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Ouyang G, Li Q, Wei Y, Dai W, Deng H, Liu Y, Li J, Li M, Luo S, Li S, Liang Y, Pan G, Yang J, Gan T. Identification of PANoptosis-related subtypes, construction of a prognosis signature, and tumor microenvironment landscape of hepatocellular carcinoma using bioinformatic analysis and experimental verification. Front Immunol 2024; 15:1323199. [PMID: 38742112 PMCID: PMC11089137 DOI: 10.3389/fimmu.2024.1323199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 04/15/2024] [Indexed: 05/16/2024] Open
Abstract
Background Hepatocellular carcinoma (HCC) is one of the most lethal malignancies worldwide. PANoptosis is a recently unveiled programmed cell death pathway, Nonetheless, the precise implications of PANoptosis within the context of HCC remain incompletely elucidated. Methods We conducted a comprehensive bioinformatics analysis to evaluate both the expression and mutation patterns of PANoptosis-related genes (PRGs). We categorized HCC into two clusters and identified differentially expressed PANoptosis-related genes (DEPRGs). Next, a PANoptosis risk model was constructed using LASSO and multivariate Cox regression analyses. The relationship between PRGs, risk genes, the risk model, and the immune microenvironment was studies. In addition, drug sensitivity between high- and low-risk groups was examined. The expression profiles of these four risk genes were elucidate by qRT-PCR or immunohistochemical (IHC). Furthermore, the effect of CTSC knock down on HCC cell behavior was verified using in vitro experiments. Results We constructed a prognostic signature of four DEPRGs (CTSC, CDCA8, G6PD, and CXCL9). Receiver operating characteristic curve analyses underscored the superior prognostic capacity of this signature in assessing the outcomes of HCC patients. Subsequently, patients were stratified based on their risk scores, which revealed that the low-risk group had better prognosis than those in the high-risk group. High-risk group displayed a lower Stromal Score, Immune Score, ESTIMATE score, and higher cancer stem cell content, tumor mutation burden (TMB) values. Furthermore, a correlation was noted between the risk model and the sensitivity to 56 chemotherapeutic agents, as well as immunotherapy efficacy, in patient with. These findings provide valuable guidance for personalized clinical treatment strategies. The qRT-PCR analysis revealed that upregulated expression of CTSC, CDCA8, and G6PD, whereas downregulated expression of CXCL9 in HCC compared with adjacent tumor tissue and normal liver cell lines. The knockdown of CTSC significantly reduced both HCC cell proliferation and migration. Conclusion Our study underscores the promise of PANoptosis-based molecular clustering and prognostic signatures in predicting patient survival and discerning the intricacies of the tumor microenvironment within the context of HCC. These insights hold the potential to advance our comprehension of the therapeutic contribution of PANoptosis plays in HCC and pave the way for generating more efficacious treatment strategies.
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Affiliation(s)
- Guoqing Ouyang
- Department of General Surgery, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China
- Guangxi Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Guangxi Medical University, Nanning, Guangxi, China
- Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Guangxi Medical University, Ministry of Education, Nanning, Guangxi, China
- Liuzhou Hepatobiliary and Pancreatic Diseases Precision Diagnosis Research Center of Engineering Technology, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China
| | - Qiuyun Li
- Department of General Surgery, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China
- Liuzhou Hepatobiliary and Pancreatic Diseases Precision Diagnosis Research Center of Engineering Technology, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China
| | - Yangnian Wei
- Department of Hepatobiliary Surgery, Ruikang Hospital, Guangxi University of Chinese Medicine, Nanning, Guangxi, China
| | - Wenbin Dai
- Department of Pathology, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China
| | - Haojian Deng
- Department of Emergency Medical, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China
| | - Youli Liu
- Department of Pathology, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China
| | - Jiaguang Li
- Department of Pathology, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China
| | - Mingjuan Li
- Department of General Surgery, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China
| | - Shunwen Luo
- Department of General Surgery, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China
| | - Shuang Li
- Department of General Surgery, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China
| | - Yunying Liang
- Department of General Surgery, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China
| | - Guandong Pan
- Department of General Surgery, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China
- Liuzhou Hepatobiliary and Pancreatic Diseases Precision Diagnosis Research Center of Engineering Technology, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China
| | - Jianqing Yang
- Department of General Surgery, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China
- Liuzhou Hepatobiliary and Pancreatic Diseases Precision Diagnosis Research Center of Engineering Technology, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China
| | - Tao Gan
- Department of General Surgery, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China
- Department of Emergency Medical, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China
- Key Specialty Department of Emergency Medicine in Guangxi, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, Guangxi, China
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Bai Z, Yan C, Nie Y, Zeng Q, Xu L, Wang S, Chang D. Glucose metabolism-based signature predicts prognosis and immunotherapy strategies for colon adenocarcinoma. J Gene Med 2024; 26:e3620. [PMID: 37973153 DOI: 10.1002/jgm.3620] [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: 08/30/2023] [Revised: 09/25/2023] [Accepted: 10/09/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND The global prevalence and metastasis rates of colon adenocarcinoma (COAD) are high, and therapeutic success is limited. Although previous research has primarily explored changes in gene phenotypes, the incidence rate of COAD remains unchanged. Metabolic reprogramming is a crucial aspect of cancer research and therapy. The present study aims to develop cluster and polygenic risk prediction models for COAD based on glucose metabolism pathways to assess the survival status of patients and potentially identify novel immunotherapy strategies and related therapeutic targets. METHODS COAD-specific data (including clinicopathological information and gene expression profiles) were sourced from The Cancer Genome Atlas (TCGA) and two Gene Expression Omnibus (GEO) datasets (GSE33113 and GSE39582). Gene sets related to glucose metabolism were obtained from the MSigDB database. The Gene Set Variation Analysis (GSVA) method was utilized to calculate pathway scores for glucose metabolism. The hclust function in R, part of the Pheatmap package, was used to establish a clustering system. The mutation characteristics of identified clusters were assessed via MOVICS software, and differentially expressed genes (DEGs) were filtered using limma software. Signature analysis was performed using the least absolute shrinkage and selection operator (LASSO) method. Survival curves, survival receiver operating characteristic (ROC) curves and multivariate Cox regression were analyzed to assess the efficacy and accuracy of the signature for prognostic prediction. The pRRophetic program was employed to predict drug sensitivity, with data sourced from the Genomics of Drug Sensitivity in Cancer (GDSC) database. RESULTS Four COAD subgroups (i.e., C1, C2, C3 and C4) were identified based on glucose metabolism, with the C4 group having higher survival rates. These four clusters were bifurcated into a new Clust2 system (C1 + C2 + C3 and C4). In total, 2175 DEGs were obtained (C1 + C2 + C3 vs. C4), from which 139 prognosis-related genes were identified. ROC curves predicting 1-, 3- and 5-year survival based on a signature containing nine genes showed an area under the curve greater than 0.7. Meanwhile, the study also found this feature to be an important predictor of prognosis in COAD and accordingly assessed the risk score, with higher risk scores being associated with a worse prognosis. The high-risk and low-risk groups responded differently to immunotherapy and chemotherapeutic agents, and there were differences in functional enrichment pathways. CONCLUSIONS This unique signature based on glucose metabolism may potentially provide a basis for predicting patient prognosis, biological characteristics and more effective immunotherapy strategies for COAD.
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Affiliation(s)
- Zilong Bai
- Department of Surgical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shanxi, China
| | - Chunyu Yan
- Department of Endocrinology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shanxi, China
| | - Yuanhua Nie
- Department of Surgical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shanxi, China
| | - Qingnuo Zeng
- Department of Surgical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shanxi, China
| | - Longwen Xu
- Department of Surgical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shanxi, China
| | - Shilong Wang
- Department of Surgical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shanxi, China
| | - Dongmin Chang
- Department of Surgical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shanxi, China
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Wang X, Chan S, Dai L, Xu Y, Yang Q, Wang M, Han Q, Chen J, Zuo X, Wang Z, Yang Y, Zhao H, Zhang G, Zhang H, Chen W. Identification of novel T cell proliferation patterns, potential biomarkers and therapeutic drugs in colorectal cancer. J Cancer 2024; 15:1234-1254. [PMID: 38356712 PMCID: PMC10861827 DOI: 10.7150/jca.91835] [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: 11/02/2023] [Accepted: 12/23/2023] [Indexed: 02/16/2024] Open
Abstract
Background: T cells are crucial components of antitumor immunity. A list of genes associated with T cell proliferation was recently identified; however, the impact of T cell proliferation-related genes (TRGs) on the prognosis and therapeutic responses of patients with colorectal cancer (CRC) remains unclear. Methods: 33 TRG expression information and clinical information of patients with CRC gathered from multiple datasets were subjected to bioinformatic analysis. Consensus clustering was used to determine the molecular subtypes associated with T cell proliferation. Utilizing the Lasso-Cox regression, a predictive signature was created and verified in external cohorts. A tumor immune environment analysis was conducted, and potential biomarkers and therapeutic drugs were identified and confirmed via in vitro and in vivo studies. Results: CRC patients were separated into two TRG clusters, and differentially expressed genes (DEGs) were identified. Patient information was divided into three different gene clusters, and the determined molecular subtypes were linked to patient survival, immune cells, and immune functions. Prognosis-associated DEGs in the three gene clusters were used to evaluate the risk score, and a predictive signature was developed. The ability of the risk score to predict patient survival and treatment response has been successfully validated using multiple datasets. To discover more possible biomarkers for CRC, the weighted gene co-expression network analysis algorithm was utilized to screen key TRG variations between groups with high- and low-risk. CDK1, BATF, IL1RN, and ITM2A were screened out as key TRGs, and the expression of key TRGs was confirmed using real-time reverse transcription polymerase chain reaction. According to the key TRGs, 7,8-benzoflavone was identified as the most significant drug molecule, and MTT, colony formation, wound healing, transwell assays, and in vivo experiments indicated that 7,8-benzoflavone significantly suppressed the proliferation and migration of CRC cells. Conclusion: T cell proliferation-based molecular subtypes and predictive signatures can be utilized to anticipate patient results, immunological landscape, and treatment response in CRC. Novel biomarker candidates and potential therapeutic drugs for CRC were identified and verified using in vitro and in vivo tests.
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Affiliation(s)
- Xu Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, Anhui, China
| | - Shixin Chan
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, Anhui, China
| | - Longfei Dai
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, Anhui, China
| | - Yuanmin Xu
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, Anhui, China
| | - Qi Yang
- Department of Gastroenterology, The First Affiliated Hospital of Wannan Medical College, Wuhu, 241000, China
| | - Ming Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, Anhui, China
| | - Qijun Han
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, Anhui, China
| | - Jiajie Chen
- Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, Anhui, China
| | - Xiaomin Zuo
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, Anhui, China
| | - Zhenglin Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, Anhui, China
| | - Yang Yang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, Anhui, China
| | - Hu Zhao
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, Anhui, China
| | - Guihong Zhang
- The Pathology Department of Anhui Medical University, Hefei 230032, Anhui, China
| | - Huabing Zhang
- Department of Biochemistry and Molecular Biology, Metabolic Disease Research Center, School of Basic Medicine, Anhui Medical University, Hefei 230032, Anhui, China
- The First Affiliated Chuzhou Hospital of Anhui Medical University, Chuzhou 239000, Anhui, China
| | - Wei Chen
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei 230032, Anhui, China
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Horaira MA, Islam MA, Kibria MK, Alam MJ, Kabir SR, Mollah MNH. Bioinformatics screening of colorectal-cancer causing molecular signatures through gene expression profiles to discover therapeutic targets and candidate agents. BMC Med Genomics 2023; 16:64. [PMID: 36991484 PMCID: PMC10053149 DOI: 10.1186/s12920-023-01488-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 03/14/2023] [Indexed: 03/31/2023] Open
Abstract
BACKGROUND Detection of appropriate receptor proteins and drug agents are equally important in the case of drug discovery and development for any disease. In this study, an attempt was made to explore colorectal cancer (CRC) causing molecular signatures as receptors and drug agents as inhibitors by using integrated statistics and bioinformatics approaches. METHODS To identify the important genes that are involved in the initiation and progression of CRC, four microarray datasets (GSE9348, GSE110224, GSE23878, and GSE35279) and an RNA_Seq profiles (GSE50760) were downloaded from the Gene Expression Omnibus database. The datasets were analyzed by a statistical r-package of LIMMA to identify common differentially expressed genes (cDEGs). The key genes (KGs) of cDEGs were detected by using the five topological measures in the protein-protein interaction network analysis. Then we performed in-silico validation for CRC-causing KGs by using different web-tools and independent databases. We also disclosed the transcriptional and post-transcriptional regulatory factors of KGs by interaction network analysis of KGs with transcription factors (TFs) and micro-RNAs. Finally, we suggested our proposed KGs-guided computationally more effective candidate drug molecules compared to other published drugs by cross-validation with the state-of-the-art alternatives of top-ranked independent receptor proteins. RESULTS We identified 50 common differentially expressed genes (cDEGs) from five gene expression profile datasets, where 31 cDEGs were downregulated, and the rest 19 were up-regulated. Then we identified 11 cDEGs (CXCL8, CEMIP, MMP7, CA4, ADH1C, GUCA2A, GUCA2B, ZG16, CLCA4, MS4A12 and CLDN1) as the KGs. Different pertinent bioinformatic analyses (box plot, survival probability curves, DNA methylation, correlation with immune infiltration levels, diseases-KGs interaction, GO and KEGG pathways) based on independent databases directly or indirectly showed that these KGs are significantly associated with CRC progression. We also detected four TFs proteins (FOXC1, YY1, GATA2 and NFKB) and eight microRNAs (hsa-mir-16-5p, hsa-mir-195-5p, hsa-mir-203a-3p, hsa-mir-34a-5p, hsa-mir-107, hsa-mir-27a-3p, hsa-mir-429, and hsa-mir-335-5p) as the key transcriptional and post-transcriptional regulators of KGs. Finally, our proposed 15 molecular signatures including 11 KGs and 4 key TFs-proteins guided 9 small molecules (Cyclosporin A, Manzamine A, Cardidigin, Staurosporine, Benzo[A]Pyrene, Sitosterol, Nocardiopsis Sp, Troglitazone, and Riccardin D) were recommended as the top-ranked candidate therapeutic agents for the treatment against CRC. CONCLUSION The findings of this study recommended that our proposed target proteins and agents might be considered as the potential diagnostic, prognostic and therapeutic signatures for CRC.
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Affiliation(s)
- Md Abu Horaira
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Ariful Islam
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Kaderi Kibria
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Jahangir Alam
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Syed Rashel Kabir
- Department of Biochemistry and Molecular Biology, University of Rajshahi, Rajshahi, 6205, Bangladesh
| | - Md Nurul Haque Mollah
- Bioinformatics Lab, Department of Statistics, University of Rajshahi, Rajshahi, 6205, Bangladesh.
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Wang X, Zuo X, Hu X, Liu Y, Wang Z, Chan S, Sun R, Han Q, Yu Z, Wang M, Zhang H, Chen W. Identification of cuproptosis-based molecular subtypes, construction of prognostic signature and characterization of immune landscape in colon cancer. Front Oncol 2023; 13:927608. [PMID: 37007145 PMCID: PMC10064275 DOI: 10.3389/fonc.2023.927608] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 02/27/2023] [Indexed: 03/19/2023] Open
Abstract
BackgroundCuproptosis is a newly discovered form of cell death induced by targeting lipoacylated proteins involved in the tricarboxylic acid cycle. However, the roles of cuproptosis-related genes (CRGs) in the clinical outcomes and immune landscape of colon cancer remain unknown.MethodsWe performed bioinformatics analysis of the expression data of 13 CRGs identified from a previous study and clinical information of patients with colon cancer obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. Colon cancer cases were divided into two CRG clusters and prognosis-related differentially expressed genes. Patient data were separated into three corresponding distinct gene clusters, and the relationships between the risk score, patient prognosis, and immune landscape were analyzed. The identified molecular subtypes correlated with patient survival, immune cells, and immune functions. A prognostic signature based on five genes was identified, and the patients were divided into high- and low-risk groups based on the calculated risk score. A nomogram model for predicting patient survival was developed based on the risk score and other clinical features.ResultsThe high-risk group showed a worse prognosis, and the risk score was related to immune cell abundance, microsatellite instability, cancer stem cell index, checkpoint expression, immune escape, and response to chemotherapeutic drugs and immunotherapy. Findings related to the risk score were validated in the imvigor210 cohort of patients with metastatic urothelial cancer treated with anti-programmed cell death ligand 1.ConclusionWe demonstrated the potential of cuproptosis-based molecular subtypes and prognostic signatures for predicting patient survival and the tumor microenvironment in colon cancer. Our findings may improve the understanding of the role of cuproptosis in colon cancer and lead to the development of more effective treatment strategies.
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Affiliation(s)
- Xu Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Xiaomin Zuo
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Xianyu Hu
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Yuyao Liu
- Department of Burns, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Zhenglin Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Shixin Chan
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Rui Sun
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Qijun Han
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Zhen Yu
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Ming Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Huabing Zhang
- The First Affiliated Chuzhou Hospital of Anhui Medical University, Chuzhou, Anhui, China
- Department of Biochemistry and Molecular Biology, Metabolic Disease Research Center, School of Basic Medicine, Anhui Medical University, Hefei, Anhui, China
- *Correspondence: Huabing Zhang, ; Wei Chen,
| | - Wei Chen
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- *Correspondence: Huabing Zhang, ; Wei Chen,
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Wang X, Xu Y, Dai L, Yu Z, Wang M, Chan S, Sun R, Han Q, Chen J, Zuo X, Wang Z, Hu X, Yang Y, Zhao H, Hu K, Zhang H, Chen W. A novel oxidative stress- and ferroptosis-related gene prognostic signature for distinguishing cold and hot tumors in colorectal cancer. Front Immunol 2022; 13:1043738. [PMID: 36389694 PMCID: PMC9660228 DOI: 10.3389/fimmu.2022.1043738] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 10/17/2022] [Indexed: 08/10/2023] Open
Abstract
Oxidative stress and ferroptosis exhibit crosstalk in many types of human diseases, including malignant tumors. We aimed to develop an oxidative stress- and ferroptosis-related gene (OFRG) prognostic signature to predict the prognosis and therapeutic response in patients with colorectal cancer (CRC). Thirty-four insertion genes between oxidative stress-related genes and ferroptosis-related genes were identified as OFRGs. We then performed bioinformatics analysis of the expression profiles of 34 OFRGs and clinical information of patients obtained from multiple datasets. Patients with CRC were divided into three OFRG clusters, and differentially expressed genes (DEGs) between clusters were identified. OFRG clusters correlated with patient survival and immune cell infiltration. Prognosis-related DEGs in three clusters were used to calculate the risk score, and a prognostic signature was constructed according to the risk score. In this study, patients in the low-risk group had better prognosis, higher immune cell infiltration levels, and better responses to fluorouracil-based chemotherapy and immune checkpoint blockade therapy than high-risk patients; these results were successfully validated with multiple independent datasets. Thus, low-risk CRC could be defined as hot tumors and high-risk CRC could be defined as cold tumors. To further identify potential biomarkers for CRC, the expression levels of five signature genes in CRC and adjacent normal tissues were further verified via an in vitro experiment. In conclusion, we identified 34 OFRGs and constructed an OFRG-related prognostic signature, which showed excellent performance in predicting survival and therapeutic responses for patients with CRC. This could help to distinguish cold and hot tumors in CRC, and the results might be helpful for precise treatment protocols in clinical practice.
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Affiliation(s)
- Xu Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Yuanmin Xu
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Longfei Dai
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Zhen Yu
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Ming Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Shixin Chan
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Rui Sun
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Qijun Han
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Jiajie Chen
- Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Xiaomin Zuo
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Zhenglin Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Xianyu Hu
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Yang Yang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Hu Zhao
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Kongwang Hu
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Huabing Zhang
- Department of Biochemistry and Molecular Biology, Metabolic Disease Research Center, School of Basic Medicine, Anhui Medical University, Hefei, Anhui, China
- The First Affiliated Chuzhou Hospital of Anhui Medical University, Chuzhou, Anhui, China
| | - Wei Chen
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
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Wang X, Sun R, Chan S, Meng L, Xu Y, Zuo X, Wang Z, Hu X, Han Q, Dai L, Bai T, Yu Z, Wang M, Yang W, Zhang H, Chen W. PANoptosis-based molecular clustering and prognostic signature predicts patient survival and immune landscape in colon cancer. Front Genet 2022; 13:955355. [PMID: 36186438 PMCID: PMC9515384 DOI: 10.3389/fgene.2022.955355] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 08/22/2022] [Indexed: 11/16/2022] Open
Abstract
PANoptosis is a newly-discovered cell death pathway that involves crosstalk and co-ordination between pyroptosis, apoptosis, and necroptosis processes. However, the roles of PANoptosis-related genes (PRGs) in prognosis and immune landscape of colon cancer remain widely unknown. Here, we performed a bioinformatics analysis of expression data of nineteen PRGs identified from previous studies and clinical data of colon cancer patients obtained from TCGA and GEO databases. Colon cancer cases were divided into two PRG clusters, and prognosis-related differentially expressed genes (PRDEGs) were identified. The patient data were then separated into two corresponding distinct gene clusters, and the relationship between the risk score, patient prognosis, and immune landscape was analyzed. The identified PRGs and gene clusters correlated with patient survival and immune system and cancer-related biological processes and pathways. A prognosis signature based on seven genes was identified, and patients were divided into high-risk and low-risk groups based on the calculated risk score. A nomogram model for prediction of patient survival was also developed based on the risk score and other clinical features. Accordingly, the high-risk group showed worse prognosis, and the risk score was related to immune cell abundance, cancer stem cell (CSC) index, checkpoint expression, and response to immunotherapy and chemotherapeutic drugs. Results of quantitative real-time polymerase chain reaction (qRT-PCR) showed that LGR5 and VSIG4 were differentially expressed between normal and colon cancer samples. In conclusion, we demonstrated the potential of PANoptosis-based molecular clustering and prognostic signatures for prediction of patient survival and tumor microenvironment (TME) in colon cancer. Our findings may improve our understanding of the role of PANoptosis in colon cancer, and enable the development of more effective treatment strategies.
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Affiliation(s)
- Xu Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Rui Sun
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Shixin Chan
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Lei Meng
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Yuanmin Xu
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Xiaomin Zuo
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Zhenglin Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Xianyu Hu
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Qijun Han
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Longfei Dai
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Tao Bai
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Zhen Yu
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Ming Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Wenqi Yang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
| | - Huabing Zhang
- Affiliated Chuzhou Hospital of Anhui Medical University, First People’s Hospital of Chuzhou, Chuzhou, Anhui, China
- Department of Biochemistry and Molecular Biology, Metabolic Disease Research Center, School of Basic Medicine, Anhui Medical University, Hefei, Anhui, China
- Correspondence: Huabing Zhang, ; Wei Chen,
| | - Wei Chen
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China
- Correspondence: Huabing Zhang, ; Wei Chen,
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8
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Janani B, Vijayakumar M, Priya K, Kim JH, Geddawy A, Shahid M, El-Bidawy MH, Al-Ghamdi S, Alsaidan M, Abdelzaher MH, Mohideen AP, Ramesh T. A network-based pharmacological investigation to identify the mechanistic regulatory pathway of andrographolide against colorectal cancer. Front Pharmacol 2022; 13:967262. [PMID: 36110531 PMCID: PMC9468871 DOI: 10.3389/fphar.2022.967262] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Traditional cancer treatments have posed numerous obstacles, including toxicity, multiple drug resistance, and financial cost. On the contrary, bioactive phytochemicals used in complementary alternative medicine have recently increased attention due to their potential to modulate a wide range of molecular mechanisms with a less toxic effect. Therefore, we investigated the potential regulatory mechanisms of andrographolide to treat colorectal cancer (CRC) using a network pharmacology approach. Target genes of andrographolide were retrieved from public databases (PharmMapper, Swiss target prediction, Targetnet, STITCH, and SuperPred), while targets related to CRC were retrieved from disease databases (Genecards and DisGeNet) and expression datasets (GSE32323 and GSE8671) were retrieved from gene expression omnibus (GEO). Protein-protein interaction networks (PPI) were generated using STRING and Cytoscape, and hub genes were identified by topology analysis and MCODE. Annotation of target proteins was performed using Gene Ontology (GO) database DAVID and signaling pathway enrichment analysis using the Kyoto Encyclopedia and Genome Database (KEGG). Survival and molecular docking analysis for the hub genes revealed three genes (PDGFRA, PTGS2, and MMP9) were involved in the overall survival of CRC patients, and the top three genes with the lowest binding energy include PDGFRA, MET, and MAPK1. MET gene upregulation and PDGFRA and PTGS2 gene downregulation are associated with the survival of CRC patients, as revealed by box plots and correlation analysis. In conclusion, this study has provided the first scientific evidence to support the use of andrographolide to inhibit cellular proliferation, migration, and growth, and induce apoptosis by targeting the hub genes (PDGFRA, PTGS2, MMP9, MAPK1, and MET) involved in CRC migration and invasion.
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Affiliation(s)
- Balakarthikeyan Janani
- Department of Biochemistry, PSG College of Arts and Science (Autonomous), Affiliated to Bharathiar University, Coimbatore, Tamil Nadu, India
| | | | - Kannappan Priya
- Department of Biochemistry, PSG College of Arts and Science (Autonomous), Affiliated to Bharathiar University, Coimbatore, Tamil Nadu, India
- *Correspondence: Kannappan Priya, ; Thiyagarajan Ramesh,
| | - Jin Hee Kim
- Department of Integrative Bioscience and Biotechnology, Sejong University, Seoul, South Korea
| | - Ayman Geddawy
- Department of Basic Medical Sciences, College of Medicine, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
- Department of Pharmacology, Faculty of Medicine, Minia University, Minia, Egypt
| | - Mohammad Shahid
- Department of Basic Medical Sciences, College of Medicine, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Mahmoud H. El-Bidawy
- Department of Basic Medical Sciences, College of Medicine, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
- Department of Physiology, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Sameer Al-Ghamdi
- Family and Community Medicine Department, College of Medicine, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Mohammed Alsaidan
- Internal Medicine Department, College of Medicine, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Mohammad Hassan Abdelzaher
- Department of Basic Medical Sciences, College of Medicine, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
- Department of Medical Biochemistry, Faculty of Medicine, Al-Azhar University, Assiut, Egypt
| | - Abubucker Peer Mohideen
- Department of Basic Medical Sciences, College of Medicine, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
| | - Thiyagarajan Ramesh
- Department of Basic Medical Sciences, College of Medicine, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
- *Correspondence: Kannappan Priya, ; Thiyagarajan Ramesh,
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9
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Cao J, Xu Y, Liu X, Cai Y, Luo B. Innovative signature establishment using lymphangiogenesis-related lncRNA pairs to predict prognosis of hepatocellular carcinoma. Heliyon 2022; 8:e10215. [PMID: 36033263 PMCID: PMC9403397 DOI: 10.1016/j.heliyon.2022.e10215] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 05/17/2022] [Accepted: 08/02/2022] [Indexed: 11/27/2022] Open
Abstract
Aims Hepatocellular carcinoma (HCC) remains a major tumoral burden globally, and its heterogeneity encumbers prognostic prediction. The lymphangiogenesis-related long non-coding RNAs (lrlncRNAs) reported to be implicated in immune response regulation show potential importance in predicting the prognostic and therapeutic outcome. Hence, this study aims to establish a lrlncRNA pairs-based signature not requiring specific expression levels of transcripts, which displays promising clinical practicality and satisfactory predictive capability. Main methods Transcriptomic and clinical information of the Liver Hepatocellular Carcinoma (LIHC) project retrieved from the TCGA portal were used to find differently expressed lrlncRNA (DElrlncRNA) via analysis performed between lymphangiogenesis-related genes (lr-genes) and lncRNAs(lrlncRNA), and to ultimately construct the signature based on lrlncRNA pairs screened out via Lasso and Cox regression analyses. Akaike information criterion (AIC) values were computed to find the cut-off point optimum for high-risk and low-risk group allocation. The signature then underwent trials in terms of its predictive value for survival, clinicopathological features, immune cells infiltration in tumoral microenvironment, selected checkpoint biomarkers and chemosensitivity. Key findings A novel lymphangiogenesis-related lncRNA pair signature was established using nine lrlncRNA pairs identified and significantly related to overall survival, clinicopathological features, immune cells infiltration and susceptibility to chemotherapy. Moreover, the signature efficacy was verified in acknowledged clinicopathological subgroups and partially validated by qRT-PCR assay in various human HCC cell lines. Significance The novel lrlncRNA-pairs based signature was shown to effectively and independently estimate HCC prognosis and help screen patients suitable for anti-tumor immunotherapy and chemotherapy.
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Affiliation(s)
- Jincheng Cao
- Department of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Yanni Xu
- Department of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Xiaodi Liu
- Department of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Yan Cai
- Department of Ultrasound, Central People's Hospital of Zhanjiang, 236 Yuanzhu Road, Zhanjiang, Guangdong 524045, China
| | - Baoming Luo
- Department of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
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10
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Identification of Six Genes as Diagnostic Markers for Colorectal Cancer Detection by Integrating Multiple Expression Profiles. JOURNAL OF ONCOLOGY 2022; 2022:3850674. [PMID: 35909904 PMCID: PMC9337943 DOI: 10.1155/2022/3850674] [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: 05/19/2022] [Revised: 06/24/2022] [Accepted: 07/04/2022] [Indexed: 12/24/2022]
Abstract
Background Many studies have demonstrated the promising utility of DNA methylation and miRNA as biomarkers for colorectal cancer (CRC) early detection. However, mRNA is rarely reported. This study aimed to identify novel fecal-based mRNA signatures. Methods The differentially expressed genes (DEGs) were first determined between CRCs and matched normal samples by integrating multiple datasets. Then, Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to reduce the number of candidates of aberrantly expressed genes. Next, the potential functions were investigated for the candidate signatures and their ability to detect CRC and pan-cancers was comprehensively evaluated. Results We identified 1841 common DEGs in two independent datasets. Functional enrichment analysis revealed they were mainly related to extracellular structure, biosynthesis, and cell adhesion. The CRC classifier was established based on six genes screened by LASSO regression. Sensitivity, specificity, and area under the ROC curve (AUC) for CRC detection were 79.30%, 80.40%, and 0.85 (0.76–0.92) in the training set, and these indexes achieved 93.20%, 41.80%, and 0.73 (0.65–0.83) in the testing set. For validation set, the sensitivity, specificity, and AUC were 98.90%, 98.00%, and 0.97 (0.94–0.99). The average sensitivities exceeded 90.00% for CRCs with different clinical features. For adenomas detection, the sensitivity and specificity were 74.50% and 64.00%. Besides, the six genes obtained an average AUC of 0.855 for pan-cancer detection. Conclusion The six-gene signatures showed ability to detect CRC and pan-cancer samples, which could be served as potential diagnostic markers.
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11
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Lei T, Zhang Y, Wang X, Liu W, Feng W, Song W. A Diagnostic Model Using Exosomal Genes for Colorectal Cancer. Front Genet 2022; 13:863747. [PMID: 35910195 PMCID: PMC9334773 DOI: 10.3389/fgene.2022.863747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Accepted: 05/19/2022] [Indexed: 12/24/2022] Open
Abstract
Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. Exosomes have great potential as liquid biopsy specimens due to their presence and stability in body fluids. However, the function and diagnostic values of exosomal genes in CRC are poorly understood. In the present study, exosomal data of CRC and healthy samples from the exoRBase 2.0 and Gene Expression Omnibus (GEO) databases were used, and 38 common exosomal genes were identified. Through the least absolute shrinkage and selection operator (Lasso) analysis, support vector machine recursive feature elimination (SVM-RFE) analysis, and logistic regression analysis, a diagnostic model of the training set was constructed based on 6 exosomal genes. The diagnostic model was internally validated in the test and exoRBase 2.0 database and externally validated in the GEO database. In addition, the co-expression analysis was used to cluster co-expression modules, and the enrichment analysis was performed on module genes. Then a protein–protein interaction and competing endogenous RNA network were constructed and 10 hub genes were identified using module genes. In conclusion, the results provided a comprehensive understanding of the functions of exosomal genes in CRC as well as a diagnostic model related to exosomal genes.
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Affiliation(s)
- Tianxiang Lei
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Laboratory of General Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yongxin Zhang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Laboratory of General Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Xiaofeng Wang
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Laboratory of General Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Wenwei Liu
- Center for Digestive Disease, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Wei Feng
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- Laboratory of General Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Wu Song
- Department of Gastrointestinal Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- *Correspondence: Wu Song,
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12
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Koppad S, Basava A, Nash K, Gkoutos GV, Acharjee A. Machine Learning-Based Identification of Colon Cancer Candidate Diagnostics Genes. BIOLOGY 2022; 11:biology11030365. [PMID: 35336739 PMCID: PMC8944988 DOI: 10.3390/biology11030365] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/16/2022] [Accepted: 02/23/2022] [Indexed: 01/27/2023]
Abstract
Simple Summary We developed a predictive approach using different machine learning methods to identify a number of genes that can potentially serve as novel diagnostic colon cancer biomarkers. Abstract Background: Colorectal cancer (CRC) is the third leading cause of cancer-related death and the fourth most commonly diagnosed cancer worldwide. Due to a lack of diagnostic biomarkers and understanding of the underlying molecular mechanisms, CRC’s mortality rate continues to grow. CRC occurrence and progression are dynamic processes. The expression levels of specific molecules vary at various stages of CRC, rendering its early detection and diagnosis challenging and the need for identifying accurate and meaningful CRC biomarkers more pressing. The advances in high-throughput sequencing technologies have been used to explore novel gene expression, targeted treatments, and colon cancer pathogenesis. Such approaches are routinely being applied and result in large datasets whose analysis is increasingly becoming dependent on machine learning (ML) algorithms that have been demonstrated to be computationally efficient platforms for the identification of variables across such high-dimensional datasets. Methods: We developed a novel ML-based experimental design to study CRC gene associations. Six different machine learning methods were employed as classifiers to identify genes that can be used as diagnostics for CRC using gene expression and clinical datasets. The accuracy, sensitivity, specificity, F1 score, and area under receiver operating characteristic (AUROC) curve were derived to explore the differentially expressed genes (DEGs) for CRC diagnosis. Gene ontology enrichment analyses of these DEGs were performed and predicted gene signatures were linked with miRNAs. Results: We evaluated six machine learning classification methods (Adaboost, ExtraTrees, logistic regression, naïve Bayes classifier, random forest, and XGBoost) across different combinations of training and test datasets over GEO datasets. The accuracy and the AUROC of each combination of training and test data with different algorithms were used as comparison metrics. Random forest (RF) models consistently performed better than other models. In total, 34 genes were identified and used for pathway and gene set enrichment analysis. Further mapping of the 34 genes with miRNA identified interesting miRNA hubs genes. Conclusions: We identified 34 genes with high accuracy that can be used as a diagnostics panel for CRC.
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Affiliation(s)
- Saraswati Koppad
- Department of Computer Science and Engineering, National Institute of Technology Karnataka, Mangalore 575025, India; (S.K.); (A.B.)
| | - Annappa Basava
- Department of Computer Science and Engineering, National Institute of Technology Karnataka, Mangalore 575025, India; (S.K.); (A.B.)
| | - Katrina Nash
- College of Medical and Dental Sciences, University of Birmingham, Birmingham B15 2TT, UK;
| | - Georgios V. Gkoutos
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK;
- Institute of Translational Medicine, University of Birmingham, Birmingham B15 2TT, UK
- NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham B15 2WB, UK
- MRC Health Data Research UK (HDR UK), Midlands Site, Birmingham B15 2TT, UK
- NIHR Experimental Cancer Medicine Centre, Birmingham B15 2TT, UK
- NIHR Biomedical Research Centre, University Hospital Birmingham, Birmingham B15 2TT, UK
| | - Animesh Acharjee
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham B15 2TT, UK;
- Institute of Translational Medicine, University of Birmingham, Birmingham B15 2TT, UK
- NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham B15 2WB, UK
- Correspondence: ; Tel.: +44-07403642022
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13
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Mohammed M, Mboya IB, Mwambi H, Elbashir MK, Omolo B. Predictors of colorectal cancer survival using cox regression and random survival forests models based on gene expression data. PLoS One 2021; 16:e0261625. [PMID: 34965262 PMCID: PMC8716055 DOI: 10.1371/journal.pone.0261625] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 12/06/2021] [Indexed: 12/30/2022] Open
Abstract
Understanding and identifying the markers and clinical information that are associated with colorectal cancer (CRC) patient survival is needed for early detection and diagnosis. In this work, we aimed to build a simple model using Cox proportional hazards (PH) and random survival forest (RSF) and find a robust signature for predicting CRC overall survival. We used stepwise regression to develop Cox PH model to analyse 54 common differentially expressed genes from three mutations. RSF is applied using log-rank and log-rank-score based on 5000 survival trees, and therefore, variables important obtained to find the genes that are most influential for CRC survival. We compared the predictive performance of the Cox PH model and RSF for early CRC detection and diagnosis. The results indicate that SLC9A8, IER5, ARSJ, ANKRD27, and PIPOX genes were significantly associated with the CRC overall survival. In addition, age, sex, and stages are also affecting the CRC overall survival. The RSF model using log-rank is better than log-rank-score, while log-rank-score needed more trees to stabilize. Overall, the imputation of missing values enhanced the model’s predictive performance. In addition, Cox PH predictive performance was better than RSF.
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Affiliation(s)
- Mohanad Mohammed
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, Scottsville, South Africa
- Faculty of Mathematical and Computer Sciences, University of Gezira, Wad Madani, Sudan
- * E-mail:
| | - Innocent B. Mboya
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, Scottsville, South Africa
- Department of Epidemiology and Biostatistics, Kilimanjaro Christian Medical University College (KCMUCo), Moshi, Tanzania
| | - Henry Mwambi
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, Scottsville, South Africa
| | - Murtada K. Elbashir
- College of Computer and Information Sciences, Jouf University, Sakaka, Saudi Arabia
| | - Bernard Omolo
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Pietermaritzburg, Scottsville, South Africa
- Division of Mathematics & Computer Science, University of South Carolina-Upstate, Spartanburg, United States of America
- School of Public Health, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa
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14
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Deng M, Lin JB, Zhao RC, Li SH, Lin WP, Zou JW, Wei W, Guo RP. Construction of a novel immune-related lncRNA signature and its potential to predict the immune status of patients with hepatocellular carcinoma. BMC Cancer 2021; 21:1347. [PMID: 34923955 PMCID: PMC8684648 DOI: 10.1186/s12885-021-09059-x] [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: 05/30/2021] [Accepted: 11/28/2021] [Indexed: 12/24/2022] Open
Abstract
Background The accuracy of existing biomarkers for predicting the prognosis of hepatocellular carcinoma (HCC) is not satisfactory. It is necessary to explore biomarkers that can accurately predict the prognosis of HCC. Methods In this study, original transcriptome data were downloaded from The Cancer Genome Atlas (TCGA) database. Immune-related long noncoding ribonucleic acids (irlncRNAs) were identified by coexpression analysis, and differentially expressed irlncRNA (DEirlncRNA) pairs were distinguished by univariate analysis. In addition, the least absolute shrinkage and selection operator (LASSO) penalized regression was modified. Next, the cutoff point was determined based on the area under the curve (AUC) and Akaike information criterion (AIC) values of the 5-year receiver operating characteristic (ROC) curve to establish an optimal model for identifying high-risk and low-risk groups of HCC patients. The model was then reassessed in terms of clinicopathological features, survival rate, tumor-infiltrating immune cells, immunosuppressive markers, and chemotherapy efficacy. Results A total of 1009 pairs of DEirlncRNAs were recognized in this study, 30 of these pairs were included in the Cox regression model for subsequent analysis. After regrouping according to the cutoff point, we could more effectively identify factors such as aggressive clinicopathological features, poor survival outcomes, specific immune cell infiltration status of tumors, high expression level of immunosuppressive biomarkers, and low sensitivity to chemotherapy drugs in HCC patients. Conclusions The nonspecific expression level signature involved with irlncRNAs shows promising clinical value in predicting the prognosis of HCC patients. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-021-09059-x.
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Affiliation(s)
- Min Deng
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng East Road, Guangzhou, China
| | - Jia-Bao Lin
- Department of Health Management Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Rong-Ce Zhao
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng East Road, Guangzhou, China
| | - Shao-Hua Li
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng East Road, Guangzhou, China
| | - Wen-Ping Lin
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng East Road, Guangzhou, China
| | - Jing-Wen Zou
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng East Road, Guangzhou, China
| | - Wei Wei
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China.,State Key Laboratory of Oncology in South China, Guangzhou, China.,Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng East Road, Guangzhou, China
| | - Rong-Ping Guo
- Department of Liver Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China. .,State Key Laboratory of Oncology in South China, Guangzhou, China. .,Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng East Road, Guangzhou, China.
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15
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Al-Harazi O, Kaya IH, El Allali A, Colak D. A Network-Based Methodology to Identify Subnetwork Markers for Diagnosis and Prognosis of Colorectal Cancer. Front Genet 2021; 12:721949. [PMID: 34790220 PMCID: PMC8591094 DOI: 10.3389/fgene.2021.721949] [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: 06/07/2021] [Accepted: 09/28/2021] [Indexed: 12/30/2022] Open
Abstract
The development of reliable methods for identification of robust biomarkers for complex diseases is critical for disease diagnosis and prognosis efforts. Integrating multi-omics data with protein-protein interaction (PPI) networks to investigate diseases may help better understand disease characteristics at the molecular level. In this study, we developed and tested a novel network-based method to detect subnetwork markers for patients with colorectal cancer (CRC). We performed an integrated omics analysis using whole-genome gene expression profiling and copy number alterations (CNAs) datasets followed by building a gene interaction network for the significantly altered genes. We then clustered the constructed gene network into subnetworks and assigned a score for each significant subnetwork. We developed a support vector machine (SVM) classifier using these scores as feature values and tested the methodology in independent CRC transcriptomic datasets. The network analysis resulted in 15 subnetwork markers that revealed several hub genes that may play a significant role in colorectal cancer, including PTP4A3, FGFR2, PTX3, AURKA, FEN1, INHBA, and YES1. The 15-subnetwork classifier displayed over 98 percent accuracy in detecting patients with CRC. In comparison to individual gene biomarkers, subnetwork markers based on integrated multi-omics and network analyses may lead to better disease classification, diagnosis, and prognosis.
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Affiliation(s)
- Olfat Al-Harazi
- Biostatistics, Epidemiology and Scientific Computing Department, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| | - Ibrahim H Kaya
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
| | - Achraf El Allali
- African Genome Center, Mohammed VI Polytechnic University, Benguerir, Morocco
| | - Dilek Colak
- Biostatistics, Epidemiology and Scientific Computing Department, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
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16
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Wang X, Chen K, Wang Z, Xu Y, Dai L, Bai T, Chen B, Yang W, Chen W. Using Immune-Related Long Non-coding Ribonucleic Acids to Develop a Novel Prognosis Signature and Predict the Immune Landscape of Colon Cancer. Front Cell Dev Biol 2021; 9:750709. [PMID: 34660608 PMCID: PMC8514752 DOI: 10.3389/fcell.2021.750709] [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: 07/31/2021] [Accepted: 09/08/2021] [Indexed: 12/24/2022] Open
Abstract
Purpose: This study aimed to construct a novel signature to predict the survival of patients with colon cancer and the associated immune landscape, based on immune-related long noncoding ribonucleic acids (irlncRNAs). Methods: Expression profiles of irlncRNAs in 457 patients with colon cancer were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed (DE) irlncRNAs were identified and irlncRNA pairs were recognized using Lasso regression and Cox regression analyses. Akaike information criterion (AIC) values of receiver operating characteristic (ROC) curve were calculated to identify the ideal cut-off point for dividing patients into two groups and constructing the prognosis signature. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to validate the expression of LINC02195 and SCARNA9 in colon cancer. Results: We identified 22 irlncRNA pairs and patients were divided into high-risk and low-risk groups based on the calculated risk score using these 22 irlncRNA pairs. The irlncRNA pairs were significantly related to patient survival. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). The area under the curve of the signature to predict 5-year survival was 0.951. The risk score correlated with tumor stage, infiltration depth, lymph node metastasis, and distant metastasis. The risk score remained significant after univariate and multivariate Cox regression analyses. A nomogram model to predict patient survival was developed based on the results of Cox regression analysis. Immune cell infiltration status, expression of some immune checkpoint genes, and sensitivity to chemotherapeutics were also related to the risk score. The results of qRT-PCR revealed that LINC02195 and SCARNA9 were significantly upregulated in colon cancer tissues. Conclusion: The constructed prognosis signature showed remarkable efficiency in predicting patient survival, immune cell infiltration status, expression of immune checkpoint genes, and sensitivity to chemotherapeutics.
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Affiliation(s)
- Xu Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Ke Chen
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Zhenglin Wang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yuanmin Xu
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Longfei Dai
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Tao Bai
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Bo Chen
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wenqi Yang
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wei Chen
- Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China
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Hameed Y, Usman M, Liang S, Ejaz S. Novel diagnostic and prognostic biomarkers of colorectal cancer: Capable to overcome the heterogeneity-specific barrier and valid for global applications. PLoS One 2021; 16:e0256020. [PMID: 34473751 PMCID: PMC8412268 DOI: 10.1371/journal.pone.0256020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2020] [Accepted: 07/28/2021] [Indexed: 02/05/2023] Open
Abstract
INTRODUCTION The heterogeneity-specific nature of the available colorectal cancer (CRC) biomarkers is significantly contributing to the cancer-associated high mortality rate worldwide. Hence, this study was initiated to investigate a system of novel CRC biomarkers that could commonly be employed to the CRC patients and helpful to overcome the heterogenetic-specific barrier. METHODS Initially, CRC-related hub genes were extracted through PubMed based literature mining. A protein-protein interaction (PPI) network of the extracted hub genes was constructed and analyzed to identify few more closely CRC-related hub genes (real hub genes). Later, a comprehensive bioinformatics approach was applied to uncover the diagnostic and prognostic role of the identified real hub genes in CRC patients of various clinicopathological features. RESULTS Out of 210 collected hub genes, in total 6 genes (CXCL12, CXCL8, AGT, GNB1, GNG4, and CXCL1) were identified as the real hub genes. We further revealed that all the six real hub genes were significantly dysregulated in colon adenocarcinoma (COAD) patients of various clinicopathological features including different races, cancer stages, genders, age groups, and body weights. Additionally, the dysregulation of real hub genes has shown different abnormal correlations with many other parameters including promoter methylation, overall survival (OS), genetic alterations and copy number variations (CNVs), and CD8+T immune cells level. Finally, we identified a potential miRNA and various chemotherapeutic drugs via miRNA, and real hub genes drug interaction network that could be used in the treatment of CRC by regulating the expression of real hub genes. CONCLUSION In conclusion, we have identified six real hub genes as potential biomarkers of CRC patients that could help to overcome the heterogenetic-specific barrier across different clinicopathological features.
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Affiliation(s)
- Yasir Hameed
- Department of Biotechnology, Institute of Biochemistry, Biotechnology and Bioinformatics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Muhammad Usman
- Department of Biotechnology, Institute of Biochemistry, Biotechnology and Bioinformatics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
| | - Shufang Liang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, 610041, P.R. China
| | - Samina Ejaz
- Department of Biochemistry, Institute of Biochemistry, Biotechnology and Bioinformatics, The Islamia University of Bahawalpur, Bahawalpur, Pakistan
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Hua S, Xie Z, Wang W, Wan Z, Chen M, Zhao S, Jiang J. Identification and Validation of a Novel Immune-Related lncRNA Signature for Bladder Cancer. Front Oncol 2021; 11:704946. [PMID: 34322391 PMCID: PMC8311739 DOI: 10.3389/fonc.2021.704946] [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: 05/04/2021] [Accepted: 06/25/2021] [Indexed: 12/21/2022] Open
Abstract
Purpose We aimed to construct an immune-related long noncoding ribonucleic acids (irlncRNA) signature to evaluate the prognosis of patients without specific expression level of these irlncRNA. Methods The raw transcriptome data were downloaded from The Cancer Genome Atlas (TCGA), irlncRNAs were filtered out using an online immune related gene database and coexpression analysis, differently expressed irlncRNA (DEirlncRNA) pairs were identified by univariate analysis. The areas under curve (AUC) were compared and the Akaike information criterion (AIC) values of receiver operating curve (ROC) was counted, the most optimal model was constructed to divide bladder cancer patients into high- and low-risk groups usingõ the cut-off point of ROC. Then, we evaluated them from multiple perspectives, such as survival time, clinic-pathological characteristics, immune-related cells infiltrating, chemotherapeutics efficacy and immune checkpoint inhibitors. Results 14 DEirlncRNA pairs were included in this signature. Patients in high-risk groups demonstrated apparent shorter survival time, more aggressive clinic-pathological characteristics, different immune-related cells infiltrating status, lower chemotherapeutics efficacy. Conclusion The irlncRNA signature demonstrated a promising prediction value for bladder cancer patients and was important in guiding clinical treatment.
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Affiliation(s)
- Shan Hua
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhiwen Xie
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenhao Wang
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhong Wan
- Department of Urology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Min Chen
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Sheng Zhao
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Juntao Jiang
- Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Wang X, Gao G, Chen Z, Chen Z, Han M, Xie X, Jin Q, Du H, Cao Z, Zhang H. Identification of the miRNA signature and key genes in colorectal cancer lymph node metastasis. Cancer Cell Int 2021; 21:358. [PMID: 34315491 PMCID: PMC8314594 DOI: 10.1186/s12935-021-02058-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 06/27/2021] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Because its metastasis to the lymph nodes are closely related to poor prognosis, miRNAs and mRNAs can serve as biomarkers for the diagnosis, prognosis, and therapy of colorectal cancer (CRC). This study aimed to identify novel gene signatures in the lymph node metastasis of CRC. METHODS GSE56350, GSE70574, and GSE95109 datasets were downloaded from the Gene Expression Omnibus (GEO) database, while data from 569 colorectal cancer cases were also downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed miRNAs (DE-miRNAs) were calculated using R programming language (Version 3.6.3), while gene ontology and enrichment analysis of target mRNAs were performed using FunRich ( http://www.funrich.org ). Furthermore, the mRNA-miRNA network was constructed using Cytoscape software (Version 3.8.0). Gene expression levels were verified using the GEO datasets. Similarly, quantitative real-time PCR (qPCR) was used to examine expression profiles from 20 paired non-metastatic and metastatic lymph node tissue samples obtained from patients with CRC. RESULTS In total, five DE-miRNAs were selected, and 34 mRNAs were identified after filtering the results. Moreover, two key miRNAs (hsa-miR-99a, hsa-miR-100) and one gene (heparan sulfate-glucosamine 3-sulfotransferase 2 [HS3ST2]) were identified. The GEO datasets analysis and qPCR results showed that the expression of key miRNA and genes were consistent with that obtained from the bioinformatic analysis. A novel miRNA-mRNA network capable of predicting the prognosis and confirmed experimentally, hsa-miR-99a-HS3ST2-hsa-miR-100, was found after expression analysis in metastasized lymph node tissue from CRC samples. CONCLUSION In summary, miRNAs and genes with potential as biomarkers were found and a novel miRNA-mRNA network was established for CRC lymph node metastasis by systematic bioinformatic analysis and experimental validation. This network may be used as a potential biomarker in the development of lymph node metastatic CRC.
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Affiliation(s)
- Xi Wang
- Department of Clinical Laboratory, The Second Affiliated Hospital of Soochow University, No. 1055 San Xiang Road, Suzhou, 215004, Jiangsu, China
| | - Guangyu Gao
- Department of Oncology, The Second Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Zhengrong Chen
- Department of Gastrointestinal Surgery, The Second Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Zhihao Chen
- Department of Orthopedics, The Second Affiliated Hospital of Soochow University, Suzhou, People's Republic of China
| | - Mingxiao Han
- Department of Clinical Laboratory, The Second Affiliated Hospital of Soochow University, No. 1055 San Xiang Road, Suzhou, 215004, Jiangsu, China
| | - Xiaolu Xie
- Department of Clinical Laboratory, The Second Affiliated Hospital of Soochow University, No. 1055 San Xiang Road, Suzhou, 215004, Jiangsu, China
| | - Qiyuan Jin
- Department of Clinical Laboratory, The Second Affiliated Hospital of Soochow University, No. 1055 San Xiang Road, Suzhou, 215004, Jiangsu, China
| | - Hong Du
- Department of Clinical Laboratory, The Second Affiliated Hospital of Soochow University, No. 1055 San Xiang Road, Suzhou, 215004, Jiangsu, China
| | - Zhifei Cao
- Department of Pathology, The Second Affiliated Hospital of Soochow University, No. 1055 San Xiang Road, Suzhou, 215004, Jiangsu, China.
| | - Haifang Zhang
- Department of Clinical Laboratory, The Second Affiliated Hospital of Soochow University, No. 1055 San Xiang Road, Suzhou, 215004, Jiangsu, China.
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Zhang Y, Mi K, Li Z, Qiang L, Lv M, Wu Y, Yuan L, Jin S. Identification of Prognostic miRNAs Associated With Immune Cell Tumor Infiltration Predictive of Clinical Outcomes in Patients With Non-Small Cell Lung Cancer. Front Oncol 2021; 11:705869. [PMID: 34277450 PMCID: PMC8281680 DOI: 10.3389/fonc.2021.705869] [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: 05/06/2021] [Accepted: 06/07/2021] [Indexed: 12/28/2022] Open
Abstract
Background A detailed means of prognostic stratification in patients with non-small cell lung cancer (NSCLC) is urgently needed to support individualized treatment plans. Recently, microRNAs (miRNAs) have been used as biomarkers due to their previously reported prognostic roles in cancer. This study aimed to construct an immune-related miRNA signature that effectively predicts NSCLC patient prognosis. Methods The miRNAs and mRNA expression and mutation data of NSCLC was obtained from The Cancer Genome Atlas (TCGA). Immune-associated miRNAs were identified using immune scores calculated by the ESTIMATE algorithm. LASSO-penalized multivariate survival models were using for development of a tumor immune-related miRNA signature (TIM-Sig), which was evaluated in several public cohorts from the Gene Expression Omnibus (GEO) and the CellMiner database. The miRTarBase was used for constructing the miRNA-target interactions. Results The TIM-Sig, including 10 immune-related miRNAs, was constructed and successfully predicted overall survival (OS) in the validation cohorts. TIM-Sig score negatively correlated with CD8+ T cell infiltration, IFN-γ expression, CYT activity, and tumor mutation burden. The correlation between TIM-Sig score and genomic mutation and cancer chemotherapeutics was also evaluated. A miRNA-target network of 10 miRNAs in TIM-Sig was constructed. Further analysis revealed that these target genes showed prognostic value in both lung squamous cell carcinoma and adenocarcinoma. Conclusions We concluded that the immune-related miRNAs demonstrated a potential value in clinical prognosis.
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Affiliation(s)
- Yuepeng Zhang
- Department of Respiratory, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Kai Mi
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Zhiheng Li
- Department of Medical Oncology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Lixia Qiang
- Department of Respiratory, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Meiyu Lv
- Department of Respiratory, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Yushan Wu
- Department of Respiratory, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Ligong Yuan
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Shoude Jin
- Department of Respiratory, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
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21
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Patil AR, Leung MY, Roy S. Identification of Hub Genes in Different Stages of Colorectal Cancer through an Integrated Bioinformatics Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:5564. [PMID: 34070979 PMCID: PMC8197092 DOI: 10.3390/ijerph18115564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/20/2021] [Accepted: 05/21/2021] [Indexed: 11/16/2022]
Abstract
Colorectal cancer (CRC) is the third most common cancer that contributes to cancer-related morbidity. However, the differential expression of genes in different phases of CRC is largely unknown. Moreover, very little is known about the role of stress-survival pathways in CRC. We sought to discover the hub genes and identify their roles in several key pathways, including oxidative stress and apoptosis in the different stages of CRC. To identify the hub genes that may be involved in the different stages of CRC, gene expression datasets were obtained from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) common among the different datasets for each group were obtained using the robust rank aggregation method. Then, gene enrichment analysis was carried out with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Finally, the protein-protein interaction networks were constructed using the Cytoscape software. We identified 40 hub genes and performed enrichment analysis for each group. We also used the Oncomine database to identify the DEGs related to stress-survival and apoptosis pathways involved in different stages of CRC. In conclusion, the hub genes were found to be enriched in several key pathways, including the cell cycle and p53 signaling pathway. Some of the hub genes were also reported in the stress-survival and apoptosis pathways. The hub DEGs revealed from our study may be used as biomarkers and may explain CRC development and progression mechanisms.
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Affiliation(s)
- Abhijeet R. Patil
- Computational Science Program, The University of Texas at El Paso, El Paso, TX 79968, USA; (A.R.P.); (M.-Y.L.)
| | - Ming-Ying Leung
- Computational Science Program, The University of Texas at El Paso, El Paso, TX 79968, USA; (A.R.P.); (M.-Y.L.)
- Border Biomedical Research Center, The University of Texas at El Paso, El Paso, TX 79968, USA
- Department of Mathematical Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA
| | - Sourav Roy
- Border Biomedical Research Center, The University of Texas at El Paso, El Paso, TX 79968, USA
- Department of Biological Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA
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22
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Endo S, Matsunaga T, Nishinaka T. The Role of AKR1B10 in Physiology and Pathophysiology. Metabolites 2021; 11:332. [PMID: 34063865 PMCID: PMC8224097 DOI: 10.3390/metabo11060332] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 05/17/2021] [Accepted: 05/19/2021] [Indexed: 12/16/2022] Open
Abstract
AKR1B10 is a human nicotinamide adenine dinucleotide phosphate (NADPH)-dependent reductase belonging to the aldo-keto reductase (AKR) 1B subfamily. It catalyzes the reduction of aldehydes, some ketones and quinones, and interacts with acetyl-CoA carboxylase and heat shock protein 90α. The enzyme is highly expressed in epithelial cells of the stomach and intestine, but down-regulated in gastrointestinal cancers and inflammatory bowel diseases. In contrast, AKR1B10 expression is low in other tissues, where the enzyme is upregulated in cancers, as well as in non-alcoholic fatty liver disease and several skin diseases. In addition, the enzyme's expression is elevated in cancer cells resistant to clinical anti-cancer drugs. Thus, growing evidence supports AKR1B10 as a potential target for diagnosing and treating these diseases. Herein, we reviewed the literature on the roles of AKR1B10 in a healthy gastrointestinal tract, the development and progression of cancers and acquired chemoresistance, in addition to its gene regulation, functions, and inhibitors.
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Affiliation(s)
- Satoshi Endo
- Laboratory of Biochemistry, Gifu Pharmaceutical University, Gifu 501-1196, Japan
| | - Toshiyuki Matsunaga
- Education Center of Green Pharmaceutical Sciences, Gifu Pharmaceutical University, Gifu 502-8585, Japan;
| | - Toru Nishinaka
- Laboratory of Biochemistry, Faculty of Pharmacy, Osaka Ohtani University, Tondabayashi 584-8540, Osaka, Japan;
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Hong W, Liang L, Gu Y, Qi Z, Qiu H, Yang X, Zeng W, Ma L, Xie J. Immune-Related lncRNA to Construct Novel Signature and Predict the Immune Landscape of Human Hepatocellular Carcinoma. MOLECULAR THERAPY. NUCLEIC ACIDS 2020; 22:937-947. [PMID: 33251044 PMCID: PMC7670249 DOI: 10.1016/j.omtn.2020.10.002] [Citation(s) in RCA: 140] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Accepted: 10/06/2020] [Indexed: 12/11/2022]
Abstract
The signature composed of immune-related long noncoding ribonucleic acids (irlncRNAs) with no requirement of specific expression level seems to be valuable in predicting the survival of patients with hepatocellular carcinoma (HCC). Here, we retrieved raw transcriptome data from The Cancer Genome Atlas (TCGA), identified irlncRNAs by co-expression analysis, and recognized differently expressed irlncRNA (DEirlncRNA) pairs using univariate analysis. In addition, we modified Lasso penalized regression. Then, we compared the areas under curve, counted the Akaike information criterion (AIC) values of 5-year receiver operating characteristic curve, and identified the cut-off point to set up an optimal model for distinguishing the high- or low-disease-risk groups among patients with HCC. We then reevaluated them from the viewpoints of survival, clinic-pathological characteristics, tumor-infiltrating immune cells, chemotherapeutics efficacy, and immunosuppressed biomarkers. 36 DEirlncRNA pairs were identified, 12 of which were included in a Cox regression model. After regrouping the patients by the cut-off point, we could more effectively differentiate between them based on unfavorable survival outcome, aggressive clinic-pathological characteristics, specific tumor immune infiltration status, low chemotherapeutics sensitivity, and highly expressed immunosuppressed biomarkers. The signature established by paring irlncRNA regardless of expression levels showed a promising clinical prediction value.
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Affiliation(s)
- Weifeng Hong
- Department of Medical Imaging, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, Guangdong 510000, China
| | - Li Liang
- Departments of Medical Oncology, Zhongshan Hospital of Fudan University, Shanghai 200032, China
- Corresponding author: Li Liang, Departments of Medical Oncology, Zhongshan Hospital of Fudan University, Shanghai 200032, China.
| | - Yujun Gu
- Department of Ultrasonic Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong 510000, China
| | - Zhenhua Qi
- Department of Anesthesiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation for Cancer Medicine, Guangzhou, Guangdong 510000, China
| | - Haibo Qiu
- Department of Gastric and Pancreatic Surgery, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation for Cancer Medicine, Guangzhou, Guangdong 510000, China
| | - Xiaosong Yang
- Department of Anesthesiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation for Cancer Medicine, Guangzhou, Guangdong 510000, China
| | - Weian Zeng
- Department of Anesthesiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation for Cancer Medicine, Guangzhou, Guangdong 510000, China
| | - Liheng Ma
- Department of Medical Imaging, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, Guangdong 510000, China
| | - Jingdun Xie
- Department of Anesthesiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation for Cancer Medicine, Guangzhou, Guangdong 510000, China
- Corresponding author: Jingdun Xie, Department of Anesthesiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation for Cancer Medicine, Guangzhou, Guangdong 510000, China.
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Chen X, Chen J, Feng Y, Guan W. Prognostic Value of SLC4A4 and its Correlation with Immune Infiltration in Colon Adenocarcinoma. Med Sci Monit 2020; 26:e925016. [PMID: 32949121 PMCID: PMC7526338 DOI: 10.12659/msm.925016] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND SLC4A4 is differentially expressed in a variety of tumors, but its significance in colon adenocarcinoma has not been determined. MATERIAL AND METHODS Transcriptomes of two cohorts, GSE41258 and GSE32323, contained in The Cancer Genome Atlas (TCGA) were analysed to determine differences in SLC4A4 expression between tumor and normal tissue and their correlations with overall survival. The relationships between SLC4A4 expression and clinical characteristics were determined by COX regression analysis and logistic regression analysis, and correlations of SLC4A4 levels with tumor infiltrating immune cells (TIICs) and genes with high mutation frequency were evaluated by Pearson correlation analysis. Molecular functions and signaling pathways that might be affected by changes in SLC4A4 expression were determined by gene set enrichment analysis (GSEA). The overall distribution of TIICs was determined by two web servers: tumor immune estimation resource (TIMER) and CIBERSORT. RESULTS SLC4A4 expression was lower in colon adenocarcinoma than in normal colon tissue, suggesting that SLC4A4 was associated with poor prognosis. Reduced SLC4A4 expression was also associated with lymph node invasion and distant metastasis and was moderately correlated with increased expression of MUC4 and SMAD4, two genes with high mutation frequency in colon adenocarcinoma. GSEA indicated that changes in SLC4A4 expression affects several biological processes, including mismatch repair, base excision repair, and DNA replication. Eight TIICs in the tumor microenvironment differed significantly in groups with low and high expression of SLC4A4. CONCLUSIONS SLC4A4 may be a novel biomarker predicting prognosis in patients with colon adenocarcinoma. TIICs differed significantly in samples with higher and lower expression of SLC4A4.
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Affiliation(s)
- Xiaoli Chen
- Department of Pathology, The First People's Hospital of Nantong, Nantong, Jiangsu, China (mainland)
| | - Jianing Chen
- Medical School of Nantong University, Nantong, Jiangsu, China (mainland)
| | - Yan Feng
- Department of Pathology, The First People's Hospital of Nantong, Nantong, Jiangsu, China (mainland)
| | - Wei Guan
- Department of Radiation Oncology, The Affiliated Hospital of Nantong University, Nantong, Jiangsu, China (mainland)
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