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Hasan MAM, Maniruzzaman M, Shin J. Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning. Sci Rep 2023; 13:3771. [PMID: 36882493 PMCID: PMC9992474 DOI: 10.1038/s41598-023-30851-1] [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: 09/09/2022] [Accepted: 03/02/2023] [Indexed: 03/09/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common lethal malignancy of the liver worldwide. Thus, it is important to dig the key genes for uncovering the molecular mechanisms and to improve diagnostic and therapeutic options for HCC. This study aimed to encompass a set of statistical and machine learning computational approaches for identifying the key candidate genes for HCC. Three microarray datasets were used in this work, which were downloaded from the Gene Expression Omnibus Database. At first, normalization and differentially expressed genes (DEGs) identification were performed using limma for each dataset. Then, support vector machine (SVM) was implemented to determine the differentially expressed discriminative genes (DEDGs) from DEGs of each dataset and select overlapping DEDGs genes among identified three sets of DEDGs. Enrichment analysis was performed on common DEDGs using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and the central hub genes were identified depending on the degree, maximum neighborhood component (MNC), maximal clique centrality (MCC), centralities of closeness, and betweenness criteria using CytoHubba. Simultaneously, significant modules were selected using MCODE scores and identified their associated genes from the PPI networks. Moreover, metadata were created by listing all hub genes from previous studies and identified significant meta-hub genes whose occurrence frequency was greater than 3 among previous studies. Finally, six key candidate genes (TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C) were determined by intersecting shared genes among central hub genes, hub module genes, and significant meta-hub genes. Two independent test datasets (GSE76427 and TCGA-LIHC) were utilized to validate these key candidate genes using the area under the curve. Moreover, the prognostic potential of these six key candidate genes was also evaluated on the TCGA-LIHC cohort using survival analysis.
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Affiliation(s)
- Md Al Mehedi Hasan
- School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Fukushima, 965-8580, Japan.,Department of Computer Science and Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh
| | - Md Maniruzzaman
- School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Fukushima, 965-8580, Japan.,Statistics Discipline, Khulna University, Khulna, 9208, Bangladesh
| | - Jungpil Shin
- School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu, Fukushima, 965-8580, Japan.
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Zhang X, Liu X, Zhu K, Zhang X, Li N, Sun T, Fan S, Dai L, Zhang J. CD5L-associated gene analyses highlight the dysregulations, prognostic effects, immune associations, and drug-sensitivity predicative potentials of LCAT and CDC20 in hepatocellular carcinoma. Cancer Cell Int 2022; 22:393. [PMID: 36494696 PMCID: PMC9733014 DOI: 10.1186/s12935-022-02820-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 12/01/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The dysregulation of CD5L has been reported in hepatocellular carcinoma (HCC). However, its functions in HCC were controversial. In this study, we aimed to identify CD5L-associated pathways and markers and explore their values in HCC diagnosis, prognosis and treatment. METHODS HCC datasets with gene expression profiles and clinical data in TCGA and ICGC were downloaded. The immune/stroma cell infiltrations were estimated with xCell. CD5L-associated pathways and CD5L-associated genes (CD5L-AGs) were identified with gene expression comparisons and gene set enrichment analysis (GSEA). Cox regression, Kaplan-Meier survival analysis, and least absolute shrinkage and selection operator (LASSO) regression analysis were performed. The correlations of the key genes with immune/stroma infiltrations, immunoregulators, and anti-cancer drug sensitivities in HCC were investigated. At protein level, the key genes dysregulations, their correlations and prognostic values were validated in clinical proteomic tumor analysis consortium (CPTAC) database. Serum CD5L and LCAT activity in 50 HCC and 30 normal samples were evaluated and compared. The correlations of serum LCAT activity with alpha-fetoprotein (AFP), albumin (ALB) and high-density lipoprotein (HDL) in HCC were also investigated. RESULTS Through systemic analyses, 14 CD5L-associated biological pathways, 256 CD5L-AGs and 28 CD5L-associated prognostic and diagnostic genes (CD5L-APDGs) were identified. A risk model consisting of LCAT and CDC20 was constructed for HCC overall survival (OS), which could discriminate HCC OS status effectively in both the training and the validation sets. CD5L, LCAT and CDC20 were shown to be significantly correlated with immune/stroma cell infiltrations, immunoregulators and 31 anti-cancer drug sensitivities in HCC. At protein level, the dysregulations of CD5L, LCAT and CDC20 were confirmed. LCAT and CDC20 were shown to be significantly correlated with proliferation marker MKI67. In serum, no significance of CD5L was shown. However, the lower activity of LCAT in HCC serum was obvious, as well as its significant positive correlations ALB and HDL concentrations. CONCLUSIONS CD5L, LCAT and CDC20 were dysregulated in HCC both at mRNA and protein levels. The LCAT-CDC20 signature might be new predicator for HCC OS. The associations of the three genes with HCC microenvironment and anti-cancer drug sensitivities would provide new clues for HCC immunotherapy and chemotherapy.
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Affiliation(s)
- Xiuzhi Zhang
- Department of Pathology, Henan Medical College, Zhengzhou, China
| | - Xiaoli Liu
- grid.414011.10000 0004 1808 090XLaboratory Department, Henan Provincial People’s Hospital, Zhengzhou, China
| | - Keke Zhu
- Department of Pathology, Henan Medical College, Zhengzhou, China
| | - Xue Zhang
- grid.207374.50000 0001 2189 3846Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
| | - Ningning Li
- Department of Pathology, Henan Medical College, Zhengzhou, China
| | - Tao Sun
- Department of Pathology, Henan Medical College, Zhengzhou, China
| | - Shasha Fan
- grid.477407.70000 0004 1806 9292Oncology Department, The First Affiliated Hospital of Hunan Normal University, Hunan Provincial People’s Hospital, Changsha, China ,grid.411427.50000 0001 0089 3695Key Laboratory of Study and Discovery of Small Targeted Molecules of Hunan Province, Hunan Normal University, Changsha, China
| | - Liping Dai
- grid.207374.50000 0001 2189 3846Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China
| | - Jinzhong Zhang
- Department of Pathology, Henan Medical College, Zhengzhou, China
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Anuraga G, Wang WJ, Phan NN, An Ton NT, Ta HDK, Berenice Prayugo F, Minh Xuan DT, Ku SC, Wu YF, Andriani V, Athoillah M, Lee KH, Wang CY. Potential Prognostic Biomarkers of NIMA (Never in Mitosis, Gene A)-Related Kinase (NEK) Family Members in Breast Cancer. J Pers Med 2021; 11:1089. [PMID: 34834441 PMCID: PMC8625415 DOI: 10.3390/jpm11111089] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 10/18/2021] [Accepted: 10/19/2021] [Indexed: 02/06/2023] Open
Abstract
Breast cancer remains the most common malignant cancer in women, with a staggering incidence of two million cases annually worldwide; therefore, it is crucial to explore novel biomarkers to assess the diagnosis and prognosis of breast cancer patients. NIMA-related kinase (NEK) protein kinase contains 11 family members named NEK1-NEK11, which were discovered from Aspergillus Nidulans; however, the role of NEK family genes for tumor development remains unclear and requires additional study. In the present study, we investigate the prognosis relationships of NEK family genes for breast cancer development, as well as the gene expression signature via the bioinformatics approach. The results of several integrative analyses revealed that most of the NEK family genes are overexpressed in breast cancer. Among these family genes, NEK2/6/8 overexpression had poor prognostic significance in distant metastasis-free survival (DMFS) in breast cancer patients. Meanwhile, NEK2/6 had the highest level of DNA methylation, and the functional enrichment analysis from MetaCore and Gene Set Enrichment Analysis (GSEA) suggested that NEK2 was associated with the cell cycle, G2M checkpoint, DNA repair, E2F, MYC, MTORC1, and interferon-related signaling. Moreover, Tumor Immune Estimation Resource (TIMER) results showed that the transcriptional levels of NEK2 were positively correlated with immune infiltration of B cells and CD4+ T Cell. Collectively, the current study indicated that NEK family genes, especially NEK2 which is involved in immune infiltration, and may serve as prognosis biomarkers for breast cancer progression.
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Affiliation(s)
- Gangga Anuraga
- PhD Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University and Academia Sinica, Taipei 11031, Taiwan; (G.A.); (H.D.K.T.); (K.-H.L.)
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; (F.B.P.); (D.T.M.X.); (S.-C.K.)
- Department of Statistics, Faculty of Science and Technology, Universitas PGRI Adi Buana, Surabaya 60234, Indonesia;
| | - Wei-Jan Wang
- Research Center for Cancer Biology, Department of Biological Science and Technology, China Medical University, Taichung 40604, Taiwan;
| | - Nam Nhut Phan
- Institute for Environmental Science, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam; (N.N.P.); (N.T.A.T.)
| | - Nu Thuy An Ton
- Institute for Environmental Science, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam; (N.N.P.); (N.T.A.T.)
| | - Hoang Dang Khoa Ta
- PhD Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University and Academia Sinica, Taipei 11031, Taiwan; (G.A.); (H.D.K.T.); (K.-H.L.)
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; (F.B.P.); (D.T.M.X.); (S.-C.K.)
| | - Fidelia Berenice Prayugo
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; (F.B.P.); (D.T.M.X.); (S.-C.K.)
| | - Do Thi Minh Xuan
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; (F.B.P.); (D.T.M.X.); (S.-C.K.)
| | - Su-Chi Ku
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; (F.B.P.); (D.T.M.X.); (S.-C.K.)
| | - Yung-Fu Wu
- Department of Medical Research, Tri-Service General Hospital, School of Medicine, National Defense Medical Center, Taipei 11490, Taiwan;
| | - Vivin Andriani
- Department of Biological Science, Faculty of Science and Technology, Universitas PGRI Adi Buana, Surabaya 60234, Indonesia;
| | - Muhammad Athoillah
- Department of Statistics, Faculty of Science and Technology, Universitas PGRI Adi Buana, Surabaya 60234, Indonesia;
| | - Kuen-Haur Lee
- PhD Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University and Academia Sinica, Taipei 11031, Taiwan; (G.A.); (H.D.K.T.); (K.-H.L.)
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; (F.B.P.); (D.T.M.X.); (S.-C.K.)
- Cancer Center, Wan Fang Hospital, Taipei Medical University, Taipei 11031, Taiwan
| | - Chih-Yang Wang
- PhD Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University and Academia Sinica, Taipei 11031, Taiwan; (G.A.); (H.D.K.T.); (K.-H.L.)
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan; (F.B.P.); (D.T.M.X.); (S.-C.K.)
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Yuan C, Yuan M, Chen M, Ouyang J, Tan W, Dai F, Yang D, Liu S, Zheng Y, Zhou C, Cheng Y. Prognostic Implication of a Novel Metabolism-Related Gene Signature in Hepatocellular Carcinoma. Front Oncol 2021; 11:666199. [PMID: 34150630 PMCID: PMC8213025 DOI: 10.3389/fonc.2021.666199] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 05/10/2021] [Indexed: 01/12/2023] Open
Abstract
Background Hepatocellular carcinoma (HCC) is one of the main causes of cancer-associated deaths globally, accounts for 90% of primary liver cancers. However, further studies are needed to confirm the metabolism-related gene signature related to the prognosis of patients with HCC. Methods Using the “limma” R package and univariate Cox analysis, combined with LASSO regression analysis, a metabolism-related gene signature was established. The relationship between the gene signature and overall survival (OS) of HCC patients was analyzed. RT-qPCR was used to evaluate the expression of metabolism-related genes in clinical samples. GSEA and ssGSEA algorithms were used to evaluate differences in metabolism and immune status, respectively. Simultaneously, data downloaded from ICGC were used as an external verification set. Results From a total of 1,382 metabolism-related genes, a novel six-gene signature (G6PD, AKR1B15, HMMR, CSPG5, ELOVL3, FABP6) was constructed based on data from TCGA. Patients were divided into two risk groups based on risk scores calculated for these six genes. Survival analysis showed a significant correlation between high-risk patients and poor prognosis. ROC analysis demonstrated that the gene signature had good predictive capability, and the mRNA expression levels of the six genes were upregulated in HCC tissues than those in adjacent normal liver tissues. Independent prognosis analysis confirmed that the risk score and tumor grade were independent risk factors for HCC. Furthermore, a nomogram of the risk score combined with tumor stage was constructed. The calibration graph results demonstrated that the OS probability predicted by the nomogram had almost no deviation from the actual OS probability, especially for 3-year OS. Both the C-index and DCA curve indicated that the nomogram provides higher reliability than the tumor stage and risk scores. Moreover, the metabolic and immune infiltration statuses of the two risk groups were significantly different. In the high-risk group, the expression levels of immune checkpoints, TGF-β, and C-ECM genes, whose functions are related to immune escape and immunotherapy failure, were also upregulated. Conclusions In summary, we developed a novel metabolism-related gene signature to provide more powerful prognostic evaluation information with potential ability to predict the immunotherapy efficiency and guide early treatment for HCC.
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Affiliation(s)
- Chaoyan Yuan
- Department of Gynecology, Minda Hospital of Hubei Minzu University, Enshi, China
| | - Mengqin Yuan
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Mingqian Chen
- Department of Gynecology, Minda Hospital of Hubei Minzu University, Enshi, China
| | - Jinhua Ouyang
- Department of Gynecology, Minda Hospital of Hubei Minzu University, Enshi, China
| | - Wei Tan
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Fangfang Dai
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Dongyong Yang
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Shiyi Liu
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yajing Zheng
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Chenliang Zhou
- Department of Intensive Care Unit, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yanxiang Cheng
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, China
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