Coexpression network analysis identified Krüppel-like factor 6 (KLF6) association with chemosensitivity in ovarian cancer

Overexpression of LILRA2 indicated poor prognosis of ovarian carcinoma: A new potential biomarker and therapeutic target

OBJECTIVE

This study aimed to assess the role of leukocyte immunoglobulin-like receptor A2 (LILRA2) in ovarian carcinoma (OC) oncogenesis and prognosis.

MATERIALS AND METHODS

Using the Cancer Genome Atlas, Genotype-Tissue Expression, and Gene Expression Omnibus databases, the association between clinicopathological profiles and LILRA2 expression was investigated using logistic regression analysis. Kaplan-Meier analysis, Cox regression analysis, and column plots predicted the clinical outcomes of patients with OC and determine the predictive value of LILRA2. The biological functions of LILRA2 were assessed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. We used single-sample Gene Set Enrichment Analysis to investigate the relationship between immune cell infiltration and LILRA2 expression.

RESULTS

LILRA2 expression in OC tumors was significantly higher than in normal tissue (P < 0.05). The high LILRA2 expression in OC was correlated with lymphatic invasion (P = 0.014). The results showed consistency indices of 0.611 [95% confidence interval (CI), 0.572-0.649] and 0.623 (95% CI, 0.584-0.663) for the overall and disease-specific survival nomograms, respectively. Cox regression analysis showed that LILRA2 was an independent risk factor for overall survival (hazard ratio [HR], 1.511; P = 0.002) and disease-specific survival (HR, 1.537; P = 0.003). Functional annotation revealed enrichment with immunoglobulin-corresponding pathways when LILRA2 expression was high.

CONCLUSION

By evaluating gene expression profiles, we demonstrated that LILRA2 has considerable potential to act as a therapeutic target and prognostic biomarker in OC.

Novel MicroRNA-Regulated Transcript Networks Are Associated with Chemotherapy Response in Ovarian Cancer

High-grade serous ovarian cancer (HGSOC) is a highly lethal gynecologic cancer, in part due to resistance to platinum-based chemotherapy reported among 20% of patients. This study aims to generate novel hypotheses of the biological mechanisms underlying chemotherapy resistance, which remain poorly understood. Differential expression analyses of mRNA- and microRNA-sequencing data from HGSOC patients of The Cancer Genome Atlas identified 21 microRNAs associated with angiogenesis and 196 mRNAs enriched for adaptive immunity and translation. Coexpression network analysis identified three microRNA networks associated with chemotherapy response enriched for lipoprotein transport and oncogenic pathways, as well as two mRNA networks enriched for ubiquitination and lipid metabolism. These network modules were replicated in two independent ovarian cancer cohorts. Moreover, integrative analyses of the mRNA/microRNA sequencing and single-nucleotide polymorphisms (SNPs) revealed potential regulation of significant mRNA transcripts by microRNAs and SNPs (expression quantitative trait loci). Thus, we report novel transcriptional networks and biological pathways associated with resistance to platinum-based chemotherapy in HGSOC patients. These results expand our understanding of the effector networks and regulators of chemotherapy response, which will help to improve the management of ovarian cancer.

LncRNA SNHG20 promotes migration and invasion of ovarian cancer via modulating the microRNA-148a/ROCK1 axis

BACKGROUND

Ovarian cancer (OC) is characterized by early metastasis and poor prognosis, which threatens the health of women worldwide. Small nucleolar RNA host gene 20 (SNHG20), a long noncoding RNA (lncRNA), has been verified to be significantly up-regulated in several tumors, including OC. MicroRNA-148a (miR-148a)/rho-kinase1 (ROCK1) axis plays an important role in the modulation of tumor development. However, whether SNHG20 can regulate OC progression through miR-148a/ROCK1 axis remains unclear. Normal human ovarian epithelial cell line and four OC cell lines were adopted for in vitro experiments. Real-time PCR was performed to assess the levels of SNHG20 and miR-148a. OC cell proliferation, apoptosis, invasion and migration were detected using clone formation, flow cytometry, transwell, and wound healing assays, respectively. Tumor xenograft assay was applied to evaluate the effect of SNHG20 on tumor growth in vivo.

RESULTS

Significant higher expression of SNHG20 was observed in OC cell lines. SNHG20 markedly promoted the invasion, migration, proliferation and inhibited the apoptosis of OC cells. SNHG20 enhanced ROCK1 expression by sponging miR-148a, and the direct binding between SNHG20/ROCK1 and miR-148a was identified.

CONCLUSION

SNHG20 promoted invasion and migration of OC via targeting miR-148a/ROCK1 axis. The present research may provide a novel insight for the therapeutic strategies of OC.

CSMD3 is Associated with Tumor Mutation Burden and Immune Infiltration in Ovarian Cancer Patients

Background

Globally, ovarian cancer (OC), the deadliest gynecologic malignancy, remains a major cause of mortality, with a rising number of cases in many low- and middle-income countries. Immunotherapy has been proven to be promising for OC. There is increasing awareness of the vital role that tumor mutation burden (TMB) plays in predicting the efficacy of immunotherapy. Women with a family history of OC are at higher risk of the disease due to gene mutations. However, whether these gene mutations are related to immune response and TMB remains to be explored.

Methods

Our present work analyzed genetic mutation data of OC patients obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) cohorts, and we identified 11 frequently mutated genes, namely, APOB, CSMD3, DST, FAT3, FLG, HMCN1, MUC16, RYR1, TP53, TTN, and USH2A, in accordance with the overlap of two databases.

Results

A statistically higher TMB was detected by whole-exome sequencing in patients with OC with CSMD3 mutation than in those with mutations in the other frequently mutated genes. Prognosis analysis performed with patients from the TCGA cohort revealed that those with CSMD3 mutation had an overall survival (OS) that was inferior to that of those with wild-type CSMD3. Gene set enrichment analysis (GSEA) and CIBERSORT analysis indicated that OC samples with CSMD3 mutations had significant involvement of pathways related to the immune response.

Conclusion

In summary, we found that CSMD3 mutation is highly correlated with increased TMB and poor clinical prognosis and that it might function as a biomarker for predicting prognosis and choosing an immunotherapy regimen.

Significance of N6-Methyladenosine RNA Methylation Regulators in Immune Infiltrates of Ovarian Cancer

N6-methyladenosine (m6A) RNA methylation regulators play an important role in the occurrence and development of tumors. Here, we aimed to identify the potential roles of m6A RNA methylation regulators in immune infiltrates of ovarian cancer. We obtained two distinct m6A patterns (m6Acluster.A and m6Acluster.B) based on the expression levels of all 21 m6A RNA methylation regulators from The Cancer Genome Atlas (TCGA) database using a consensus clustering algorithm. Differential analysis of m6Acluster.A and m6Acluster.B identified 196 m6A-related genes. We further validated the m6A regulation mechanism based on the 196 m6A-related genes using another consensus clustering algorithm. Considering individual differences, principal component analysis algorithms were used to calculate an m6A score for each sample in order to quantify the m6A patterns. A low m6A score was associated with immune activation and enhanced response to immune checkpoint inhibitors, whereas a high m6A score was associated with tumor progression. Finally, we successfully verified the correlation between m6A regulators and immune microenvironment in OC using our microarray analysis data. In summary, m6A regulators play non-negligible roles in immune infiltrates of ovarian cancer. Our investigation of m6A patterns may help to guide future immunotherapy strategies for advanced ovarian cancer.

Identification of Hub Genes and Small Molecule Drugs Associated with Acquired Resistance to Gefitinib in Non-Small Cell Lung Cancer

Targeting EGFR, epidermal growth factor receptor tyrosine kinase inhibitors (EGFR TKIs), brings lights to the treatment of non-small cell lung cancer (NSCLC). Although T790M mutation responded as one of the main reasons of acquired resistance, still 15% of the resistance patients can't be explained by the known mechanisms. The purpose of this research was to identify some new mechanisms of gefitinib acquired resistance, and to predict small molecules drugs which may reverse drug resistance by integrated bioinformatics analysis. The GSE34228 data package containing the microarray data of acquired gefitinib-resistant cell line (PC9GR) and gefitinib-sensitive cell line (PC9) from the GEO database were downloaded, and gene co-expression networks by weighted gene co-expression network analysis (WGCNA) were constructed to identified key modules and key genes related to gefitinib resistance. Furthermore, the significantly differentially expressed genes (DEGs) between the two cell types were screened out, and a protein-protein interaction (PPI) network to obtain the key genes of DEGs was accordingly constructed. Through the above two methods, 4 hub genes, PI3, S100A8, AXL and PNPLA4 were mined as the most relevant to gefitinib resistance. Among them, PI3, S100A8 were down-regulated in PC9GR cell samples, while AXL, PNPLA4 were up-regulated. The gene set enrichment analysis (GSEA) for single gene showed that the four hub genes were mainly correlated with cell proliferation and cycle. Besides, small molecule drugs with the potential to overcome resistance, such as Emetine and cephaeline, were screened by CMap database. Consistent with this, in vitro experiments results have shown that emetine and cephaeline can increase the sensitivity of drug-resistant cells to gefitinib, and the mechanism may be related to the regulation of PI3 and S100A8. In conclusion, 4 hub genes were found to be related to the occurrence of gefitinib resistance in non-small cell lung cancer, and several small molecule drugs were screened out as potential therapeutic agents to overcome gefitinib resistance, which may lead a new way for the treatment of NSCLC of acquired resistance to gefitinib.

Integrated bioinformatic analysis identifies UBE2Q1 as a potential prognostic marker for high grade serous ovarian cancer

Background

High grade serous ovarian cancer (HGSOC) accounts for nearly 60% of total cases of epithelial ovarian cancer. It is the most aggressive subtype, which shows poor prognosis and low patient survival. For better management of HGSOC patients, new prognostic biomarkers are required to facilitate improved treatment strategies and ensure suitable healthcare decisions.

Methods

We performed genome wide expression analysis of HGSOC patient samples to identify differentially expressed genes (DEGs) using R based Limma package, Clust and other statistical tools. The identified DEGs were subjected to weighted gene co-expression network analysis (WGCNA) to identify co-expression patterns of relevant genes. Module trait and gene ontology analyses were performed to establish important gene co-expression networks and their biological functions. Overlapping the most relevant DEG cluster 4 with prominent WGCNA cyan module identified strongest correlation of UBE2Q1 with ovarian cancer and its prognostic significance on survival probability of ovarian cancer patients was investigated. The predictive value of UBE2Q1 as a potential biomarker was analysed by correlating its expression with 12-months relapse free survival of patients in response to platin/taxane, the standard first-line chemotherapy for ovarian cancer, and analysing area under the ROC curve.

Results

An integrated gene expression analysis and WGCNA, identified UBE2Q1 as a potential prognostic marker associated with poor relapse-free survival and response outcome to platin/taxane treatment of patients with high grade serous ovarian cancer.

Conclusions

Our study identifies a potential UBE2Q1 – B4GALT3 functional axis in ovarian cancer, where only the E2 conjugating enzyme showed a poor prognostic impact on the disease.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-07928-z.

Four genes relevant to pathological grade and prognosis in ovarian cancer

BACKGROUND: Ovarian cancer is the common tumor in female, the prognostic of which is influenced by a series of factors. In this study, 4 genes relevant to pathological grade in ovarian cancer were screened out by the construction of weighted gene co-expression network analysis. METHODS: GSE9891 with 298 ovarian cancer cases had been used to construct co-expression networks. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses was used to analyze the possible mechanism of genes involved in the malignant process of ovarian cancer. Hub genes were validated in other independent datasets, such as GSE63885, GSE26193 and GSE30161. Survival analysis based on the hub genes was performed by website of Kaplan Meier-plotter. RESULTS: The result based on weighted gene co-expression network analysis indicated that turquoise module has the highest association with pathological grade. Gene Ontology enrichment analysis revealed that the genes in turquoise module main enrichment in inflammatory response and immune response. Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed that the genes in turquoise module main enrichment in cytokine-cytokine receptor interaction and chemokine signaling pathway. In turquoise module, a total of 4 hub genes (MS4A4A, CD163, CPR65, MS4A6A) were identified. Then, 4 hub genes were effectively verified in the test datasets (GSE63885, GSE26193 and GSE30161) and tissue samples from Shengjing Hospital of China Medical University. Survival analysis indicated that the 4 hub genes were associated with poor progression-free survival of ovarian cancer. CONCLUSIONS: In conclusion, 4 hub genes (MS4A4A, CD163, CPR65, MS4A6A) were verified associated with pathological grade of ovarian cancer. Moreover, MS4A4A, CD163, MS4A6A may serve as a surface marker for M2 macrophages. Targeting the 4 hub genes may can improve the prognosis of ovarian cancer.

Significance of hub genes and immune cell infiltration identified by bioinformatics analysis in pelvic organ prolapse

Objective

Pelvic organ prolapse (POP) refers to the decline of pelvic organ position and dysfunction caused by weak pelvic floor support. The aim of the present study was to screen the hub genes and immune cell infiltration related to POP disease.

Methods

Microarray data of 34 POP tissues in the GSE12852 gene expression dataset were used as research objects. Weighted gene co-expression network analysis (WGCNA) was performed to elucidate the hub module and hub genes related to POP occurrence. Gene function annotation was performed using the DAVID tool. Differential analysis based on the GSE12852 dataset was carried out to explore the expression of the selected hub genes in POP and non-POP tissues, and RT-qPCR was used to validate the results. The differential immune cell infiltration between POP and non-POP tissues was investigated using the CIBERSORT algorithm.

Results

WGCNA revealed the module that possessed the highest correlation with POP occurrence. Functional annotation indicated that the genes in this module were mainly involved in immunity. ZNF331, THBS1, IFRD1, FLJ20533, CXCR4, GEM, SOD2, and SAT were identified as the hub genes. Differential analysis and RT-qPCR demonstrated that the selected hub genes were overexpressed in POP tissues as compared with non-POP tissues. The CIBERSORT algorithm was employed to evaluate the infiltration of 22 immune cell types in POP tissues and non-POP tissues. We found greater infiltration of activated mast cells and neutrophils in POP tissues than non-POP tissues, while the infiltration of resting mast cells was lower in POP tissues. Moreover, we investigated the relationship between the type of immune cell infiltration and hub genes by Pearson correlation analysis. The results indicate that activated mast cells and neutrophils had a positive correlation with the hub genes, while resting mast cells had a negative correlation with the hub genes.

Conclusions

Our research identified eight hub genes and the infiltration of three types of immune cells related to POP occurrence. These hub genes may participate in the pathogenesis of POP through the immune system, giving them a certain diagnostic and therapeutic value.

Significance of tumor mutation burden combined with immune infiltrates in the progression and prognosis of ovarian cancer

Background

Ovarian cancer (OC) is the most malignant tumor in the female reproductive system. About 75% of OC in complete remission of clinical symptoms still develop a recurrence. Therefore, searching for new treatment methods plays an important role in improving the prognosis of OC.

Methods

We downloaded the MAF files, RNA-seq data and clinical information from the TCGA database. The “maftools” package in R software was used to visualize the OC mutation data. We calculated the tumor mutation burden (TMB) of OC and analyzed its correlation with clinicopathological parameters and prognostic value. Tumor mutation burden related signature model was constructed to predict the overall survival (OS) of OC.

Results

The results revealed that there was a statistical correlation between TMB and FIGO stage, grade and tumor residual size of ovarian cancer patients. The Kaplan–Meier curve indicated that a high TMB is associated with better clinical outcomes of OC. The difference analysis indicated 24 upregulated genes and 619 downregulated genes in the high-TMB group compared with the low-TMB group. Besides, the TMBRS model based on five hub genes (RBMS3, PLA2G5, CDH2, AMHR2 and ADAMTS8) was constructed to predict the OS of OC. The ROC curve and validation data sets all revealed that the TMBRS model was reliable in predicting recurrence risk. Immune microenvironment analysis indicated the correlations between TMB and infiltrating immune cells.

Conclusions

Our results suggest that TMB plays an important role in the prognosis and guiding immunotherapy of OC. By detecting the TMB of OC, clinicians can more accurately treat patients with immunotherapy, thereby improving their survival rate.

Non-coding somatic mutations converge on the PAX8 pathway in ovarian cancer

The functional consequences of somatic non-coding mutations in ovarian cancer (OC) are unknown. To identify regulatory elements (RE) and genes perturbed by acquired non-coding variants, here we establish epigenomic and transcriptomic landscapes of primary OCs using H3K27ac ChIP-seq and RNA-seq, and then integrate these with whole genome sequencing data from 232 OCs. We identify 25 frequently mutated regulatory elements, including an enhancer at 6p22.1 which associates with differential expression of ZSCAN16 (P = 6.6 × 10-4) and ZSCAN12 (P = 0.02). CRISPR/Cas9 knockout of this enhancer induces downregulation of both genes. Globally, there is an enrichment of single nucleotide variants in active binding sites for TEAD4 (P = 6 × 10-11) and its binding partner PAX8 (P = 2×10-10), a known lineage-specific transcription factor in OC. In addition, the collection of cis REs associated with PAX8 comprise the most frequently mutated set of enhancers in OC (P = 0.003). These data indicate that non-coding somatic mutations disrupt the PAX8 transcriptional network during OC development.

Identification of Biomarkers Related to Systemic Sclerosis With or Without Pulmonary Hypertension Using Co-expression Analysis

Systemic sclerosis (SSc), also known as scleroderma, is an autoimmune disease with multiple system involvement, and pulmonary complications, including pulmonary hypertension (PH), are leading causes of death. This study aimed to develop early biomarkers to distinguish SSc with or without PH from normal population using bioinformatics approaches. The gene expression profile GSE22356, which contains 10 SSc samples with PH, 10 SSc samples without PH, and 10 normal samples, was obtained from the Gene Expression Omnibus database. First, we constructed co-expression networks and identified critical gene modules using the weighted gene co-expression network analysis. Then, functional enrichment analysis of significant modules was performed. Finally, the "real" hub gene was screened out by intramodule analysis and protein-protein interaction networks, and the receiver operating characteristic analysis was conducted. A total of 5046 genes were screened out to construct co-expression networks, and 18 modules were identified. Of these modules, the turquoise module had a strong correlation with SSc only, whereas the midnightblue module showed an obvious positive correlation with SSc with PH. Functional enrichment analysis indicated that the turquoise module was mainly enriched in transcription of DNA template and its regulation and protein ubiquitination and involved in apoptosis and pyrimidine metabolism pathway. The midnightblue module was significantly associated with inflammatory and immune response and pathways in Staphylococcus aureus infection and Chagas disease. The "real" hub genes in the turquoise module were WDR36, POLR1B, and SRSF1, and those in midnightblue were TLR2 and TNFAIP6.

Identification of Hub Genes in High-Grade Serous Ovarian Cancer Using Weighted Gene Co-Expression Network Analysis

Background

High-grade serous ovarian cancer (HGSOC) is the most malignant gynecologic tumor. This study reveals biomarkers related to HGSOC incidence and progression using the bioinformatics method.

Material/Methods

Five gene expression profiles were downloaded from GEO. Differentially-expressed genes (DEGs) in HGSOC and normal ovarian tissue samples were screened using limma and the function of DEGs was annotated by KEGG and GO analysis using clusterProfiler. A co-expression network utilizing the WGCNA package was established to define several hub genes from the key module. Furthermore, survival analysis was performed, followed by expression validation with datasets from TCGA and GTEx. Finally, we used single-gene GSEA to detect the function of prognostic hub genes.

Results

Out of the 1874 DEGs detected from 114 HGSOC versus 49 normal tissue samples, 956 were upregulated and 919 were downregulated. The functional annotation indicated that upregulated DEGs were mostly enriched in cell cycle, whereas the downregulated DEGs were enriched in the MAPK or Ras signaling pathway. Two modules significantly associated with HGSOC were excavated through WGCNA. After survival analysis and expression validation of hub genes, we found that 2 upregulated genes (MAD2L1 and PKD2) and 3 downregulated genes (DOCK5, FANCD2 and TBRG1) were positively correlated with HGSOC prognosis. GSEA for single-hub genes revealed that MAD2L1 and PKD2 were associated with proliferation, while DOCK5, FANCD2, and TBRG1 were associated with immune response.

Conclusions

We found that FANCD2, PKD2, TBRG1, and DOCK5 had prognostic value and could be used as potential biomarkers for HGSOC treatment.

Co-Expression Network Analysis Identified Genes Associated with Cancer Stem Cell Characteristics in Lung Squamous Cell Carcinoma

Objective: Cancer stem cells are self-renewal cells in tumors and can produce heterogeneous tumor cells, which play an important role in the development of lung squamous cell carcinoma (LSCC). In our research, we aimed to explore the expression of genes related to LSCC stem cells.Methods: We downloaded the RNAseq data, the pathological and prognostic profiles of LSCC cases from the public database TCGA. The mRNA expression-based stiffness index (mRNAsi) of LSCC was calculated and the prognostic value of mRNAsi was discussed. Then, we constructed a weighted gene co-expression network analysis (WGCNA) to screen key genes related to mRNAsi of LSCC.Results: MRNAsi is an independent prognostic factor in LSCC. We screened 5 key genes (BUB1, BIRC5, CCNB2, KIF15 and SPAG5) related to mRNAsi of LSCC based on WGCNA. The key genes were highly expressed in the tumor samples compared to the normal samples. In addition, there is a strong interaction between proteins of these key genes and a strong co-expression relationship at the transcriptional level.Conclusions: To conclude, mRNAsi play an important role in LSCC. Five key genes (BUB1, BIRC5, CCNB2, KIF15 and SPAG5) related to mRNAsi were screened, which may act as therapeutic targets for inhibiting the stem cell characteristics of LSCC.