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Liu C, Yuan ZY, Zhang XX, Chang JJ, Yang Y, Sun SJ, Du Y, Zhan HQ. Novel molecular classification and prognosis of papillary renal cell carcinoma based on a large-scale CRISPR-Cas9 screening and machine learning. Heliyon 2024; 10:e23184. [PMID: 38163209 PMCID: PMC10754875 DOI: 10.1016/j.heliyon.2023.e23184] [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: 07/11/2023] [Revised: 11/18/2023] [Accepted: 11/28/2023] [Indexed: 01/03/2024] Open
Abstract
Papillary renal cell carcinoma (PRCC) is a highly heterogeneous cancer, and PRCC patients with advanced/metastatic subgroup showed obviously shorter survival compared to other kinds of renal cell carcinomas. However, the molecular mechanism and prognostic predictors of PRCC remain unclear and are worth deep studying. The aim of this study is to identify novel molecular classification and construct a reliable prognostic model for PRCC. The expression data were retrieved from TCGA, GEO, GTEx and TARGET databases. CRISPR data was obtained from Depmap database. The key genes were selected by the intersection of CRISPR-Cas9 screening genes, differentially expressed genes, and genes with prognostic capacity in PRCC. The molecular classification was identified based on the key genes. Drug sensitivity, tumor microenvironment, somatic mutation, and survival were compared among the novel classification. A prognostic model utilizing multiple machine learning algorithms based on the key genes was developed and tested by independent external validation set. Our study identified three clusters (C1, C2 and C3) in PRCC based on 41 key genes. C2 had obviously higher expression of the key genes and lower survival than C1 and C3. Significant differences in drug sensitivity, tumor microenvironment, and mutation landscape have been observed among the three clusters. By utilizing 21 combinations of 9 machine learning algorithms, 9 out of 41 genes were chosen to construct a robust prognostic signature, which exhibited good prognostic ability. SERPINH1 was identified as a critical gene for its strong prognostic ability in PRCC by univariate and multiple Cox regression analyses. Quantitative real-time PCR and Western blot demonstrated that SERPINH1 mRNA and protein were highly expressed in PRCC cells compared with normal human renal cells. This study exhibited a new molecular classification and prognostic signature for PRCC, which may provide a potential biomarker and therapy target for PRCC patients.
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Affiliation(s)
- Chang Liu
- Department of Pathology, School of Basic Medical Sciences, Anhui Medical University, Hefei, 230032, PR China
- Department of Pathology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, PR China
| | - Zhan-Yuan Yuan
- Department of Plastic Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, 230601, PR China
| | - Xiao-Xun Zhang
- Department of Pathology, School of Basic Medical Sciences, Anhui Medical University, Hefei, 230032, PR China
| | - Jia-Jun Chang
- Department of Pathology, School of Basic Medical Sciences, Anhui Medical University, Hefei, 230032, PR China
| | - Yang Yang
- First School of Clinical Medicine, Anhui Medical University, Hefei, 230032, PR China
| | - Sheng-Jia Sun
- School of Clinical Medicine, Anhui Medical University, Hefei, 230031, PR China
| | - Yinan Du
- Department of Pathogenic microbiology, School of Basic Medical Sciences, Anhui Medical University, Hefei, 230032, PR China
| | - He-Qin Zhan
- Department of Pathology, School of Basic Medical Sciences, Anhui Medical University, Hefei, 230032, PR China
- Department of Pathology, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, PR China
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Fei H, Han X, Wang Y, Li S. Novel immune-related gene signature for risk stratification and prognosis prediction in ovarian cancer. J Ovarian Res 2023; 16:205. [PMID: 37858138 PMCID: PMC10585734 DOI: 10.1186/s13048-023-01289-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: 09/27/2021] [Accepted: 09/28/2023] [Indexed: 10/21/2023] Open
Abstract
BACKGROUND The immune system played a multifaceted role in ovarian cancer (OC) and was a significant mediator of ovarian carcinogenesis. Various immune cells and immune gene products played an integrated role in ovarian cancer (OC) progression, proved the significance of the immune microenvironment in prognosis. Therefore, we aimed to establish and validate an immune gene prognostic signature for OC patients' prognosis prediction. METHODS Differently expressed Immune-related genes (DEIRGs) were identified in 428 OC and 77 normal ovary tissue specimens from 9 independent GEO datasets. The Cancer Genome Atlas (TCGA) cohort was used as a training cohort, Univariate Cox analysis was used to identify prognostic DEIRGs in TCGA cohort. Then, an immune gene-based risk model for prognosis prediction was constructed using the LASSO regression analysis, and validated the accuracy and stability of the model in 374 and 93 OC patients in TCGA training cohort and International Cancer Genome Consortium (ICGC) validation cohort respectively. Finally, the correlation among risk score model, clinicopathological parameters, and immune cell infiltration were analyzed. RESULTS Five DEIRGs were identified to establish the immune gene signature and divided OC patients into the low- and high-risk groups. In TCGA and ICGC datasets, patients in the low-risk group showed a substantially higher survival rate than high-risk group. Receiver operating characteristic (ROC) curves, t-distributed stochastic neighbor embedding (t-SNE) analysis and principal component analysis (PCA) showed the good performance of the risk model. Clinicopathological correlation analysis proved the risk score model could serve as an independent prognostic factor in 2 independent datasets. CONCLUSIONS The prognostic model based on immune-related genes can function as a superior prognostic indicator for OC patients, which could provide evidence for individualized treatment and clinical decision making.
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Affiliation(s)
- Hongjun Fei
- Department of Reproductive Genetics, International Peace Maternity and Child Health Hospital, Shanghai Key Laboratory of Embryo Original Diseases, Shanghai Municipal Key Clinical Specialty, Shanghai Jiao Tong University School of Medicine, No.910, Hengshan Road, Shanghai, 200030, China.
| | - Xu Han
- Department of Reproductive Genetics, International Peace Maternity and Child Health Hospital, Shanghai Key Laboratory of Embryo Original Diseases, Shanghai Municipal Key Clinical Specialty, Shanghai Jiao Tong University School of Medicine, No.910, Hengshan Road, Shanghai, 200030, China
| | - Yanlin Wang
- Department of Reproductive Genetics, International Peace Maternity and Child Health Hospital, Shanghai Key Laboratory of Embryo Original Diseases, Shanghai Municipal Key Clinical Specialty, Shanghai Jiao Tong University School of Medicine, No.910, Hengshan Road, Shanghai, 200030, China
| | - Shuyuan Li
- Department of Reproductive Genetics, International Peace Maternity and Child Health Hospital, Shanghai Key Laboratory of Embryo Original Diseases, Shanghai Municipal Key Clinical Specialty, Shanghai Jiao Tong University School of Medicine, No.910, Hengshan Road, Shanghai, 200030, China.
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Fei H, Han X, Wang Y, Li S. Mining Prognostic Biomarkers of Thyroid Cancer Patients Based on the Immune-Related Genes and Development of a Reliable Prognostic Risk Model. Mediators Inflamm 2023; 2023:6503476. [PMID: 37554551 PMCID: PMC10406562 DOI: 10.1155/2023/6503476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 04/21/2023] [Accepted: 07/10/2023] [Indexed: 08/10/2023] Open
Abstract
PURPOSE Tumor immunity serves an essential role in the occurrence and development of thyroid cancer (THCA). The aim of this study is to establish an immune-related prognostic model for THCA patients by using immune-related genes (IRGs). METHODS Wilcox test was used to screen the differentially expressed immune-related genes (DEIRGs) in THCA and normal tissues, then the DEIRGs related to prognosis were identified using univariate Cox regression analysis. According to The Cancer Genome Atlas (TCGA) cohort, we developed a least absolute shrinkage and selection operator (LASSO) regression prognostic model and performed validation analyses regard to the predictive value of the model in internal (TCGA) and external (International Cancer Genome Consortium) cohorts respectively. Finally, we analyzed the correlation among the prognostic model, clinical variables, and immune cell infiltration. RESULTS Eighty-two of 2,498 IRGs were differentially expressed between THCA and normal tissues, and 18 of them were related to prognosis. LASSO Cox regression analysis identified seven DEIRGs with the greatest prognostic value to construct the prognostic model. The risk model showed high predictive value for the survival of THCA in two independent cohorts. The risk score according to the risk model was positively associated with poor survival and the infiltration levels of immune cells, it can evaluate the prognosis of THCA patients independent of any other clinicopathologic feature. The prognostic value and genetic alternations of seven risk genes were evaluated separately. CONCLUSION Our study established and verified a dependable prognostic model associated with immune for THCA, both the identified IRGs and immune-related risk model were clinically significant, which is conducive to promoting individualized immunotherapy against THCA.
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Affiliation(s)
- Hongjun Fei
- Department of Reproductive Genetics, International Peace Maternity and Child Health Hospital, Shanghai Key Laboratory of Embryo Original Diseases, Shanghai Municipal Key Clinical Specialty, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Xu Han
- Department of Reproductive Genetics, International Peace Maternity and Child Health Hospital, Shanghai Key Laboratory of Embryo Original Diseases, Shanghai Municipal Key Clinical Specialty, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Yanlin Wang
- Department of Reproductive Genetics, International Peace Maternity and Child Health Hospital, Shanghai Key Laboratory of Embryo Original Diseases, Shanghai Municipal Key Clinical Specialty, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
| | - Shuyuan Li
- Department of Reproductive Genetics, International Peace Maternity and Child Health Hospital, Shanghai Key Laboratory of Embryo Original Diseases, Shanghai Municipal Key Clinical Specialty, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
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Zhang W, Liu W, Yang Y, Xiao C, Xiao Y, Tan X, Pang Q, Wu H, Hua M, Shi X. Integrative analysis of transcriptomic landscape and urinary signature reveals prognostic biomarkers for clear cell renal cell carcinoma. Front Oncol 2023; 13:1102623. [PMID: 37035174 PMCID: PMC10079990 DOI: 10.3389/fonc.2023.1102623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 03/14/2023] [Indexed: 04/11/2023] Open
Abstract
Background Clear cell renal cell carcinoma (ccRCC) patients with venous tumor thrombus (VTT) have poor prognosis. We aimed to reveal features of ccRCC with VTT and develop a urine-based prognostic classifier to predict ccRCC prognosis through integrative analyses of transcriptomic landscape and urinary signature. Methods RNA sequencing was performed in five patients with ccRCC thrombus-tumor-normal tissue triples, while mass spectrometry was performed for urine samples from 12 ccRCC and 11 healthy controls. A urine-based classifier consisting of three proteins was developed to predict patients' survival and validated in an independent cohort. Results Transcriptomic analysis identified 856 invasion-associated differentially expressed genes (DEGs). Furthermore, proteomic analysis showed 133 differentially expressed proteins (DEPs). Integration of transcriptomic landscape and urinary signature reveals 6 urinary detectable proteins (VSIG4, C3, GAL3ST1, TGFBI, AKR1C3, P4HB) displaying abundance changes consistent with corresponding genes in transcriptomic profiling. According to TCGA database, VSIG4, TGFBI, and P4HB were significantly overexpressed in patients with shorter survival and might be independent prognostic factors for ccRCC (all p<0.05). A prognostic classifier consisting of the three DEPs highly associated with survival performed satisfactorily in predicting overall survival (HR=2.0, p<0.01) and disease-free survival (HR=1.6, p<0.001) of ccRCC patients. The ELISA analysis of urine samples from an independent cohort confirmed the satisfied predictive power of the classifier for pathological grade (AUC=0.795, p<0.001) and stage (AUC=0.894, p<0.001). Conclusion Based on integrative analyses of transcriptomic landscape and urinary signature, the urine-based prognostic classifier consisting of VSIG4, TGFBI, and P4HB has satisfied predictive power of ccRCC prognosis and may facilitate ccRCC molecular subtyping and treatment.
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Affiliation(s)
- Wei Zhang
- Department of Urology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Wenqiang Liu
- Department of Urology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Yiren Yang
- Department of Urology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Chengwu Xiao
- Department of Urology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Yutian Xiao
- Department of Urology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Xiaojie Tan
- Department of Epidemiology, Naval Medical University, Shanghai, China
| | - Qingyang Pang
- Department of Urology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Han Wu
- Department of Urology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Meimian Hua
- Department of Urology, Changhai Hospital, Naval Medical University, Shanghai, China
- *Correspondence: Xiaolei Shi, ; Meimian Hua,
| | - Xiaolei Shi
- Department of Urology, Changhai Hospital, Naval Medical University, Shanghai, China
- *Correspondence: Xiaolei Shi, ; Meimian Hua,
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A Novel Autophagy-Related Prognostic Risk Model and a Nomogram for Survival Prediction of Oral Cancer Patients. BIOMED RESEARCH INTERNATIONAL 2022; 2022:2067540. [PMID: 35036428 PMCID: PMC8758260 DOI: 10.1155/2022/2067540] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 12/11/2021] [Indexed: 12/26/2022]
Abstract
Background. This study is aimed at constructing a risk signature to predict survival outcomes of ORCA patients. Methods. We identified differentially expressed autophagy-related genes (DEARGs) based on the RNA sequencing data in the TCGA database; then, four independent survival-related ARGs were identified to construct an autophagy-associated signature for survival prediction of ORCA patients. The validity and robustness of the prognostic model were validated by clinicopathological data and survival data. Subsequently, four independent prognostic DEARGs that composed the model were evaluated individually. Results. The expressions of 232 autophagy-related genes (ARGs) in 127 ORCA and 13 control tissues were compared, and 36 DEARGs were filtered out. We performed functional enrichment analysis and constructed protein–protein interaction network for 36 DEARGs. Univariate and multivariate Cox regression analyses were adopted for searching prognostic ARGs, and an autophagy-associated signature for ORCA patients was constructed. Eventually, 4 desirable independent survival-related ARGs (WDR45, MAPK9, VEGFA, and ATIC) were confirmed and comprised the prognostic model. We made use of multiple ways to verify the accuracy of the novel autophagy-related signature for survival evaluation, such as receiver-operator characteristic curve, Kaplan–Meier plotter, and clinicopathological correlational analyses. Four independent prognostic DEARGs that formed the model were also associated with the prognosis of ORCA patients. Conclusions. The autophagy-related risk model can evaluate OS for ORCA patients independently since it is accurate and stable. Four prognostic ARGs that composed the model can be studied deeply for target treatment.
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Zhao H, Zhang J, Fu X, Mao D, Qi X, Liang S, Meng G, Song Z, Yang R, Guo Z, Tong B, Sun M, Zuo B, Li G. Integrated bioinformatics analysis of the NEDD4 family reveals a prognostic value of NEDD4L in clear-cell renal cell cancer. PeerJ 2021; 9:e11880. [PMID: 34458018 PMCID: PMC8378337 DOI: 10.7717/peerj.11880] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 07/07/2021] [Indexed: 12/20/2022] Open
Abstract
The members of the Nedd4-like E3 family participate in various biological processes. However, their role in clear cell renal cell carcinoma (ccRCC) is not clear. This study systematically analyzed the Nedd4-like E3 family members in ccRCC data sets from multiple publicly available databases. NEDD4L was identified as the only NEDD4 family member differentially expressed in ccRCC compared with normal samples. Bioinformatics tools were used to characterize the function of NEDD4L in ccRCC. It indicated that NEDD4L might regulate cellular energy metabolism by co-expression analysis, and subsequent gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. A prognostic model developed by the LASSO Cox regression method showed a relatively good predictive value in training and testing data sets. The result revealed that NEDD4L was associated with biosynthesis and metabolism of ccRCC. Since NEDD4L is downregulated and dysregulation of metabolism is involved in tumor progression, NEDD4L might be a potential therapeutic target in ccRCC.
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Affiliation(s)
- Hui Zhao
- Department of Urology, Affiliated Hospital of Weifang Medical University, Weifang, China.,Department of Urology, China Rehabilitation Research Centre, Rehabilitation School of Capital Medical University, Beijing, China
| | - Junjun Zhang
- Department of Oncology, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Xiaoliang Fu
- Department of Urology, The Second Affiliated Hospital of Air Force Medical University, Xian, China
| | - Dongdong Mao
- Department of Urology, Affiliated Hospital of Weifang Medical University, Weifang, China
| | - Xuesen Qi
- Department of Urology, Affiliated Hospital of Weifang Medical University, Weifang, China
| | - Shuai Liang
- Department of Urology, Affiliated Hospital of Weifang Medical University, Weifang, China
| | - Gang Meng
- Department of Urology, Affiliated Hospital of Weifang Medical University, Weifang, China
| | - Zewen Song
- Department of Oncology, The Third Xiangya Hospital of Central South University, Changsha, China
| | - Ru Yang
- Henan Key Laboratory of Neurorestoratology, The First Affliated Hospital of Xinxiang Medical University, Weihui, China
| | - Zhenni Guo
- College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, China
| | - Binghua Tong
- College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, China
| | - Meiqing Sun
- College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, China
| | - Baile Zuo
- Tumor Molecular Immunology and Immunotherapy Laboratory, School of Laboratory Medicine, Xinxiang Medical University, Xinxiang, China
| | - Guoyin Li
- College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou, China.,Academy of Medical Science, Zhengzhou University, Zhengzhou, China
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