Weighted gene coexpression network analysis reveals hub genes involved in cholangiocarcinoma progression and prognosis

Mitochondrial ribosomal proteins: potential targets for cancer prognosis and therapy

Mitochondrial ribosomal proteins (MRPs) are essential components of mitochondrial ribosomes, responsible for translating proteins encoded by mitochondrial DNA and maintaining mitochondrial energy metabolism and function. Emerging evidence suggests that MRPs exhibit significant expression changes in multiple cancer types, profoundly affecting tumor biology through modulating oxidative stress levels, inducing metabolic reprogramming, disrupting cell cycle regulation, inhibiting apoptosis, promoting mitophagy, and remodeling the tumor microenvironment. Specifically, MRPs have been implicated in tumor cell proliferation, migration, invasion, and apoptosis, highlighting their potential as therapeutic targets. This review summarizes the multifaceted roles of MRPs in cancer, focusing on their impact on the tumor microenvironment and their potential as prognostic biomarkers and therapeutic targets. We also explore the implications of MRPs in precision oncology, particularly in patient stratification and the design of metabolic targeted therapies, offering new insights and research directions for the precise prevention and treatment of cancer.

Integrated Analysis of the Metabolome and Transcriptome During Apple Ripening to Highlight Aroma Determinants in Ningqiu Apples

We investigated the dynamic changes in volatile aroma compound profiles (types and concentrations) and associated gene expression patterns in both the peel and pulp tissues of apples during fruit maturation. This study aimed to elucidate the metabolic regulatory mechanisms underlying volatile aroma biosynthesis in Malus domestica "Ningqiu" apples, thereby providing theoretical support for the comprehensive utilization of aroma resources. Our methodological framework integrated headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS), ultra-high-performance liquid chromatography-orbitrap mass spectrometry (UHPLC-OE-MS), and Illumina high-throughput sequencing to generate comprehensive metabolomic and transcriptomic profiles of peel and pulp tissues. Critical differential aroma compound classes were identified, including esters, aldehydes, alcohols, terpenoids, and ketones, with their metabolic pathways systematically mapped through KEGG functional annotation. Our findings revealed substantial transcriptomic and metabolomic divergence across carotenoid, terpenoid, and fatty acid metabolic pathways. Integrative analysis of multi-omics data revealed 26 and 31 putative biologically significant hub genes in peel and pulp tissues, respectively, putatively associated with the observed metabolic signatures. Among these, five core genes-farnesyl diphosphate synthase (FDPS1.X1), alcohol acyltransferases (AAT1 and AAT3), alcohol dehydrogenase (ADH3), and carotenoid cleavage dioxygenase (CCD3)-were recognized as shared regulatory determinants between both tissue types. Furthermore, terpene synthase (TPS7) emerged as a peel-specific regulatory factor, while hydroperoxide lyase (HPL2), alcohol dehydrogenases (ADH2 and ADH4), and alcohol acyltransferase (AAT2) were identified as pulp-exclusive modulators of metabolic differentiation. The experimental findings provide foundational insights into the molecular basis of aroma profile variation in Malus domestica "Ningqiu" and establish a functional genomics framework for precision breeding initiatives targeting fruit quality optimization through transcriptional regulatory network manipulation.

Prediction of influenza A virus-human protein-protein interactions using XGBoost with continuous and discontinuous amino acids information

Influenza A virus (IAV) has the characteristics of high infectivity and high pathogenicity, which makes IAV infection a serious public health threat. Identifying protein-protein interactions (PPIs) between IAV and human proteins is beneficial for understanding the mechanism of viral infection and designing antiviral drugs. In this article, we developed a sequence-based machine learning method for predicting PPI. First, we applied a new negative sample construction method to establish a high-quality IAV-human PPI dataset. Then we used conjoint triad (CT) and Moran autocorrelation (Moran) to encode biologically relevant features. The joint consideration utilizing the complementary information between contiguous and discontinuous amino acids provides a more comprehensive description of PPI information. After comparing different machine learning models, the eXtreme Gradient Boosting (XGBoost) model was determined as the final model for the prediction. The model achieved an accuracy of 96.89%, precision of 98.79%, recall of 94.85%, F1-score of 96.78%. Finally, we successfully identified 3,269 potential target proteins. Gene ontology (GO) and pathway analysis showed that these genes were highly associated with IAV infection. The analysis of the PPI network further revealed that the predicted proteins were classified as core proteins within the human protein interaction network. This study may encourage the identification of potential targets for the discovery of more effective anti-influenza drugs. The source codes and datasets are available at https://github.com/HVPPIlab/IVA-Human-PPI/.

Mitochondrial Ribosomal Proteins and Cancer

Mitochondria play key roles in maintaining cell life and cell function, and their dysfunction can lead to cell damage. Mitochondrial ribosomal proteins (MRPs) are encoded by nuclear genes and are assembled within the mitochondria. MRPs are pivotal components of the mitochondrial ribosomes, which are responsible for translating 13 mitochondrial DNA-encoded proteins essential for the mitochondrial respiratory chain. Recent studies have underscored the importance of MRPs in cancer biology, revealing their altered expression patterns in various types of cancer and their potential as both prognostic biomarkers and therapeutic targets. Herein, we review the current knowledge regarding the multiple functions of MRPs in maintaining the structure of the mitochondrial ribosome and apoptosis, their implications for cancer susceptibility and progression, and the innovative strategies being developed to target MRPs and mitoribosome biogenesis in cancer therapy. This comprehensive overview aims to provide insights into the role of MRPs in cancer biology and highlight promising strategies for future precision oncology.

An integrated mRNA-lncRNA signature for overall survival prediction in cholangiocarcinoma

The combination of mRNA and lncRNA profiles for establishing an integrated mRNA-lncRNA prognostic signature has remained unexplored in cholangiocarcinoma (CCA) patients. We utilized a training dataset of 36 samples from The Cancer Genome Atlas dataset and a validation cohort (GSE107943) of 30 samples from Gene Expression Omnibus. Two mRNAs (CFHR3 and PIWIL4) and 2 lncRNAs (AC007285.1 and AC134682.1) were identified to construct the integrated signature through a univariate Cox regression (P-value = 1.35E-02) and a multivariable Cox analysis (P-value = 3.07E-02). Kaplan-Meier curve showed that patients with low risk scores had notably prolonged overall survival than those with high risk scores (P-value = 4.61E-03). Subsequently, the signature was validated in GSE107943 cohort with an area under the curve of 0.750 at 1-year and 0.729 at 3-year. The signature was not only independent from diverse clinical features (P-value = 3.07E-02), but also surpassed other clinical characteristics as prognostic biomarkers with area under the curve of 0.781 at 3-year. Moreover, the weighted gene co-expression network analysis and gene enrichment analyses found that the integrated signature were associated with metabolic-related biological process and lipid metabolism pathway, which has been implicated in the pathogenesis of CCA. Taken together, we developed an integrated mRNA-lncRNA signature that had an independent prognostic value in the risk stratification of patients with CCA.

Characteristic Analysis of Featured Genes Associated with Cholangiocarcinoma Progression

The noninvasive diagnosis of cholangiocarcinoma (CCA) is insufficiently accurate. Therefore, the discovery of new prognostic markers is vital for the understanding of the CCA mechanism and related treatment. The information on CCA patients in The Cancer Genome Atlas database was used for weighted gene co-expression network analysis. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied to analyze the modules of interest. By using receiver operating characteristic (ROC) analysis to analyze the Human Protein Atlas (HPA), the featured genes were subsequently verified. In addition, clinical samples and GSE119336 cohort data were also collected for the validation of these hub genes. Using WGCNA, we identified 61 hub genes that regulated the progression and prognosis of CCA. Eight hub genes (VSNL1, TH, PCP4, IGDCC3, RAD51AP2, MUC2, BUB1, and BUB1B) were identified which exhibited significant interactions with the tumorigenic mechanism and prognosis of CCA. In addition, GO and KEGG clarified that the blue and magenta modules were involved with chromosome segregation, mitotic and oocyte meiosis, the cell cycle, and sister chromatid segregation. Four hub genes (VSNL1, PCP4, BUB1, and BUB1B) were also verified as featured genes of progression and prognosis by the GSE119336 cohort data and five human tissue samples.

Detection of key mRNAs in liver tissue of hepatocellular carcinoma patients based on machine learning and bioinformatics analysis

One methodology extensively used to develop biomarkers is the precise detection of highly responsive genes that can distinguish cancer samples from healthy samples. The purpose of this study was to screen for potential hepatocellular carcinoma (HCC) biomarkers based on non-fusion integrative multi-platform meta-analysis method. The gene expression profiles of liver tissue samples from two microarray platforms were initially analyzed using a meta-analysis based on an empirical Bayesian method to robust discover differentially expressed genes in HCC and non-tumor tissues. Then, using the bioinformatics technique of weighted correlation network analysis, the highly associated prioritized Differentially Expressed Genes (DEGs) were clustered. Co-expression network and topological analysis were utilized to identify sub-clusters and confirm candidate genes. Next, a diagnostic model was developed and validated using a machine learning algorithm. To construct a prognostic model, the Cox proportional hazard regression analysis was applied and validated. We identified three genes as specific biomarkers for the diagnosis of HCC based on accuracy and feasibility. The diagnostic model's area under the curve was 0.931 with confidence interval of 0.923-0.952.•Non-fusion integrative multi-platform meta-analysis method.•Classification methods and biomarkers recognition via machine learning method.•Biomarker validation models.

Immune-Related lncRNAs with WGCNA Identified the Function of SNHG10 in HBV-Related Hepatocellular Carcinoma

Objective. The hepatitis B virus (HBV) infection led to hepatitis, which was one of common reasons for hepatocellular carcinoma (HCC). The immune microenvironment alteration played a crucial role in this process. The study aimed to identify immune-related long noncoding RNAs (lncRNAs) in HBV-related HCC and explore potential mechanisms. Methods. In total, 1,072 immune‐related genes (IRGs) were enriched in different co-expression modules with weighted gene co-expression network analysis (WGCNA) combining the corresponding clinical features in HBV-related HCC. The immune-related lncRNAs were selected from the crucial co-expression model based on the correlation analysis with IRGs. The immune-related lncRNAs were furtherly used to construct prognostic signature by the Cox proportional hazards regression and Lasso regression. Furthermore, the proliferation and migration ability of lncRNA SNHG10 were verified in vitro. Results. A total of nine co-expression modules were identified by WGCNA of which the “red” co-expression module was most correlated with various clinical characteristics. Additionally, the IRGs in this module were significantly enriched in multiple immune-related pathways. The twelve immune-related lncRNAs prognostic signature (HAND2-AS1, LINC00844, SNHG10, MALAT1, LINC00460, LBX2-AS1, MIR31HG, SEMA6A-AS1, LINC1278, LINC00514, CTBP-AS2, and LINC00205) was constructed. The risk score was an independent risk factor in HBV-related HCC and verified by principal components analysis (PCA), nomogram, and PCR between different cell lines. Moreover, the proportion of immune cells were significantly different between high-risk score group and low-risk score group. The malignant behavior of Hep3B was significantly different between si-lncRNA SNHG10 and control group. Conclusions. The immune-related lncRNAs prognostic signature provided some potential biomarkers and molecular mechanisms in HBV-related HCC.

Potential of Mitochondrial Ribosomal Genes as Cancer Biomarkers Demonstrated by Bioinformatics Results

Next-generation sequencing and bioinformatics analyses have clearly revealed the roles of mitochondrial ribosomal genes in cancer development. Mitochondrial ribosomes are composed of three RNA components encoded by mitochondrial DNA and 82 specific protein components encoded by nuclear DNA. They synthesize mitochondrial inner membrane oxidative phosphorylation (OXPHOS)-related proteins and participate in various biological activities via the regulation of energy metabolism and apoptosis. Mitochondrial ribosomal genes are strongly associated with clinical features such as prognosis and foci metastasis in patients with cancer. Accordingly, mitochondrial ribosomes have become an important focus of cancer research. We review recent advances in bioinformatics research that have explored the link between mitochondrial ribosomes and cancer, with a focus on the potential of mitochondrial ribosomal genes as biomarkers in cancer.

Diagnostic Value of FibroTouch and Non-invasive Fibrosis Indexes in Hepatic Fibrosis with Different Aetiologies

BACKGROUND

Liver biopsy is the gold standard for staging liver fibrosis, but it has numerous drawbacks, mainly associated with bleeding and bile fistula risks. A number of non-invasive techniques have been investigated, but they all have their own disadvantages. To avoid the risks mentioned above and to improve the diagnostic value, we still need to search for a more accurate non-invasive method to evaluate the degree of liver fibrosis.

AIM

This study aimed to evaluate the diagnostic performance of FibroTouch versus other non-invasive fibrosis indexes in hepatic fibrosis of different aetiologies.

METHODS

This study retrospectively enrolled 227 patients with chronic hepatic liver disease admitted to the first hospital of Lanzhou University from 2017 to 2020. Liver biopsy was performed in all of the patients, and their biochemical indicators were all tested. Non-invasive indexes including the fibrosis index based on four factors (FIB-4), the aminotransferase-to-platelet ratio index (APRI), and the gamma-glutamyl transpeptidase-to-platelet ratio index (GPRI) were all calculated. Transient elastography was performed using FibroTouch.

RESULTS

The correlation between FibroTouch and the pathology of liver fibrosis was significantly higher than that between the non-invasive fibrosis indexes and the biopsy results (r = 0.771, p < 0.05). The area under the receiver operating curve (AUC) of FibroTouch was significantly higher than that of FIB-4, APRI, and GPRI for the diagnosis of significant fibrosis (≥ S2 fibrosis stage), advanced fibrosis (≥ S3 fibrosis stage), and cirrhosis (= S4 fibrosis stage) (p < 0.05). The patients were grouped according to different aetiologies. The diagnostic value of FibroTouch had much higher credibility in different fibrosis stages for different causes compared with other non-invasive indexes. The AUC of FibroTouch showed both higher specificity and higher sensitivity than FIB-4, APRI, and GPRI for different liver fibrosis stages with different aetiologies.

CONCLUSIONS

FibroTouch demonstrates the highest diagnostic value for liver fibrosis and cirrhosis among non-invasive methods, showing better results than FIB-4, APRI, and GPRI, and surpassed only by liver biopsy. FibroTouch is reliable in assessing liver fibrosis with different aetiologies.

A comprehensive transcriptomic landscape of cholangiocarcinoma based on bioinformatics analysis from large cohort of patients

Cholangiocarcinoma (CCA) is a group of malignancies emerging in the biliary tree and is associated with a poor patient prognosis. Although the anatomical location is the only worldwide accepted classification basis, it still has bias. The current study integrates the whole-genome expression data from several big cohorts in the literature, to screen and provide a comprehensive bioinformatic analysis, in order to better classify molecular subtypes and explore an underlying cluster mechanism related to anatomy and geographical regions. Differentially expressed protein-coding genes (DEGs) were identified for CCA as well as subtypes. Biological function enrichment analysis-Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis-was applied and identified different DEGs enriched signaling pathways in CCA subtypes. A co-expression network was presented by Weighted gene co-expression network analysis package and modules related to specific phenotypes were identified. Combined with DEGs, hub genes in the given module were demonstrated through protein-protein interaction network analysis. Finally, DEGs which significantly related to patient overall survival and disease-free survival time were selected, including ARHGAP21, SCP2, UBIAD1, TJP2, RAP1A and HDAC9.

Systematic Analysis of the Transcriptome Profiles and Co-Expression Networks of Tumour Endothelial Cells Identifies Several Tumour-Associated Modules and Potential Therapeutic Targets in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the sixth most common cancer and the third most common cause of cancer-related death, with tumour associated liver endothelial cells being thought to be major drivers in HCC progression. This study aims to compare the gene expression profiles of tumour endothelial cells from the liver with endothelial cells from non-tumour liver tissue, to identify perturbed biologic functions, co-expression modules, and potentially drugable hub genes that could give rise to novel therapeutic targets and strategies. Gene Set Variation Analysis (GSVA) showed that cell growth-related pathways were upregulated, whereas apoptosis induction, immune and inflammatory-related pathways were downregulated in tumour endothelial cells. Weighted Gene Co-expression Network Analysis (WGCNA) identified several modules strongly associated to tumour endothelial cells or angiogenic activated endothelial cells with high endoglin (ENG) expression. In tumour cells, upregulated modules were associated with cell growth, cell proliferation, and DNA-replication, whereas downregulated modules were involved in immune functions, particularly complement activation. In ENG⁺ cells, upregulated modules were associated with cell adhesion and endothelial functions. One downregulated module was associated with immune system-related functions. Querying the STRING database revealed known functional-interaction networks underlying the modules. Several possible hub genes were identified, of which some (for example FEN1, BIRC5, NEK2, CDKN3, and TTK) are potentially druggable as determined by querying the Drug Gene Interaction database. In summary, our study provides a detailed picture of the transcriptomic differences between tumour and non-tumour endothelium in the liver on a co-expression network level, indicates several potential therapeutic targets and presents an analysis workflow that can be easily adapted to other projects.

Identifying Biomarkers to Predict the Progression and Prognosis of Breast Cancer by Weighted Gene Co-expression Network Analysis

Breast cancer (BC) is the leading cause of cancer death among women worldwide. The molecular mechanisms of its pathogenesis are still to be investigated. In our study, differentially expressed genes (DEGs) were screened between BC and normal tissues. Based on the DEGs, a weighted gene co-expression network analysis (WGCNA) was performed in 683 BC samples, and eight co-expressed gene modules were identified. In addition, by relating the eight co-expressed modules to clinical information, we found the blue module and pathological stage had a significant correlation (r = 0.24, p = 1e–10). Validated by multiple independent datasets, using one-way ANOVA, survival analysis and expression level revalidation, we finally screened 12 hub genes that can predict BC progression and prognosis. Functional annotation analysis indicated that the hub genes were enriched in cell division and cell cycle regulation. Importantly, higher expression of the 12 hub genes indicated poor overall survival, recurrence-free survival, and disease-free survival in BC patients. In addition, the expression of the 12 hub genes showed a significantly positive correlation with the expression of cell proliferation marker Ki-67 in BC. In summary, our study has identified 12 hub genes associated with the progression and prognosis of BC; these hub genes might lead to poor outcomes by regulating the cell division and cell cycle. These hub genes may serve as a biomarker and help to distinguish different pathological stages for BC patients.

Abnormal Expression of Mitochondrial Ribosomal Proteins and Their Encoding Genes with Cell Apoptosis and Diseases

Mammalian mitochondrial ribosomes translate 13 proteins encoded by mitochondrial genes, all of which play roles in the mitochondrial respiratory chain. After a long period of reconstruction, mitochondrial ribosomes are the most protein-rich ribosomes. Mitochondrial ribosomal proteins (MRPs) are encoded by nuclear genes, synthesized in the cytoplasm and then, transported to the mitochondria to be assembled into mitochondrial ribosomes. MRPs not only play a role in mitochondrial oxidative phosphorylation (OXPHOS). Moreover, they participate in the regulation of cell state as apoptosis inducing factors. Abnormal expressions of MRPs will lead to mitochondrial metabolism disorder, cell dysfunction, etc. Many researches have demonstrated the abnormal expression of MRPs in various tumors. This paper reviews the basic structure of mitochondrial ribosome, focuses on the structure and function of MRPs, and their relationships with cell apoptosis and diseases. It provides a reference for the study of the function of MRPs and the disease diagnosis and treatment.

Weighted Gene Co-expression Network Analysis of Key Biomarkers Associated With Bronchopulmonary Dysplasia

Bronchopulmonary dysplasia (BPD) is a complex disorder resulting from interactions between genes and the environment. The accurate molecular etiology of BPD remains largely unclear. This study aimed to identify key BPD-associated genes and pathways functionally enriched using weighted gene co-expression network analysis (WGCNA). We analyzed microarray data of 62 pre-term patients with BPD and 38 pre-term patients without BPD from Gene Expression Omnibus (GEO). WGCNA was used to construct a gene expression network, and genes were classified into definite modules. In addition, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of BPD-related hub genes were performed. Firstly, we constructed a weighted gene co-expression network, and genes were divided into 10 modules. Among the modules, the yellow module was related to BPD progression and severity and included the following hub genes: MMP25, MMP9, SIRPA, CKAP4, SLCO4C1, and SLC2A3; and the red module included some co-expression molecules that displayed a continuous decline in expression with BPD progression and included the following hub genes: LEF1, ITK, CD6, RASGRP1, IL7R, SKAP1, CD3E, and ICOS. GO and KEGG analyses showed that high expression of inflammatory response-related genes and low expression of T cell receptor activation-related genes are significantly correlated with BPD progression. The present WGCNA-based study thus provides an overall perspective of BPD and lays the foundation for identifying potential pathways and hub genes that contribute to the development of BPD.

Identification of hub methylated-CpG sites and associated genes in oral squamous cell carcinoma

To improve personalized diagnosis and prognosis for oral squamous cell carcinoma (OSCC) by identification of hub methylated‐CpG sites and associated genes, weighted gene comethylation network analysis (WGCNA) was performed to examine and identify hub modules and CpG sites correlated with OSCC. Here, WGCNA modeling yielded blue and brown comethylation modules that were significantly associated with OSCC status. Following screening of the differentially expressed genes (DEGs) from gene expression microarrays and differentially methylated‐CpG sites (DCGs), integrated multiomics analysis of the DEGs, DCGs, and hub CpG sites from the modules was performed to investigate their correlations. Expression levels of 16 CpG sites‐associated genes were negatively correlated with methylation patterns of promoter. Moreover, Kaplan‐Meier survival analysis of the hub CpG sites and associated genes was carried out using 2 public databases, MethSurv and GEPIA. Only 5 genes, ACTA1, ACTN2, OSR1, SYNGR1, and ZNF677, had significant overall survival using GEPIA. Hypermethylated‐CpG sites ACTN2‐cg21376883 and OSR1‐cg06509239 were found to be associated with poor survival by MethSurv. Methylation status of specific site and expression levels of associated genes were determined using clinical samples by quantitative methylation‐specific PCR and real‐time PCR. Pearson's correlation analysis showed that methylation levels of cg06509239 and cg18335068 were negatively related to OSR1 and ZNF677 expression levels, respectively. Our classification schema using multiomics analysis represents a screening framework for identification of hub CpG sites and associated genes.

Identification of potential key genes in gastric cancer using bioinformatics analysis

Gastric cancer (GC) is one of the most common types of cancer worldwide. Patients must be identified at an early stage of tumor progression for treatment to be effective. The aim of the present study was to identify potential biomarkers with diagnostic value in patients with GC. To examine potential therapeutic targets for GC, four Gene Expression Omnibus (GEO) datasets were downloaded and screened for differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were subsequently performed to study the function and pathway enrichment of the identified DEGs. A protein-protein interaction (PPI) network was constructed. The CytoHubba plugin of Cytoscape was used to calculate the degree of connectivity of proteins in the PPI network, and the two genes with the highest degree of connectivity were selected for further analysis. Additionally, the two DEGs with the largest and smallest log Fold Change values were selected. These six key genes were further examined using Oncomine and the Kaplan-Meier plotter platform. A total of 99 upregulated and 172 downregulated genes common to all four GEO datasets were screened. The DEGs were primarily enriched in the Biological Process terms: ‘extracellular matrix organization’, ‘collagen catabolic process’ and ‘cell adhesion’. These three KEGG pathways were significantly enriched in the categories: ‘ECM-receptor interaction’, ‘protein digestion and absorption’, and ‘focal adhesion’. Based on Oncomine, expression of ATP4A and ATP4B were downregulated in GC, whereas expression of the other genes were all upregulated. The Kaplan-Meier plotter platform confirmed that upregulated expression of the identified key genes was significantly associated with worse overall survival of patients with GC. The results of the present study suggest that FN1, COL1A1, INHBA and CST1 may be potential biomarkers and therapeutic targets for GC. Additional studies are required to explore the potential value of ATP4A and ATP4B in the treatment of GC.

Feature selection with the Fisher score followed by the Maximal Clique Centrality algorithm can accurately identify the hub genes of hepatocellular carcinoma

This study aimed to select the feature genes of hepatocellular carcinoma (HCC) with the Fisher score algorithm and to identify hub genes with the Maximal Clique Centrality (MCC) algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed to examine the enrichment of terms. Gene set enrichment analysis (GSEA) was used to identify the classes of genes that are overrepresented. Following the construction of a protein-protein interaction network with the feature genes, hub genes were identified with the MCC algorithm. The Kaplan–Meier plotter was utilized to assess the prognosis of patients based on expression of the hub genes. The feature genes were closely associated with cancer and the cell cycle, as revealed by GO, KEGG and GSEA enrichment analyses. Survival analysis showed that the overexpression of the Fisher score–selected hub genes was associated with decreased survival time (P < 0.05). Weighted gene co-expression network analysis (WGCNA), Lasso, ReliefF and random forest were used for comparison with the Fisher score algorithm. The comparison among these approaches showed that the Fisher score algorithm is superior to the Lasso and ReliefF algorithms in terms of hub gene identification and has similar performance to the WGCNA and random forest algorithms. Our results demonstrated that the Fisher score followed by the application of the MCC algorithm can accurately identify hub genes in HCC.

Identification of key genes and pathways associated with cholangiocarcinoma development based on weighted gene correlation network analysis

Background

As the most frequently occurred tumor in biliary tract, cholangiocarcinoma (CCA) is mainly characterized by its late diagnosis and poor outcome. It is therefore urgent to identify specific genes and pathways associated with its progression and prognosis.

Materials and Methods

The differentially expressed genes in The Cancer Genome Atlas were analyzed to build the co-expression network by Weighted gene co-expression network analysis (WGCNA). Gene ontology (GO) as well as Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted for the selected genes. Module-clinical trait relationships were analyzed to explore the association with clinicopathological parameters. Log-rank tests and cox regression were used to identify the prognosis-related genes.

Results

The most related modules with CCA development were tan module containing 181 genes and salmon module with 148 genes. GO analysis suggested enrichment terms of digestion, hormone transport and secretion, epithelial cell proliferation, signal release, fibroblast activation, response to acid chemical, wnt, Nicotinamide adenine dinucleotide phosphate metabolism. KEGG analysis demonstrated 15 significantly altered pathways including glutathione metabolism, wnt, central carbon metabolism, mTOR, pancreatic secretion, protein digestion, axon guidance, retinol metabolism, insulin secretion, salivary secretion, fat digestion. Key genes of SOX2, KIT, PRSS56, WNT9A, SLC4A4, PRRG4, PANX2, PIR, RASSF8, MFSD4A, INS, RNF39, IL1R2, CST1, and PPP3CA might be potential prognostic markers for CCA, of which RNF39 and PRSS56 also showed significant correlation with clinical stage.

Discussion

Differentially expressed genes and key modules contributing to CCA development were identified by WGCNA. Our results offer novel insights into the characteristics in the etiology, prognosis, and treatment of CCA.