Identification of Hub Genes Associated With Immune Infiltration and Predict Prognosis in Hepatocellular Carcinoma via Bioinformatics Approaches

Chen, Huaping; Wu, Junrong; Lu, Liuyi; Hu, Zuojian; Li, Xi; Huang, Li; Zhang, Xiaolian; Chen, Mingxing; Qin, Xue; Xie, Li

doi:10.3389/fgene.2020.575762

Cited by 25 publications

(21 citation statements)

References 62 publications

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“…In previous studies, the differential expression of MAD2L1 in many tumors was analyzed using the TIMER2.0 database [ 35 , 36 ]. It was found that the expression of the MAD2L1 gene was higher in BLCA, BRCA, CESC, CHOL, COAD, ESCA, GBM, HNSC, KIRC, LUAD, LUSC, PRAD, READ, STAD, and UCEC than in their corresponding paracancerous tissues.…”

Section: Discussionmentioning

confidence: 99%

Identification of MAD2L1 as a Potential Biomarker in Hepatocellular Carcinoma via Comprehensive Bioinformatics Analysis

Chen

Yang

Zhang

et al. 2022

BioMed Research International

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Background. Hepatocellular carcinoma (HCC) is widely acknowledged as a malignant tumor with rapid progression, high recurrence rate, and poor prognosis. At present, there is a paucity of reliable biomarkers at the clinical level to guide the management of HCC and improve patient outcomes. Our research is aimed at assessing the prognostic value of MAD2L1 in HCC. Methods. Four datasets, GSE121248, GSE101685, GSE85598, and GSE62232, were selected from the GEO database to analyze differentially expressed genes (DEGs) between HCC and normal liver tissues. After functional analysis, we constructed a protein-protein interaction network (PPI) for DEGs and identified core genes in this network with high connectivity with other genes. We assessed the relationship between core genes and the pathogenesis and prognosis of HCC. Finally, we explored the gene regulatory signaling mechanisms involved in HCC pathogenesis. Results. 145 DEGs were screened from the intersection of the four GEO datasets. MAD2L1 was associated with most genes according to the PPI network and was selected as a candidate gene for further study. Survival analysis suggested that high MAD2L1 expression in HCC correlated with a worse prognosis. In addition, real-time quantitative PCR (RT-qPCR), western blot (WB), and immunohistochemistry (IHC) findings suggested that the expression of MAD2L1 was abnormally increased in HCC tissues and cells compared to paraneoplastic tissues and normal hepatocytes. Conclusion. We found that high MAD2L1 expression in HCC was significantly associated with overall patient survival and clinical features. We also explored the potential biological properties of this gene.

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Section: Discussionmentioning

confidence: 99%

Identification of MAD2L1 as a Potential Biomarker in Hepatocellular Carcinoma via Comprehensive Bioinformatics Analysis

Chen

Yang

Zhang

et al. 2022

BioMed Research International

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“…Additionally, in gastric and lung cancers, the high expression of PRC1 has often been associated with early lymph node metastasis and poor prognosis ( 40 , 41 ). Previous bioinformatics analyses have revealed that PRC1 was associated with immune invasion of hepatocellular carcinoma ( 42 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of Immune-Related Breast Cancer Chemotherapy Resistance Genes via Bioinformatics Approaches

Han

Wang

et al. 2022

Front. Oncol.

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Chemotherapy resistance in breast cancer is an important factor affecting the prognosis of breast cancer patients. We computationally analyzed the differences in gene expression before and after chemotherapy in breast cancer patients, drug-sensitive groups, and drug-resistant groups. Through functional enrichment analysis, immune microenvironment analysis, and other computational analysis methods, we identified PRC1, GGTLC1, and IRS1 as genes that may mediate breast cancer chemoresistance through the immune pathway. After validation of certain other clinical datasets and in vitro cellular assays, we found that the above three genes influenced drug resistance in breast cancer patients and were closely related to the tumor immune microenvironment. Our finding that chemoresistance in breast cancer could be influenced by the mediation of tumor immunity expanded our knowledge of how to address this problem and could guide future research involving chemoresistance.

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“…Yong found that RACGAP1 overexpression was significantly related to poor prognosis in HCC patients, and could promote proliferation ability of HCC cells via inhibiting activation of the Hippo and YAP pathways (35). Chen found that ten hub genes associated with immune infiltration, including RACGAP1 and AURKA, could predict survival outcome in HCC via bioinformatics analysis (36). The down-regulated ACOT12 was significantly associated with poor diagnosis in metastatic HCC patients (37).…”

Section: Discussionmentioning

confidence: 99%

A Novel Five-Gene Signature for Prognosis Prediction in Hepatocellular Carcinoma

Zhang

Kong

2021

Front. Oncol.

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Hepatocellular carcinoma (HCC) has been a global health issue and attracted wide attention due to its high incidence and poor outcomes. In this study, our purpose was to explore an effective prognostic marker for HCC. Five cohort profile datasets from GEO (GSE25097, GSE36376, GSE62232, GSE76427 and GSE101685) were integrated with TCGA-LIHC and GTEx dataset to identify differentially expressed genes (DEGs) between normal and cancer tissues in HCC patients, then 5 upregulated differentially expressed genes and 32 downregulated DEGs were identified as common DEGs in total. Next, we systematically explored the relationship between the expression of 37 common DEGs in tumor tissues and overall survival (OS) rate of HCC patients in TCGA and constructed a novel prognostic model composed of five genes (AURKA, PZP, RACGAP1, ACOT12 and LCAT). Furthermore, the predicted performance of the five-gene signature was verified in ICGC and another independent clinical samples cohort, and the results demonstrated that the signature performed well in predicting the OS rate of patients with HCC. What is more, the signature was an independent hazard factor for HCC patients when considering other clinical factors in the three cohorts. Finally, we found the signature was significantly associated with HCC immune microenvironment. In conclusion, the prognostic five-gene signature identified in our present study could efficiently classify patients with HCC into subgroups with low and high risk of longer overall survival time and help clinicians make decisions for individualized treatment.

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Identification of Hub Genes Associated With Immune Infiltration and Predict Prognosis in Hepatocellular Carcinoma via Bioinformatics Approaches

Cited by 25 publications

References 62 publications

Identification of MAD2L1 as a Potential Biomarker in Hepatocellular Carcinoma via Comprehensive Bioinformatics Analysis

Identification of MAD2L1 as a Potential Biomarker in Hepatocellular Carcinoma via Comprehensive Bioinformatics Analysis

Identification of Immune-Related Breast Cancer Chemotherapy Resistance Genes via Bioinformatics Approaches

A Novel Five-Gene Signature for Prognosis Prediction in Hepatocellular Carcinoma

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