Prognostic signature of ovarian cancer based on 14 tumor microenvironment-related genes

MUL1 identified as mitochondria-linked biomarker promoting cisplatin resistance in OC cells

Ovarian cancer (OC) ranks among the prevalent tumors affecting the female reproductive system. The aim of this study was to evaluate mitochondria-associated platinum resistance genes using organoid models. Univariate Cox regression, LASSO and multivariate Cox regression analyses were performed on The Cancer Genome Atlas (TCGA) database to construct 2-gene prognostic signature (MUL1 and SSBP1), and GSE26712 dataset was used for external validation. In addition, the relationship between MUL1 and platinum resistance was examined by organoid culture, lentiviral transduction, CCK8 assay, and Western blot. The results showed that patients in the high-risk group exhibited significantly worse OS (P = 0.002, P = 0.017). Drug sensitivity analysis revealed that platinum resistance increased with the upregulation of MUL1 expression (Cor = 0.5154, P = 0.02). Our experimental findings demonstrated that knockout of the MUL1 gene significantly increased apoptosis and enhanced the sensitivity of the OC cell line A2780 to cisplatin. Through this study, we have provided strong evidence for further research on prognostic risk factors and individualized treatment in OC patients, and provided new insights into addressing platinum resistance in OC.

Analysis of prognostic value of lactate metabolism-related genes in ovarian cancer based on bioinformatics

BACKGROUND

Recent studies have provided evidence supporting the functional role and mechanism of lactate in suppressing anticancer immunity. However, there is no systematic analysis of lactate metabolism-related genes (LMRGs) and ovarian cancer (OV) prognosis.

RESULTS

Six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) were selected as prognostic genes and a prognostic model was utilized. Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) analyses were further performed and indicated that the prognostic model was effective. Subsequently, the neoplasm_cancer_status and RiskScore were determined as independent prognostic factors, and a nomogram was established with relatively accurate forecasting ability. Additionally, 2 types of immune cells (Central memory CD8 T cell and Immature B cell), 4 types of immune functions (APC co inhibition, DCs, Tfh and Th1 cells), 9 immune checkpoints (BTLA, CTLA4, IDO1, LAG3, VTCN1, CXCL10, CXCL9, IFNG, CD27) and tumor immune dysfunction and exclusion (TIDE) scores were significantly different between risk groups. The expression of 6 genes were verified by quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) and the expression of 6 genes were higher in the high-grade serous carcinoma (HGSC) samples.

CONCLUSION

A prognostic model related to lactate metabolism was established for OV based on six genes (CCL18, CCND1, MXRA5, NRBP2, OLFML2B and THY1) that could provide new insights into therapy.

Immunotherapy for Ovarian Cancer: Disappointing or Promising?

Ovarian cancer, one of the deadliest malignancies, lacks effective treatment, despite advancements in surgical techniques and chemotherapy. Thus, new therapeutic approaches are imperative to improving treatment outcomes. Immunotherapy, which has demonstrated considerable success in managing various cancers, has already found its place in clinical practice. This review aims to provide an overview of ovarian tumor immunotherapy, including its basics, key strategies, and clinical research data supporting its potential. In particular, this discussion highlights promising strategies such as checkpoint inhibitors, vaccines, and pericyte transfer, both individually and in combination. However, the advancement of new immunotherapies necessitates large controlled randomized trials, which will undoubtedly shape the future of ovarian cancer treatment.

Construction of a prognostic signature for serous ovarian cancer based on lactate metabolism-related genes

Background

The key biochemical feature of malignant tumor is the conversion of energy metabolism from oxidative phosphorylation to glycolysis, which provides sufficient capacity and raw materials for tumor cell rapid growth. Our study aims to construct a prognostic signature for ovarian cancer based on lactate metabolism-related genes (LMRGs).

Methods

Data of ovarian cancer and non-diseased ovarian data were downloaded from TCGA and the GTEx database, respectively. LMRGs were obtained from GeneCards and MSigDB databases, and the differentially expressed LMRGs were identified using limma and DESeq2 R packages. Cox regression analysis and LASSO were performed to determine the LMRGs associated with OS and develop the prognostic signature. Then, clinical significance of the prognostic signature in ovarian cancer was assessed.

Results

A total of 485 differentially expressed LMRGs in ovarian tissue were selected for subsequent analysis, of which 324 were up-regulated and 161 were down regulated. We found that 22 LMRGs were most significantly associated with OS by using the univariate regression analysis. The prognostic scoring model was consisted of 12 LMRGs (SLCO1B3, ERBB4, SLC28A1, PDSS1, BDH1, AIFM1, TSFM, PPARGC1A, HGF, FGFR1, ABCC8, TH). Kaplan-Meier survival analysis indicated that poorer overall survival (OS) in the high-risk group patients (P<0.0001). This prognostic signature could be an independent prognostic indicator after adjusting to other clinical factors. The calibration curves of nomogram for the signature at 1, 2, and 3 years and the ROC curve demonstrated good agreement between the predicted and observed survival rates of ovarian cancer patients. Furthermore, the high-risk group patients have much lower expression level of immune checkpoint-TDO2 compared with the low-risk group (P=0.024).

Conclusions

We established a prognostic signature based on LMRGs for ovarian cancer, and highlighted emerging evidence indicating that this prognostic signature is a promising approach for predicting ovarian cancer prognosis and guiding clinical therapy.