Ten hub genes associated with progression and prognosis of pancreatic carcinoma identified by co-expression analysis

Discovery and validation of a 10-gene predictive signature for response to adjuvant chemotherapy in stage II and III colon cancer

Identifying patients with stage II and III colon cancer who will benefit from 5-fluorouracil (5-FU)-based adjuvant chemotherapy is crucial for the advancement of personalized cancer therapy. We employ a semi-supervised machine learning approach to analyze a large dataset with 933 stage II and III colon cancer samples. Our analysis leverages gene regulatory networks to discover an 18-gene prognostic signature and to explore a 10-gene signature that potentially predicts chemotherapy benefits. The 10-gene signature demonstrates strong prognostic power and shows promising potential to predict chemotherapy benefits. We establish a robust clinical assay on the NanoString nCounter platform, validated in a retrospective formalin-fixed paraffin-embedded (FFPE) cohort, which represents an important step toward clinical application. Our study lays the groundwork for improving adjuvant chemotherapy and potentially expanding into immunotherapy decision-making in colon cancer. Future prospective studies are needed to validate and establish the clinical utility of the 10-gene signature in clinical settings.

Function of NEK2 in clear cell renal cell carcinoma and its effect on the tumor microenvironment

BACKGROUND

Previous studies have revealed the critical functions of NEK2 in controlling the cell cycle which is linked to poor prognosis in multiple tumor types, but less research has been devoted to clear cell renal cell carcinoma (ccRCC).

METHODS

We downloaded clinical data from the gene expression omnibus (GEO) and TCGA databases together with transcriptional and mutational datasets. Strongly coexpressed genes with NEK2 were extracted from TCGA-KIRC cohort, and were submitted to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for functional analyses. According to NEK2 levels, the survival status, mutational characteristics, response to immunotherapy and sensitivity to drugs of the patients were studied. The potential correlations between NEK2 levels and immune cell state as well as immune cell infiltration were examined using the GEPIA, TIMER and TISIDB databases. Double immunofluorescence (IF) was performed to identify the NEK2 overexpression and relationship with CD8 in ccRCC.

RESULTS

The NEK2 gene was overexpressed and would enhance the nuclear division and cell cycle activities in ccRCC. ccRCC patients with high NEK2 expression had worse clinical outcomes, higher mutation burden and better therapeutic response. Moreover, NEK2 gene overexpression was positively related to various immune cell marker sets, which was also proved by validation cohort, and more infiltration of various immune cells.

CONCLUSION

ccRCC patients with NEK2 high expression have a poorer prognosis than those with NEK2 low expression, resulting from its function of promoting proliferation, accompanied by increased infiltration of CD8 + T cells and Tregs and T-cell exhaustion and will respond better to proper treatments.

CCNA2 and NEK2 regulate glioblastoma progression by targeting the cell cycle

Glioblastoma (GBM) is characterized by significant heterogeneity, leading to poor survival outcomes for patients, despite the implementation of comprehensive treatment strategies. The roles of cyclin A2 (CCNA2) and NIMA related kinase 2 (NEK2) have been extensively studied in numerous cancers, but their specific functions in GBM remain to be elucidated. The present study aimed to investigate the potential molecular mechanisms of CCNA2 and NEK2 in GBM. CCNA2 and NEK2 expression and prognosis in glioma were evaluated by bioinformatics methods. In addition, the distribution of CCNA2 and NEK2 expression in GBM subsets was determined using pseudo-time analysis and tricycle position of single-cell sequencing. Gene Expression Omnibus and Kyoto Encyclopedia of Genes and Genome databases were employed and enrichment analyses were conducted to investigate potential signaling pathways in GBM subsets and a nomogram was established to predict 1-, 2- and 3-year overall survival probability in GBM. CCNA2 and NEK2 expression levels were further validated by western blot analysis and immunohistochemical staining in GBM samples. High expression of CCNA2 and NEK2 in glioma indicates poor clinical outcomes. Single-cell sequencing of GBM revealed that these genes were upregulated in a subset of positive neural progenitor cells (P-NPCs), which showed significant proliferation and progression properties and may activate G2M checkpoint pathways. A comprehensive nomogram predicts 1-, 2- and 3-year overall survival probability in GBM by considering P-NPCs, age, chemotherapy and radiotherapy scores. CCNA2 and NEK2 regulate glioblastoma progression by targeting the cell cycle, thus indicating the potential of novel therapy directed to CCNA2 and NEK2 in GBM.

The development of prognostic gene markers associated with disulfidptosis in gastric cancer and their application in predicting drug response

Background

Gastric cancer (GC) is a malignancy known for its high fatality rate. Disulfidptosis, a potentially innovative therapeutic strategy for cancer treatment, has been proposed. Nevertheless, the specific involvement of disulfidptosis in the context of GC remains uncertain.

Methods

The mRNA expression profiles were obtained from the TCGA and GEO databases. Univariate and LASSO Cox regression analyses were employed to identify differentially expressed genes and develop a risk model for disulfidptosis-related genes. The performance of the model was evaluated using Kaplan-Meier curve, ROC curve, and nomogram. Univariate and multivariate Cox regression analyses were conducted to determine if the risk model could serve as an independent prognostic factor. The biological function of the identified genes was assessed through GO, KEGG, and GSEA analyses. The prediction of drug response was conducted employing the package "pRRophetic". Furthermore, gene expression was determined using qRT-PCR.

Results

An eight-gene signature were identified and utilized to categorize patients into low- and high-risk groups. Survival, receiver operating characteristic (ROC) curve, and Cox analyses provided clarification that these eight hub genes served as a favorable independent prognostic factor for patients with GC. A nomogram was constructed by integrating clinical parameters with the risk signatures, demonstrating high precision in predicting 1-, 3-, and 5-year survival rates. Additionally, drug sensitivity was different in the high-risk and low-risk groups, and the expression of three genes was verified by qRT-PCR.

Conclusion

The prognostic risk model developed in this study demonstrates the potential to accurately forecast the prognosis of patients with GC.

HDACi-dependent Microenvironmental Normalization Overcomes Tumor Burden-induced T-cell Exhaustion

PURPOSE

T-cell exhaustion limits immunotherapy for the treatment of solid tumors. Although immune checkpoint blockade and adoptive T-cell therapy (ACT) can mediate tumor regression, their potency is often determined by tumor burden. Here, we identified tumor burden-related pathway changes that are conducive to T-cell exhaustion. We then determined whether microenvironmental reprogramming via epigenetic modulation could reverse T-cell exhaustion and improve immunotherapeutic responsiveness.

EXPERIMENTAL DESIGN

We developed a murine syngeneic tumor model wherein an increased burden ablated therapeutic responsiveness to ACT, which corresponded with systemic induction of T-cell exhaustion. Transcriptome analysis of these large tumors allowed us to characterize changes to immunosuppressive pathway expression during class I histone deacetylase inhibitor MS-275 treatment. We then measured the therapeutic impact of MS-275 during ACT and assessed T-cell exhaustion by transcriptome/phenotypic analysis.

RESULTS

ACT durably regressed small tumors but failed to control large tumors, which were associated with systemic T-cell exhaustion and ablation of T-cell responses. Large tumors were defined by an immunosuppressive pathway signature. MS-275 reversed this pathway signature and promoted durable regression of large tumors during ACT. Prototypical exhaustion marker Tim-3 was selectively upregulated in transferred T cells despite displaying a reduced exhaustion signature. Instead, we observed enhanced activation-dependent signaling correlating with enrichment of the IL2-STAT5 signaling axis. Activated CD8+ T-cell responses were predominantly skewed toward terminal effector cell-like CD44+ Tim-3hi TCF1- CD127- KLRG1+ differentiation.

CONCLUSIONS

Tumor burden-induced pathway changes can be reversed through epigenetic reprogramming, enabling the conversion from T-cell exhaustion to effector lineage differentiation.

LRRK2 Gly2019Ser Mutation Promotes ER Stress via Interacting with THBS1/TGF-β1 in Parkinson's Disease

The gene mutations of LRRK2, which encodes leucine-rich repeat kinase 2 (LRRK2), are associated with one of the most prevalent monogenic forms of Parkinson's disease (PD). However, the potential effectors of the Gly2019Ser (G2019S) mutation remain unknown. In this study, the authors investigate the effects of LRRK2 G2019S on endoplasmic reticulum (ER) stress in induced pluripotent stem cell (iPSC)-induced dopamine neurons and explore potential therapeutic targets in mice model. These findings demonstrate that LRRK2 G2019S significantly promotes ER stress in neurons and mice. Interestingly, inhibiting LRRK2 activity can ameliorate ER stress induced by the mutation. Moreover, LRRK2 mutation can induce ER stress by directly interacting with thrombospondin-1/transforming growth factor beta1 (THBS1/TGF-β1). Inhibition of LRRK2 kinase activity can effectively suppress ER stress and the expression of THBS1/TGF-β1. Knocking down THBS1 can rescue ER stress by interacting with TGF-β1 and behavior burden caused by the LRRK2 mutation, while suppression of TGF-β1 has a similar effect. Overall, it is demonstrated that the LRRK2 mutation promotes ER stress by directly interacting with THBS1/TGF-β1, leading to neural death in PD. These findings provide valuable insights into the pathogenesis of PD, highlighting potential diagnostic markers and therapeutic targets.

Identification of key immune cell-related genes involved in tumorigenesis and prognosis of cervical squamous cell carcinoma

The infiltration of immune cells can significantly affect the prognosis and immune therapy of patients with cervical squamous cell carcinoma (CSCC). This study aimed to explore key immune cell-related genes in the tumorigenesis and prognosis of CSCC. The module significantly related to immunity was screened by weighted gene co-expression network analysis (WGCNA) and ESTIMATE analysis, followed by correlation analysis with clinical traits. Key candidate genes were intersected with the protein-protein interaction (PPI) network genes for immune-related genes. The relationship between immune cell infiltration and key genes was analyzed. Tumor immune dysfunction and exclusion (TIDE) and immunophenoscore (IPS) predicted the response to immunotherapy in CSCC patients. Clinically, quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry were manipulated for analyzing the changes in mRNA and protein expression of key genes in cancer. Western blot was conducted to assess the correlation between key genes and immune infiltration. The brown module was notably associated with the immune microenvironment of CSCC, from which three immune-related key genes (TYROBP, CCL5, and HLA-DRA) were obtained. High expression of these genes was significantly positively associated with the infiltration abundance of T cells, B cells, and other immune cells. High expression levels of three key genes were confirmed in para-cancer tissue and correlated with the abundance of immune cells. The high-expression group of key genes was more sensitive to immunotherapy. We provide a theoretical basis for searching for potential targets for effective treatment and diagnosis of CSCC and provide new ideas for developing novel immunotherapy strategies.

Analysis of the characteristics of immune infiltration in endometrial carcinoma and its relationship with prognosis based on bioinformatics

To explore immune-related molecules that affect the prognosis of endometrial carcinoma (EC) using bioinformatic data mining. The expression data related to EC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. After differential expression analysis, the intersection with immune related genes in the ImmPort database was used to obtain immune related differentially expressed genes (IRDEGs). The correlation between clinicopathological information and the prognosis of IRDEGs was further analyzed to obtain prognosis related differentially expressed immune genes (PRDEIG). Gene correlation analysis and Gene Set Enrichment Analysis (GSEA) enrichment analysis showed that PRDEIG was enriched in cancer-related functional pathways. We then analyzed the relationship between PRDEIG and immune cell infiltration, and further analyzed the mRNA and protein expression of PRDEIG in EC using TCGA and the human protein expression atlas (THPA) databases. After the intersection of the differential expression analysis results and immune-related genes, 4 IRDEGs were obtained: osteoglycin (OGN), LTBP4, CXCL12, and SPP1. After analyzing the relationship between 4 IRDEGs and clinicopathological parameters and prognosis of patients with EC, revealed that only OGN was not only related to tumor immunity, but also affected the prognosis of patients with EC. Gene correlation and GSEA enrichment of OGN were analyzed. The results showed that OGN was significantly enriched in 6 functional pathways: epithelial mesenchymal transition, KRAS signaling up, myogenesis, UV response, allograft rejection and apical junction. In addition, it was also found that OGN was significantly correlated with a variety of immune cells. The results of TCGA and THPA database showed that the mRNA and protein expression levels of OGN decreased in EC. OGN may affect the epithelial mesenchymal transformation (EMT) of tumor by affecting the infiltration of tumor immune cells.

Kinesin family member 23 knockdown inhibits cell proliferation and epithelial-mesenchymal transition in esophageal carcinoma by inactivating the Wnt/β-catenin pathway

Kinesin family member 23 (KIF23) serves as a tumor-promoting gene with prognostic values in various tumors. However, the role of KIF23 in esophageal carcinoma (ESCA) progression is largely unknown. The overlapping differentially expressed genes (DEGs) in GSE12452, GSE17351, and GSE20347 datasets were identified via GEO2R tool and Venn diagram software. KIF23 expression was analyzed using GSE12452, GSE17351, and GSE20347 datasets, GEPIA database, and qRT-PCR. Cell proliferation was assessed by CCK-8 and EdU incorporation assays. Gene set enrichment analysis (GSEA) analysis was performed to investigate the pathways associated with the regulatory mechanisms of KIF23 in ESCA. The expression of E-cadherin, vimentin, N-cadherin, and matrix metalloproteinase-9 (MMP-9) and alternation of Wnt/β-catenin pathway were detected by western blot analysis. We identified two overlapping upregulated DEGs, among which KIF23 was selected for subsequent experiments. KIF23 was overexpressed in ESCA samples and cells, and knockdown of KIF23 retarded cell proliferation in ESCA cells. Besides, KIF23 knockdown suppressed epithelial-mesenchymal transition (EMT) process in ESCA cells, as evidenced by the increase of E-cadherin expression and the reduction of vimentin, N-cadherin, and MMP-9 expression. GSEA analysis suggested that Wnt signaling pathway was the significant pathway related to KIF23. Moreover, we demonstrated that KIF23 silencing inhibited the Wnt/β-catenin pathway in ESCA cells. Activation of Wnt/β-catenin pathway by SKL2001 reversed the effects of KIF23 silencing on cell proliferation and EMT in ESCA cells. In conclusion, KIF23 knockdown inhibited the proliferation and EMT in ESCA cells through blockage of Wnt/β-catenin pathway.

FZD1/KLF10-hsa-miR-4762-5p/miR-224-3p-circular RNAs axis as prognostic biomarkers and therapeutic targets for glioblastoma: a comprehensive report

BACKGROUND

The circular RNA (circRNA) plays a vital role in the pathogenesis of tumors as a competitive endogenous RNA (ceRNA). Given the high aggressiveness and fatality rate of glioblastoma (GBM) as well as poor prognosis, it is necessary to construct a circRNA-related ceRNA network for further studies on the mechanism of GBM and identify possible biomarkers as well as therapeutic drugs.

METHODS

Three datasets from the gene expression omnibus (GEO) database were downloaded to distinguish differential circRNAs, microRNAs, and messenger RNAs respectively in GBM. With the help of GEPIA2, circBank, CSCD, TargetScan, miRDB, and miRTarBase databases, we established a circRNAs-related ceRNA network in GBM. Functional enrichments were employed to profile the most relevant mRNAs to indirectly clarify the mechanisms of the ceRNA network. Based on the expression profile data and survival information of GBM patients from the GEO and the cancer genome atlas (TCGA) databases, we performed survival analysis to select prognostic mRNAs and constructed a novel circRNA-miRNA-mRNA central regulatory subnetwork. The DGIdb database was used to find potential drug-gene interactions.

RESULTS

The datasets obtained from the GEO and TCGA databases were analyzed, and 504 differentially expressed mRNAs (DEmRNAs), 71 differentially expressed microRNAs (DEmiRNAs), and 270 differentially expressed circRNAs (DEcircRNAs) were screened out. The novel ceRNA regulatory network included 22 circRNAs, 11 miRNAs, and 15 mRNAs. FZD1 and KLF10 were significantly correlated with the overall survival rate of patients with GBM (P < 0.05). The final survival subnetwork contained six circRNAs, two miRNAs, and two mRNAs. Two small-molecule compounds and one antibody could be used as therapeutic drugs for GBM. Interestingly, the Wnt signaling pathway appeared in both KEGG and GO functional terms.

CONCLUSIONS

Results of this study demonstrate that FZD1 and KLF10 may exert regulatory functions in GBM, and the ceRNA-mediated network could be a therapeutic strategy for GBM.

A 9-gene prognostic signature for kidney renal clear cell carcinoma overall survival based on co-expression and regression analyses

This research attempted to screen potential signatures associated with KIRC progression and overall survival by weighted gene co-expression network analysis (WGCNA) and Cox regression. The KIRC-associated mRNA expression and clinical data were accessed from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened by differential analysis. A co-expression network was constructed by "WGCNA". Based on WGCNA module, GO and KEGG analyses were performed. Protein-protein interaction (PPI) network was constructed. Prognostic signatures were screened by Lasso-Cox regression. Prognostic model was evaluated by Receiver Operating Characteristic (ROC) and Kaplan-Meier (K-M) curves. Multivariate Cox and nomogram were introduced to examine whether risk score could be an independent marker. qRT-PCR was introduced to determine expression of 9 hub genes in KIRC clinical tumor tissues and adjacent tissues, respectively. Genes in the green module were highly associated with clinical status, and green module genes were significantly enriched in mitotic nuclear division, cell cycle, and p53 signaling pathway. Twenty-six candidates were subsequently screened out from the green module. Next, a 9-gene prognostic model (DLGAP5, NUF2, TOP2A, RRM2, HJURP, PLK1, AURKB, KIF18A, CCNB2) was constructed. The predicting ability of the model was optimal. Some cancer-related signaling pathways were differently activated between two risk score groups. Additionally, under-expression of some signature genes (AURKB, CCNB2, PLK1, RRM2, TOP2A) was associated with better survival rate for KIRC patients. Meanwhile, all 9 hub genes were substantially overexpressed in KIRC patients. A KIRC prognostic signature was screened in this study, contributing valuable findings to KIRC biomarker development.

Extracellular matrix is the main targeted environment in early stage of pancreatic ductal adenocarcinoma

Aim

Due to weak diagnosis and treatment of pancreatic ductal adenocarcinoma (PDAC), detection of PDAC possible biomarkers in early stage is the main aim of this study.

Background

PDAC is known as an exocrine cancer with a 5-year overall survival of 11%.

Methods

Gene expression profiles of early stage of PDAC tissue and normal tissue are downloaded from gene expression omnibus (GEO) and evaluated via GEO2R. The significant differentially expressed genes (DEGs) are investigated via protein-protein interaction (PPI) network analysis and gene ontology.

Results

Among 104 DEGs, ALB, COL1A1, COL1A2, MMP1, POSTN, PLAU, and COL3A1 were pointed out as hub nodes. "Gelatin degradation by MMP1, 2, 3, 7, 8, 9, 12, 13" group of 52 biological terms were identified as the main affected terms.

Conclusion

In conclusion, ALB, MMP1, and COL1A1 genes were highlighted as possible biomarkers of early stage of PDAC. Dysfunction of extracellular matrix was identified as a main event in patients.

Identification of genes for seed isoflavones based on bulk segregant analysis sequencing in soybean natural population

We identified four hub genes for isoflavone biosynthesis based on BSA-seq and WGCNA methods and validated that GmIE3-1 positively contribute to isoflavone accumulation in soybean. Soybean isoflavones are secondary metabolites of great interest owing to their beneficial impact on human health. Herein, we profiled the seed isoflavone content by HPLC in 1551 soybean accessions grown in two locations for two years and constructed two extreme pools with high (4065.1 µg g^-1) and low (1427.23 µg g^-1) isoflavone contents to identify candidate genes involved in isoflavone biosynthesis pathways using bulk segregant analysis sequencing (BSA-seq) approach. The results showed that the average sequencing depths were 50.3× and 65.7× in high and low pools, respectively. A total of 23,626 polymorphic SNPs and 5299 InDels were detected between both pools and 1492 genes with different variations were identified. Based on differential genes in BSA-seq and weighted gene co-expression network analysis (WGCNA), four hub genes, Glyma.06G290400 (designated as GmIE3-1), Glyma.01G239200, Glyma.01G241500, Glyma.13G256100 were identified, encoding E3 ubiquitin-protein ligase, arm repeat protein interacting with ABF2, zinc metallopeptidase EGY3, and dynamin-related protein 3A, respectively. The allelic variation in GmIE3-1 showed a significant influence on isoflavone accumulation. The virus-induced gene silencing (VIGS) and RNAi hairy root transformation of GmIE3-1 revealed partial suppression of this gene could cause a significant decrease (P < 0.0001) of total isoflavone content, suggesting GmIE3-1 is a positive regulator for isoflavones. The present study demonstrated that the BSA-seq approach combined with WGCNA, VIGS and hairy root transformation can efficiently identify isoflavone candidate genes in soybean natural population.

LAMC2 marks a tumor-initiating cell population with an aggressive signature in pancreatic cancer

BACKGROUND

Tumor-initiating cells (TIC), also known as cancer stem cells, are considered a specific subpopulation of cells necessary for cancer initiation and metastasis; however, the mechanisms by which they acquire metastatic traits are not well understood.

METHODS

LAMC2 transcriptional levels were evaluated using publicly available transcriptome data sets, and LAMC2 immunohistochemistry was performed using a tissue microarray composed of PDAC and normal pancreas tissues. Silencing and tracing of LAMC2 was performed using lentiviral shRNA constructs and CRISPR/Cas9-mediated homologous recombination, respectively. The contribution of LAMC2 to PDAC tumorigenicity was explored in vitro by tumor cell invasion, migration, sphere-forming and organoids assays, and in vivo by tumor growth and metastatic assays. mRNA sequencing was performed to identify key cellular pathways upregulated in LAMC2 expressing cells. Metastatic spreading induced by LAMC2- expressing cells was blocked by pharmacological inhibition of transforming growth factor beta (TGF-β) signaling.

RESULTS

We report a LAMC2-expressing cell population, which is endowed with enhanced self-renewal capacity, and is sufficient for tumor initiation and differentiation, and drives metastasis. mRNA profiling of these cells indicates a prominent squamous signature, and differentially activated pathways critical for tumor growth and metastasis, including deregulation of the TGF-β signaling pathway. Treatment with Vactosertib, a new small molecule inhibitor of the TGF-β type I receptor (activin receptor-like kinase-5, ALK5), completely abrogated lung metastasis, primarily originating from LAMC2-expressing cells.

CONCLUSIONS

We have identified a highly metastatic subpopulation of TICs marked by LAMC2. Strategies aimed at targeting the LAMC2 population may be effective in reducing tumor aggressiveness in PDAC patients. Our results prompt further study of this TIC population in pancreatic cancer and exploration as a potential therapeutic target and/or biomarker.

Identification of Four Hub Genes as Promising Biomarkers for the Evaluation of Ovarian Cancer Prognosis

Ovarian malignant growth is perhaps the most lethal disease in females. There are no exact biomarkers for the early determination of ovarian disease. We obtained a total of 12 gene clusters through WGCNA and studied the azure gene modules related to the lymphatic infiltration of ovarian cancer further. What’s more, endurance investigation was utilized to decide three qualities connected with the by and large and infection-free endurance in ovarian disease patients, including GOGA8B [Hazard Ratio (HR)=1.53, p=0.037, 95% CI: 1.05–2.24], LRRC26 (HR=0.7, p =0.045, 95% CI: 0.48–1.01), and CCDC114 (HR = 0.72, p = 0.042, 95% CI: 0.53–0.98). A prognostic risk score model was built to anticipate the endurance pace of patients at 1, 3, and 5 years, individually. The area under the receiver operating characteristic (ROC) curve (AUC) of the training set was 0.749, 0.764, and 0.784, respectively; the test AUC was 0.601, 0.623, and 0.709. Our review gives a point of view on significant possible biomarkers for the determination, anticipation, and therapy of ovarian malignant growth.

Systematic analysis competing endogenous RNA coexpression network as a potentially prediction prognostic biomarker for colon adenocarcinoma

Colon adenocarcinoma (COAD) is one of the most common types of colon cancer, represents a major public health issue due to its high incidence and mortality. Competing endogenous RNAs (ceRNAs) hypothesis has generated a great interest in the study of molecular biological mechanisms of cancer progression. The aim of this study was to identify potential prediction prognostic biomarker associated with progression of COAD and illuminate regulatory mechanisms. Two RNA sequencing datasets downloaded from the Genotype-Tissue Expression and TCGA. The differentially expressed RNAs were analyzed. Weighted correlation network analysis was used to analyze the similarity of genes model with a trait in the network. Interactions between lncRNAs, miRNAs, and target mRNAs were predicted by MiRcode, starBase, miRTarBase, miRDB, and TargetScan, and the risk score of mRNAs was established. Based on the identified prognostic signature and independent clinical factors, then the nomogram survival model was built. Totally, we identified 3537 differentially expressed mRNAs, 2379 lncRNAs, and 449 microRNAs. Based on the 8 prognosis-associated mRNAs (CCNA2 + CEBPA + NEBL + SOX9 + DLG4 + RIMKLB + TCF7L1 + TUB), the risk score was proposed. After the independent clinical prognostic factors were identified, the nomogram survival model was built. LncRNA-miRNA-mRNA ceRNA network was built by 68 lncRNAs, 4 miRNAs, and 6 mRNAs, which might serve as prognostic biomarkers of COAD. These findings suggest several genes in ceRNA network might be novel important prognostic biomarkers and potential targets for COAD. CeRNA networks could provide further insight into the mRNA-related regulatory mechanism and COAD prognosis.

MicroRNA-409-5p inhibits cell proliferation, and induces G2/M phase arrest and apoptosis by targeting DLGAP5 in ovarian cancer cells

MicroRNA (miRNA/miR)-409-5p has been reported to be implicated in prostate and breast cancers; however, its functional role in ovarian cancer (OC) remains unclear. Therefore the aim of the present study was to investigate the clinical significance and biological function of miR-409-5p in OC. Here, reverse transcription-quantitative PCR analysis was performed to detect miR-409-5p expression in OC tissues and cell lines. The association between miR-409-5p expression and the clinicopathological characteristics of patients with OC was assessed using the Fisher's exact test. Furthermore, the Cell Counting Kit-8 assay was performed to assess cell proliferation. Cell cycle distribution and apoptosis were evaluated via flow cytometric analysis, and the target gene of miR-409-5p was validated via the dual-luciferase reporter assay. The results demonstrated that miR-409-5p expression was significantly downregulated in OC tissues and cell lines compared with adjacent normal tissues and epithelial cells, respectively. In addition, low miR-409-5p expression was significantly associated with tumor size (P=0.044) and the International Federation of Gynecology and Obstetrics staging system (P=0.005). Notably, overexpression of miR-409-5p suppressed cell proliferation, and induced G₂/M phase arrest and apoptosis of OC cells. Mechanistically, discs large-associated protein 5 (DLGAP5) was identified as a novel target of miR-409-5p, which was negatively regulated by miR-409-5p. DLGAP5 expression was significantly upregulated in OC tissues and cell lines compared with adjacent normal tissues and epithelial cells, respectively. Furthermore, overexpression of DLGAP5 reversed the effects of miR-409-5p on SKOV-3 cell proliferation, and G₂/M phase and apoptosis. Taken together, these results suggest that miR-409-5p acts as a tumor suppressor in OC by modulating DLGAP5 expression.

Integrated bioinformatics data analysis reveals a risk signature and PKD1 induced progression in endometrial cancer patients with postmenopausal status

Background: Endometrial cancer (EC) is one of the most common type of female genital malignancies. The purpose of the present study was to reveal the underlying oncogene and mechanism that played a pivotal role in postmenopausal EC patients.

Methods: Weighted gene co-expression network analysis (WGCNA) was conducted using the microarray dataset and clinical data of EC patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify significant gene modules and hub genes associated with postmenopausal status in EC patients. LASSO regression was conducted to build and validate the risk model. Finally, expression of hub gene was validated in pre- and post-menopausal EC patients in our center.

Results: 1240 common genes were used to construct the WGCNA model. According to the WGCNA results, we identified a brown module with 471 genes which was significantly associated with postmenopausal status in EC patients. Furthermore, we constructed an 11-gene risk signature to predict the overall survival of EC patients. The Kaplan–Meier curve and area under the ROC curve (AUC) of this model showed high accuracy in prediction. We also validate the risk model in patients in our center and it also has a high accuracy. Among the 11 genes, PKD1 was recognized as a potential biomarker in the progression of EC patients with postmenopausal status.

Conclusion: Taken together, we uncovered a common PKD1-mediated mechanism underlying postmenopausal EC patients’ progression by integrated analyses. This finding may improve targeted therapy for EC patients.

The Big Picture of Neurodegeneration: A Meta Study to Extract the Essential Evidence on Neurodegenerative Diseases in a Network-Based Approach

The common features of all neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Amyotrophic Lateral Sclerosis (ALS), and Huntington's disease, are the accumulation of aggregated and misfolded proteins and the progressive loss of neurons, leading to cognitive decline and locomotive dysfunction. Still, they differ in their ultimate manifestation, the affected brain region, and the kind of proteinopathy. In the last decades, a vast number of processes have been described as associated with neurodegenerative diseases, making it increasingly harder to keep an overview of the big picture forming from all those data. In this meta-study, we analyzed genomic, transcriptomic, proteomic, and epigenomic data of the aforementioned diseases using the data of 234 studies in a network-based approach to study significant general coherences but also specific processes in individual diseases or omics levels. In the analysis part, we focus on only some of the emerging findings, but trust that the meta-study provided here will be a valuable resource for various other researchers focusing on specific processes or genes contributing to the development of neurodegeneration.

Robust prognostic model based on immune infiltration-related genes and clinical information in ovarian cancer

Immune infiltration of ovarian cancer (OV) is a critical factor in determining patient's prognosis. Using data from TCGA and GTEx database combined with WGCNA and ESTIMATE methods, 46 genes related to OV occurrence and immune infiltration were identified. Lasso and multivariate Cox regression were applied to define a prognostic score (IGCI score) based on 3 immune genes and 3 types of clinical information. The IGCI score has been verified by K‐M curves, ROC curves and C‐index on test set. In test set, IGCI score (C‐index = 0.630) is significantly better than AJCC stage (C‐index = 0.541, p < 0.05) and CIN25 (C‐index = 0.571, p < 0.05). In addition, we identified key mutations to analyse prognosis of patients and the process related to immunity. Chi‐squared tests revealed that 6 mutations are significantly (p < 0.05) related to immune infiltration: BRCA1, ZNF462, VWF, RBAK, RB1 and ADGRV1. According to mutation survival analysis, we found 5 key mutations significantly related to patient prognosis (p < 0.05): CSMD3, FLG2, HMCN1, TOP2A and TRRAP. RB1 and CSMD3 mutations had small p‐value (p < 0.1) in both chi‐squared tests and survival analysis. The drug sensitivity analysis of key mutation showed when RB1 mutation occurs, the efficacy of six anti‐tumour drugs has changed significantly (p < 0.05).

Identification of Hub Genes for Early Diagnosis and Predicting Prognosis in Colon Adenocarcinoma

Colon adenocarcinoma (COAD) is among the most common digestive system malignancies worldwide, and its pathogenesis and gene signatures remain unclear. This study explored the genetic characteristics and molecular mechanisms underlying colon cancer development. Three gene expression data sets were obtained from the Gene Expression Omnibus (GEO) database. GEO2R was used to determine differentially expressed genes (DEGs) between COAD and normal tissues. Then, the intersection of the data sets was obtained. Metascape was used to perform the functional enrichment analyses. Next, STRING was used to build protein-protein interaction (PPI) networks. Hub genes were identified and analysed using Cytoscape. Next, survival analysis and expression analysis of the hub genes were performed. ROC curve analysis was performed for further test of the diagnostic efficacy. Finally, alterations in the hub genes were predicted and analysed by cBioPortal. Altogether, 436 DEGs were detected. The DEGs were mainly enriched in cell cycle phase transition, nuclear division, meiotic nuclear division, and cytokinesis. Based on PPI networks, 20 hub genes were selected. Among them, 6 hub genes (CCNB1, CCNA2, AURKA, NCAPG, DLGAP5, and CENPE) showed significant prognostic value in colon cancer (P < 0.05), while 5 hub genes (CDK1, CCNB1, CCNA2, MAD2L1, and DLGAP5) were associated with early colon cancer diagnosis and ROC curve analysis showed good diagnostic accuracy. In conclusion, integrated bioinformatics analysis was used to identify hub genes that reveal the potential mechanism of carcinogenesis and progression of colon cancer. The hub genes might be novel biomarkers for early diagnosis, treatment, and prognosis of colon cancer.

UBE2T is a prognostic biomarker and correlated with Th2 cell infiltrates in retinoblastoma

OBJECTIVE

This study aimed to screen anaplasia-related genes that influence the progression of retinoblastoma (RB) and to identify immune cells associated with the poor prognosis.

METHODS

Differentially expressed genes (DEGs) between retina and RB samples were acquired from gene expression omnibus (GEO) database. Candidate hub genes were screened by taking intersections among the co-expressed genes, the hub nodes, and DEGs of the validation set. The hub genes were identified by receiver operating characteristic (ROC) and quantitative real-time PCR (qPCR). Immune infiltration levels of RB tissues were estimated using single-sample gene set enrichment analysis (ssGSEA). The functions of RB cells were detected by CCK8, EDU and flow cytometry assays.

RESULTS

665 DEGs involved in the genesis and progression of RB were acquired from GEO database. 29 candidate hub genes were screened by examining 43 co-expressed genes and 63 hub nodes. 9 hub genes (CHEK1, EXO1, FANCI, GTSE1, MELK, MKI67, NCAPH, PRC1, and UBE2T) strongly related to the anaplastic grades were validated by ROC curve analysis (AUC >0.8). Based on the ssGSEA scores, the immune infiltration levels of Th2 cells were positively associated with anaplastic grade. qPCR assay showed that 9 hub genes were upregulated in RB cells, and UBE2T expressed remarkably high. CCK 8, EDU, and flow cytometry assays revealed that UBE2T silencing inhibited the proliferation of RB cells and incited apoptosis.

CONCLUSIONS

The increased infiltration of Th2 cells and upregulated expression of 9 hub genes predict a poor prognosis of RB. UBE2T can be a therapeutic target for RB treatment.

Screening and identification of biomarkers associated with the immune infiltration of intracerebral hemorrhage

BACKGROUND

Recent studies showed that inflammation and immunity might play essential roles in the progression of intracerebral hemorrhage (ICH). However, the underlying mechanisms for changes at the cellular and molecular levels after ICH remain unclear.

METHODS

We downloaded the microarray dataset of ICH from the Gene Expression Omnibus (GEO) database. The differential expression gene analysis was obtained by weighted gene co-expression network analysis (WGCNA). We got the hub genes and performed the biological functions and signaling pathways of these genes by Metascape. GSVA algorithm was used to evaluate the potential physical function of time-varying ICH samples. We used single-sample gene set enrichment analysis (ssGSEA) to assess the immune signatures infiltration and analyzed the correlation between hub genes and immune signatures.

RESULTS

The data sets of all 22 ICH samples in GSE125512 were examined by the WGCNA R package. We finally screened five hub genes (GAPDH, PF4, SELP, APP, and PPBP) in the royal blue module. Metascape analysis displayed the biological processes related to inflammation and immunology. Cell adhesion molecule binding, myeloid leukocyte activation, CXCR chemokine receptor binding, and regulation of cytokine production were the most enriched pathophysiological process. The immune signatures infiltration analyses showed that ICH patients' early and late samples had different activity and abundance of immune-related cells and types.

CONCLUSIONS

GAPDH, PF4, SELP, APP, and PPBP are identified as potential biomarkers for predicting the progression of ICH. This study may help us better understand the immunologic mechanism and shed new light on the promising approaches of immunotherapy for ICH patients.

PRC1 and RACGAP1 are Diagnostic Biomarkers of Early HCC and PRC1 Drives Self-Renewal of Liver Cancer Stem Cells

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related deaths across the world. Due to the lack of reliable markers for early HCC detection, most HCC patients are diagnosed in middle/late stages. Liver cancer stem cells (CSCs), which are drivers of liver tumorigenesis, usually emerge in the early HCC stage and are also termed as liver tumor initiation cells (TIC). Liver CSCs contribute to initiation, propagation, and metastasis of HCC and also play a key role in tumor therapy. Taking advantage of online-available data sets, bioinformatic analyses, and experimental confirmation, here we have screened out PRC1 and RACGAP1 as reliable markers for early HCC detection. PRC1 or RACGAP1 knockdown dramatically inhibited the proliferation, migration, and invasion capacities of HCC cells, conferring PRC1 and RACGAP1 as predominant modulators for HCC propagation and metastasis. Moreover, the sphere formation capacity of HCC cells was impaired after PRC1 knockdown, revealing the function of PRC1 as a modulator for liver CSC self-renewal. Furthermore, the inhibitor of PRC1 had same phenotypes as PRC1 knockdown in HCC cells. Altogether, PRC1 and RACGAP1 are identified both as prognosis markers for early HCC detection and therapeutic targets for liver cancer and liver CSCs, adding additional layers for the early prognosis and therapy of HCC.

Novel insight into pancreatic adenocarcinoma pathogenesis using liquid association analysis

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy associated with a poor prognosis. High-throughput disease-related-gene expression data provide valuable information on gene interaction, which consequently lead to deeper insight about pathogenesis. The co-expression analysis is a common approach that is used to investigate gene interaction. However, such an approach solely is inadequate to reveal the complexity of the gene interaction. The three-way interaction model is known as a novel approach applied to decode the complex relationship between genes.

METHODS

In the current study, the liquid association method was used to capture the statistically significant triplets involved in the PDAC pathogenesis. Subsequently, gene set enrichment and gene regulatory network analyses were performed to trace the biological relevance of the statistically significant triplets.

RESULTS

The results of the current study suggest that "response to estradiol" and "Regulation of T-cell proliferation" are two critical biological processes that may be associated with the PDAC pathogenesis. Additionally, we introduced six switch genes, namely Lamc2, Klk1, Nqo1, Aox1, Tspan1, and Cxcl12, which might be involved in PDAC triggering.

CONCLUSION

In the current study, for the first time, the critical genes and pathways involved in the PDAC pathogenesis were investigated using the three-way interaction approach. As a result, two critical biological processes, as well as six potential biomarkers, were suggested that might be involved in the PDAC triggering. Surprisingly, strong evidence for the biological relevance of our results can be found in the literature.

Identification of key genes associated with papillary thyroid microcarcinoma characteristics by integrating transcriptome sequencing and weighted gene co-expression network analysis

OBJECTIVE

Papillary thyroid microcarcinoma (PTMC) is the most prevalent histological type of thyroid carcinoma. Despite the overall favorable prognosis of PTMC, some cases exhibit aggressive phenotypes. The identification of robust biomarkers may improve early PTMC diagnosis. In this study, we integrated high-throughput transcriptome sequencing, bioinformatic analyses and experimental validation to identify key genes associated with the malignant characteristics of PTMC.

METHODS

Total RNA was extracted from 24 PTMC samples and 7 non-malignant thyroid tissue samples, followed by RNA sequencing. The differentially expressed genes (DEGs) were identified and used to construct co-expression networks by weighted gene co-expression network analysis (WGCNA). Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed, and protein-protein interaction networks were constructed. Key modules and hub genes showing a strong correlation with the malignant characteristics of PTMC were identified and validated.

RESULTS

The green-yellow and turquoise modules generated by WGCNA were strongly associated with the malignant characteristics of PTMC. Functional enrichment analysis revealed that genes in the green-yellow module participated in cell motility and metabolism, whereas those in the turquoise module participated in several oncogenic biological processes. Nine real hub genes (FHL1, NDRG2, NEXN, SYNM, COL1A1, FN1, LAMC2, POSTN, and TGFBI) were identified and validated at the transcriptional and translational levels. Our preliminary results indicated their diagnostic potentials in PTMC.

CONCLUSIONS

In this study, we identified key co-expression modules and nine malignancy-related genes with potential diagnostic value in PTMC.

Exploring the potential biomarkers for prognosis of glioblastoma via weighted gene co-expression network analysis

BACKGROUND

Glioblastoma (GBM) is the most common malignant tumor in the central system with a poor prognosis. Due to the complexity of its molecular mechanism, the recurrence rate and mortality rate of GBM patients are still high. Therefore, there is an urgent need to screen GBM biomarkers to prove the therapeutic effect and improve the prognosis.

RESULTS

We extracted data from GBM patients from the Gene Expression Integration Database (GEO), analyzed differentially expressed genes in GEO and identified key modules by weighted gene co-expression network analysis (WGCNA). GSE145128 data was obtained from the GEO database, and the darkturquoise module was determined to be the most relevant to the GBM prognosis by WGCNA (r =  - 0.62, p = 0.01). We performed enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to reveal the interaction activity in the selected modules. Then Kaplan-Meier survival curve analysis was used to extract genes closely related to GBM prognosis. We used Kaplan-Meier survival curves to analyze the 139 genes in the darkturquoise module, identified four genes (DARS/GDI2/P4HA2/TRUB1) associated with prognostic GBM. Low expression of DARS/GDI2/TRUB1 and high expression of P4HA2 had a poor prognosis. Finally, we used tumor genome map (TCGA) data, verified the characteristics of hub genes through Co-expression analysis, Drug sensitivity analysis, TIMER database analysis and GSVA analysis. We downloaded the data of GBM from the TCGA database, the results of co-expression analysis showed that DARS/GDI2/P4HA2/TRUB1 could regulate the development of GBM by affecting genes such as CDC73/CDC123/B4GALT1/CUL2. Drug sensitivity analysis showed that genes are involved in many classic Cancer-related pathways including TSC/mTOR, RAS/MAPK.TIMER database analysis showed DARS expression is positively correlated with tumor purity (cor = 0.125, p = 1.07e-02)), P4HA2 expression is negatively correlated with tumor purity (cor =-0.279, p = 6.06e-09). Finally, GSVA analysis found that DARS/GDI2/P4HA2/TRUB1 gene sets are closely related to the occurrence of cancer.

CONCLUSION

We used two public databases to identify four valuable biomarkers for GBM prognosis, namely DARS/GDI2/P4HA2/TRUB1, which have potential clinical application value and can be used as prognostic markers for GBM.

Identification of Novel Diagnostic Biomarkers in Prostate Adenocarcinoma Based on the Stromal-Immune Score and Analysis of the WGCNA and ceRNA Network

Prostate cancer is still a significant global health burden in the coming decade. Novel biomarkers for detection and prognosis are needed to improve the survival of distant and advanced stage prostate cancer patients. The tumor microenvironment is an important driving factor for tumor biological functions. To investigate RNA prognostic biomarkers for prostate cancer in the tumor microenvironment, we obtained relevant data from The Cancer Genome Atlas (TCGA) database. We used the bioinformatics tools Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm and weighted coexpression network analysis (WGCNA) to construct tumor microenvironment stromal-immune score-based competitive endogenous RNA (ceRNA) networks. Then, the Cox regression model was performed to screen RNAs associated with prostate cancer survival. The differentially expressed gene profile in tumor stroma was significantly enriched in microenvironment functions, like immune response, cancer-related pathways, and cell adhesion-related pathways. Based on these differentially expressed genes, we constructed three ceRNA networks with 152 RNAs associated with the prostate cancer tumor microenvironment. Cox regression analysis screened 31 RNAs as the potential prognostic biomarkers for prostate cancer. The most interesting 8 prognostic biomarkers for prostate cancer included lncRNA LINC01082, miRNA hsa-miR-133a-3p, and genes TTLL12, PTGDS, GAS6, CYP27A1, PKP3, and ZG16B. In this systematic study for ceRNA networks in the tumor environment, we screened out potential biomarkers to predict prognosis for prostate cancer. Our findings might apply a valuable tool to improve prostate cancer clinical management and the new target for mechanism study and therapy.

Gene Expression Characteristics of Tumor and Adjacent Non-Tumor Tissues of Pancreatic Ductal Adenocarcinoma (PDAC) In-Silico

BACKGROUND

One of the deadliest and most prevalent cancer is pancreatic ductal adenocarcinoma (PDAC). Microarray has become an important tool in the research of PDAC genes and target therapeutic drugs.

OBJECTIVES

This study intends to clarify the promising prognostic and biomarker targets in PDAC using GSE78229 and GSE62452 datasets, publicly accessible at the Gene Expression Omnibus database.

MATERIALS AND METHODS

Utilizing GEOquery, Bio base, gplots, and ggplot2 packages in the R program, this study detects 428 differentially expressed genes that are further applied to build a co-expression network by the weighted correlation network analysis (WGCNA). The turquoise module presented a higher correlation with PDAC progression. 79 candidate genes were selected based on the co-expression and protein-protein interaction (PPI) networks. In addition, the functional enrichment analysis was studied.

RESULTS

Five significant KEGG pathways linked to PDAC were detected, in which the endoplasmic reticulum protein processing pathway was remarked to be vital. The resulting 19 hub genes as HSPA4, PABPC1, HSP90B1, PPP1CC, USP9X, EIF2S3, MSN, RAB10, BMPR2, P4HB, UBC, B2M, SLC25A5, MMP7, SPTBN1, RALB, DNAJB1, CENPE, and PDIA6 were identified by the Network Analyst web tool founded on PPI network by the STRING. These were identified as the most connected hub proteins. The quantification of the expression of levels and survival probabilities were analyzed overall survival (OS) of the real hub genes and were investigated by Kaplan-Meier (KM) plotter through The Cancer Genome Atlas Program (TCGA) database.

CONCLUSIONS

The protein-protein interactions and KEGG pathway enrichment by DAVID indicated that some pathways were involved in PDAC, such as "pathways in cancer (hsa05200)", "protein processing in the endoplasmic reticulum (hsa04141)", "antigen processing and presentation (hsa04612)", "dopaminergic synapse (hsa04728)", and "measles (hsa05162)"; in which these pathways, the "protein processing in endoplasmic reticulum (hsa04141)", was further studied because of its closely relationship with PDAC. The rest of the hub genes reviewed throughout the study might be promising targets for diagnosing and treating PDAC and relevant diseases.

DEP domain containing 1B (DEPDC1B) exerts the tumor promoter in hepatocellular carcinoma through activating p53 signaling pathway via kinesin family member 23 (KIF23)

Hepatocellular carcinoma (HCC) is closely associated with chronic liver disease and possesses a high incidence. DEP domain containing 1B (DEPDC1B) expression has been found to be upregulated in HCC according to bioinformatics analysis. This paper sought to study the specific role of DEPDC1B in HCC. The data of DEPDC1B expression and individual overall survival in HCC and normal liver tissues were acquired from UALCAN database. The association between DEPDC1B and the downstream signal, kinesin family member 23 (KIF23), was determined using LinkedOmics and STRING database, and subsequently confirmed by co-immunoprecipitation assay. The expression levels of DEPDC1B and KIF23 in normal hepatic epithelial cells and HCC cell lines were assessed by RT-qPCR and Western blotting, respectively. Following transfection with small interference RNA-DEPDC1B, the influences of DEPDC1B knockdown on cell proliferation, colony formation, cell cycle, cell invasion, migration, and KIF23 expression were evaluated. In addition, the effects of KIF23 overexpression on the above aspects of HCC cells were also determined, as well as the expression level of p53 signaling-related proteins. The results indicated that DEPDC1B was highly expressed in HCC cells. DEPDC1B knockdown inhibited the proliferation, migration, invasion, cycle, and KIF23 expression in HCC cells. Moreover, KIF23 overexpression reversed the inhibitory effect of DEPDC1B knockdown in HCC cells and the activation of the p53 signaling. In conclusion, DEPDC1B knockdown exerts anti-cancer role in HCC by activating the p53 signaling through KIF23.

Hydroxysteroid 17-Beta Dehydrogenase 6 Is a Prognostic Biomarker and Correlates with Immune Infiltrates in Hepatocellular Carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a common malignancy worldwide with poor outcomes. Therefore, it is important to identify a valuable prognostic biomarker for HCC. The present study aimed to identify novel prognostic biomarkers for HCC and evaluate the potential role of hub genes in HCC.

METHODS

Weighted gene co-expression network analysis and protein-protein interaction analysis were performed to identify important potential prognostic genes. The expression of hub genes was confirmed by the GEPIA, Oncomine, UALCAN, and HPA database. Furthermore, survival analysis of hub genes was performed using the Kaplan-Meier plotter database. Finally, we investigated the association between hub genes and immune factors in HCC through GSEA, the TIMER, and TISIDB database.

RESULTS

HSD17B6 expression was significantly lower in HCC than in normal tissues. Low HSD17B6 expression is associated with poorer overall survival and progression-free survival in HCC patients, particularly at medium disease stages (stage II and III or grade III). HSD17B6 showed a strong correlation with tumor-infiltrating B cells, CD4 + and CD8 + T cells, macrophages, neutrophils, and dendritic cells. Somatic copy number alteration might be the main cause of the negative correlation between HSD17B6 expression and immune infiltration. HSD17B6 expression in HCC negatively correlated with the expression of several immune cell markers, including exhausted T cell markers, PD-1 and CTLA-4, suggesting its role in regulating tumor immunity.

CONCLUSIONS

HSD17B6 is a potential prognostic biomarker that determines cancer progression and is correlated with tumor immune cells infiltration in HCC.

Identification of hub genes and construction of an mRNA-miRNA-lncRNA network of gastric carcinoma using integrated bioinformatics analysis

Background

Gastric carcinoma (GC) is one of the most common cancer globally. Despite its worldwide decline in incidence and mortality over the past decades, gastric cancer still has a poor prognosis. However, the key regulators driving this process and their exact mechanisms have not been thoroughly studied. This study aimed to identify hub genes to improve the prognostic prediction of GC and construct a messenger RNA-microRNA-long non-coding RNA(mRNA-miRNA-lncRNA) regulatory network.

Methods

The GSE66229 dataset, from the Gene Expression Omnibus (GEO) database, and The Cancer Genome Atlas (TCGA) database were used for the bioinformatic analysis. Differential gene expression analysis methods and Weighted Gene Co-expression Network Analysis (WGCNA) were used to identify a common set of differentially co-expressed genes in GC. The genes were validated using samples from TCGA database and further validation using the online tools GEPIA database and Kaplan-Meier(KM) plotter database. Gene set enrichment analysis(GSEA) was used to identify hub genes related to signaling pathways in GC. The RNAInter database and Cytoscape software were used to construct an mRNA-miRNA-lncRNA network.

Results

A total of 12 genes were identified as the common set of differentially co-expressed genes in GC. After verification of these genes, 3 hub genes, namely CTHRC1, FNDC1, and INHBA, were found to be upregulated in tumor and associated with poor GC patient survival. In addition, an mRNA-miRNA-lncRNA regulatory network was established, which included 12 lncRNAs, 5 miRNAs, and the 3 hub genes.

Conclusions

In summary, the identification of these hub genes and the establishment of the mRNA-miRNA-lncRNA regulatory network provide new insights into the underlying mechanisms of gastric carcinogenesis. In addition, the identified hub genes, CTHRC1, FNDC1, and INHBA, may serve as novel prognostic biomarkers and therapeutic targets.

Identification and Validation of CYBB, CD86, and C3AR1 as the Key Genes Related to Macrophage Infiltration of Gastric Cancer

Gastric cancer (GC) is rampant around the world. Most of the GC cases are detected in advanced stages with poor prognosis. The identification of marker genes for early diagnosis is of great significance. Studying the tumor environment is helpful to acknowledge the process of tumorigenesis, development, and metastasis. Twenty-two kinds of immune cells were calculated by CIBERSORT from Gene Expression Omnibus (GEO) database. Subsequently, higher infiltration of macrophages M0 was discovered in GC compared with normal tissues. WGCNA was utilized to construct the network and then identify key modules and genes related to macrophages in TCGA. Finally, 18 hub genes were verified. In the PPI bar chart, the top 3 genes were chosen as hub genes involved in most pathways. On the TIMER and THPA websites, it is verified that the expression levels of CYBB, CD86, and C3AR1 genes in tumor tissues were higher than those in normal tissues. These genes may work as biomarkers or targets for accurate diagnosis and treatment of GC in the future. Our findings may be a new strategy for the treatment of GC.

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.

Transmembrane signaling molecules play a key role in the pathogenesis of IgA nephropathy: a weighted gene co-expression network analysis study

BACKGROUND

Immunoglobulin A nephropathy (IgAN) is one of the most common primary glomerulonephritis and a serious health concern worldwide; though still the underlying molecular mechanisms of IgAN are yet to be known and there is no efficient treatment for this disease. The main goal of this study was to explore the IgAN underlying pathogenic pathways, plus identifying the disease correlated modules and genes using the weighted gene co-expression network analysis (WGCNA) algorithm.

RESULTS

GSE104948 dataset (the expression data from glomerular tissue of IgAN patients) was analyzed and the identified differentially expressed genes (DEGs) were introduced to the WGCNA algorithm for building co-expression modules. Genes were classified into six co-expression modules. Genes of the disease's most correlated module were mainly enriched in the immune system, cell-cell communication and transmembrane cell signaling pathways. The PPI network was constructed by genes in all the modules and after hub-gene identification and validation steps, 11 genes, mostly transmembrane proteins (CD44, TLR1, TLR2, GNG11, CSF1R, TYROBP, ITGB2, PECAM1), as well as DNMT1, CYBB and PSMB9 were identified as potentially key players in the pathogenesis of IgAN. In the constructed regulatory network, hsa-miR-129-2-3p, hsa-miR-34a-5p and hsa-miR-27a-3p, as well as STAT3 were spotted as top molecules orchestrating the regulation of the hub genes.

CONCLUSIONS

The excavated hub genes from the hearts of co-expressed modules and the PPI network were mostly transmembrane signaling molecules. These genes and their upstream regulators could deepen our understanding of IgAN and be considered as potential targets for hindering its progression.

MicroRNA‑190b expression predicts a good prognosis and attenuates the malignant progression of pancreatic cancer by targeting MEF2C and TCF4

MicroRNAs (miRNAs/miRs) are key components of regulatory networks in cancer. Although miR-190b is an important tumor-related miRNA, its role in pancreatic cancer has not been extensively investigated. The aim of the present study was to examine the expression of miR-190b in pancreatic cancer cell lines and tissues and evaluate its effects on cancer progression. Reverse transcription-quantitative PCR (RT-qPCR) analysis was used to measure miR-190b expression levels in human pancreatic cancer cell lines and tissues, and the association between miR-190b expression and clinicopathological characteristics was assessed. An in vitro Transwell invasion assay and an in vivo metastasis formation assay were performed using pancreatic cancer cells. The effect of miR-190b on pancreatic cancer cell proliferation was evaluated using a Cell Counting Kit-8 assay based on an in vivo xenograft mouse model. The direct targets of miR-190b were predicted using bioinformatics tools and were validated through western blotting and luciferase reporter assays. Pancreatic cancer cell lines and tissues were found to express lower levels of miR-190b compared with normal cells and adjacent non-tumor tissues. Furthermore, high expression of miR-190b was found to be positively correlated with low T, N and American Joint Committee on Cancer classifications, and predicted a good prognosis. miR-190b was shown to exert suppressive effects on cancer cell proliferation, invasion and metastasis. In addition, it was also found that miR-190b directly targeted myocyte enhancer factor 2C (MEF2C) and transcription factor 4 (TCF4) in pancreatic cancer, thus serving as a tumor suppressor and a predictor of good prognosis in pancreatic cancer. The immunohistochemistry and RT-qPCR results indicated that the MEF2C and TCF4 expression levels were negatively correlated with the miR-190b expression levels. The findings of the present study highlight the value of miR-190b as a novel target candidate for pancreatic cancer diagnosis and therapy.

Screening and Identification of Hub Genes in the Corticosteroid Resistance Network in Human Airway Epithelial Cells via Microarray Analysis

Background and Objective: Corticosteroid resistance is a major barrier to chronic obstructive pulmonary disease (COPD), but the exact mechanism of corticosteroid resistance in COPD has been less well studied. Methods: The microarray dataset GSE11906, which includes genomic and clinical data on COPD, was downloaded from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) were identified using R software. Gene set enrichment analysis (GSEA) and Kyoto Encyclopedia of Genes (KEGG) were utilized to enrich and analyze the gene cohort related to the response to steroid hormones, respectively. The Connectivity Map (CMap) database was used to screen corticosteroid resistance-related drugs that might exert a potential therapeutic effect. STRING was used to construct a protein-protein interaction (PPI) network of the gene cohort, and the CytoHubba plug-in of Cytoscape was used to screen the hub genes in the PPI network. The expression levels of hub genes in cigarette smoke extract (CSE)-stimulated bronchial epithelial cells were assayed by quantitative real-time PCR and western blotting. Results: Twenty-one genes were found to be correlated with the response to steroid hormones. In the CMap database, 32 small-molecule compounds that might exert a therapeutic effect on corticosteroid resistance in COPD were identified. Nine hub genes were extracted from the PPI network. The expression levels of the BMP4, FOS, FN1, EGFR, and SPP1 proteins were consistent with the microarray data obtained from molecular biology experiments. Scopoletin significantly restrained the increases in the levels of AKR1C3, ALDH3A1, FN1 and reversed the decreases of phosphorylated GR and HDAC2 caused by CSE exposure. Conclusion: The BMP4, FOS, FN1, EGFR, and SPP1 genes are closely correlated with CSE-induced glucocorticoid resistance in airway epithelial cells. Scopoletin may be a potential drug for the treatment of glucocorticoid resistance caused by CSE.

A New Stemness-Related Prognostic Model for Predicting the Prognosis in Pancreatic Ductal Adenocarcinoma

Objective

This study is aimed at identifying stemness-related genes in pancreatic ductal adenocarcinoma (PDAC).

Methods

The RNA-seq data of PADC patients were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. The mRNA expression-based stemness index (mRNAsi) and epigenetically regulated mRNAsi (EREG-mRNAsi) of PADC patients were evaluated. The mRNAsi-related gene sets in PADC were identified by weighted gene coexpression network analysis (WGCNA). The key genes were further analyzed using functional enrichment analysis. The Kaplan-Meier survival analysis and the Cox proportional hazards model were used to evaluate the prognostic value of the key genes. Prognostic hub genes were used to establish nomograms. The receiver operating characteristic (ROC) curves, concordance index (C-index), and calibration curves were used to assess the discrimination and accuracy of the nomogram. Finally, these results were validated in the Gene Expression Omnibus (GEO) database.

Results

A total of 36 key genes related to mRNAsi were identified by WGCNA. A prognostic gene signature compromising seven genes (TPX2, ZWINT, UBE2C, CCNB2, CDK1, BUB1, and BIRC5) was established to predict the overall survival (OS) of PADC patients. The Cox regression analysis revealed that the risk score was an independent prognostic factor for PADC. Patients were then divided into the high-risk and low-risk groups. The ROC curves, C-index, and calibration curves indicated good performance of the prognostic signature in the TCGA and GEO datasets. Moreover, the nomogram incorporating clinical parameters showed better sensitivity and specificity for predicting the OS of PADC patients.

Conclusion

The stemness-related prognostic model successfully predicted the OS of PADC patients and could be used for the treatment of PADC.

Knockdown of DLGAP5 suppresses cell proliferation, induces G2/M phase arrest and apoptosis in ovarian cancer

Discs large-associated protein 5 (DLGAP5) is a microtubule-associated protein and is reported to exert oncogenic role in tumorigenesis, including lung cancer and hepatocellular carcinoma. However, the prognostic value and biological function of DLGAP5 in ovarian cancer (OC) still remain unclear. The present study investigated the expression pattern of DLGAP5 by searching the Oncomine microarray database. The correlation between DLGAP5 and survival prognosis of OC patients was analyzed by the online tool KM-plotter. Knockdown of DLGAP5 was achieved by transfection with small interfering RNA targeting DLGAP5 in two OC cell lines (SKOV3 and CAOV3). Cell proliferation was assessed by Cell Counting Kit-8 assay and colony-formation assay. Flow cytometry was utilized to determine the effects of DLGAP5 on cell cycle distribution and apoptosis. The present study data showed that DLGAP5 was significantly upregulated in OC and its higher expression was associated with poor survival prognosis. Knockdown of DLGAP5 significantly suppressed cell proliferation, induced cell cycle G₂/M phase arrest and apoptosis. Western blot analysis further demonstrated that DLGAP5 knockdown downregulated the expression of CDK1, Cyclin B1 and Bcl-2, but upregulated Bax expression. Collectively, these data demonstrate that DLGAP5 might be a promising prognostic therapeutic target for OC treatment.

Cell-specific gene association network construction from single-cell RNA sequence

The recent development of a high throughput single-cell RNA sequence devises the opportunity to study entire transcriptomes in the smallest detail. It also leads to the characterization of molecules and subtypes of a cell. Cancer epigenetics induced not only from individual molecules but also from the dysfunction of the system and the coupling effect of genes. While rapid advances are being made in the development of tools for single-cell RNA-seq data analysis, few slants are noticed in the potential advantages of single-cell network construction.Here, we used network perturbation theory with significant analysis to develop a cell-specific network that provides an insight into gene-gene association based on molecular expressions in a single-cell resolution. Besides, using this method, we can characterize each cell by inspecting how genes are connected and can identify the hub genes using network degree theory. Pathway & Gene enrichment analysis of the identified cell-specific high network degree genes supported the effectiveness of this method. This method could be beneficial for personalized drug design and even therapeutics.

Identification of the Real Hub Gene and Construction of a Novel Prognostic Signature for Pancreatic Adenocarcinoma Based on the Weighted Gene Co-expression Network Analysis and Least Absolute Shrinkage and Selection Operator Algorithms

Background: Pancreatic adenocarcinoma (PAAD) has a considerably bad prognosis, and its pathophysiologic mechanism remains unclear. Thus, we aimed to identify real hub genes to better explore the pathophysiology of PAAD and construct a prognostic panel to better predict the prognosis of PAAD using the weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) algorithms. Methods: WGCNA identified the modules most closely related to the PAAD stage and grade based on the Gene Expression Omnibus. The module genes significantly associated with PAAD progression and prognosis were considered as the real hub genes. Eligible genes in the most significant module were selected for construction and validation of a multigene prognostic signature based on the LASSO-Cox regression analysis in The Cancer Genome Atlas and the International Cancer Genome Consortium databases, respectively. Results: The brown module identified by WGCNA was most closely associated with the clinical characteristics of PAAD. Scaffold attachment factor B (SAFB) was significantly associated with PAAD progression and prognosis, and was identified as the real hub gene of PAAD. Moreover, both transcriptional and translational levels of SAFB were significantly lower in PAAD tissues compared with normal pancreatic tissues. In addition, a novel multigene-independent prognostic signature consisting of SAFB, SP1, and SERTAD3 was identified and verified. The predictive accuracy of our signature was superior to that of previous studies, especially for predicting 3- and 5-year survival probabilities. Furthermore, a prognostic nomogram based on independent prognostic variables was developed and validated using calibration curves. The predictive ability of this nomogram was also superior to the well-established AJCC stage and histological grade. The potential mechanisms of different prognoses between the high- and low-risk subgroups were also investigated using functional enrichment analysis, GSEA, ssGSEA, immune checkpoint analysis, and mutation profile analysis. Conclusion: SAFB was identified as the real hub gene of PAAD. A novel multigene-independent prognostic signature was successfully identified and validated to better predict PAAD prognosis. An accurate nomogram was also developed and verified to aid in the accurate treatment of tumors, as well as in early intervention.

Multi-Dimensional Scaling Analysis of Key Regulatory Genes in Prostate Cancer Using the TCGA Database

Prostate cancer (PC) is a polygenic disease with multiple gene interactions. Therefore, a detailed analysis of its epidemiology and evaluation of risk factors can help to identify more accurate predictors of aggressive disease. We used the transcriptome data from a cohort of 243 patients from the Cancer Genome Atlas (TCGA) database. Key regulatory genes involved in proliferation activity, in the regulation of stress, and in the regulation of inflammation processes of the tumor microenvironment were selected to test a priori multi-dimensional scaling (MDS) models and create a combined score to better predict the patients' survival and disease-free intervals. Survival was positively correlated with cortisol expression and negatively with Mini-Chromosome Maintenance 7 (MCM7) and Breast-Related Cancer Antigen2 (BRCA2) expression. The disease-free interval was negatively related to the expression of enhancer of zeste homolog 2 (EZH2), MCM7, BRCA2, and programmed cell death 1 ligand 1 (PD-L1). MDS suggested two separate pathways of activation in PC. Within these two dimensions three separate clusters emerged: (1) cortisol and brain-derived neurotrophic factor BDNF, (2) PD-L1 and cytotoxic-T-lymphocyte-associated protein 4 (CTL4); (3) and finally EZH2, MCM7, BRCA2, and c-Myc. We entered the three clusters of association shown in the MDS in several Kaplan-Meier analyses. It was found that only Cluster 3 was significantly related to the interval-disease free, indicating that patients with an overall higher activity of regulatory genes of proliferation and DNA repair had a lower probability to have a longer disease-free time. In conclusion, our data study provided initial evidence that selecting patients with a high grade of proliferation and DNA repair activity could lead to an early identification of an aggressive PC with a potentials for metastatic development.

Identification of hub genes and their novel diagnostic and prognostic significance in pancreatic adenocarcinoma

OBJECTIVE

The main reasons for the poor prognoses of pancreatic adenocarcinoma (PA) patients are rapid early-stage progression, advanced stage metastasis, and chemotherapy resistance. Identification of novel diagnostic and prognostic biomarkers of PA is therefore urgently needed.

METHODS

Three mRNA microarray datasets were obtained from the Gene Expression Omnibus database to select differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses for hub genes were performed using DAVID. Correlations between expression levels of hub genes and cancer-infiltrating immune cells were investigated by TIMER. Cox proportional hazard regression analyses were also performed. Serum hub genes were screened using the HPA platform and verified for diagnostic value using ELISAs.

RESULTS

We identified 59 hub genes among 752 DEGs. GO analysis indicated that these 59 hub genes were mainly involved in the defense response to viruses and the type I interferon signaling pathway. We also discovered that RSAD2 and SMC4 were associated with immune cell infiltration in the PA microenvironment. Additionally, DLGAP5 mRNA might be used as an independent risk factor for the prognoses of PA patients. Furthermore, the protein encoded by ISG15, which exists in peripheral blood, was validated as a potential diagnostic biomarker that distinguished PA patients from healthy controls (area under the curve: 0.902, 95% confidence interval: 0.819-0.961).

CONCLUSIONS

Our study suggested that RSAD2 and SMC4 were associated with immune cell infiltration in the PA microenvironment, while DLGAP5 mRNA expression might be an independent risk factor for the survival prognoses of PA patients. Moreover, ELISAs indicated that serum ISG15 could be a potential novel diagnostic biomarker for PA.

Seven key hub genes identified by gene co-expression network in cutaneous squamous cell carcinoma

Background

Cutaneous squamous cell carcinoma (cSCC) often follows actinic keratosis (AK) and is the second most common skin cancer worldwide. To reduce metastasis risk, it is important to diagnose and treat cSCC early. This study aimed to identify hub genes associated with cSCC and AK.

Methods

This study used three datasets GSE45216, GSE98774, and GSE108008. We combined samples from the GSE45216 and GSE98774 datasets into the new dataset GSE45216–98774. We applied a weighted gene co-expression network analysis (WGCNA) to investigate key modules and hub genes associated with cSCC and AK. We considered the hub genes found in both the GSE45216–98774 and GSE108008 datasets as validated hub genes. We tested whether the expression of hub genes could predict patient survival outcomes in other cancers using TCGA pan-cancer data.

Results

We identified modules most relevant to cSCC and AK. Additionally, we identified and validated seven hub genes of cSCC: GATM, ARHGEF26, PTHLH, MMP1, POU2F3, MMP10 and GATA3. We did not find validated hub genes for AK. Each hub gene was significantly associated with the survival of various cancer types. Only GATA3 was significantly associated with melanoma survival.

Conclusions

We applied WGCNA to find seven hub genes that play important roles in cSCC tumorigenesis. These results provide new insights that help explain the pathogenesis of cSCC. These hub genes may become biomarkers or therapeutic targets for accurate diagnosis and treatment of cSCC in the future.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08604-y.

Development and validation of a novel N6-methyladenosine (m6A)-related multi- long non-coding RNA (lncRNA) prognostic signature in pancreatic adenocarcinoma

Accumulating evidence has unveiled the pivotal roles of N6-methyladenosine (m6A) in pancreatic adenocarcinoma (PAAD). However, there are not many researches to predict the prognosis of PAAD using m6A-related long non-coding RNAs (lncRNAs). Raw data from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and the Genotype-Tissue Expression project (GTEx) were utilized to comprehensively analyze the expression and prognostic performances of 145 m6A-related lncRNAs in PAAD and to develop and validate a novel m6A-related multi-lncRNA prognostic signature (m6A-LPS) for PAAD patients. In total, 57 differentially expressed m6A-related lncRNAs with prognostic values were identified. Based on LASSO-Cox regression analysis, m6A-LPS was constructed and verified by using five-lncRNA expression profiles for TCGA and ICGC cohorts. PAAD patients were then divided into high- and low-risKBIE_A_1933868k subgroups with different clinical outcomes according to the median risk score; this was further verified by time-dependent receiver operating characteristic curves. Risk scores were significantly associated with clinical parameters such as histological grade and cancer status among PAAD patients. A nomogram consisting of risk score, grade, and cancer status was generated to predict the survival probability of PAAD patients, as also demonstrated by calibration curves. Discrepancies in cellular processes, signaling pathways, and immune status between the high- and low-risk subgroups were investigated by functional and single-sample gene set enrichment analyses. In conclusion, the novel m6A-LPS for PAAD patients was developed and validated, which might provide new insight into clinical decision-making and precision medicine.

Identification and Validation of Hub Genes Associated With Hepatocellular Carcinoma Via Integrated Bioinformatics Analysis

Hepatocellular carcinoma (HCC) is the most common malignant tumor of the liver, with high morbidity and mortality, yet its molecular mechanisms of tumorigenesis are still unclear. In this study, gene expression profile of GSE62232 was downloaded from the Gene Expression Omnibus (GEO). The RNA-seq expression data and relative clinical information were retrieved from the Cancer Genome Atlas (TCGA) database. The datasets were analyzed by differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA) to obtain the overlapping genes. Then, we performed a functional enrichment analysis to understand the potential biological functions of these co-expression genes. Finally, we constructed the protein-protein interaction (PPI) analysis combined with survival analysis. MARCO, CLEC4M, FCGR2B, LYVE1, TIMD4, STAB2, CFP, CLEC4G, CLEC1B, FCN2, FCN3 and FOXO1 were identified as the candidate hub genes using the CytoHubba plugin of Cytoscape. Based on survival analysis, the lower expression of FCN3 and FOXO1 were associated with worse overall survival (OS) in HCC patients. Furthermore, the expression levels of FCN3 and FOXO1 were validated by the Human Protein Atlas (HPA) database and the qRT-PCR. In summary, our findings contribute new ideas for the precise early diagnosis, clinical treatment and prognosis of HCC in the future.

Weighted Gene Co-Expression Network Analysis Identifies Five Hub Genes Associated with Metastasis in Synovial Sarcoma

BACKGROUND

Synovial sarcoma (SS) refers to a malignant soft tissue sarcoma (STS) which often occurs in children and adults and has a poor prognosis in elderly patients. Patients with local lesions can be treated with extensive surgical resection combined with adjuvant or radiotherapy, whereas about half of the cases have recurrent diseases and metastatic lesions, and five-year survival ratio is assessed within the range of 27% - 55% only.

METHOD

We downloaded a set of expression profile data (GSE40021) related to SS metastasis based on the Gene Expression Omnibus (GEO) database, and selected distinctly represented genes (DEGs) related to tumor metastasis. WGCNA was used to emphasize the DEGs related to tumor metastasis and obtain co-expression modules. Then, the module most related to SS metastasis was screened out. The genes enriched in this module were analyzed by Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway improvement analysis. Cytoscape software was used for constructing protein-protein interaction (PPI) networks, and hub genes were screened in Oncomine analysis.

RESULT

We selected 514 DEGs, consisting of 210 up-regulated genes and 304 down-regulated genes. Through WGCAN, we got seven co-expression modules and the module most related to SS metastasis was the turquoise module, which contained 66 genes. Finally, we screened out five hub genes (HJURP, NCAPG, TPX2, CENPA, NDC80) through CytoHubba and Oncomine analysis.

CONCLUSION

In this study, we screened five hub genes that may help in clinical diagnosis and serve as the latent purpose of SS treatment.

RTP4 is a novel prognosis-related hub gene in cutaneous melanoma

OBJECTIVE

Melanoma accounts for 80% of skin cancer deaths. The pathogenesis of melanoma is regulated by gene networks. Thus, we aimed here to identify gene networks and hub genes associated with melanoma and to further identify their underlying mechanisms.

METHODS

GTEx (normal skin) and TCGA (melanoma tumor) RNA-seq datasets were employed for this purpose. We conducted weighted gene co-expression network analysis (WGCNA) to identify key modules and hub genes associated with melanoma. Log-rank analysis and multivariate Cox model analysis were performed to identify prognosis genes, which were validated using two independent melanoma datasets. We also evaluated the correlation between prognostic gene and immune cell infiltration.

RESULTS

The blue module was the most relevant for melanoma and was thus considered the key module. Intersecting genes were identified between this module and differentially expressed genes (DEGs). Finally, 72 genes were identified and verified as hub genes using the Oncomine database. Log-rank analysis and multivariate Cox model analysis identified 13 genes that were associated with the prognosis of the metastatic melanoma group, and RTP4 was validated as a prognostic gene using two independent melanoma datasets. RTP4 was not previously associated with melanoma. When we evaluated the correlation between prognostic gene and immune cell infiltration, we discovered that RTP4 was associated with immune cell infiltration. Further, RTP4 was significantly associated with genes encoding components of immune checkpoints (PDCD1, TIM-3, and LAG3).

CONCLUSIONS

RTP4 is a novel prognosis-related hub gene in cutaneous melanoma. The novel gene RTP4 identified here will facilitate the exploration of the molecular mechanism of the pathogenesis and progression of melanoma and the discovery of potential new target for drug therapy.

Gene expression of cytokinesis regulators PRC1, KIF14 and CIT has no prognostic role in colorectal and pancreatic cancer

Colorectal cancer is one of the most common cancers and pancreatic cancer is among the most fatal and difficult to treat. New prognostic biomarkers are urgently needed to improve the treatment of colorectal and pancreatic cancer. Protein regulating cytokinesis 1 (PRC1), kinesin family member 14 (KIF14) and citron Rho-interacting serine/threonine kinase (CIT) serve important roles in cytokinesis, are strongly associated with cancer progression and have prognostic potential. The present study aimed to investigate the prognostic relevance of the PRC1, KIF14 and CIT genes in colorectal and pancreatic cancer. PRC1, KIF14 and CIT transcript expression was assessed by reverse transcription-quantitative PCR in tumors and paired distant unaffected mucosa from 67 patients with colorectal cancer and tumors and paired non-neoplastic control tissues from 48 patients with pancreatic cancer. The extent of transcript dysregulation between tumor and control tissues and between groups of patients divided by main clinical characteristics, namely patients' age and sex, disease stage, localization and grade, was determined. Finally, the associations of transcript levels in tumors with disease-free interval and overall survival time were evaluated. PRC1, KIF14 and CIT transcripts were upregulated in tumors compared with control tissues. PRC1, KIF14 and CIT levels strongly correlated to each other in both colorectal and pancreatic tumor and control tissues after correction for multiple testing. However, no significant associations were found among the transcript levels of PRC1, KIF14 and CIT and disease-free interval or overall survival time. In summary, the present study demonstrated mutual correlation of PRC1, KIF14 and CIT cytokinesis regulators with no clear prognostic value in pancreatic and colorectal cancers. Hence, according to the results of the present study, transcript levels of these genes cannot be clinically exploited as prognostic biomarkers in colorectal or pancreatic cancer patients.

Construction of a prognostic model with histone modification-related genes and identification of potential drugs in pancreatic cancer

Background

Pancreatic cancer (PC) is a highly fatal and aggressive disease with its incidence and mortality quite discouraging. An effective prediction model is urgently needed for the accurate assessment of patients’ prognosis to assist clinical decision-making.

Methods

Gene expression data and clinicopathological data of the samples were acquired from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases. Differential expressed genes (DEGs) analysis, univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, random forest screening and multivariate Cox regression analysis were applied to construct the risk signature. The effectiveness and independence of the model were validated by time-dependent receiver operating characteristic (ROC) curve, Kaplan–Meier (KM) survival analysis and survival point graph in training set, test set, TCGA entire set and GSE57495 set. The validity of the core gene was verified by immunohistochemistry and our own independent cohort. Meanwhile, functional enrichment analysis of DEGs between the high and low risk groups revealed the potential biological pathways. Finally, CMap database and drug sensitivity assay were utilized to identify potential small molecular drugs as the risk model-related treatments for PC patients.

Results

Four histone modification-related genes were identified to establish the risk signature, including CBX8, CENPT, DPY30 and PADI1. The predictive performance of risk signature was validated in training set, test set, TCGA entire set and GSE57495 set, with the areas under ROC curve (AUCs) for 3-year survival were 0.773, 0.729, 0.775 and 0.770 respectively. Furthermore, KM survival analysis, univariate and multivariate Cox regression analysis proved it as an independent prognostic factor. Mechanically, functional enrichment analysis showed that the poor prognosis of high-risk population was related to the metabolic disorders caused by inadequate insulin secretion, which was fueled by neuroendocrine aberration. Lastly, a cluster of small molecule drugs were identified with significant potentiality in treating PC patients.

Conclusions

Based on a histone modification-related gene signature, our model can serve as a reliable prognosis assessment tool and help to optimize the treatment for PC patients. Meanwhile, a cluster of small molecule drugs were also identified with significant potentiality in treating PC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01928-6.