Identification and validation of key modules and hub genes associated with the pathological stage of oral squamous cell carcinoma by weighted gene co-expression network analysis

Analysis of Hyperosmotic Tolerance Mechanisms in Gracilariopsis lemaneiformis Based on Weighted Co-Expression Network Analysis

We conducted transcriptome sequencing on salt-tolerant mutants X5 and X3, and a control (Ctr) strain of Gracilariopsis lemaneiformis after treatment with artificial seawater at varying salinities (30‱, 45‱, and 60‱) for 3 weeks. Differentially expressed genes were identified and a weighted co-expression network analysis was conducted. The blue, red, and tan modules were most closely associated with salinity, while the black, cyan, light cyan, and yellow modules showed a close correlation with strain attributes. KEGG enrichment of genes from the aforementioned modules revealed that the key enrichment pathways for salinity attributes included the proteasome and carbon fixation in photosynthesis, whereas the key pathways for strain attributes consisted of lipid metabolism, oxidative phosphorylation, soluble N-ethylmaleimide-sensitive factor-activating protein receptor (SNARE) interactions in vesicular transport, and porphyrin and chlorophyll metabolism. Gene expression for the proteasome and carbon fixation in photosynthesis was higher in all strains at 60‱. In addition, gene expression in the proteasome pathway was higher in the X5-60 than Ctr-60 and X3-60. Based on the above data and relevant literature, we speculated that mutant X5 likely copes with high salt stress by upregulating genes related to lysosome and carbon fixation in photosynthesis. The proteasome may be reset to adjust the organism's proteome composition to adapt to high-salt environments, while carbon fixation may aid in maintaining material and energy metabolism for normal life activities by enhancing carbon dioxide uptake via photosynthesis. The differences between the X5-30 and Ctr-30 expression of genes involved in the synthesis of secondary metabolites, oxidative phosphorylation, and SNARE interactions in vesicular transport suggested that the X5-30 may differ from Ctr-30 in lipid metabolism, energy metabolism, and vesicular transport. Finally, among the key pathways with good correlation with salinity and strain traits, the key genes with significant correlation with salinity and strain traits were identified by correlation analysis.

Identification of differentially expressed genes and pathways in BEAS-2B cells upon long-term exposure to particulate matter (PM2.5) from biomass combustion using bioinformatics analysis

BACKGROUND

Long-term exposure to PM2.5 from burning domestic substances has been linked to an increased risk of lung disease, but the underlying mechanisms are unclear. This study is to explore the hub genes and pathways involved in PM2.5 toxicity in human bronchial epithelial BEAS-2B cells.

METHODS

The GSE158954 dataset is downloaded from the GEO database. Differentially expressed genes (DEGs) were screened using the limma package in RStudio (version 4.2.1). In addition, DEGs analysis was performed by Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. A protein-protein interaction (PPI) network was constructed, MCODE plug-in and the cytoHubba plug-in in Cytoscape software was used to identify the hub genes. Finally, CytoHubba and DEGs were used to integrate the hub genes, and preliminary validation was performed by comparing the toxicology genomics database (CTD). Differential immune cell infiltration was investigated using the CIBERSORT algorithm.

RESULTS

A total of 135 DEGs were identified, of which 57 were up-regulated and 78 were down-regulated. Functional enrichment analyses in the GO and KEGG indicated the potential involvement of DEGs was mainly enriched in the regulation of endopeptidase activity and influenza A. Gene Set Enrichment Analysis revealed that Chemical Carcinogenesis - DNA adducts were remarkably enriched in PM2.5 groups. 53 nodes and 198 edges composed the PPI network. Besides, 5 direct-acting genes were filtered at the intersection of cytohubba plug-in, MCODE plug-in and CTD database. There is a decreasing trend of dendritic cells resting after BEAS-2B cells long-term exposure to PM2.5.

CONCLUSIONS

The identified DEGs, modules, pathways, and hub genes provide clues and shed light on the potential molecular mechanisms of BEAS-2B cells upon long-term exposure to PM2.5.

Highly expressed FAM189B predicts poor prognosis in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most malignant tumors with persistently high morbidity and mortality. However, the expression, prognostic and clinical significance of FAM189 family genes in HCC remain largely unknown. In this study, the expression levels of FAM189 family genes in HCC were analyzed through TCGA-LIHC and ICGC-LIRI-JP cohorts, and further validated in multiple independent GEO datasets. It was found that the expression of FAM189B was significantly upregulated in HCC tumor tissues, while the expression of FAM189A1 and FAM189A2 was not significantly changed between tumor and adjacent tissues. Further analysis revealed that upregulated copy number variation contributed to increased expression of FAM189B in HCC. Survival analysis showed that highly expressed FAM189B was significantly correlated with unfavorable prognosis, including overall survival, disease-specific survival, and progression-free interval. Univariate and multivariate Cox regression analysis showed that FAM189B was a potential novel prognosis factor for HCC patients. In addition, the association between FAM189B expression and clinical and molecular characteristics was analyzed. High expression of FAM189B was associated with high AFP level, high predicted risk metastasis signature, and TP53 mutation, while there was no significant association between FAM189B expression and cancer stage or tumor grade of HCC. Gene set enrichment analysis revealed that highly expressed FAM189B was closely related with signal pathways and biological processes associated with cell proliferation and cell cycle in HCC. In conclusion, this study suggested that FAM189B was highly expressed in HCC and highly expressed FAM189B may serve as an effective prognostic indicator and a potential therapeutic target for HCC patients.

STAT3/HIF1A and EMT specific transcription factors regulated genes: Novel predictors of breast cancer metastasis

Metastasis, the fatal hallmark of breast cancer (BC), is a serious hurdle for therapy. Current prognostic approaches are not sufficient to predict the metastasis risk for BC patients. Therefore, in the present study, we analyzed gene expression data from GSE139038 and TCGA database to develop predictive markers for BC metastasis. Initially, the data from GSE139038 which contained 65 samples consisting of 41 breast tumor tissues, 18 paired morphologically normal tissues and 6 from non-malignant breast tissues were analyzed for differentially expressed genes (DEGs). DEGs were obtained from three different comparisons: paired morphologically normal (MN) versus tumor samples (C), apparently normal (AN) versus tumor samples (C), and paired morphologically normal (MN) versus apparently normal samples (AN). Multiple bioinformatic methods were employed to evaluate metastasis, EMT and triple negative breast cancer (TNBC) specific genes. Further, regulation of gene expression, clinicopathological factors and DNA methylation patterns of DEGs in BC were validated with TCGA datasets. Our bioinformatic analysis showed that 40 genes were upregulated and 294 were found to be downregulated between AN vs C; 124 were upregulated and 760 genes were downregulated between MN vs C; 4 were upregulated and 13 were downregulated between MN vs AN. Analysis using TCGA dataset revealed 18 genes were significantly altered in nodal positive BC patients compared to nodal negative BC patients. Our study showed novel candidate genes as predictive markers for BC metastasis which can also be used for therapeutic targets for BC treatment.

Potential Novel Modules and Hub Genes as Prognostic Candidates of Thyroid Cancer by Weighted Gene Co-Expression Network Analysis

Background

Although thyroid cancer (THCA) is one of the most common type of endocrine malignancy, its highly complex molecular mechanisms of carcinogenesis are not completely known.

Materials and Methods

In this study, weighted gene co-expression network analysis (WGCNA) was utilized to construct gene co-expression networks and evaluate the relations between modules and clinical traits to identify potential prognostic biomarkers for THCA patients. RNA-seq data and clinical data were downloaded from The Cancer Genome Atlas (TCGA). Other independent datasets from the Gene Expression Omnibus (GEO) database and the Human Protein Atlas database were performed to validate findings.

Results

Finally, 11 co-expression modules were constructed and four hub genes, CCDC146, SLC4A4, TDRD9 and MUM1L1, were identified and validated statistically, which were considerably interrelated to worse survival of THCA patients.

Conclusion

This research study revealed four hub genes may be considered candidate prognostic biomarkers and potential therapeutic targets for THCA patients in the future.

A Transcription Factor-Based Risk Model for Predicting the Prognosis of Prostate Cancer and Potential Therapeutic Drugs

Background

Prostate cancer (PC) is one of the most critical cancers affecting men's health worldwide. The development of many cancers involves dysregulation or mutations in key transcription factors. This study established a transcription factor-based risk model to predict the prognosis of PC and potential therapeutic drugs.

Materials and Methods

In this study, RNA-sequencing data were downloaded and analyzed using The Cancer Genome Atlas dataset. A total of 145 genes related to the overall survival rate of PC patients were screened using the univariate Cox analysis. The Kdmist clustering method was used to classify prostate adenocarcinoma (PRAD), thereby determining the cluster related to the transcription factors. The support vector machine-recursive feature elimination method was used to identify genes related to the types of transcription factors and the key genes specifically upregulated or downregulated were screened. These genes were further analyzed using Lasso to establish a model. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for the functional analysis. The TIMER algorithm was used to quantify the abundance of immune cells in PRAD samples. The chemotherapy response of each GBM patient was predicted based on the public pharmacogenomic database, Genomics of Drug Sensitivity in Cancer (GDSC, http://www.cancerrxgene.org). The R package "pRRophetic" was applied to drug sensitivity (IC50) value prediction.

Results

We screened 10 genes related to prognosis, including eight low-risk genes and two high-risk genes. The receiver operating characteristic (ROC) curve was 0.946. Patients in the high-risk score group had a poorer prognosis than those in the low-risk score group. The average area under the curve value of the model at different times was higher than 0.8. The risk score was an independent prognostic factor. Compared with the low-risk score group, early growth response-1 (EGR1), CACNA2D1, AC005831.1, SLC52A3, TMEM79, IL20RA, CRACR2A, and FAM189A2 expressions in the high-risk score group were decreased, while AC012181.1 and TRAPPC8 expressions were increased. GO and KEGG analyses showed that prognosis was related to various cancer signaling pathways. The proportion of B_cell, T_cell_CD4, and macrophages in the high-risk score group was significantly higher than that in the low-risk score group. A total of 25 classic immune checkpoint genes were screened out to express abnormally high-risk scores, and there were significant differences. Thirty mutant genes were identified; in the high- and low-risk score groups, SPOP, TP53, and TTN had the highest mutation frequency, and their mutations were mainly missense mutations. A total of 36 potential drug candidates for the treatment of PC were screened and identified.

Conclusions

Ten genes of both high-and low-risk scores were associated with the prognosis of PC. PC prognosis may be related to immune disorders. SPOP, TP53, and TTN may be potential targets for the prognosis of PC.

miRNA interplay: mechanisms and consequences in cancer

Canonically, microRNAs (miRNAs) control mRNA expression. However, studies have shown that miRNAs are also capable of targeting non-coding RNAs, including long non-coding RNAs and miRNAs. The latter, termed a miRNA:miRNA interaction, is a form of self-regulation. In this Review, we discuss the three main modes of miRNA:miRNA regulation: direct, indirect and global interactions, and their implications in cancer biology. We also discuss the cell-type-specific nature of miRNA:miRNA interactions, current experimental approaches and bioinformatic techniques, and how these strategies are not sufficient for the identification of novel miRNA:miRNA interactions. The self-regulation of miRNAs and their impact on gene regulation has yet to be fully understood. Investigating this hidden world of miRNA self-regulation will assist in discovering novel regulatory mechanisms associated with disease pathways.

Screening and Identification of Key Common and Specific Genes and Their Prognostic Roles in Different Molecular Subtypes of Breast Cancer

Breast cancer is one of the most common cancers. Although the present molecular classification improves the treatment effect and prognosis of breast cancer, the heterogeneity of the molecular subtype remains very complex, and the applicability and effectiveness of treatment methods are still limited leading to poorer patient prognosis than expected. Further identification of more refined molecular typing based on gene expression profile will yield better understanding of the heterogeneity, improving treatment effects and prolonging prognosis of patients. Here, we downloaded the mRNA expression profiles and corresponding clinical data of patients with breast cancer from public databases and performed typical molecular typing using PAM50 (Prediction Analysis of Microarray 50) method. Comparative analyses were performed to screen the common and specific differentially expressed genes (DEGs) between cancer and corresponding para-cancerous tissues in each breast cancer subtype. The GO and KEGG analyses of the DEGs were performed to enrich the common and specific functional progress and signaling pathway involved in breast cancer subtypes. A total of 38 key common and specific DEGs were identified and selected based on the validated results, GO/KEGG enrichments, and the priority of expression, including four common DEGs and 34 specific DEGs in different subtypes. The prognostic value of these key common and specific DEGs was further analyzed to obtain useful potential markers in clinic. Finally, the potential roles and the specific prognostic values of the common and specific DEGs were speculated and summarized in total breast cancer and different subtype breast cancer based on the results of these analyses. The findings of our study provide the basis of more refined molecular typing of breast cancer, potential new therapeutic targets and prognostic markers for different breast cancer subtypes

Development and validation of a novel survival model for head and neck squamous cell carcinoma based on autophagy-related genes

BACKGROUND

In view of the critical role of autophagy-related genes (ARGs) in the pathogenesis of various diseases including cancer, this study aims to identify and evaluate the potential value of ARGs in head and neck squamous cell carcinoma (HNSCC).

METHODS

RNA sequencing and clinical data in The Cancer Genome Atlas (TCGA) were analyzed by univariate Cox regression analysis and Lasso Cox regression analysis model established a novel 13- autophagy related prognostic genes, which were used to build a prognostic risk model. A multivariate Cox proportional regression model and the survival analysis were used to evaluate the prognostic risk model. Moreover, the efficiency of prognostic risk model was tested by receiver operating characteristic (ROC) curve analysis based on data from TCGA database and Gene Expression Omnibus (GEO). Besides, the other independent datasets from Human Protein Atlas dataset (HPA) also applied.

RESULTS

13 ARGs (GABARAPL1, ITGA3, USP10, ST13, MAPK9, PRKN, FADD, IKBKB, ITPR1, TP73, MAP2K7, CDKN2A, and EEF2K) with prognostic value were identified in HNSCC patients. Subsequently, a prognostic risk model was established based on 13 ARGs, and significantly stratified HNSCC patients into high- and low-risk groups in terms of overall survival (OS) (HR = 0.379，95% CI: 0.289-0.495, p < 0.0001). The multivariate Cox analysis revealed that this model was an independent prognostic factor (HR = 1.506, 95% CI = 1.330-1.706, P < 0.001). The areas under the ROC curves (AUC) were significant for both the TCGA and GEO, with AUC of 0.685 and 0.928 respectively. Functional annotation revealed that model significantly enriched in many critical pathways correlated with tumorigenesis, including the p53 pathway, IL2 STAT5 signaling, TGF beta signaling, PI3K Ak mTOR signaling by gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). In addition, we developed a nomogram shown some clinical net could be used as a reference for clinical decision-making.

CONCLUSIONS

Collectively, we developed and validated a novel robust 13-gene signatures for HNSCC prognosis prediction. The 13 ARGs could serve as an independent and reliable prognostic biomarkers and therapeutic targets for the HNSCC patients.