Aberrant MAPK Signaling Offers Therapeutic Potential for Treatment of Ovarian Carcinoma

Novel protein-based prognostic signature linked to immunotherapeutic efficiency in ovarian cancer

BACKGROUND

Personalized medicine remains an unmet need in ovarian cancer due to its heterogeneous nature and complex immune microenvironments, which has gained increasing attention in the era of immunotherapy. A key obstacle is the lack of reliable biomarkers to identify patients who would benefit significantly from the therapy. While conventional clinicopathological factors have exhibited limited efficacy as prognostic indicators in ovarian cancer, multi-omics profiling presents a promising avenue for comprehending the interplay between the tumor and immune components. Here we aimed to leverage the individual proteomic and transcriptomic profiles of ovarian cancer patients to develop an effective protein-based signature capable of prognostication and distinguishing responses to immunotherapy.

METHODS

The workflow was demonstrated based on the Reverse Phase Protein Array (RPPA) and RNA-sequencing profiles of ovarian cancer patients from The Cancer Genome Atlas (TCGA). The algorithm began by clustering patients using immune-related gene sets, which allowed us to identify immune-related proteins of interest. Next, a multi-stage process involving LASSO and Cox regression was employed to distill a prognostic signature encompassing five immune-related proteins. Based on the signature, we subsequently calculated the risk score for each patient and evaluated its prognostic performance by comparing this model with conventional clinicopathological characteristics.

RESULTS

We developed and validated a protein-based prognostic signature in a cohort of 377 ovarian cancer patients. The risk signature outperformed conventional clinicopathological factors, such as age, grade, stage, microsatellite instability (MSI), and homologous recombination deficiency (HRD) status, in terms of prognoses. Patients in the high-risk group had significantly unfavorable overall survival (p < 0.001). Moreover, our signature effectively stratified patients into subgroups with distinct immune landscapes. The high-risk group exhibited higher levels of CD8 T-cell infiltration and a potentially greater proportion of immunotherapy responders. The co-activation of the TGF-β pathway and cancer-associated fibroblasts could impair the ability of cytotoxic T cells to eliminate cancer cells, leading to poor outcomes in the high-risk group.

CONCLUSIONS

The protein-based signature not only aids in evaluating the prognosis but also provides valuable insights into the tumor immune microenvironments in ovarian cancer. Together our findings highlight the importance of a thorough understanding of the immunosuppressive tumor microenvironment in ovarian cancer to guide the development of more effective immunotherapies.

Highly expressed of BID indicates poor prognosis and mediates different tumor microenvironment characteristics in clear cell renal cell carcinoma

BACKGROUND

Studies have found that BH3 interacting domain death agonist (BID) is closely related to the occurrence and development of many kinds of tumors. However, little attention has been paid to the situation of BID in clear cell renal cell carcinoma (ccRCC). So, our aim was to explore the effect of BID in ccRCC.

METHODS

Survival analysis, ROC curve, correlation analysis and Cox regression analysis were executed to analyze the prognostic value and clinical correlation of BID in ccRCC. The risk prognosis model was constructed in the training cohort and further validated in the internal testing cohort, ICGC cohort, and GEO cohort. Transcriptome sequencing and immunohistochemical staining of clinical specimens were used to validate the results of bioinformatics analysis. The GSEA, ESTIMATE algorithm, CIBERSORT algorithm, ssGSEA, TIDE score, correlation and difference analysis were used to analyze the effects of BID on immune infiltration in tumor microenvironment (TME).

RESULTS

BID was highly expressed in ccRCC tissues, which was verified by transcriptome sequencing and immunohistochemical staining of clinical specimens. Patients with high expression of BID had a worse prognosis. BID is an independent prognostic factor for ccRCC. The prognostic model based on BID can accurately predict the prognosis of patients in different cohorts. In addition, the expression levels of BID was closely related to immunomodulatory molecules such as PD-1, LAG3, and CTLA4. Enrichment analysis indicated that BID was significantly enriched in immune-related responses and cancer-related pathways. The change of BID expression mediates different characteristics of immune infiltration in TME.

CONCLUSIONS

BID is highly expressed in ccRCC, which is a reliable biomarker of ccRCC prognosis. It is closely related to TME, and may be a potential target for immunotherapy in patients with ccRCC.

Construction and validation of molecular subtype and signature of immune cell-related telomeric genes and prediction of prognosis and immunotherapy efficacy in ovarian cancer patients

BACKGROUND

Ovarian cancer (OVC) has emerged as a fatal gynecological malignancy as a result of a lack of reliable methods for early detection, limited biomarkers and few treatment options. Immune cell-related telomeric genes (ICRTGs) show promise as potential biomarkers.

METHODS

ICRTGs were discovered using weighted gene co-expression network analysis (WGCNA). ICRTGs were screened for significant prognosis using one-way Cox regression analysis. Subsequently, molecular subtypes of prognosis-relevant ICRTGs were constructed and validated for OVC, and the immune microenvironment's landscape across subtypes was compared. OVC prognostic models were built and validated using prognosis-relevant ICRTGs. Additionally, chemotherapy susceptibility drugs for OVC patients in the low- and high-risk groups of ICRTGs were screened using genomics of drug susceptibility to cancer (GDSC). Finally, the immunotherapy response in the low- and high-risk groups was detected using the data from GSE78220. We conducted an immune index correlation analysis of ICRTGs with significant prognoses. The MAP3K4 gene, for which the prognostic correlation coefficient is the highest, was validated using tissue microarrays for a prognostic-immune index correlation.

RESULTS

WGCNA analysis constructed a gene set of ICRTGs and screened 22 genes with prognostic significance. Unsupervised clustering analysis revealed the best molecular typing for two subtypes. The Gene Set Variation Analysis algorithm was used to calculate telomere scores and validate the molecular subtyping. A prognostic model was constructed using 17 ICRTGs. In the The Cancer Genome Atlas-OVC training set and the Gene Expression Omnibus validation set (GSE30161), the risk score model's predicted risk groups and the actual prognosis were shown to be significantly correlated. GDSC screened Axitinib, Bexarotene, Embelin and the GSE78220 datasets and demonstrated that ICRTGs effectively distinguished the group that responds to immunotherapy from the non-responsive group. Additionally, tissue microarray validation results revealed that MAP3K4 significantly predicted patient prognosis. Furthermore, MAP3K4 exhibited a positive association with PD-L1 and a negative relationship with the M1 macrophage markers CD86 and INOS.

CONCLUSIONS

ICRTGs may be reliable biomarkers for the molecular typing of patients with OVC, enabling the prediction of prognosis and immunotherapy efficacy.

SAR131675 exhibits anticancer activity on human ovarian cancer cells through inhibition of VEGFR-3/ERK1/2/AKT signaling pathway

Vascular endothelial growth factor receptor-3 (VEGFR-3) is known to participate in tumorigenesis and lymphangiogenesis, and as such, has the potential to serve as a molecular target for cancer therapy. SAR131675 is a highly selective VEGFR-3 antagonist that has an inhibitive effect on lymphatic cell growth. However, the anticancer effects and underlying mechanisms of SAR131675 in ovarian cancer remain poorly understood. In this study, we investigated the pathological role of VEGFR-3, and the effects of SAR131675 on proliferation, cell cycle, migration, and apoptosis in ovarian cancer cells. Our results showed that the mRNA and protein of VEGFR-3 were expressed in OVCAR3 and SKOV3 ovarian cancer cells, and this receptor was activated following stimulation with 50 ng/ml VEGF-C Cys156Ser (VEGF-CS), a selective ligand for VEGFR-3. Enhancing VEGFR-3 phosphorylation by treatment of ovarian cancer cells with VEGF-CS resulted in increased levels of phosphorylated extracellular signal-regulated kinases 1/2 (ERK1/2) and AKT. Moreover, our data demonstrated that SAR131675 inhibited VEGF-CS-mediated proliferation, colony formation, and migration of cancer cells in a dose-dependent manner. In addition, inhibition of VEGFR-3 activation with SAR131675 significantly increased cell cycle arrest and promoted apoptosis in both OVCAR3 and SKOV3 cells. Mechanistically, SAR131675 effectively suppressed the VEGF-CS-induced phosphorylation of VEGFR-3 and its downstream effectors including activated ERK1/2 and AKT in ovarian cancer cells. Our results reveal an anticancer activity of SAR131675 on the growth and migration of ovarian cancer cells, which may be through inhibiting VEGFR-3/ERK1/2/AKT pathway. SAR131675 may serve as an effective targeted drug for ovarian cancer.

Differential molecular pathway expression according to chemotherapeutic response in ovarian clear cell carcinoma

OBJECTIVE

Ovarian clear cell carcinoma (OCCC) is a distinct entity from epithelial ovarian cancer. The prognosis of advanced and recurrent disease is very poor due to resistance to chemotherapeutic agents. Our aim was to explore the molecular alterations among OCCC patients with different chemotherapeutic responses and to obtain insights into potential biomarkers.

METHODS

Twenty-four OCCC patients were included in this study. The patients were divided into two groups based on the relapse time after the first-line platinum-based chemotherapy: the platinum-sensitive group (PS) and the platinum-resistant group (PR). Gene expression profiling was performed using NanoString nCounter PanCancer Pathways Panel.

RESULTS

Gene expression analysis comparing PR vs. PS identified 32 differentially expressed genes: 17 upregulated genes and 15 downregulated genes. Most of these genes are involved in the PI3K, MAPK and Cell Cycle-Apoptosis pathways. In particular, eight genes are involved in two or all three pathways.

CONCLUSION

The dysregulated genes in the PI3K, MAPK, and Cell Cycle-Apoptosis pathways identified and postulated mechanisms could help to probe biomarkers of OCCC platinum sensitivity, providing a research basis for further exploration of targeted therapy.