Abstract
Simple SummaryThe expression of tumor microenvironment-related genes is known to be correlated with ovarian cancer patients’ prognosis. Immunotherapeutic targets are in part located in this complex cluster of cells and soluble factors. In our study, we constructed a prognostic 11-gene signature for advanced serous ovarian cancer from tumor microenvironment-related genes through lasso regression. The established risk score can quantify the prognosis of ovarian cancer patients more accurately and is able to predict the putative biological response of cancer samples to a programmed death ligand 1 blocking immunotherapy. This might empower the role of immunotherapy in ovarian cancer through its usage in future study protocols.(1) Background: The tumor microenvironment is involved in the growth and proliferation of malignant tumors and in the process of resistance towards systemic and targeted therapies. A correlation between the gene expression profile of the tumor microenvironment and the prognosis of ovarian cancer patients is already known. (2) Methods: Based on data from The Cancer Genome Atlas (379 RNA sequencing samples), we constructed a prognostic 11-gene signature (SNRPA1, CCL19, CXCL11, CDC5L, APCDD1, LPAR2, PI3, PLEKHF1, CCDC80, CPXM1 and CTAG2) for Fédération Internationale de Gynécologie et d’Obstétrique stage III and IV serous ovarian cancer through lasso regression. (3) Results: The established risk score was able to predict the 1-, 3- and 5-year prognoses more accurately than previously known models. (4) Conclusions: We were able to confirm the predictive power of this model when we applied it to cervical and urothelial cancer, supporting its pan-cancer usability. We found that immune checkpoint genes correlate negatively with a higher risk score. Based on this information, we used our risk score to predict the biological response of cancer samples to an anti-programmed death ligand 1 immunotherapy, which could be useful for future clinical studies on immunotherapy in ovarian cancer.
Highlights
Ovarian cancer (OC) has the highest mortality rate among malignancies originating in the female reproductive system
We showed that our 11-gene risk model (SNRPA1, CCL19, CXCL11, CDC5L, APCDD1, LPAR2, PI3, PLEKHF1, CCDC80, CPXM1 and CTAG2) is able to indicate the prognosis of advanced-stage serous ovarian cancer (SOC) patients
Our study proposes a tumor microenvironment (TME)-related risk model to be implemented in the assessment of advanced-stage SOC patients
Summary
Ovarian cancer (OC) has the highest mortality rate among malignancies originating in the female reproductive system. Given the continuous improvement of both diagnostic techniques and treatment methods, the prevalence and survival rate of OC patients have increased during the past 15 years. OC accounts for 3.3% of all malignant diseases in women in Germany and the cancer-related mortality rate is high, accounting for 5.6% of all malignancy-related deaths [2]. Advanced-stage serous ovarian cancer (SOC) is the cause of most OC deaths. 80% of patients significantly benefit from surgery and chemotherapy, they usually relapse and metastasize and are, incurable. Two-thirds will experience a relapse after receiving several lines of therapy and die within 10 years of the first diagnosis [3,4]
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