Hepatocellular carcinoma (HCC) is a devastating disease worldwide. Though many efforts have been made to elucidate the process of HCC, its molecular mechanisms of development remain elusive due to its complexity. To explore the stepwise carcinogenic process from pre-neoplastic lesions to the end stage of HCC, we employed weighted gene co-expression network analysis (WGCNA) which has been proved to be an effective method in many diseases to detect co-expressed modules and hub genes using eight pathological stages including normal, cirrhosis without HCC, cirrhosis, low-grade dysplastic, high-grade dysplastic, very early and early, advanced HCC and very advanced HCC. Among the eight consecutive pathological stages, five representative modules are selected to perform canonical pathway enrichment and upstream regulator analysis by using ingenuity pathway analysis (IPA) software. We found that cell cycle related biological processes were activated at four neoplastic stages, and the degree of activation of the cell cycle corresponded to the deterioration degree of HCC. The orange and yellow modules enriched in energy metabolism, especially oxidative metabolism, and the expression value of the genes decreased only at four neoplastic stages. The brown module, enriched in protein ubiquitination and ephrin receptor signaling pathways, correlated mainly with the very early stage of HCC. The darkred module, enriched in hepatic fibrosis/hepatic stellate cell activation, correlated with the cirrhotic stage only. The high degree hub genes were identified based on the protein-protein interaction (PPI) network and were verified by Kaplan-Meier survival analysis. The novel five high degree hub genes signature that was identified in our study may shed light on future prognostic and therapeutic approaches. Our study brings a new perspective to the understanding of the key pathways and genes in the dynamic changes of HCC progression. These findings shed light on further investigations

https://www.mdpi.com/2073-4425/9/2/92/pdf

Identification of Key Pathways and Genes in the Dynamic Progression of HCC Based on WGCNA

Cell division cycle 20 (CDC20) drives prostate cancer progression via stabilization of β-catenin in cancer stem-like cells.

Small-cell lung carcinoma (SCLC) and large-cell neuroendocrine lung carcinoma (LCNEC) are high-grade lung neuroendocrine tumors (NET). However, comparative protein expression within SCLC and LCNEC remains unclear. Here, protein expression profiles were obtained via mass spectrometry-based proteomic analysis. Weighted gene co-expression network analysis (WGCNA) identified co-expressed modules and hub genes. Of 34 identified modules, six were significant and selected for protein-protein interaction (PPI) network analysis and pathway enrichment. Within the six modules, the activation of cellular processes and complexes, such as alternative mRNA splicing, translation initiation, nucleosome remodeling and deacetylase (NuRD) complex, SWItch/Sucrose Non-Fermentable (SWI/SNF) superfamily-type complex, chromatin remodeling pathway, and mRNA metabolic processes, were significant to SCLC. Modules enriched in processes, including signal recognition particle (SRP)-dependent co-translational protein targeting to membrane, nuclear-transcribed mRNA catabolic process of nonsense-mediated decay (NMD), and cellular macromolecule catabolic process, were characteristically activated in LCNEC. Novel high-degree hub genes were identified for each module. Master and upstream regulators were predicted via causal network analysis. This study provides an understanding of the molecular differences in tumorigenesis and malignancy between SCLC and LCNEC and may help identify potential therapeutic targets.

Identification of key modules and hub genes for small-cell lung carcinoma and large-cell neuroendocrine lung carcinoma by weighted gene co-expression network analysis of clinical tissue-proteomes

Tumor microenvironment constitutes a complex network in which tumor cells communicate among them and with stromal and immune cells. It has been shown that cancer cells are able to exchange genetic materials through small extracellular vesicles (EVs), a heterogeneous group of vesicles with different size and shape, cargo content, and function. The importance to investigate populations of circulating EVs would be of great importance as prostate cancer (PCa) biomarkers. In several neoplasms as well as in PCa, nanometer-sized EVs of endosomal origin are implicated in supporting tumor growth and metastatic spread by both altering local stroma cells and creating a protumor environment that favors the formation of pre-metastatic niches. Several techniques are applicable for the isolation and analysis of PCa-derived small EVs and are illustrated in this article. Due to the high sensitivity and specificity of these techniques, small EVs have become ideal candidates for early diagnosis. Moreover, we discuss the role of small EVs during PCa carcinogenesis, as well as in modulating the development of drug resistance to hormonal therapy and chemotherapy, thus underlining the potential of EV-tailored strategies in PCa patients.

Exploring Small Extracellular Vesicles for Precision Medicine in Prostate Cancer.

Prostate cancer (PCa) is one of the most common malignancies for males, but very little is known about its pathogenesis. This study aimed to identify novel biomarkers associated with PCa prognosis and elucidate the underlying molecular mechanism. First, The Cancer Genome Atlas (TCGA) RNA-sequencing data were utilized to identify differentially expressed genes (DEGs) between tumor and normal samples. The DEGs were then applied to construct a co-expression and mined using structure network analysis. The magenta module that was highly related to the Gleason score (r = 0.46, p = 3e-26) and tumor stage (r = 0.38, p = 2e-17) was screened. Subsequently, all genes of the magenta module underwent function annotation. From the key module, CCNA2, CKAP2L, NCAPG, and NUSAP1 were chosen as the four candidate genes. Finally, internal (TCGA) and external data sets (GSE32571, GSE70770, and GSE141551) were combined to validate and predict the value of real hub genes. The results show that the above genes are up-regulated in PCa samples, and higher expression levels show significant association with higher Gleason scores and tumor T stage. Moreover, receiver operating characteristic curve and survival analysis validate the excellent value of hub genes in PCa progression and prognosis. In addition, the protein levels of these four genes also remain higher in tumor tissues when compared with normal tissues. Gene set enrichment analysis and gene set variation analysis for a single gene reveal the close relation with cell proliferation. Meanwhile, 11 small molecular drugs that have the potential to treat PCa were also screened. In conclusion, our research identified four potential prognostic genes and several candidate molecular drugs for treating PCa.

/pdf/four-novel-prognostic-genes-related-to-prostate-cancer-cbabt288dx.pdf

Four Novel Prognostic Genes Related to Prostate Cancer Identified Using Co-expression Structure Network Analysis.

Prostate cancer is one of the leading causes of cancer death for male. In the present study, we applied an integrated bioinformatics approach to provide a novel perspective and identified some hub genes of prostate cancer. Microarray data of fifty-nine prostate cancer were downloaded from Gene Expression Omnibus. Gene Ontology and pathway analysis were applied for differentially expressed genes between high and low grade prostate cancer. Weighted gene coexpression network analysis was applied to construct gene network and classify genes into different modules. The most related module to high grade prostate cancer was identified and hub genes in the module were revealed. Ingenuity pathway analysis was applied to check the chosen module’s relationship to high grade prostate cancer. Hub gene’s expression profile was verified with clinical samples and a dataset from The Cancer Genome Atlas project. 3193 differentially expressed genes were filtered and gene ontology and pathway analysis revealed some cancer- and sex hormone-related results. Weighted gene coexpression network was constructed and genes were classified into six modules. The red module was selected and ingenuity pathway analysis confirmed its relationship with high grade prostate cancer. Hub genes were identified and their expression profile was also confirmed. The present study applied integrate bioinformatics approaches to generate a holistic view of high grade prostate cancer and identified hub genes could serve as prognosis markers and potential treatment targets.

Identification of prognostic markers of high grade prostate cancer through an integrated bioinformatics approach.

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Identification of prognostic markers of high grade prostate cancer through an integrated bioinformatics approach.