, China. ORCID: https://orcid.org/0000-0002-2118-1117 (JZ), https://orcid.org/0000-0002-2716-9338 (XRY). Tel: +86-21-64041990, Fax: +86-21-64037181, E-mail: (JZ) or (XRY)
| |
Collapse
|
18
|
Dai S, Yao D. An immune-associated ten-long noncoding RNA signature for predicting overall survival in cervical cancer. Transl Cancer Res 2021; 10:5295-5306. [PMID: 35116378 PMCID: PMC8799008 DOI: 10.21037/tcr-21-2390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 11/25/2021] [Indexed: 01/27/2023]
Abstract
BACKGROUND Several immune-associated long non-coding RNA (lncRNA) signatures have been reported as prognostic models in different types of cancers; however, the immune-associated lncRNA signature for predicting overall survival (OS) in cervical cancer is unknown. METHODS The lncRNA expression profiles and clinical data of cervical cancer were acquired from The Cancer Genome Atlas (TCGA) dataset. Immune-associated genes were extracted from the Molecular Signatures Database (MSigDB), and the immune-associated lncRNAs were extracted for Cox regression analysis. Principal component analysis (PCA) was used to distinguish the high and low risk status of cervical cancer patients. Gene Set Enrichment Analysis (GSEA) was used for functional analyses. RESULTS Cox regression analyses and the least absolute shrinkage and selection operator (LASSO) Cox regression model were used to construct an immune-associated ten-lncRNA signature (containing AL021807.1, AL109976.1, LINC02446, MIR4458HG, AC004540.2, AC009065.8, AC083809.1, AC055822.1, AP000904.1, and FBXL19-AS1) for predicting OS in cervical cancer. The signature segregated the cervical cancer patients into 2 groups (high-risk group and low-risk group). The Kaplan-Meier survival curves of AL021807.1, AL109976.1, LINC02446, and MIR4458HG were statistically significant (P<0.05) and the others (including AC004540.2, AC009065.8, AC083809.1, AC055822.1, AP000904.1, and FBXL19-AS1) were not statistically significant (P>0.05). The Kaplan-Meier survival curves of the signature were statistically significant (P=1.134e-10), and the 5-year survival rate was 0.444 in the high-risk group [95% confidence interval (CI): 0.334 to 0.590] and 0.884 in the low-risk group (95% CI: 0.807 to 0.969). The area under curve (AUC) of the receiver operating characteristic (ROC) curve of the signature was 0.833. The concordance index (C-index) of the signature was 0.788 (95% CI: 0.730 to 0.846, P=1.884778e-22). The PCA successfully distinguished the high-risk group and low-risk group based on the signature. The GSEA showed that the signature-related protein coding genes (PCGs) may participate in immunologic biological processes and pathways. CONCLUSIONS This study revealed that the immune-associated ten-lncRNA signature is an independent factor for cervical cancer prognosis prediction, providing a bright future for immunotherapy of cervical cancer patients.
Collapse
Affiliation(s)
- Shengkang Dai
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
- People’s Hospital of Baise, Baise, China
| | - Desheng Yao
- Department of Gynecologic Oncology, Guangxi Medical University Cancer Hospital, Nanning, China
| |
Collapse
|
19
|
Liu Y, Wang L, Fang L, Liu H, Tian H, Zheng Y, Fan T, Li C, He J. A Multi-Center Validated Subtyping Model of Esophageal Cancer Based on Three Metabolism-Related Genes. Front Oncol 2021; 11:772145. [PMID: 34760709 PMCID: PMC8573269 DOI: 10.3389/fonc.2021.772145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 10/11/2021] [Indexed: 01/23/2023] Open
Abstract
Metabolic reprogramming is a hallmark of malignancy. Understanding the characteristics of metabolic reprogramming in esophageal squamous cell carcinoma (ESCC) helps uncover novel targets for cancer progression. In this study, 880 metabolism-related genes were identified from microarray data and then filtered to divide patients into two subgroups using consensus clustering, which exhibits significantly different overall survival. After a differential analysis between two subtypes, 3 genes were screened out to construct a two subtypes decision model on the training cohort (GSE53624), defined as high-risk and low-risk subtypes. These risk models were then verified in two public databases (GSE53622 and TCGA-ESCC), an independent cohort of 49 ESCC patients by RT-qPCR and an external cohort of 95 ESCC patients by immunohistochemistry analysis (IHC). Furthermore, the immune cell infiltration of regulatory T cells (Tregs) and plasma cells showed a significant difference between the high and low-risk subtypes in the IHC experiment with 119 ESCC patients. In conclusion, our study indicated that three metabolism-related prognostic genes could stratify patients into subgroups and were associated with immune infiltration, clinical features and clinical outcomes.
Collapse
Affiliation(s)
- Yu Liu
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Liyu Wang
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lingling Fang
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hengchang Liu
- Department of Colorectal Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - He Tian
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yujia Zheng
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Tao Fan
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chunxiang Li
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jie He
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| |
Collapse
|
20
|
Zhang G, Su L, Lv X, Yang Q. A novel tumor doubling time-related immune gene signature for prognosis prediction in hepatocellular carcinoma. Cancer Cell Int 2021; 21:522. [PMID: 34627241 PMCID: PMC8502295 DOI: 10.1186/s12935-021-02227-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 09/24/2021] [Indexed: 12/30/2022] Open
Abstract
Background Hepatocellular carcinoma (HCC) has become a global health issue of wide concern due to its high prevalence and poor therapeutic efficacy. Both tumor doubling time (TDT) and immune status are closely related to the prognosis of HCC patients. However, the association between TDT-related genes (TDTRGs) and immune-related genes (IRGs) and the value of their combination in predicting the prognosis of HCC patients remains unclear. The current study aimed to discover reliable biomarkers for anticipating the future prognosis of HCC patients based on the relationship between TDTRGs and IRGs. Methods Tumor doubling time-related genes (TDTRGs) were acquired from GSE54236 by using Pearson correlation test and immune-related genes (IRGs) were available from ImmPort. Prognostic TDTRGs and IRGs in TCGA-LIHC dataset were determined to create a prognostic model by the LASSO-Cox regression and stepwise Cox regression analysis. International Cancer Genome Consortium (ICGC) and another cohort of individual clinical samples acted as external validations. Additionally, significant impacts of the signature on HCC immune microenvironment and reaction to immune checkpoint inhibitors were observed. Results Among the 68 overlapping genes identified as TDTRG and IRG, a total of 29 genes had significant prognostic relevance and were further selected by performing a LASSO-Cox regression model based on the minimum value of λ. Subsequently, a prognostic three-gene signature including HECT domain and ankyrin repeat containing E3 ubiquitin protein ligase 1 (HACE1), C-type lectin domain family 1 member B (CLEC1B), and Collectin sub-family member 12 (COLEC12) was finally identified by stepwise Cox proportional modeling. The signature exhibited superior accuracy in forecasting the survival outcomes of HCC patients in TCGA, ICGC and the independent clinical cohorts. Patients in high-risk subgroup had significantly increased levels of immune checkpoint molecules including PD-L1, CD276, CTLA4, CXCR4, IL1A, PD-L2, TGFB1, OX40 and CD137, and are therefore more sensitive to immune checkpoint inhibitors (ICIs) treatment. Finally, we first found that overexpression of CLEC1B inhibited the proliferation and migration ability of HuH7 cells. Conclusions In summary, the prognostic signature based on TDTRGs and IRGs could effectively help clinicians classify HCC patients for prognosis prediction and individualized immunotherapies. Supplementary Information The online version contains supplementary material available at 10.1186/s12935-021-02227-w.
Collapse
Affiliation(s)
- Genhao Zhang
- Department of Blood Transfusion, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Lisa Su
- Department of Genetic and Prenatal Diagnosis Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xianping Lv
- Department of Blood Transfusion, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Qiankun Yang
- Department of Blood Transfusion, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| |
Collapse
|
21
|
Zhu W, Jiang H, Xie S, Xiao H, Liu Q, Chen N, Wan P, Lu S. Downregulation of PPA2 expression correlates with poor prognosis of kidney renal clear cell carcinoma. PeerJ 2021; 9:e12086. [PMID: 34567842 PMCID: PMC8428262 DOI: 10.7717/peerj.12086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 08/07/2021] [Indexed: 11/23/2022] Open
Abstract
Background Kidney renal clear cell carcinoma (KIRC) is the most common subtype of kidney cancer. Inorganic pyrophosphatase (PPA2) is an enzyme that catalyzes the hydrolysis of pyrophosphate to inorganic phosphate; few studies have reported its significance in cancers. Therefore, we aimed to explore the prognostic value of PPA2 in KIRC. Methods PPA2 expression was detected via immunohistochemistry in a tissue chip containing specimens from 150 patients with KIRC. We evaluated the correlation between PPA2 expression, clinicopathological characteristics, and survival. Data from online databases and another cohort (paraffin-embedded specimens from 10 patients with KIRC) were used for external validation. Results PPA2 expression was significantly lower in KIRC tissues than in normal renal tissues (p < 0.0001). Low expression of PPA2 was significantly associated with a high histologic grade and poor prognosis. The differential expression of PPA2 was validated at the gene and protein levels. Multivariate Cox regression analysis showed that PPA2 expression was an independent prognostic factor in patients with KIRC. Gene set enrichment analysis suggested that decreased expression of PPA2 might be related to the epithelial-mesenchymal transition in KIRC. Conclusions Our study demonstrated that PPA2 is an important energy metabolism-associated biomarker correlated with a favorable prognosis in KIRC.
Collapse
Affiliation(s)
- Wenbiao Zhu
- Department of Pathology, Meizhou People's Hospital, Meizhou, Guangdong, China
| | - Huiming Jiang
- Department of Urology, Meizhou People's Hospital, Meizhou, Guangdong, China
| | - Shoucheng Xie
- Department of Pathology, Meizhou People's Hospital, Meizhou, Guangdong, China
| | - Huanqin Xiao
- Department of Pathology, Meizhou People's Hospital, Meizhou, Guangdong, China
| | - Qinghua Liu
- Department of Pathology, Meizhou People's Hospital, Meizhou, Guangdong, China
| | - Nanhui Chen
- Department of Urology, Meizhou People's Hospital, Meizhou, Guangdong, China
| | - Pei Wan
- Department of Urology, Meizhou People's Hospital, Meizhou, Guangdong, China
| | - Shanming Lu
- Department of Pathology, Meizhou People's Hospital, Meizhou, Guangdong, China
| |
Collapse
|
22
|
Zhao Y, Zhang J, Wang S, Jiang Q, Xu K. Identification and Validation of a Nine-Gene Amino Acid Metabolism-Related Risk Signature in HCC. Front Cell Dev Biol 2021; 9:731790. [PMID: 34557495 PMCID: PMC8452960 DOI: 10.3389/fcell.2021.731790] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 08/12/2021] [Indexed: 12/29/2022] Open
Abstract
Background: Hepatocellular carcinoma (HCC) is the world’s second most deadly cancer, and metabolic reprogramming is its distinguishing feature. Among metabolite profiling, variation in amino acid metabolism supports tumor proliferation and metastasis to the most extent, yet a systematic study on the role of amino acid metabolism-related genes in HCC is still lacking. An effective amino acid metabolism-related prediction signature is urgently needed to assess the prognosis of HCC patients for individualized treatment. Materials and Methods: RNA-seq data of HCC from the TCGA-LIHC and GSE14520 (GPL3921) datasets were defined as the training set and validation set, respectively. Amino acid metabolic genes were extracted from the Molecular Signature Database. Univariate Cox and LASSO regression analyses were performed to build a predictive risk signature. K-M curves, ROC curves, and univariate and multivariate Cox regression were conducted to evaluate the predictive value of this risk signature. Functional enrichment was analyzed by GSEA and CIBERSORTx software. Results: A nine-gene amino acid metabolism-related risk signature including B3GAT3, B4GALT2, CYB5R3, GNPDA1, GOT2, HEXB, HMGCS2, PLOD2, and SEPHS1 was constructed to predict the overall survival (OS) of HCC patients. Patients were separated into high-risk and low-risk groups based on risk scores and low-risk patients had lower risk scores and longer survival time. Univariate and multivariate Cox regression verified that this signature was an independent risk factor for HCC. ROC curves showed that this risk signature can effectively predict the 1-, 2-, 3- and 5-year survival times of patients with HCC. Additionally, prognostic nomograms were established based on the training set and validation set. These genes were closely correlated with the immune regulation. Conclusion: Our study identified a nine-gene amino acid metabolism-related risk signature and built predictive nomograms for OS in HCC. These findings will help us to personalize the treatment of liver cancer patients.
Collapse
Affiliation(s)
- Yajuan Zhao
- Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Junli Zhang
- Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shuhan Wang
- Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qianqian Jiang
- Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Keshu Xu
- Division of Gastroenterology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| |
Collapse
|
23
|
Zhang K, Fang T, Shao Y, Wu Y. TGF-β-MTA1-SMAD7-SMAD3-SOX4-EZH2 Signaling Axis Promotes Viability, Migration, Invasion and EMT of Hepatocellular Carcinoma Cells. Cancer Manag Res 2021; 13:7087-7099. [PMID: 34531686 PMCID: PMC8439444 DOI: 10.2147/cmar.s297765] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Accepted: 07/29/2021] [Indexed: 12/12/2022] Open
Abstract
Introduction Enhancer of zeste homolog 2 (EZH2) is implicated in hepatocellular carcinoma (HCC), but whether transforming growth factor-β (TGF-β)-metastasis associated 1 (MTA1)-SMAD7-SMAD3-SRY-Box Transcription Factor 4 (SOX4)-EZH2 signaling axis, in which EZH2 participates, is also involved in HCC remained unknown. Methods Data on EZH2 expression in liver hepatocellular carcinoma (LIHC) and its relation with prognosis of HCC patients were predicted and analyzed using online databases. Following transfection with or without TGF-β1, HCC cell viability, migration and invasion were determined with MTT, Scratch and Transwell assays. Relative expressions of epithelial-to-mesenchymal transition (EMT)-related factors (N-Cadherin, Vimentin, and E-Cadherin) and TGF-β-MTA1-SMAD7-SMAD3-SOX4-EZH2 signaling axis factors (TGF-β, MTA1, SMAD7, phosphorylated-SMAD3, SOX4 and EZH2) were calculated via reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and Western blot. Results EZH2 was upregulated in HCC, which was related to poor prognosis. Silencing EZH2 suppressed EZH2 expression and HCC cell viability, migration, and invasion, and increased E-Cadherin expression yet decreased N-Cadherin and Vimentin expression, whereas EZH2 overexpression did conversely. Also, silencing EZH2 reversed the effects of TGF-β1 on promoting viability, migration, and invasion, as well as N-Cadherin and Vimentin expressions, yet suppressing E-Cadherin expression in HCC cells. In addition, TGF-β1 promoted TGF-β, MTA1, SOX4 and EZH2 expressions and p-SMAD3/SMAD3 ratio yet suppressed SMAD7, whereas silencing EZH2 solely reversed the effects of TGF-β1 on EZH2 expression in HCC cells. Conclusion The present study provides a theoretical basis for TGF-β-MTA1-SMAD7-SMAD3-SOX4-EZH2 signaling cascade in viability, migration, invasion, and EMT of HCC cells. Inhibiting these signals may represent a therapeutic pathway for the treatment of metastatic HCC.
Collapse
Affiliation(s)
- Kangjun Zhang
- Hepatic Surgery Department, The Third People's Hospital of Shenzhen, Shenzhen City, Guangdong Province, People's Republic of China
| | - Taishi Fang
- Hepatic Surgery Department, The Third People's Hospital of Shenzhen, Shenzhen City, Guangdong Province, People's Republic of China
| | - Yajie Shao
- Department of Anesthesiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan City, Hubei Province, People's Republic of China
| | - Yanhui Wu
- Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan City, Hubei Province, People's Republic of China
| |
Collapse
|
24
|
Yuan Y, Liu M, Hou P, Liang L, Sun X, Gan L, Liu T. Identification of a metabolic signature to predict overall survival for colorectal cancer. Scand J Gastroenterol 2021; 56:1078-1087. [PMID: 34261388 DOI: 10.1080/00365521.2021.1948605] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
PURPOSE Metabolic genes are associated with the occurrence and development of tumors. Metabolic-related risk models have showed partly prognostic predictive ability in cancers. However, the correlation between metabolic-related genes (MRGs) and the outcome of colorectal cancer is still poorly understood. PATIENTS AND METHODS TCGA database is used as the training cohort; while GSE39582 is the verification cohort. The least absolute shrinkage and selection operator Cox regression analysis were utilized to identify the MRGs and establish a genetic risk scoring model. A nomogram by integrating MRGs risk scores with TNM stage was constructed. The potential biological mechanisms were explored using gene set enrichment analysis. Associations of the signature with immune cell infiltrations and the tumor mutation burden (TMB) were also uncovered by Spearman rank test. RESULTS A six-gene metabolic signature was identified. Based on the risk scoring model with the signature, patients were divided into two groups (high-risk versus low-risk). The overall survival (OS) duration of patients with high-risk were quite shorter than those of low-risk patients (TCGA: p < .001, GSE39582: p < .001). Metabolic-related pathways were major enriched in low-risk group, while the high-risk group exhibited multiple immune-related pathways. Moreover, our signature was more linear dependent with antigen-presenting cell than effector immune cells, and a positive correction were seen between our signature and TMB. CONCLUSION Our research has discovered a six-gene metabolic signature to predict the OS of colorectal cancer. These genes may play significant roles in colorectal cancer regulating tumor microenvironment and serving as potential biomarkers for anti-cancer therapy.
Collapse
Affiliation(s)
- Yitao Yuan
- Department of Medical Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Mengling Liu
- Department of Medical Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Pengcong Hou
- Department of Medical Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Li Liang
- Department of Medical Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xun Sun
- Department of Medical Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Lu Gan
- Department of Medical Oncology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Tianshu Liu
- Department of Medical Oncology, Zhongshan Hospital, Fudan University, Shanghai, China.,Center of Evidence‑Based Medicine, Fudan University, Shanghai, China.,Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| |
Collapse
|
25
|
Wang Z, Fu Y, Xia A, Chen C, Qu J, Xu G, Zou X, Wang Q, Wang S. Prognostic and predictive role of a metabolic rate-limiting enzyme signature in hepatocellular carcinoma. Cell Prolif 2021; 54:e13117. [PMID: 34423480 PMCID: PMC8488553 DOI: 10.1111/cpr.13117] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 07/27/2021] [Accepted: 08/10/2021] [Indexed: 12/15/2022] Open
Abstract
OBJECTIVES Abnormal expression of metabolic rate-limiting enzymes drives the occurrence and progression of hepatocellular carcinoma (HCC). This study aimed to elucidate the comprehensive model of metabolic rate-limiting enzymes associated with the prognosis of HCC. MATERIALS AND METHODS HCC animal model and TCGA project were used to screen out differentially expressed metabolic rate-limiting enzyme. Cox regression, least absolute shrinkage and selection operation (LASSO) and experimentally verification were performed to identify metabolic rate-limiting enzyme signature. The area under the receiver operating characteristic curve (AUC) and prognostic nomogram were used to assess the efficacy of the signature in the three HCC cohorts (TCGA training cohort, internal cohort and an independent validation cohort). RESULTS A classifier based on three rate-limiting enzymes (RRM1, UCK2 and G6PD) was conducted and serves as independent prognostic factor. This effect was further confirmed in an independent cohort, which indicated that the AUC at year 5 was 0.715 (95% CI: 0.653-0.777) for clinical risk score, whereas it was significantly increased to 0.852 (95% CI: 0.798-0.906) when combination of the clinical with signature risk score. Moreover, a comprehensive nomogram including the signature and clinicopathological aspects resulted in significantly predict the individual outcomes. CONCLUSIONS Our results highlighted the prognostic value of rate-limiting enzymes in HCC, which may be useful for accurate risk assessment in guiding clinical management and treatment decisions.
Collapse
Affiliation(s)
- Zhangding Wang
- Department of Gastroenterology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Yao Fu
- Department of Pathology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Anliang Xia
- Department of Hepatobiliary Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Chen Chen
- Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China
| | - Jiamu Qu
- Department of Hepatobiliary Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Guifang Xu
- Department of Gastroenterology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Xiaoping Zou
- Department of Gastroenterology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Qiang Wang
- Department of Hepatobiliary Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China
| | - Shouyu Wang
- Department of Hepatobiliary Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China.,Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing, China.,Center for Public Health Research, Medical School of Nanjing University, Nanjing, China
| |
Collapse
|
26
|
Identification of prognostic long non-coding RNA signature with potential drugs in hepatocellular carcinoma. Aging (Albany NY) 2021; 13:18789-18805. [PMID: 34285143 PMCID: PMC8351707 DOI: 10.18632/aging.203322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 07/05/2021] [Indexed: 12/24/2022]
Abstract
Hepatocellular carcinoma (HCC) is the primary malignancy in the liver with high rate of death and recurrence. Novel prognostic model would be crucial for early diagnosis and improved clinical decision. The study aims to provide an effective lncRNA-based signature to predict survival time and tumor recurrence for HCC. Based on public database, lncRNA-based classifiers for overall survival and tumor recurrence were built with regression analysis and cross validation strategy. According to the risk-score of the classifiers, the whole cohorts were divided into groups with high and low risk. Afterwards, the efficiency of the lncRNA-based classifiers was evaluated and compared with other clinical factors. Finally, candidate small molecules for high risk groups were further screened using drug response databases to explore potential drugs for HCC treatment.
Collapse
|
27
|
Su L, Zhang G, Kong X. A Novel Five-Gene Signature for Prognosis Prediction in Hepatocellular Carcinoma. Front Oncol 2021; 11:642563. [PMID: 34336648 PMCID: PMC8322700 DOI: 10.3389/fonc.2021.642563] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 04/06/2021] [Indexed: 01/12/2023] Open
Abstract
Hepatocellular carcinoma (HCC) has been a global health issue and attracted wide attention due to its high incidence and poor outcomes. In this study, our purpose was to explore an effective prognostic marker for HCC. Five cohort profile datasets from GEO (GSE25097, GSE36376, GSE62232, GSE76427 and GSE101685) were integrated with TCGA-LIHC and GTEx dataset to identify differentially expressed genes (DEGs) between normal and cancer tissues in HCC patients, then 5 upregulated differentially expressed genes and 32 downregulated DEGs were identified as common DEGs in total. Next, we systematically explored the relationship between the expression of 37 common DEGs in tumor tissues and overall survival (OS) rate of HCC patients in TCGA and constructed a novel prognostic model composed of five genes (AURKA, PZP, RACGAP1, ACOT12 and LCAT). Furthermore, the predicted performance of the five-gene signature was verified in ICGC and another independent clinical samples cohort, and the results demonstrated that the signature performed well in predicting the OS rate of patients with HCC. What is more, the signature was an independent hazard factor for HCC patients when considering other clinical factors in the three cohorts. Finally, we found the signature was significantly associated with HCC immune microenvironment. In conclusion, the prognostic five-gene signature identified in our present study could efficiently classify patients with HCC into subgroups with low and high risk of longer overall survival time and help clinicians make decisions for individualized treatment.
Collapse
Affiliation(s)
- Lisa Su
- Department of Genetic and Prenatal Diagnosis Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Genhao Zhang
- Department of Blood Transfusion, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiangdong Kong
- Department of Genetic and Prenatal Diagnosis Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| |
Collapse
|
28
|
Identification of Multiple Hub Genes and Pathways in Hepatocellular Carcinoma: A Bioinformatics Analysis. BIOMED RESEARCH INTERNATIONAL 2021; 2021:8849415. [PMID: 34337056 PMCID: PMC8292096 DOI: 10.1155/2021/8849415] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 05/02/2021] [Accepted: 06/25/2021] [Indexed: 12/22/2022]
Abstract
Hepatocellular carcinoma (HCC) is a common malignant tumor of the digestive system, and its early asymptomatic characteristic increases the difficulty of diagnosis and treatment. This study is aimed at obtaining some novel biomarkers with diagnostic and prognostic meaning and may find out potential therapeutic targets for HCC. We screen differentially expressed genes (DEGs) from the HCC gene expression profile GSE14520 using GEO2R. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted by using the clusterProfiler software while a protein-protein interaction (PPI) network was performed based on the STRING database. Then, prognosis analysis of hub genes was conducted using The Cancer Genome Atlas (TCGA) database. Quantitative real-time polymerase chain reaction (qRT-PCR) was utilized to further verify the expression of hub genes and explore the correlation between gene expression and clinicopathological parameters. A total of 1053 DEGs were captured, containing 497 upregulated genes and 556 downregulated genes. GO and KEGG analysis indicated that the downregulated DEGs were mainly enriched in the fatty acid catabolic process while upregulated DEGs were primarily enriched in the cell cycle. Simultaneously, ten hub genes (CYP3A4, UGT1A6, AOX1, UGT1A4, UGT2B15, CDK1, CCNB1, MAD2L1, CCNB2, and CDC20) were identified by the PPI network. Five prognosis-related hub genes (CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20) were uncovered by the survival analysis based on TCGA database. The ten hub genes were further validated by qRT-PCR using samples obtained from our hospital. The prognosis-related hub genes such as CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20 could be considered potential diagnosis biomarkers and prognosis targets for HCC. We also use Oncomine for further verification, and we found CCNB1, CCNB2, CDK1, and CYP3A4 which were highly expressed in HCC. Meanwhile, CCNB1, CCNB2, and CDK1 are highly expressed in almost all cancer types, which may play an important role in cancer. Still, further functional study should be conducted to explore the underlying mechanism and biological effect in the near future.
Collapse
|
29
|
Li Q, Jin L, Jin M. Novel Hypoxia-Related Gene Signature for Risk Stratification and Prognosis in Hepatocellular Carcinoma. Front Genet 2021; 12:613890. [PMID: 34194464 PMCID: PMC8236897 DOI: 10.3389/fgene.2021.613890] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Accepted: 05/05/2021] [Indexed: 12/12/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common form of liver cancer with limited therapeutic options and low survival rate. The hypoxic microenvironment plays a vital role in progression, metabolism, and prognosis of malignancies. Therefore, this study aims to develop and validate a hypoxia gene signature for risk stratification and prognosis prediction of HCC patients. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases were used as a training cohort, and one Gene Expression Omnibus database (GSE14520) was served as an external validation cohort. Our results showed that eight hypoxia-related genes (HRGs) were identified by the least absolute shrinkage and selection operator analysis to develop the hypoxia gene signature and demarcated HCC patients into the high- and low-risk groups. In TCGA, ICGC, and GSE14520 datasets, patients in the high-risk group had worse overall survival outcomes than those in the low-risk group (all log-rank P < 0.001). Besides, the risk score derived from the hypoxia gene signature could serve as an independent prognostic factor for HCC patients in the three independent datasets. Finally, a nomogram including the gene signature and tumor-node-metastasis stage was constructed to serve clinical practice. In the present study, a novel hypoxia signature risk model could reflect individual risk classification and provide therapeutic targets for patients with HCC. The prognostic nomogram may help predict individualized survival.
Collapse
Affiliation(s)
- Quanxiao Li
- Department of Radiation Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.,Department of Interventional Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.,Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, China
| | - Limin Jin
- Department of Anesthesia, The First Hospital of Jilin University, Changchun, China
| | - Meng Jin
- Department of Radiation Oncology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| |
Collapse
|
30
|
Zheng Y, Cheng Y, Zhang C, Fu S, He G, Cai L, Qiu L, Huang K, Chen Q, Xie W, Chen T, Huang M, Bai Y, Pan M. Co-amplification of genes in chromosome 8q24: a robust prognostic marker in hepatocellular carcinoma. J Gastrointest Oncol 2021; 12:1086-1100. [PMID: 34295559 DOI: 10.21037/jgo-21-205] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 05/06/2021] [Indexed: 01/07/2023] Open
Abstract
Background Hepatocellular carcinoma (HCC) is a leading cause of tumor-associated death worldwide, owing to its high 5-year postoperative recurrence rate and inter-individual heterogeneity. Thus, a prognostic model is urgently needed for patients with HCC. Several researches have reported that copy number amplification of the 8q24 chromosomal region is associated with low survival in many cancers. In the present work, we set out to construct a multi-gene model for prognostic prediction in HCC. Methods RNA sequencing and copy number variant data of tumor tissue samples of HCC from The Cancer Genome Atlas (n=328) were used to identify differentially expressed messenger RNAs of genes located on the chromosomal 8q24 region by the Wilcox test. Univariate Cox and Lasso-Cox regression analyses were carried out for the screening and construction of a prognostic multi-gene signature in The Cancer Genome Atlas cohort (n=119). The multi-gene signature was validated in a cohort from the International Cancer Genome Consortium (n=240). A nomogram for prognostic prediction was built, and the underpinning molecular mechanisms were studied by Gene Set Enrichment Analysis. Results We successfully established a 7-gene prognostic signature model to predict the prognosis of patients with HCC. Using the model, we divided individuals into high-risk and low-risk sets, which showed a significant difference in overall survival in the training dataset (HR =0.17, 95% CI: 0.1-0.28; P<0.001) and in the testing dataset (HR = 0.42, 95% CI: 0.23-0.74; P=0.002). Multivariate Cox regression analysis showed the signature to be an independent prognostic factor of HCC survival. A nomogram including the prognostic signature was constructed and showed a better predictive performance in short-term (1 and 3 years) than in long-term (5 years) survival. Furthermore, Gene Set Enrichment Analysis identified several pathways of significance, which may aid in explaining the underlying molecular mechanism. Conclusions Our 7-gene signature is a reliable prognostic marker for HCC, which may provide meaningful information for therapeutic customization and treatment-related decision making.
Collapse
Affiliation(s)
- Yongjian Zheng
- Second Department of Hepatobiliary Surgery, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Yuan Cheng
- Second Department of Hepatobiliary Surgery, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Cheng Zhang
- Second Department of Hepatobiliary Surgery, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Shunjun Fu
- Second Department of Hepatobiliary Surgery, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Guolin He
- Second Department of Hepatobiliary Surgery, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Lei Cai
- Second Department of Hepatobiliary Surgery, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Ling Qiu
- Second Department of Surgery, Dongfeng People's Hospital, Guangzhou, China
| | - Kunhua Huang
- Second Department of Hepatobiliary Surgery, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Qunhui Chen
- Second Department of Hepatobiliary Surgery, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| | - Wenzhuan Xie
- The Research and Development Center of Precision Medicine, 3D Medicines Inc., Shanghai, China
| | - Tingting Chen
- The Research and Development Center of Precision Medicine, 3D Medicines Inc., Shanghai, China
| | - Mengli Huang
- The Research and Development Center of Precision Medicine, 3D Medicines Inc., Shanghai, China
| | - Yuezong Bai
- The Research and Development Center of Precision Medicine, 3D Medicines Inc., Shanghai, China
| | - Mingxin Pan
- Second Department of Hepatobiliary Surgery, Zhujiang Hospital of Southern Medical University, Guangzhou, China
| |
Collapse
|
31
|
Chen L, Zeng H, Zhang M, Luo Y, Ma X. Histopathological image and gene expression pattern analysis for predicting molecular features and prognosis of head and neck squamous cell carcinoma. Cancer Med 2021; 10:4615-4628. [PMID: 33987946 PMCID: PMC8267162 DOI: 10.1002/cam4.3965] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/15/2021] [Accepted: 04/19/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Histopathological image features offer a quantitative measurement of cellular morphology, and probably help for better diagnosis and prognosis in head and neck squamous cell carcinoma (HNSCC). METHODS We first used histopathological image features and machine-learning algorithms to predict molecular features of 212 HNSCC patients from The Cancer Genome Atlas (TCGA). Next, we divided TCGA-HNSCC cohort into training set (n = 149) and test set (n = 63), and obtained tissue microarrays as an external validation set (n = 126). We identified the gene expression profile correlated to image features by bioinformatics analysis. RESULTS Histopathological image features combined with random forest may predict five somatic mutations, transcriptional subtypes, and methylation subtypes, with area under curve (AUC) ranging from 0.828 to 0.968. The prediction model based on image features could predict overall survival, with 5-year AUC of 0.831, 0.782, and 0.751 in training, test, and validation sets. We next established an integrative prognostic model of image features and gene expressions, which obtained better performance in training set (5-year AUC = 0.860) and test set (5-year AUC = 0.826). According to histopathological transcriptomics risk score (HTRS) generated by the model, high-risk and low-risk patients had different survival in training set (HR = 4.09, p < 0.001) and test set (HR=3.08, p = 0.019). Multivariate analysis suggested that HTRS was an independent predictor in training set (HR = 5.17, p < 0.001). The nomogram combining HTRS and clinical factors had higher net benefit than conventional clinical evaluation. CONCLUSIONS Histopathological image features provided a promising approach to predict mutations, molecular subtypes, and prognosis of HNSCC. The integration of image features and gene expression data had potential for improving prognosis prediction in HNSCC.
Collapse
Affiliation(s)
- Linyan Chen
- Department of Biotherapy, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Hao Zeng
- Department of Biotherapy, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Mingxuan Zhang
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Yuling Luo
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, Cancer Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| |
Collapse
|
32
|
Lei C, Chen W, Wang Y, Zhao B, Liu P, Kong Z, Liu D, Dai C, Wang Y, Wang Y, Ma W. Prognostic Prediction Model for Glioblastoma: A Metabolic Gene Signature and Independent External Validation. J Cancer 2021; 12:3796-3808. [PMID: 34093788 PMCID: PMC8176239 DOI: 10.7150/jca.53827] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 04/21/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Glioblastoma (GBM) is the most common primary malignant intracranial tumor and closely related to metabolic alteration. However, few accepted prognostic models are currently available, especially models based on metabolic genes. Methods: The transcriptome data were obtained for all of the patients diagnosed with GBM from the Gene Expression Omnibus (GEO) (training cohort, n=369) and The Cancer Genome Atlas (TCGA) (validation cohort, n=152) with the following variables: age at diagnosis, sex, follow-up and overall survival (OS). Metabolic genes according to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were contracted, and a Lasso regression model was constructed. Survival was assessed by univariate or multivariate Cox proportional hazards regression and Kaplan-Meier analysis, and an independent external validation was also conducted to examine the model. Results: There were 341 metabolic genes showed significant differences between normal brain and GBM tissues in both the training and validation cohorts, among which 56 genes were dramatically correlated to the OS of patients. Lasso regression revealed that the metabolic prognostic model was composed of 18 genes, including COX10, COMT, and GPX2 with protective effects, as well as OCRL and RRM2 with unfavorable effects. Patients classified as high-risk by the risk score from this model had markedly shorter OS than low-risk patients (P<0.0001), and this significant result was also observed in independent external validation (P<0.001). Conclusions: The prognosis of GBM was dramatically related to metabolic pathways, and our metabolic prognostic model had high accuracy and application value in predicting the OS of GBM patients.
Collapse
Affiliation(s)
- Chuxiang Lei
- Department of Vascular Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, China
| | - Wenlin Chen
- Department of Neurosurgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, China
| | - Yuekun Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, China
| | - Binghao Zhao
- Department of Neurosurgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, China
| | - Penghao Liu
- Department of Neurosurgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, China
| | - Ziren Kong
- Department of Neurosurgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, China
| | - Delin Liu
- Department of Neurosurgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, China
| | - Congxin Dai
- Department of Neurosurgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, China
| | - Yaning Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, China
| | - Yu Wang
- Department of Neurosurgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, China
| | - Wenbin Ma
- Department of Neurosurgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, No. 1 Shuaifuyuan, Dongcheng District, Beijing, China
| |
Collapse
|
33
|
Development and validation of epithelial mesenchymal transition-related prognostic model for hepatocellular carcinoma. Aging (Albany NY) 2021; 13:13822-13845. [PMID: 33929972 PMCID: PMC8202896 DOI: 10.18632/aging.202976] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 03/27/2021] [Indexed: 12/24/2022]
Abstract
Epithelial cell transformation (EMT) plays an important role in the pathogenesis and metastasis of hepatocellular carcinoma (HCC). We aimed to establish a genetic risk model to evaluate HCC prognosis based on the expression levels of EMT-related genes. The data of HCC patients were collected from TCGA and ICGC databases. Gene expression differential analysis, univariate analysis, and lasso combined with stepwise Cox regression were used to construct the prognostic model. Kaplan–Meier curve, receiver operating characteristic (ROC) curve, calibration analysis, Harrell’s concordance index (C-index), and decision curve analysis (DCA) were used to evaluate the predictive ability of the risk model or nomogram. GO and KEGG were used to analyze differently expressed EMT genes, or genes that directly or indirectly interact with the risk-associated genes. A 10-gene signature, including TSC2, ACTA2, SLC2A1, PGF, MYCN, PIK3R1, EOMES, BDNF, ZNF746, and TFDP3, was identified. Kaplan–Meier survival analysis showed a significant prognostic difference between high- and low-risk groups of patients. ROC curve analysis showed that the risk score model could effectively predict the 1-, 3-, and 5-year overall survival rates of patients with HCC. The nomogram showed a stronger predictive effect than clinical indicators. C-index, DCA, and calibration analysis demonstrated that the risk score and nomogram had high accuracy. The single sample gene set enrichment analysis results confirmed significant differences in the types of infiltrating immune cells between patients in the high- and low-risk groups. This study established a new prediction model of risk gene signature for predicting prognosis in patients with HCC, and provides a new molecular tool for the clinical evaluation of HCC prognosis.
Collapse
|
34
|
Huo J, Wu L, Zang Y. Development and Validation of a Metabolic-related Prognostic Model for Hepatocellular Carcinoma. J Clin Transl Hepatol 2021; 9:169-179. [PMID: 34007798 PMCID: PMC8111106 DOI: 10.14218/jcth.2020.00114] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 01/03/2021] [Accepted: 01/26/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND AND AIMS Growing evidence suggests that metabolic-related genes have a significant impact on the occurrence and development of hepatocellular carcinoma (HCC). However, the prognostic value of metabolic-related genes for HCC has not been fully revealed. METHODS mRNA sequencing and clinical data were obtained from The Cancer Genome Atlas and the GTEx Genotype-Tissue Expression comprehensive database. Differentially expressed metabolic-related genes in tumor tissues (n=374) and normal tissues (n=160) were identified by the Wilcoxon test. Time-dependent receiver operating characteristic curve analysis, univariate multivariate Cox regression analysis and Kaplan-Meier survival analysis were used to evaluate the predictive effectiveness and independence of the prognostic model. Two independent cohorts (International Cancer Genome Consortiums and GSE14520) were applied to verify the prognostic model. RESULTS Our study included a total of 793 patients with HCC. We constructed a risk score consisting of five metabolic-genes (BDH1, RRM2, CYP2C9, PLA2G7, and TXNRD1). For the overall survival rate, the low-risk group had a considerably higher rate than the high-risk group. Univariate and multivariate Cox regression analyses indicated that the risk score was an independent predictor for the prognosis of HCC. CONCLUSIONS We constructed and validated a novel prognostic model, which may provide support for the precise treatment of HCC.
Collapse
Affiliation(s)
| | - Liqun Wu
- Correspondence to: Liqun Wu, Liver Disease Center, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, Shandong 266003, China. Tel: +86-18661809789, Fax: +86-532-82913225, E-mail:
| | | |
Collapse
|
35
|
Weng J, Zhou C, Zhou Q, Chen W, Yin Y, Atyah M, Dong Q, Shi Y, Ren N. Development and Validation of a Metabolic Gene-Based Prognostic Signature for Hepatocellular Carcinoma. J Hepatocell Carcinoma 2021; 8:193-209. [PMID: 33824863 PMCID: PMC8018394 DOI: 10.2147/jhc.s300633] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 02/23/2021] [Indexed: 12/12/2022] Open
Abstract
Background Hepatocellular carcinoma (HCC) is a malignant tumor with great variation in prognosis among individuals. Changes in metabolism influence disease progression and clinical outcomes. The objective of this study was to determine the overall survival (OS) risk of HCC patients from a metabolic perspective. Patients and Methods The model was constructed using the least absolute shrinkage and selection operator (LASSO) COX regression based on The Cancer Genome Atlas (TCGA, n=342) dataset. The International Cancer Genome Consortium (ICGC, n=232), GSE14520 (n=242) datasets, and a clinical cohort (n=64) were then used to assess the prognostic value of the signature. Results A 10 metabolic gene-based signature was constructed and verified as a robust and independent prognostic classifier in public and real-world validation cohorts. Meanwhile, the signature enabled the identification of HCC molecular subtypes, yielding an AUC value of 0.678 [95% CI: 0.592–0.763]. Besides, the signature was associated with metabolic processes like glycolysis, supported by a clear correlation between the risk score and expression of rate-limiting enzymes. Furthermore, high-risk tumor was likely to have a high tumor infiltration status of immunosuppressive cells, as well as elevated expression of some immune checkpoint molecules. For final clinical translation, a nomogram integrating the signature and tumor stage was established, and showed improved predictive accuracy of 3- and 5-year OS and brought more net benefit to patients. Conclusion We developed a prognostic signature based on 10 metabolic genes, which has proven to be an independent and reliable prognostic predictor for HCC and reflects the metabolic and immune characteristics of tumors.
Collapse
Affiliation(s)
- Jialei Weng
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China
| | - Chenhao Zhou
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China.,Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Qiang Zhou
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China
| | - Wanyong Chen
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China.,Institute of Fudan Minhang Academic Health System, Minhang Hospital, Key Laboratory of Shanghai Municipal Health Commission, Fudan University, Shanghai, People's Republic of China
| | - Yirui Yin
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China
| | - Manar Atyah
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China
| | - Qiongzhu Dong
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.,Institute of Fudan Minhang Academic Health System, Minhang Hospital, Key Laboratory of Shanghai Municipal Health Commission, Fudan University, Shanghai, People's Republic of China.,Institutes of Biomedical Sciences, Fudan University, Shanghai, People's Republic of China
| | - Yi Shi
- Biomedical Research Centre, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Ning Ren
- Department of Liver Surgery, Liver Cancer Institute, Zhongshan Hospital, Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai, People's Republic of China.,Institute of Fudan Minhang Academic Health System, Minhang Hospital, Key Laboratory of Shanghai Municipal Health Commission, Fudan University, Shanghai, People's Republic of China
| |
Collapse
|
36
|
Zuo D, Li C, Liu T, Yue M, Zhang J, Ning G. Construction and validation of a metabolic risk model predicting prognosis of colon cancer. Sci Rep 2021; 11:6837. [PMID: 33767290 PMCID: PMC7994414 DOI: 10.1038/s41598-021-86286-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 03/12/2021] [Indexed: 01/31/2023] Open
Abstract
Metabolic genes have played a significant role in tumor development and prognosis. In this study, we constructed a metabolic risk model to predict the prognosis of colon cancer based on The Cancer Genome Atlas (TCGA) and validated the model by Gene Expression Omnibus (GEO). We extracted 753 metabolic genes and identified 139 differentially expressed genes (DEGs) from TCGA database. Then we conducted univariate cox regression analysis and Least Absolute Shrinkage and Selection Operator Cox regression analysis to identify prognosis-related genes and construct the metabolic risk model. An eleven-gene prognostic model was constructed after 1000 resamples. The gene signature has been proved to have an excellent ability to predict prognosis by Kaplan-Meier analysis, time-dependent receiver operating characteristic, risk score, univariate and multivariate cox regression analysis based on TCGA. Then we validated the model by Kaplan-Meier analysis and risk score based on GEO database. Finally, we performed a weighted gene co-expression network analysis and protein-protein interaction network on DEGs, and Kyoto Encyclopedia of Genes and Genomes pathways and Gene Ontology enrichment analyses were conducted. The results of functional analyses showed that most significantly enriched pathways focused on metabolism, especially glucose and lipid metabolism pathways.
Collapse
Affiliation(s)
- Didi Zuo
- grid.430605.4Department of Endocrinology and Metabolism, The First Hospital of Jilin University, Changchun, Jilin Province China
| | - Chao Li
- grid.430605.4Department of Colorectal and Anal Surgery, The First Hospital of Jilin University, Changchun, Jilin China
| | - Tao Liu
- grid.430605.4Department of Colorectal and Anal Surgery, The First Hospital of Jilin University, Changchun, Jilin China
| | - Meng Yue
- grid.430605.4Department of Colorectal and Anal Surgery, The First Hospital of Jilin University, Changchun, Jilin China
| | - Jiantao Zhang
- grid.430605.4Department of Colorectal and Anal Surgery, The First Hospital of Jilin University, Changchun, Jilin China
| | - Guang Ning
- grid.430605.4Department of Endocrinology and Metabolism, The First Hospital of Jilin University, Changchun, Jilin Province China ,grid.16821.3c0000 0004 0368 8293Key Laboratory for Endocrine and Metabolic Diseases of Ministry of Health of China, Shanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Shanghai Institute for Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| |
Collapse
|
37
|
A dual immune signature of CD8+ T cells and MMP9 improves the survival of patients with hepatocellular carcinoma. Biosci Rep 2021; 41:228011. [PMID: 33656546 PMCID: PMC7969702 DOI: 10.1042/bsr20204219] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 02/20/2021] [Accepted: 03/02/2021] [Indexed: 12/12/2022] Open
Abstract
The 5-year survival of hepatocellular carcinoma (HCC) is difficult due to the high recurrence rate and metastasis. Tumor infiltrating immune cells (TICs) and immune-related genes (IRGs) bring hope to improve survival and treatment of HCC patients. However, there are problems in predicting immune signatures and identifying novel therapeutic targets. In the study, the CIBERSORT algorithm was used to evaluate 22 immune cell infiltration patterns in gene expression omnibus (GEO) and the cancer genome atlas (TCGA) data. Eight immune cells were found to have significant infiltration differences between the tumor and normal groups. The CD8+ T cells immune signature was constructed by least absolute shrinkage and selection operator (LASSO) algorithm. The high infiltration level of CD8+ T cells could significantly improve survival of patients. The weighted gene co-expression network analysis (WGCNA) algorithm identified MMP9 was closely related to the overall survival of HCC patients. K-M survival and tROC analysis confirmed that MMP9 had an excellent prognostic prediction. Cox regression showed that a dual immune signature of CD8+ T cells and MMP9 was independent survival factor in HCC. Therefore, a dual prognostic immune signature could improve the survival of patient and may provide a new strategy for the immunotherapy of HCC.
Collapse
|
38
|
Zhu G, Xia H, Tang Q, Bi F. An epithelial-mesenchymal transition-related 5-gene signature predicting the prognosis of hepatocellular carcinoma patients. Cancer Cell Int 2021; 21:166. [PMID: 33712026 PMCID: PMC7953549 DOI: 10.1186/s12935-021-01864-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 03/03/2021] [Indexed: 02/07/2023] Open
Abstract
Background Tumor metastasis is one of the leading reasons of the dismal prognosis of hepatocellular carcinoma (HCC). Epithelial-mesenchymal transition (EMT) is closely associated with tumor metastasis including HCC. The purpose of this study is to construct and validate an EMT-related gene signature for predicting the prognosis of HCC patients. Methods Gene expression data of HCC patients was downloaded from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) was performed to found the EMT-related gene sets which were obviously distinct between normal samples and paired HCC samples. Cox regression analysis was used to develop an EMT-related prognostic signature, and the performance of the signature was evaluated by Kaplan–Meier curves and time-dependent receiver operating characteristic (ROC) curves. A nomogram incorporating the independent predictors was established. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the expression levels of the hub genes in HCC cell lines, and the role of PDCD6 in the metastasis of HCC was determined by functional experiments. Results An EMT-related 5-gene signature (PDCD6, TCOF1, TRIM28, EZH2 and FAM83D) was constructed using univariate and multivariate Cox regression analysis. Based on the signature, the HCC patients were classified into high- and low-risk groups, and patients in high-risk group had a poor prognosis. Time-dependent ROC and Cox regression analyses suggested that the signature could predict HCC prognosis exactly and independently. The predictive capacity of the signature was also validated in two external cohorts. GSEA results showed that many cancer-related signaling pathways such as PI3K/Akt/mTOR pathway and TGF-β/SMAD pathway were enriched in high-risk group. The result of qRT-PCR revealed that PDCD6, TCOF1 and FAM83D were highly expressed in HCC cancer cells. Among them, PDCD6 were found to promote cell migration and invasion. Conclusion The EMT-related 5-gene signature can serve as a promising prognostic biomarker for HCC patients and may provide a novel mechanism of HCC metastasis. Supplementary Information The online version contains supplementary material available at 10.1186/s12935-021-01864-5.
Collapse
Affiliation(s)
- Gongmin Zhu
- Department of Abdominal Oncology, Cancer Center and Laboratory of Molecular Targeted Therapy in Oncology, West China Hospital, Sichuan University, No.37 guoxue lane, Chengdu, 610041, Sichuan Province, China
| | - Hongwei Xia
- Department of Abdominal Oncology, Cancer Center and Laboratory of Molecular Targeted Therapy in Oncology, West China Hospital, Sichuan University, No.37 guoxue lane, Chengdu, 610041, Sichuan Province, China
| | - Qiulin Tang
- Department of Abdominal Oncology, Cancer Center and Laboratory of Molecular Targeted Therapy in Oncology, West China Hospital, Sichuan University, No.37 guoxue lane, Chengdu, 610041, Sichuan Province, China
| | - Feng Bi
- Department of Abdominal Oncology, Cancer Center and Laboratory of Molecular Targeted Therapy in Oncology, West China Hospital, Sichuan University, No.37 guoxue lane, Chengdu, 610041, Sichuan Province, China.
| |
Collapse
|
39
|
Zhou D, Liu X, Wang X, Yan F, Wang P, Yan H, Jiang Y, Yang Z. A prognostic nomogram based on LASSO Cox regression in patients with alpha-fetoprotein-negative hepatocellular carcinoma following non-surgical therapy. BMC Cancer 2021; 21:246. [PMID: 33685417 PMCID: PMC7938545 DOI: 10.1186/s12885-021-07916-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 02/15/2021] [Indexed: 12/14/2022] Open
Abstract
Background Alpha-fetoprotein-negative hepatocellular carcinoma (AFP-NHCC) (< 8.78 ng/mL) have special clinicopathologic characteristics and prognosis. The aim of this study was to apply a new method to establish and validate a new model for predicting the prognosis of patients with AFP-NHCC. Methods A total of 410 AFP-negative patients with clinical diagnosed with HCC following non-surgical therapy as a primary cohort; 148 patients with AFP-NHCC following non-surgical therapy as an independent validation cohort. In primary cohort, independent factors for overall survival (OS) by LASSO Cox regression were all contained into the nomogram1; by Forward Stepwise Cox regression were all contained into the nomogram2. Nomograms performance and discriminative power were assessed with concordance index (C-index) values, area under curve (AUC), Calibration curve and decision curve analyses (DCA). The results were validated in the validation cohort. Results The C-index of nomogram1was 0.708 (95%CI: 0.673–0.743), which was superior to nomogram2 (0.706) and traditional modes (0.606–0.629). The AUC of nomogram1 was 0.736 (95%CI: 0.690–0.778). In the validation cohort, the nomogram1 still gave good discrimination (C-index: 0.752, 95%CI: 0.691–0.813; AUC: 0.784, 95%CI: 0.709–0.847). The calibration curve for probability of OS showed good homogeneity between prediction by nomogram1 and actual observation. DCA demonstrated that nomogram1 was clinically useful. Moreover, patients were divided into three distinct risk groups for OS by the nomogram1: low-risk group, middle-risk group and high-risk group, respectively. Conclusions Novel nomogram based on LASSO Cox regression presents more accurate and useful prognostic prediction for patients with AFP-NHCC following non-surgical therapy. This model could help patients with AFP-NHCC following non-surgical therapy facilitate a personalized prognostic evaluation.
Collapse
Affiliation(s)
- Dongdong Zhou
- Center for Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, No. 8 Jing Shun East Street, Beijing, 100015, People's Republic of China
| | - Xiaoli Liu
- Center for Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, No. 8 Jing Shun East Street, Beijing, 100015, People's Republic of China
| | - Xinhui Wang
- Center for Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, No. 8 Jing Shun East Street, Beijing, 100015, People's Republic of China
| | - Fengna Yan
- Center for Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, No. 8 Jing Shun East Street, Beijing, 100015, People's Republic of China
| | - Peng Wang
- Center for Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, No. 8 Jing Shun East Street, Beijing, 100015, People's Republic of China
| | - Huiwen Yan
- Center for Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, No. 8 Jing Shun East Street, Beijing, 100015, People's Republic of China.,First Clinical Medical College, Beijing University of Chinese Medicine, Chaoyang District, Beijing, 100029, People's Republic of China
| | - Yuyong Jiang
- Center for Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, No. 8 Jing Shun East Street, Beijing, 100015, People's Republic of China
| | - Zhiyun Yang
- Center for Integrative Medicine, Beijing Ditan Hospital, Capital Medical University, No. 8 Jing Shun East Street, Beijing, 100015, People's Republic of China.
| |
Collapse
|
40
|
Guo X, Sun Z, Jiang S, Jin X, Wang H. Identification and validation of a two-gene metabolic signature for survival prediction in patients with kidney renal clear cell carcinoma. Aging (Albany NY) 2021; 13:8276-8289. [PMID: 33686951 PMCID: PMC8034923 DOI: 10.18632/aging.202636] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 10/22/2020] [Indexed: 12/27/2022]
Abstract
Metabolic reprogramming contributes to the high mortality of advanced stage kidney renal clear cell carcinoma (KIRC), the most common renal cancer subtype. This study aimed to identify a metabolism-related gene (MRG) signature to improve survival prediction in KIRC patients. We downloaded RNA sequencing data and corresponding clinical information for KIRC and control samples from The Cancer Genome Atlas database and identified, based on an MRG dataset in the Molecular Signatures Database, 123 MRGs with differential expression in KIRC. Following Cox regression analysis and least absolute shrinkage and selection operator selection, RRM2 and ALDH6A1 were identified as prognosis-related genes and used to construct a prognostic signature with independent prognostic significance. After risk score-based patient separation, stratified survival analysis indicated that high-risk patients showed poorer overall survival than low-risk patients. We then constructed a clinical nomogram that showed a concordance index of 0.774 and good performance based upon calibration curves. Gene set enrichment analysis revealed several metabolic pathways significantly enriched in the target genes. The two-gene metabolic signature identified herein may represent a highly valuable tool for KIRC prognosis prediction, and might also help identify new metabolism-related biomarkers and therapeutic targets for KIRC.
Collapse
Affiliation(s)
- Xudong Guo
- Department of Urology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
| | - Zhuolun Sun
- Department of Urology, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, Guangdong, China
| | - Shaobo Jiang
- Department of Urology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
| | - Xunbo Jin
- Department of Urology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
| | - Hanbo Wang
- Department of Urology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan 250021, Shandong, China
| |
Collapse
|
41
|
Sun X, Zhou ZR, Fang Y, Ding S, Lu S, Wang Z, Wang H, Chen X, Shen K. A novel metabolic gene signature-based nomogram to predict overall survival in breast cancer. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:367. [PMID: 33842588 PMCID: PMC8033348 DOI: 10.21037/atm-20-4813] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Background Breast cancer risk prediction is often based on clinicopathological characteristics despite the high heterogeneity derived from gene expression. Metabolic alteration is a hallmark of cancer, and thus, the integration of a metabolic signature with clinical parameters is necessary to predict disease outcomes in breast cancers. Methods Metabolic genes were downloaded from the Gene Set Enrichment Analysis (GSEA) dataset. Genes with statistical significance in the univariate analysis were applied in the least absolute shrinkage and selection operator (LASSO) analysis to build a gene signature in the GSE20685 dataset. Clinicopathological characteristics and risk scores with prognostic significance were incorporated into the nomogram to predict the overall survival (OS) of patients. The Cancer Genome Atlas (TCGA) and GSE866166 datasets were used as the validation datasets. Time-dependent receiver operating characteristic (tROC) curves and calibration plots were used to assess the accuracy and discrimination of the model. Results A 55-gene metabolic gene signature (MGS) was constructed, and was significantly related to OS both in the discovery (P<0.001) and validation (P<0.001) datasets. The MGS was an independent prognostic factor and could divide patients into high- and low-risk groups regardless of their different prediction analysis of microarray 50 (PAM50) subtypes. Time-dependent ROC curves indicated that the risk scores based on the MGS [area under the ROC curve (AUC): 0.931] were superior to the those based on the American Joint Committee on Cancer (AJCC) stage (AUC: 0.781) and PAM50 (AUC: 0.675). A nomogram based on the AJCC stage and risk score could predict OS, and the calibration curves showed good agreement to the actual outcome, indicating that the nomogram may have practical utility. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analysis indicated that this MGS was primarily enriched in amino acid pathways. Conclusions Our results demonstrated that the MGS was superior to existing risk predictors such as PAM50 and AJCC stage. By combining clinical factors (AJCC stage) and the MGS, a nomogram was constructed and showed good predictive ability for OS in breast cancer.
Collapse
Affiliation(s)
- Xi Sun
- Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhi-Rui Zhou
- Radiation Oncology Center, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yan Fang
- Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shuning Ding
- Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Shuangshuang Lu
- Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zheng Wang
- Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Hui Wang
- Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaosong Chen
- Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Kunwei Shen
- Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| |
Collapse
|
42
|
Wu X, Lan T, Li M, Liu J, Wu X, Shen S, Chen W, Peng B. Six Metabolism Related mRNAs Predict the Prognosis of Patients With Hepatocellular Carcinoma. Front Mol Biosci 2021; 8:621232. [PMID: 33869278 PMCID: PMC8045485 DOI: 10.3389/fmolb.2021.621232] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 01/11/2021] [Indexed: 12/19/2022] Open
Abstract
Background: Hepatocellular carcinoma (HCC) is one of the most common aggressive solid malignant tumors and current research regards HCC as a type of metabolic disease. This study aims to establish a metabolism-related mRNA signature model for risk assessment and prognosis prediction in HCC patients. Methods: HCC data were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Enrichment Analysis (GSEA) website. Least absolute shrinkage and selection operator (LASSO) was used to screen out the candidate mRNAs and calculate the risk coefficient to establish the prognosis model. A high-risk group and low-risk group were separated for further study depending on their median risk score. The reliability of the prediction was evaluated in the validation cohort and the whole cohort. Results: A total of 548 differential mRNAs were identified from HCC samples (n = 374) and normal controls (n = 50), 45 of which were correlated with prognosis. A total of 373 samples met the screening criteria and there were randomly divided into the training cohort (n = 186) and the validation cohort (n = 187). In the training cohort, six metabolism-related mRNAs were used to construct a prognostic model with a LASSO regression model. Based on the risk model, the overall survival rate of the high-risk cohort was significantly lower than that of the low-risk cohort. The results of a time-ROC curve proved that the risk score (AUC = 0.849) had a higher prognostic value than the pathological grade, clinical stage, age or gender. Conclusion: The model constructed by the six metabolism-related mRNAs has a significant value for survival prediction and can be applied to guide the evaluation of HCC and the designation of clinical therapy.
Collapse
Affiliation(s)
- Xiwen Wu
- Department of Hepatic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Tian Lan
- Department of Pancreaticobiliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Muqi Li
- Department of Hepatic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Junfeng Liu
- Department of Pathology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xukun Wu
- Department of Hepatic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Shunli Shen
- Department of Hepatic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Wei Chen
- Department of Pancreaticobiliary Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Baogang Peng
- Department of Hepatic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| |
Collapse
|
43
|
Nie Y, Liu L, Liu Q, Zhu X. Identification of a metabolic-related gene signature predicting the overall survival for patients with stomach adenocarcinoma. PeerJ 2021; 9:e10908. [PMID: 33614297 PMCID: PMC7877239 DOI: 10.7717/peerj.10908] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 01/15/2021] [Indexed: 12/11/2022] Open
Abstract
Background The reprogramming of energy metabolism and consistently altered metabolic genes are new features of cancer, and their prognostic roles remain to be further studied in stomach adenocarcinoma (STAD). Methods Messenger RNA (mRNA) expression profiles and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA) and the GSE84437 databases from the Gene Expression Omnibus (GEO) database. A univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression model established a novel metabolic signature based on TCGA. The area under the receiver operating characteristic (ROC) curve (AUROC) and a nomogram were calculated to assess the predictive accuracy. Results A novel metabolic-related signature (including acylphosphatase 1, RNA polymerase I subunit A, retinol dehydrogenase 12, 5-oxoprolinase, ATP-hydrolyzing, malic enzyme 1, nicotinamide N-methyltransferase, gamma-glutamyl transferase 5, deoxycytidine kinase, galactosidase alpha, DNA polymerase delta 3, glutathione S-transferase alpha 2, N-acyl sphingosine amidohydrolase 1, and N-acyl sphingosine amidohydrolase 1) was identified. In both TCGA and GSE84437, patients in the high-risk group showed significantly poorersurvival than the patients in the low-risk group. A good predictive value was shown by the AUROC and nomogram. Furthermore, gene set enrichment analyses (GSEAs) revealed several significantly enriched pathways, which may help in explaining the underlying mechanisms. Conclusions A novel robust metabolic-related signature for STAD prognosis prediction was conducted. The signature may reflect the dysregulated metabolic microenvironment and can provided potential biomarkers for metabolic therapy in STAD.
Collapse
Affiliation(s)
- Yuan Nie
- Department of Gastroenterology, First Affiliated Hospital of Nanchang University, Nan Chang, China
| | - Linxiang Liu
- Department of Gastroenterology, First Affiliated Hospital of Nanchang University, Nan Chang, China
| | - Qi Liu
- Department of Gastroenterology, First Affiliated Hospital of Nanchang University, Nan Chang, China
| | - Xuan Zhu
- Department of Gastroenterology, First Affiliated Hospital of Nanchang University, Nan Chang, China
| |
Collapse
|
44
|
Xu W, Guo W, Lu P, Ma D, Liu L, Yu F. Identification of an autophagy-related gene signature predicting overall survival for hepatocellular carcinoma. Biosci Rep 2021; 41:BSR20203231. [PMID: 33351066 PMCID: PMC7812060 DOI: 10.1042/bsr20203231] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2020] [Revised: 12/06/2020] [Accepted: 12/22/2020] [Indexed: 12/18/2022] Open
Abstract
The poor prognosis of hepatocellular carcinoma (HCC) calls for the development of accurate prognostic models. The growing number of studies indicating a correlation between autophagy activity and HCC indicates there is a commitment to finding solutions for the prognosis of HCC from the perspective of autophagy. We used a cohort in The Cancer Genome Atlas (TCGA) to evaluate the expression of autophagy-related genes in 371 HCC samples using univariate Cox and lasso Cox regression analysis, and the prognostic features were identified. A prognostic model was established by combining the expression of selected genes with the multivariate Cox regression coefficient of each gene. Eight autophagy-related genes were selected as prognostic features of HCC. We established the HCC prognostic risk model in TCGA dataset using these identified prognostic genes. The model's stability was confirmed in two independent verification sets (GSE14520 and GSE36376). The model had a good predictive power for the overall survival (OS) of HCC (hazard ratio = 2.32, 95% confidence interval = 1.76-3.05, P<0.001). Moreover, the risk score computed by the model did not depend on other clinical parameters. Finally, the applicability of the model was demonstrated through a nomogram (C-index = 0.701). In the present study, we established an autophagy-related risk model having a high prediction accuracy for OS in HCC. Our findings will contribute to the definition of prognosis and establishment of personalized therapy for HCC patients.
Collapse
Affiliation(s)
- Wenfang Xu
- Department of Biochemistry and Molecular Biology, School
of Basic Medical Sciences and Institutes of Biomedical Sciences, Fudan University,
Shanghai, China
- NHC Key Laboratory of Reproduction Regulation (Shanghai
Institute of Planned Parenthood Research), Fudan University, Shanghai, China
| | - Wenke Guo
- NHC Key Laboratory of Reproduction Regulation (Shanghai
Institute of Planned Parenthood Research), Fudan University, Shanghai, China
| | - Ping Lu
- NHC Key Laboratory of Reproduction Regulation (Shanghai
Institute of Planned Parenthood Research), Fudan University, Shanghai, China
| | - Duan Ma
- Department of Biochemistry and Molecular Biology, School
of Basic Medical Sciences and Institutes of Biomedical Sciences, Fudan University,
Shanghai, China
| | - Lei Liu
- Department of Biochemistry and Molecular Biology, School
of Basic Medical Sciences and Institutes of Biomedical Sciences, Fudan University,
Shanghai, China
| | - Fudong Yu
- NHC Key Laboratory of Reproduction Regulation (Shanghai
Institute of Planned Parenthood Research), Fudan University, Shanghai, China
| |
Collapse
|
45
|
Wang W, Wang L, Xie X, Yan Y, Li Y, Lu Q. A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma. BMC Cancer 2021; 21:6. [PMID: 33402113 PMCID: PMC7786458 DOI: 10.1186/s12885-020-07692-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Accepted: 11/25/2020] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Hepatocellular carcinoma (HCC) remains the most frequent liver cancer, accounting for approximately 90% of primary liver cancers worldwide. The recurrence-free survival (RFS) of HCC patients is a critical factor in devising a personal treatment plan. Thus, it is necessary to accurately forecast the prognosis of HCC patients in clinical practice. METHODS Using The Cancer Genome Atlas (TCGA) dataset, we identified genes associated with RFS. A robust likelihood-based survival modeling approach was used to select the best genes for the prognostic model. Then, the GSE76427 dataset was used to evaluate the prognostic model's effectiveness. RESULTS We identified 1331 differentially expressed genes associated with RFS. Seven of these genes were selected to generate the prognostic model. The validation in both the TCGA cohort and GEO cohort demonstrated that the 7-gene prognostic model can predict the RFS of HCC patients. Meanwhile, the results of the multivariate Cox regression analysis showed that the 7-gene risk score model could function as an independent prognostic factor. In addition, according to the time-dependent ROC curve, the 7-gene risk score model performed better in predicting the RFS of the training set and the external validation dataset than the classical TNM staging and BCLC. Furthermore, these seven genes were found to be related to the occurrence and development of liver cancer by exploring three other databases. CONCLUSION Our study identified a seven-gene signature for HCC RFS prediction that can be used as a novel and convenient prognostic tool. These seven genes might be potential target genes for metabolic therapy and the treatment of HCC.
Collapse
Affiliation(s)
- Wenhua Wang
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, 330006, Jiangxi, China.,Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Lingchen Wang
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, 330006, Jiangxi, China.,Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Xinsheng Xie
- Center for Experimental Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Yehong Yan
- Department of General Surgery, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Yue Li
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, 330006, Jiangxi, China.,Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, 330006, Jiangxi, China
| | - Quqin Lu
- Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang, 330006, Jiangxi, China. .,Department of Biostatistics and Epidemiology, School of Public Health, Nanchang University, Nanchang, 330006, Jiangxi, China.
| |
Collapse
|
46
|
Cao Y, Lu X, Li Y, Fu J, Li H, Li X, Chang Z, Liu S. Identification of a six-gene metabolic signature predicting overall survival for patients with lung adenocarcinoma. PeerJ 2020; 8:e10320. [PMID: 33344071 PMCID: PMC7718790 DOI: 10.7717/peerj.10320] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 10/17/2020] [Indexed: 12/13/2022] Open
Abstract
Background Lung cancer is the leading cause of cancer-related deaths worldwide. Lung adenocarcinoma (LUAD) is one of the main subtypes of lung cancer. Hundreds of metabolic genes are altered consistently in LUAD; however, their prognostic role remains to be explored. This study aimed to establish a molecular signature that can predict the prognosis in patients with LUAD based on metabolic gene expression. Methods The transcriptome expression profiles and corresponding clinical information of LUAD were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. The differentially expressed genes (DEGs) between LUAD and paired non-tumor samples were identified by the Wilcoxon rank sum test. Univariate Cox regression analysis and the lasso Cox regression model were used to construct the best-prognosis molecular signature. A nomogram was established comprising the prognostic model for predicting overall survival. To validate the prognostic ability of the molecular signature and the nomogram, the Kaplan-Meier survival analysis, Cox proportional hazards model, and receiver operating characteristic analysis were used. Results The six-gene molecular signature (PFKP, PKM, TPI1, LDHA, PTGES, and TYMS) from the DEGs was constructed to predict the prognosis. The molecular signature demonstrated a robust independent prognostic ability in the training and validation sets. The nomogram including the prognostic model had a greater predictive accuracy than previous systems. Furthermore, a gene set enrichment analysis revealed several significantly enriched metabolic pathways, which suggests a correlation of the molecular signature with metabolic systems and may help explain the underlying mechanisms. Conclusions Our study identified a novel six-gene metabolic signature for LUAD prognosis prediction. The molecular signature could reflect the dysregulated metabolic microenvironment, provide potential biomarkers for predicting prognosis, and indicate potential novel metabolic molecular-targeted therapies.
Collapse
Affiliation(s)
- Yubo Cao
- Department of Medical Oncology, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Xiaomei Lu
- Department of Pathophysiology, China Medical University, Shenyang, China
| | - Yue Li
- Department of Medical Oncology, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Jia Fu
- Department of Medical Oncology, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Hongyuan Li
- Department of Medical Oncology, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Xiulin Li
- Department of Medical Oncology, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Ziyou Chang
- Department of Medical Oncology, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
| | - Sa Liu
- Department of Medical Oncology, The Fourth Affiliated Hospital of China Medical University, Shenyang, China
| |
Collapse
|
47
|
Wu X, Yao Y, Li Z, Ge H, Wang D, Wang Y. Identification of a Transcriptional Prognostic Signature From Five Metabolic Pathways in Oral Squamous Cell Carcinoma. Front Oncol 2020; 10:572919. [PMID: 33425725 PMCID: PMC7793793 DOI: 10.3389/fonc.2020.572919] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 11/02/2020] [Indexed: 12/18/2022] Open
Abstract
Dysregulated metabolic pathways have been appreciated to be intimately associated with tumorigenesis and patient prognosis. Here, we sought to develop a novel prognostic signature based on metabolic pathways in patients with primary oral squamous cell carcinoma (OSCC). The original RNA-seq data of OSCC from The Cancer Genome Atlas (TCGA) project and Gene Expression Omnibus (GEO) database were transformed into a metabolic pathway enrichment score matrix by single-sample gene set enrichment analysis (ssGSEA). A novel prognostic signature based on metabolic pathways was constructed by LASSO and stepwise Cox regression analysis in the training cohort and validated in both testing and validation cohorts. The optimal cut-off value was obtained using the Youden index by receiver operating characteristic (ROC) curve. The overall survival curves were plotted by the Kaplan-Meier method. A time-dependent ROC curve analysis with 1, 3, 5 years as the defining point was performed to evaluate the predictive value of this prognostic signature. A 5-metabolic pathways prognostic signature (5MPS) for OSCC was constructed which stratified patients into subgroups with favorable or inferior survival. It served as an independent prognostic factor for patient survival and had a satisfactory predictive performance for OSCC. Our results developed a novel prognostic signature based on dysregulated metabolic pathways in OSCC and provided support for aberrant metabolism underlying OSCC tumorigenesis.
Collapse
Affiliation(s)
- Xiang Wu
- Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Nanjing, China
| | - Yuan Yao
- Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Nanjing, China
| | - Zhongwu Li
- Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Nanjing, China
| | - Han Ge
- Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Nanjing, China.,Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, China
| | - Dongmiao Wang
- Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, China
| | - Yanling Wang
- Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Nanjing, China.,Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Stomatology, Nanjing Medical University, Nanjing, China
| |
Collapse
|
48
|
Zhang Z, Wang L, Wang Q, Zhang M, Wang B, Jiang K, Ye Y, Wang S, Shen Z. Molecular Characterization and Clinical Relevance of RNA Binding Proteins in Colorectal Cancer. Front Genet 2020; 11:580149. [PMID: 33193701 PMCID: PMC7597397 DOI: 10.3389/fgene.2020.580149] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2020] [Accepted: 09/17/2020] [Indexed: 12/22/2022] Open
Abstract
Abnormal expression of RNA binding proteins (RBPs) has been reported across various cancers. However, the potential role of RBPs in colorectal cancer (CRC) remains unclear. In this study, we performed a systematic bioinformatics analysis of RBPs in CRC. We downloaded CRC data from The Cancer Genome Atlas (TCGA) database. Our analysis identified 242 differentially expressed RBPs between tumor and normal tissues, including 200 upregulated and 42 downregulated RBPs. Next, we found eight RBPs (RRS1, PABPC1L, TERT, SMAD6, UPF3B, RP9, NOL3, and PTRH1) related to the prognoses of CRC patients. Among these eight prognosis-related RBPs, four RBPs (NOL3, PTRH1, UPF3B, and SMAD6) were selected to construct a prognostic risk score model. Furthermore, our results indicated that the prognostic risk score model accurately predicted the prognosis of CRC patients [area under the receiver operating characteristic curve (AUC)for 3- and 5-year overall survival (OS) and was 0.645 and 0.672, respectively]. Furthermore, we developed a nomogram based on a prognostic risk score model. The nomogram was able to demonstrate the wonderful performance in predicting 3- and 5-year OS. Additionally, we validated the clinical value of four risk genes in the prognostic risk score model and identified that these risk genes were associated with tumorigenesis, lymph node metastasis, distant metastasis, clinical stage, and prognosis. Finally, we used the TIMER and Human Protein Atlas (HPA)database to validate the expression of four risk genes at the transcriptional and translational levels, respectively, and used a clinical cohort to validate the roles of NOL3 and UPF3B in predicting the prognosis of CRC patients. In summary, our study demonstrated that RBPs have an effect on CRC tumor progression and might be potential prognostic biomarkers for CRC patients.
Collapse
Affiliation(s)
- Zhen Zhang
- Department of Gastroenterological Surgery, Peking University People's Hospital, Beijing, China.,Laboratory of Surgical Oncology, Beijing Key Laboratory of Colorectal Cancer Diagnosis and Treatment Research, Peking University People's Hospital, Beijing, China
| | - Ling Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Hepatopancreatobiliary Surgery Department I, Peking University Cancer Hospital and Institute, Beijing, China
| | - Quan Wang
- Department of Gastroenterological Surgery, Peking University People's Hospital, Beijing, China.,Laboratory of Surgical Oncology, Beijing Key Laboratory of Colorectal Cancer Diagnosis and Treatment Research, Peking University People's Hospital, Beijing, China
| | - Mengmeng Zhang
- Department of Gastroenterological Surgery, Peking University People's Hospital, Beijing, China.,Laboratory of Surgical Oncology, Beijing Key Laboratory of Colorectal Cancer Diagnosis and Treatment Research, Peking University People's Hospital, Beijing, China
| | - Bo Wang
- Department of Gastroenterological Surgery, Peking University People's Hospital, Beijing, China.,Laboratory of Surgical Oncology, Beijing Key Laboratory of Colorectal Cancer Diagnosis and Treatment Research, Peking University People's Hospital, Beijing, China
| | - Kewei Jiang
- Department of Gastroenterological Surgery, Peking University People's Hospital, Beijing, China.,Laboratory of Surgical Oncology, Beijing Key Laboratory of Colorectal Cancer Diagnosis and Treatment Research, Peking University People's Hospital, Beijing, China
| | - Yingjiang Ye
- Department of Gastroenterological Surgery, Peking University People's Hospital, Beijing, China.,Laboratory of Surgical Oncology, Beijing Key Laboratory of Colorectal Cancer Diagnosis and Treatment Research, Peking University People's Hospital, Beijing, China
| | - Shan Wang
- Department of Gastroenterological Surgery, Peking University People's Hospital, Beijing, China.,Laboratory of Surgical Oncology, Beijing Key Laboratory of Colorectal Cancer Diagnosis and Treatment Research, Peking University People's Hospital, Beijing, China
| | - Zhanlong Shen
- Department of Gastroenterological Surgery, Peking University People's Hospital, Beijing, China.,Laboratory of Surgical Oncology, Beijing Key Laboratory of Colorectal Cancer Diagnosis and Treatment Research, Peking University People's Hospital, Beijing, China
| |
Collapse
|
49
|
Zhao Z, He B, Cai Q, Zhang P, Peng X, Zhang Y, Xie H, Wang X. A model of twenty-three metabolic-related genes predicting overall survival for lung adenocarcinoma. PeerJ 2020; 8:e10008. [PMID: 33024640 PMCID: PMC7520091 DOI: 10.7717/peerj.10008] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Accepted: 08/31/2020] [Indexed: 01/27/2023] Open
Abstract
Background The highest rate of cancer-related deaths worldwide is from lung adenocarcinoma (LUAD) annually. Metabolism was associated with tumorigenesis and cancer development. Metabolic-related genes may be important biomarkers and metabolic therapeutic targets for LUAD. Materials and Methods In this study, the gleaned cohort included LUAD RNA-SEQ data from the Cancer Genome Atlas (TCGA) and corresponding clinical data (n = 445). The training cohort was utilized to model construction, and data from the Gene Expression Omnibus (GEO, GSE30219 cohort, n = 83; GEO, GSE72094, n = 393) were regarded as a testing cohort and utilized for validation. First, we used a lasso-penalized Cox regression analysis to build a new metabolic-related signature for predicting the prognosis of LUAD patients. Next, we verified the metabolic gene model by survival analysis, C-index, receiver operating characteristic (ROC) analysis. Univariate and multivariate Cox regression analyses were utilized to verify the gene signature as an independent prognostic factor. Finally, we constructed a nomogram and performed gene set enrichment analysis to facilitate subsequent clinical applications and molecular mechanism analysis. Result Patients with higher risk scores showed significantly associated with poorer survival. We also verified the signature can work as an independent prognostic factor for LUAD survival. The nomogram showed better clinical application performance for LUAD patient prognostic prediction. Finally, KEGG and GO pathways enrichment analyses suggested several especially enriched pathways, which may be helpful for us investigative the underlying mechanisms.
Collapse
Affiliation(s)
- Zhenyu Zhao
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Central South University, Changsha, Hunan, China
| | - Boxue He
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Central South University, Changsha, Hunan, China
| | - Qidong Cai
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Central South University, Changsha, Hunan, China
| | - Pengfei Zhang
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Central South University, Changsha, Hunan, China
| | - Xiong Peng
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Central South University, Changsha, Hunan, China
| | - Yuqian Zhang
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Central South University, Changsha, Hunan, China
| | - Hui Xie
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Central South University, Changsha, Hunan, China
| | - Xiang Wang
- Department of Thoracic Surgery, The Second Xiangya Hospital of Central South University, Central South University, Changsha, Hunan, China.,Hunan Key Laboratory of Early Diagnosis and Precise Treatment of Lung Cancer, The Second Xiangya Hospital of Central South University, Central South University, Changsha, Hunan, China
| |
Collapse
|
50
|
A Novel Signature Based on mTORC1 Pathway in Hepatocellular Carcinoma. JOURNAL OF ONCOLOGY 2020; 2020:8291036. [PMID: 33014055 PMCID: PMC7512110 DOI: 10.1155/2020/8291036] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 08/15/2020] [Accepted: 09/01/2020] [Indexed: 12/11/2022]
Abstract
Background mTORC1 signal pathway plays a role in the initiation and progression of hepatocellular carcinoma (HCC), but no relevant gene signature was developed. This research aimed to explore the potential correlation between the mTORC1 signal pathway and HCC and establish the related gene signature. Methods HCC cases were retrieved from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. The genes included in mTORC1-associated signature were selected by performing univariate and multivariate Cox regression analyses and lasso regression analysis. The protein expression level of included genes was verified by The Human Protein Altas. Then, the signature was verified by survival analysis and multiple receiver operating characteristic (ROC) curve. Moreover, the correlation between signature and immune cells infiltration was investigated. Furthermore, a nomogram was established and evaluated by C-index and calibration plot. Results The signature was established with the six genes (ETF1, GSR, SKAP2, HSPD1, CACYBP, and PNP). Three genes (ETF1, GSR, and HSPD1) have verified their protein expression level in HCC. Under the grouping from signature, patients in the high-risk group showed worse survival than those in the low-risk group in both three datasets. The signature was found to be significantly associated with the infiltration of B cells, CD4+ T-cells, CD8+ T-cells, dendritic cells, macrophages, and neutrophils. The univariate and multivariate Cox regression analysis indicated that mTORC1-related signature could be the potential independent prognostic factor in HCC. Finally, the nomogram involving age, gender, stage, and signature has been established and verified. Conclusion The mTORC1-associated gene signature established and validated in our research could be used as a potential prognostic factor in HCC.
Collapse
|