Identification of Multiple Hub Genes and Pathways in Hepatocellular Carcinoma: A Bioinformatics Analysis

Genetic alterations of KRAS and TP53 in intrahepatic cholangiocarcinoma associated with poor prognosis

The aim of this study is to investigate certain genetic features of intrahepatic cholangiocarcinoma (ICCA). A total of 12 eligible ICCA patients were enrolled, and tumor tissues from the patients were subjected to next-generation sequencing of a multi-genes panel. Tumor mutation burden (TMB), mutated genes, copy number variants (CNVs), and pathway enrichment analysis were performed. The median TMB was 2.76 Mutation/Mb (range, 0-36.62 Mutation/Mb) in ICCA patients. The top two most commonly mutated genes in ICCA were KRAS (33%) and TP53 (25%). The co-mutations of KRAS and TP53 were 16.7% (2/12) in ICCA patients. Notably, patient P6 with the highest TMB did not have KRAS and TP53 mutations. Additionally, TP53 and/or KRAS alterations were significantly associated with poor progression-free survival than those with wild type (1.4 months vs 18 months). DNA damage repair and homologs recombinant repair deficiencies were significantly associated with high TMB in ICCA cases. In conclusion, we found that certain genetic mutations of TP53 and KRAS could predict poor prognosis in ICCA patients.

Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning

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.

CD5L-associated gene analyses highlight the dysregulations, prognostic effects, immune associations, and drug-sensitivity predicative potentials of LCAT and CDC20 in hepatocellular carcinoma

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.

Glycolysis-related gene dihydrolipoamide acetyltransferase promotes poor prognosis in hepatocellular carcinoma through the Wnt/β-catenin and PI3K/Akt signaling pathways

Background

Recent research suggests that dihydrolipoamide acetyltransferase (DLAT), which is a copper-induced cell death-related gene, is involved in multiple biological events in tumors. This study sought to investigate the relationship between DLAT and hepatocellular carcinoma (HCC).

Methods

In the Cancer Genome Atlas (TCGA) database, we first identified the differentially expressed gene (i.e., DLAT), then confirmed DLAT expression, and found a link between it and the prognosis of HCC patients. An internal validation nomogram was built based on a multivariate Cox regression analysis. Data from the Tumor Immune Estimation Resource (TIMER) database was used to examine the association between DLT and immunological cells. A gene set enrichment analysis (GSEA) was conducted to investigate the probable mechanism of action. Finally, in vitro cytological research was conducted to further examin the involvement of DLAT in HCC-related unfavorable biological events.

Results

The database screenings showed that DLAT was a differentially expressed molecule; that is, DLAT was more highly expressed in the cancer tissues than normal tissues. TCGA results and Kaplan-Meier-plotter data sets showed that HCC patients with reduced DLAT expression had greater disease-specific survival (DSS), overall survival (OS), and progression-free interval (PFI). The prediction model had a concordance index of 0.659 (0.614-0.704), which indicates high accuracy. According to the TIMER database, tumor cells in the HCC microenvironment may be able to bypass the immune system due to the expression of DLAT. The in vitro cytological tests showed that DLAT knockdown significantly decreased the proliferation and invasion of the HCC cells. It also inhibited the activity of the phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K)/protein kinase B (Akt) and Wnt/β-catenin signaling pathways.

Conclusions

Decreased DLAT expression significantly prolongs the OS, PFI, and DSS of HCC patients. DLAT may be employed as a new predictive biomarker for HCC, and may be linked to the immune system in HCC patients. The tumor microenvironment (TME) may have a significant effect on the ability of tumor cells to evade the immune system. The PI3K/Akt and Wnt/β-catenin signaling pathways may affect the prognosis of HCC by interfering with DLAT. Given these findings, HCC may be an ideal target for the development of anti-cancer therapies.

A review on the role of cyclin dependent kinases in cancers

The Cyclin-dependent kinase (CDK) class of serine/threonine kinases has crucial roles in the regulation of cell cycle transition and is mainly involved in the pathogenesis of cancers. The expression of CDKs is controlled by a complex regulatory network comprised of genetic and epigenetic mechanisms, which are dysregulated during the progression of cancer. The abnormal activation of CDKs results in uncontrolled cancer cell proliferation and the induction of cancer stem cell characteristics. The levels of CDKs can be utilized to predict the prognosis and treatment response of cancer patients, and further understanding of the function and underlying mechanisms of CDKs in human tumors would pave the way for future cancer therapies that effectively target CDKs. Defects in the regulation of cell cycle and mutations in the genes coding cell-cycle regulatory proteins lead to unrestrained proliferation of cells leading to formation of tumors. A number of treatment modalities have been designed to combat dysregulation of cell cycle through affecting expression or activity of CDKs. However, effective application of these methods in the clinical settings requires recognition of the role of CDKs in the progression of each type of cancer, their partners, their interactions with signaling pathways and the effects of suppression of these kinases on malignant features. Thus, we designed this literature search to summarize these findings at cellular level, as well as in vivo and clinical levels.