Liancheng Zhu

Development of a lactylation-related molecular classification and machine learning-based gene signature to predict survival, response to immunotherapy for ovarian cancer

Lactylation is acknowledged as a regulator of numerous biological processes related to cancer. Research on the ability of lactylation-related genes (LacRGs) to predict prognosis and immunotherapeutic response in ovarian cancer (OC) patients is limited. Consensus clustering was utilized to identify prognostic differentially expressed genes (DEGs) across clusters. A consensus lactylation-related gene signature (LRGS) was established from TCGA-OC and four independent GSE datasets through a machine learning-based integrative approach. LRGS demonstrates consistent and robust performance as an independent risk factor for overall survival. Furthermore, while the low-LRGS group is more likely to exhibit the "hot tumor" phenotype, it also shows a more favorable prognosis and enhanced responsiveness to immunotherapy. Patients exhibiting a high LRGS demonstrated a reduced probability of deriving benefit from immunotherapy and faced a poor prognosis. The oncogenic role of the risk gene RPS6KA2 was preliminarily validated. An in-depth analysis of the LacRGs data may yield valuable insights and enhance the molecular classification of OC. The identification of LRGS serves as a crucial factor in the early prognosis of patients and the selection of potential candidates for immunotherapy. The findings have significant implications for individual patients with OC.

Multiomics evaluation and machine learning optimize molecular classification, prediction of prognosis and immunotherapy response for ovarian cancer

Ovarian cancer (OC), owing to its substantial heterogeneity and high invasiveness, has historically been devoid of precise, individualized treatment options. This study aimed to establish integrated consensus subtypes of OC using different multiomics integration methodologies. We integrated five distinct multiomics datasets from multicentric cohorts to identify high-resolution molecular subgroups using a combination of 10 and 101 clustering and machine learning algorithms, respectively, to develop a robust consensus multiomics-related machine learning signature (CMMS). Two cancer subtypes with prognostic significance were identified using multiomics clustering analysis. 10 essential genes were identified in the CMMS. Patients in the high CMMS group exhibited a poorer prognosis, with a "cold tumor" phenotype and an immunosuppressive state with reduced immune cell infiltration. In contrast, patients in the low CMMS group exhibited a more favorable prognosis, with immune activation and a "hot tumor" phenotype characterized by increased tumor mutation burden, tumor neoantigen burden, PD-L1 expression, and enriched M1 macrophages. Eight independent immunotherapy datasets were validated to further corroborate our findings regarding patients in the low CMMS group who responded better to immunotherapy. CMMS detection has significant utility in the prognosis of patients at an early stage and identification of potential candidates for immunotherapy.

Nomogram for predicting postoperative cancer-specific early death in patients with epithelial ovarian cancer based on the SEER database: a large cohort study

Abstract Purpose Ovarian cancer is a common gynecological malignant tumor. Poor prognosis is strongly associated with early death, but there is no effective tool to predict this. This study aimed to construct a nomogram for predicting cancer-specific early death in patients with ovarian cancer. Methods We used data from the Surveillance, Epidemiology, and End Results database of patients with ovarian cancer registered from 1988 to 2016. Important independent prognostic factors were determined by univariate and multivariate logistic regression and LASSO Cox regression. Several risk factors were considered in constructing the nomogram. Nomogram discrimination and calibration were evaluated using C-index, internal validation, and receiver operating characteristic (ROC) curves. Results A total of 4769 patients were included. Patients were assigned to the training set ( n  = 3340; 70%) and validation set ( n  = 1429; 30%). Based on the training set, eight variables were shown to be significant factors for early death and were incorporated in the nomogram: American Joint Committee on Cancer (AJCC) stage, residual lesion size, chemotherapy, serum CA125 level, tumor size, number of lymph nodes examined, surgery of primary site, and age. The concordance indices and ROC curves showed that the nomogram had better predictive ability than the AJCC staging system and good clinical practicability. Internal validation based on validation set showed good consistency between predicted and observed values for early death. Conclusion Compared with predictions made based on AJCC stage or residual lesion size, the nomogram could provide more robust predictions for early death in patients with ovarian cancer.

MRPL15 is a novel prognostic biomarker and therapeutic target for epithelial ovarian cancer

AbstractPurposeTo analyze the role of six human epididymis protein 4 (HE4)‐related mitochondrial ribosomal proteins (MRPs) in ovarian cancer and selected MRPL15, which is most closely related to the tumorigenesis and prognosis of ovarian cancer, for further analyses.MethodsUsing STRING database and MCODE plugin in Cytoscape, six MRPs were identified among genes that are upregulated in response to HE4 overexpression in epithelial ovarian cancer cells. The Cancer Genome Atlas (TCGA) ovarian cancer, GTEX, Oncomine, and TISIDB were used to analyze the expression of the six MRPs. The prognostic impact and genetic variation of these six MRPs in ovarian cancer were evaluated using Kaplan‐Meier Plotter and cBioPortal, respectively. MRPL15 was selected for immunohistochemistry and GEO verification. TCGA ovarian cancer data, gene set enrichment analysis, and Enrichr were used to explore the mechanism of MRPL15 in ovarian cancer. Finally, the relationship between MRPL15 expression and immune subtype, tumor‐infiltrating lymphocytes, and immune regulatory factors was analyzed using TCGA ovarian cancer data and TISIDB.ResultsSix MRPs (MRPL10, MRPL15, MRPL36, MRPL39, MRPS16, and MRPS31) related to HE4 in ovarian cancer were selected. MRPL15 was highly expressed and amplified in ovarian cancer and was related to the poor prognosis of patients. Mechanism analysis indicated that MRPL15 plays a role in ovarian cancer through pathways such as the cell cycle, DNA repair, and mTOR 1 signaling. High expression of MRPL15 in ovarian cancer may be associated with its amplification and hypomethylation. Additionally, MRPL15 showed the lowest expression in C3 ovarian cancer and was correlated with proliferation of CD8+ T cells and dendritic cells as well as TGFβR1 and IDO1 expression.ConclusionMRPL15 may be a prognostic indicator and therapeutic target for ovarian cancer. Because of its close correlation with HE4, this study provides insights into the mechanism of HE4 in ovarian cancer.

A methylation-driven genes prognostic signature and the immune microenvironment in epithelial ovarian cancer

Abstract Aberrant gene methylation has been implicated in the development and progression of tumors. In this study, we aimed to identity methylation-driven genes involved in epithelial ovarian cancer (EOC) to establish a prognostic signature for patients with EOC. We identified and verified 6 MDGs that are closely related to the prognosis of ovarian cancer. A prognostic risk score model and nomogram for predicting the prognosis of ovarian cancer were constructed based on the six MDGs. It can also effectively reflect the immune environment and immunotherapy response of ovarian cancer. These MDGs have great significance to the implementation of individualized treatment and disease monitoring of ovarian cancer patients.