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Abstract Background Necroptosis is a novel type of programmed cell death distinct from apoptosis. However, the role of necroptosis in ovarian cancer (OC) remains unclear. The present study investigated the prognostic value of necroptosis-related genes (NRGs) and the immune landscape in OC. Methods The gene expression profiling and clinical information were downloaded from the TCGA and GTEx databases. Differentially expressed NRGs (DE-NRGs) between OC and normal tissueswere identified. The regression analyses were conducted to screen the prognostic NRGs and construct the predictive risk model. Patients were then divided into high- and low-risk groups, and the GO and KEGG analyses were performed to explore bioinformatics functions between the two groups. Subsequently, the risk level and immune status correlations were assessed through the ESTIMATE and CIBERSORT algorithms. The tumor mutation burden (TMB) and the drug sensitivity were also analyzed based on the two-NRG signature in OC. Results Totally 42 DE-NRGs were identified in OC. The regression analyses screened out two NRGs (MAPK10 and STAT4) with prognostic values for overall survival. The ROC curve showed a better predictive ability in five-year OS using the risk score. Immune-related functions were significantly enriched in the high- and low-risk group. Macrophages M1, T cells CD4 memory activated, T cells CD8, and T cells regulatory infiltration immune cells were associated with the low-risk score. The lower tumor microenvironment score was demonstrated in the high-risk group. Patients with lower TMB in the low-risk group showed a better prognosis, and a lower TIDE score suggested a better immune checkpoint inhibitor response in the high-risk group. Besides, cisplatin and paclitaxel were found to be more sensitive in the low-risk group. Conclusions MAPK10 and STAT4 can be important prognosis factors in OC, and the two-gene signature performs well in predicting survival outcomes. Our study provided novel ways of OC prognosis estimation and potential treatment strategy.

Abstract Tumor microenvironment and chemokines play a significant role in cancer chemoresistance. This study was designed to reveal the important role of CXCL2 in platinum resistance in epithelial ovarian cancer (EOC). Differently expressed (DE) genes were screen out based on analysis of GSE114206 dataset in GEO database. The expression of DE chemokines was further validated in platinum- resistant and sensitive EOC. Cell viability assay and cell apoptosis assay were performed to explore the roles of CXCL2 in EOC. Cell stemness characteristics and the signaling pathway regulated by CXCL2 were also investigated in this study. As the results showed, CXCL2 was identified up-regulated in platinum-resistant EOC. The functional assays showed overexpressing CXCL2 or co-culturing with recombinant human CXCL2 promoted cell resistance to cisplatin. Conversely, knocking down CXCL2 or co-culturing with neutralizing antibody to CXCL2 increased cell response to cisplatin. CXCL2 overexpressing maintained cell stemness and activated ATR/CHK1 signaling pathway in EOC. Moreover, we further demonstrated that CXCL2-mediated resistance to cisplatin could be saved by SB225002, the inhibitor of CXCL2 receptor, as well as be rescued by SAR-020106, the inhibitor of ATR/CHK1 signaling pathway. This study identified a CXCL2-mediated mechanism in EOC platinum resistance. Our findings provided a novel target for chemoresistance prevention in EOC.

Abstract Background Chemotherapy resistance remains a barrier to improving the prognosis of epithelial ovarian cancer (EOC). ALKBH5 has recently been shown to be one of the RNA N6-methyladenosine (m6A) demethyltransferases associated with various cancers, but its role in cancer therapeutic resistance remains unclear. This study aimed to investigate the role of AlkB homolog 5 (ALKBH5) in cisplatin-resistant EOC. Methods Functional assays were performed both in vitro and in vivo. RNA sequencing (RNA-seq), m6A-modified RNA immunoprecipitation sequencing (MeRIP-seq), chromatin immunoprecipitation, RNA immunoprecipitation, and luciferase reporter and actinomycin-D assays were performed to investigate RNA/RNA interaction and m6A modification of the ALKBH5-HOXA10 loop. Results ALKBH5 was upregulated in cisplatin-resistant EOC and promoted cancer cell cisplatin resistance both in vivo and in vitro. Notably, HOXA10 formed a loop with ALKBH5 and was found to be the upstream transcription factor of ALKBH5. HOXA10 overexpression also facilitated EOC cell chemoresistance both in vivo and in vitro. Collective results of MeRIP-seq and RNA-seq showed that JAK2 is the m6A-modified gene targeted by ALKBH5. The JAK2/STAT3 signaling pathway was activated by overexpression of the ALKBH5-HOXA10 loop, resulting in EOC chemoresistance. Cell sensitivity to cisplatin was rescued by ALKBH5 and HOXA10 knockdown or inhibition of the JAK2/STAT3 signaling pathway in EOC cells overexpressing ALKBH5-HOXA10. Conclusions The ALKBH5-HOXA10 loop jointly activates the JAK2/STAT3 signaling pathway by mediating JAK2 m6A demethylation, promoting EOC resistance to cisplatin. Thus, inhibition of the expression of the ALKBH5-HOXA10 loop may be a potential strategy to overcome cisplatin resistance in EOC.

Abstract Background Endometrial cancer is the fourth most common cancer in women. The death rate for endometrial cancer has increased. Glycolysis of cellular respiration is a complex reaction and is the first step in most carbohydrate catabolism, which was proved to participate in tumors. Methods We analyzed the sample data of over 500 patients from TCGA database. The bioinformatic analysis included GSEA, cox and lasso regression analysis to select prognostic genes, as well as construction of a prognostic model and a nomogram for OS evaluation. The immunohistochemistry staining, survival analysis and expression level validation were also performed. Maftools package was for mutation analysis. GSEA identified Glycolysis was the most related pathway to EC. qRT-PCR verified the expression level of hub gene in clinical samples. Results According to the prognostic model using the train set, 9 glycolysis-related genes including B3GALT6, PAM, LCT, GMPPB, GLCE, DCN, CAPN5, GYS2 and FBP2 were identified as prognosis-related genes. Based on nine gene signature, the EC patients could be classified into high and low risk subgroups, and patients with high risk score showed shorter survival time. Time-dependent ROC analysis and Cox regression suggested that the risk score predicted EC prognosis accurately and independently. Analysis of test and train sets yielded consistent results A nomogram which incorporated the 9-mRNA signature and clinical features was also built for prognostic prediction. Immunohistochemistry staining and TCGA validation showed that expression levels of these genes do differ between EC and normal tissue samples. GSEA revealed that the samples of the low-risk group were mainly concentrated on Bile Acid Metabolism. Patients in the low-risk group displayed obvious mutation signatures compared with those in the high-risk group. The expression levels of B3GALT6, DCN, FBP2 and GYS2 are lower in tumor samples and higher in normal tissue samples. The expression of CAPN5 and LCT in clinical sample tissues is just the opposite. Conclusion This study found that the Glycolysis pathway is associated with EC and screened for hub genes on the Glycolysis pathway, which may serve as new target for the treatment of EC.

Abstract Background Endometrial cancer (EC) is one kind of women cancers. Bioinformatic technology could screen out relative genes which made targeted therapy becoming conventionalized. Methods GSE17025 were downloaded from GEO. The genomic data and clinical data were obtained from TCGA. R software and bioconductor packages were used to identify the DEGs. Clusterprofiler was used for functional analysis. STRING was used to assess PPI information and plug-in MCODE to screen hub modules in Cytoscape. The selected genes were coped with functional analysis. CMap could find EC-related drugs that might have potential effect. Univariate and multivariate Cox proportional hazards regression analyses were performed to predict the risk of each patient. Kaplan–Meier curve analysis could compare the survival time. ROC curve analysis was performed to predict value of the genes. Mutation and survival analysis in TCGA database and UALCAN validation were completed. Immunohistochemistry staining from Human Protein Atlas database. GSEA, ROC curve analysis, Oncomine and qRT-PCR were also performed. Results Functional analysis showed that the upregulated DEGs were strikingly enriched in chemokine activity, and the down-regulated DEGs in glycosaminoglycan binding. PPI network suggested that NCAPG was the most relevant protein. CMap identified 10 small molecules as possible drugs to treat EC. Cox analysis showed that BCHE, MAL and ASPM were correlated with EC prognosis. TCGA dataset analysis showed significantly mutated BHCE positively related to EC prognosis. MAL and ASPM were further validated in UALCAN. All the results demonstrated that the two genes might promote EC progression. The profile of ASPM was confirmed by the results from immunohistochemistry. ROC curve demonstrated that the mRNA levels of two genes exhibited difference between normal and tumor tissues, indicating their diagnostic efficiency. qRT-PCR results supported the above results. Oncomine results showed that DNA copy number variation of MAL was significantly higher in different EC subtypes than in healthy tissues. GSEA suggested that the two genes played crucial roles in cell cycle. Conclusion BCHE, MAL and ASPM are tumor-related genes and can be used as potential biomarkers in EC treatment.

Uterine Corpus Endometrial Carcinoma (UCEC) is the most common gynecological cancer. Here, we have investigated the significance of immune-related genes in predicting the prognosis and response of UCEC patients to immunotherapy and chemotherapy. Based on the Cancer Genome Atlas (TCGA) database, the single-sample gene-set enrichment analysis (ssGSEA) scores was utilized to obtain enrichment of 29 immune signatures. Univariate, multivariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to generate an immune-related prognostic signature (IRPS). The biological functions of IRPS-associated genes were evaluated using GSEA, Tumor Immune Estimation Resource (TIMER) Database analysis, Mutation analysis, Immunophenoscore (IPS) analysis, Gene Expression Profiling Interactive Analysis (GEPIA), Genomics of Drug Sensitivity in Cancer (GDSC) and Immune Cell Abundance Identifier (ImmuCellAI). Potential small molecule drugs for UCEC were predicted using the connectivity map (Cmap). The mRNA and protein expression levels of IRPS-associated genes were tested via quantitative real-time PCR (qPCR) and immunohistology. Two immune-related genes (CCL13 and KLRC1) were identified to construct the IRPS. Both genes were related to the prognosis of UCEC patients (P < 0.05). The IRPS could distinguish patients with different prognosis and was closely associated with the infiltration of several types of immune cells. Our findings showed that patients with low IRPS benefited more from immunotherapy and developed stronger response to several chemotherapies, which was also confirmed by the results of ImmuCellAI. Finally, we identified three small molecular drugs that might improve the prognosis of patients with high IRPS. IRPS can be utilized to predict the prognosis of UCEC patients and provide valuable information about their therapeutic response to immunotherapy and chemotherapy.