A novel five‐gene signature predicts overall survival of patients with hepatocellular carcinoma

Wang, Zhigang; Pan, Leyu; Guo, Deliang; Luo, Xiaofeng; Tang, Jie; Yang, Weihua; Zhang, Yuxian; Luo, Anni; Gu, Yang; Pan, Yuxuan

doi:10.1002/cam4.3900

Cited by 29 publications

(28 citation statements)

References 36 publications

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“…miR-147a is involved in the regulation of cancer malignancy ( Lee et al, 2021 ), and was previously proposed as a potential prognostic factor for non-small cell lung cancer ( Lu and Luan, 2019 ). miR-103a-3p has been reported to be associated with cisplatin resistance in non-small cell lung cancer ( Wang Z. et al, 2021 ), and docetaxel resistance in prostate cancer ( Yi et al, 2022 ). miR-103a was also reported as a new regulator of Wnt signaling pathway in colorectal carcinoma ( Fasihi et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Gene co-expression network analysis revealed novel biomarkers for ovarian cancer

Kasavi

2022

Front. Genet.

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Ovarian cancer is the second most common gynecologic cancer and remains the leading cause of death of all gynecologic oncologic disease. Therefore, understanding the molecular mechanisms underlying the disease, and the identification of effective and predictive biomarkers are invaluable for the development of diagnostic and treatment strategies. In the present study, a differential co-expression network analysis was performed via meta-analysis of three transcriptome datasets of serous ovarian adenocarcinoma to identify novel candidate biomarker signatures, i.e. genes and miRNAs. We identified 439 common differentially expressed genes (DEGs), and reconstructed differential co-expression networks using common DEGs and considering two conditions, i.e. healthy ovarian surface epithelia samples and serous ovarian adenocarcinoma epithelia samples. The modular analyses of the constructed networks indicated a co-expressed gene module consisting of 17 genes. A total of 11 biomarker candidates were determined through receiver operating characteristic (ROC) curves of gene expression of module genes, and miRNAs targeting these genes were identified. As a result, six genes (CDT1, CNIH4, CRLS1, LIMCH1, POC1A, and SNX13), and two miRNAs (mir-147a, and mir-103a-3p) were suggested as novel candidate prognostic biomarkers for ovarian cancer. Further experimental and clinical validation of the proposed biomarkers could help future development of potential diagnostic and therapeutic innovations in ovarian cancer.

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Section: Discussionmentioning

confidence: 99%

Gene co-expression network analysis revealed novel biomarkers for ovarian cancer

Kasavi

2022

Front. Genet.

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“…For instance, a study based on bioinformatics reported that MITD1, involved in cell cycle regulation, can be employed as an independent predictive factor for individuals with HCC, and MITD1 expression is associated with clinicopathological characteristics and natural killer cell infiltration ( 16 ). Additionally, increasing findings using bioinformatics demonstrated that DEGs between HCC and neighboring non-malignant tissues were remarkably enriched in cell cycle pathway ( 17 - 19 ). Therefore, the utility of bioinformatics to study the function of cell cycle-linked genes in HCC pathogenesis is necessary for improving the therapeutic effect of HCC.…”

Section: Discussionmentioning

confidence: 99%

Characterization of diagnostic and prognostic significance of cell cycle-linked genes in hepatocellular carcinoma

Wang¹,

Yu²,

Zhang³

et al. 2021

Transl Cancer Res TCR

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Background The high degree of heterogeneity of hepatocellular carcinoma (HCC) imposes a significant challenge to predict the prognosis. Currently, increasing evidence has indicated that cell cycle-linked genes are strongly linked to occurrence and progress of HCC. Herein, we purposed to create a prediction model on the basis of cell cycle-linked genes. Methods The transcriptome along with clinicopathological data abstracted from The Cancer Genome Atlas (TCGA) were used as a training cohort. Lasso regression analysis was employed to create a prediction model in TCGA cohort. The data of samples obtained from the International Cancer Genome Consortium (ICGC) data resource were applied in the verification of the model. A series of bioinformatics analyzed the relationship of the risk signature with overall survival (OS), biological function, and clinicopathological features. Results Six cell cycle-linked genes ( PLK1, CDC20, HSP90AA1, CHEK1, HDAC1 , and NDC80 ) were chosen to create the prognostic model, demonstrating a good prognostic capacity. Further analyses indicated that the model could independently assess the OS of HCC patients. A single-sample gene set enrichment analysis (ssGSEA) indicated that the risk signature was remarkably linked to immune status. Additionally, there was a remarkable association of the risk signature with TP53 mutation frequency, as well as immune checkpoint molecule expression levels. Conclusions We created a prediction model using six cell cycle-linked genes to predict HCC prognosis. The six genes are expected to be novel markers for HCC diagnosis, as well as treatment.

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“…The Kaplan-Meier analysis revealed that our signature was still closely associated with worse survival regardless of these clinicopathological features of HCC (Figures 4C-F and Supplementary Figure 4A). To further assess the predictive efficacy of our signature on OS in HCC patients, we compared it with the other four published TCGA HCC signatures (23,(25)(26)(27). The ROC curves and time-dependent c-index proved that our signature was more accurate in prognosis prediction than the four published signatures (Figures 4G, H and Supplementary Figure 4B).…”

Section: Prognostic Value Of the Ferroptosis-associated Long Non-codi...mentioning

confidence: 92%

A Novel Ferroptosis-Related Long Non-Coding RNA Prognostic Signature Correlates With Genomic Heterogeneity, Immunosuppressive Phenotype, and Drug Sensitivity in Hepatocellular Carcinoma

Liu

Zhang

et al. 2022

Front. Immunol.

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Hepatocellular carcinoma (HCC) with high heterogeneity is a common malignancy worldwide, but effective treatments are limited. Ferroptosis plays a critical role in tumors as a novel iron-dependent and reactive oxygen species-reliant type of cell death. Several studies have shown that long non-coding RNAs (lncRNAs) can drive HCC initiation and progression. However, the prognostic value of ferroptosis-related lncRNAs in patients with HCC has not been explored comprehensively. Gene set variation analysis (GSVA) based on gene set and RNA-seq profiles obtained from public databases indicated that ferroptosis is suppressed in HCC patients. Ferroptosis-related differentially expressed lncRNAs were screened by Pearson’s test. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression were performed to establish a novel five ferroptosis-related lncRNA signature in the training cohort with 60% patients, which was further verified in the testing cohort with 40% patients. Dimensionality reduction analysis, Kaplan–Meier curve, receiver operating characteristic (ROC) curve, independent prognostic analysis, and stratification analysis confirmed that our signature had a high clinical application value in predicting the overall survival of HCC patients. Compared to the clinicopathological factors and the other four published HCC prognostic signatures, the current risk model had a better predictive value. The comparison results of functional enrichment, tumor immune microenvironment, genomic heterogeneity, and drug sensitivity between the high- and low-risk groups showed that the risk score is associated with extensive genomic alterations, immunosuppressive tumor microenvironment, and clinical treatment response. Finally, cell experiments showed that silencing LNCSRLR expression inhibited the growth, proliferation, migration, and invasion of the HCC cell line. Thus, the model can function as an efficient indicator for predicting clinical prognosis and treatment of anticancer drugs in HCC patients.

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A novel five‐gene signature predicts overall survival of patients with hepatocellular carcinoma

Cited by 29 publications

References 36 publications

Gene co-expression network analysis revealed novel biomarkers for ovarian cancer

Gene co-expression network analysis revealed novel biomarkers for ovarian cancer

Characterization of diagnostic and prognostic significance of cell cycle-linked genes in hepatocellular carcinoma

A Novel Ferroptosis-Related Long Non-Coding RNA Prognostic Signature Correlates With Genomic Heterogeneity, Immunosuppressive Phenotype, and Drug Sensitivity in Hepatocellular Carcinoma

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