Mingxin Yu

Immune Subtype Profiling and Establishment of Prognostic Immune-Related lncRNA Pairs in Human Ovarian Cancer

This study collected immune-related genes (IRGs) and used gene expression data from TCGA database to construct a molecular subtype of ovarian cancer (OV) based on immune-related lncRNA gene pairs (IRLnc_GPs). The relationships between molecular subtypes and prognosis and clinical characteristics were further explored. IRGs were acquired from the ImmPort database, and round-robin pairing of immune-related lncRNAs was performed. The NMF algorithm was used to identify molecular subtypes, and the immune score of a single sample was calculated through ESTIMATE, TIMER, ssGSEA, MCPcounter, and CIBERSORT. The relationship between molecular subtypes and immune microenvironments was identified. A hypergeometric test was used to test the lncRNA pairs among the OV molecular subtypes (C1 and C2 subtypes). The BH method was used to screen the different lncRNA pairs, and a predictive risk model was constructed and verified. Finally, correlation analysis between the risk model, immune checkpoint genes, and chemotherapy drugs was carried out. Based on IRLnc_GP to classify 373 OV samples of TCGA, the samples were divided into two subtypes, and the prognosis between the subtypes showed significant differences. The C1 subtype with a poor prognosis was more related to the pathways of tumor occurrence and development. We identified 180 differential lncRNA pairs between subtypes and constructed a prognostic risk model based on 8 IRLnc_GPs. In the independent dataset, the distribution of subtypes in functional modules was different and highly repeatable. There were significant differences in the molecular and clinical characteristics of the subtypes and the drug sensitivity of immunotherapy/chemotherapy. In conclusion, the risk model established based on IRLnc_GP can better evaluate the prognosis of OV samples and can also assess the effects of different drug treatments in the high- and low-risk groups, providing new insights and ideas for the treatment of OV.

Identification and Validation of Invasion-Related Molecular Subtypes and Prognostic Features for Cervical Cancer

Background. As one of the main causes leading to female cancer deaths, cervical cancer shows malignant features of local infiltration and invasion into adjacent organs and tissues. This study was designed to categorize novel molecular subtypes according to cervical cancer invasion and screen reliable prognostic markers. Methods. Invasion-related gene sets and expression profiles of invasion-related genes were collected from the CancerSEA database and The Cancer Genome Atlas (TCGA), respectively. Samples were clustered by nonnegative matrix factorization (NMF) to obtain different molecular subgroups, immune microenvironment characteristics of which were further systematically compared. Limma was employed to screen differentially expressed gene sets in different subtypes, followed by Lasso analysis for dimension reduction. Multivariate and univariate Cox regression analysis was performed to determine prognostic characteristics. The Kaplan-Meier test showed the prognostic differences of patients with different risks. Additionally, receiver operating characteristic (ROC) curves were applied to validate the prognostic model performance. A nomogram model was developed using clinical and prognostic characteristics of cervical cancer, and its prediction accuracy was reflected by calibration curve. Results. This study filtered 19 invasion-related genes with prognosis significance in cervical cancer and 2 molecular subtypes (C1, C2). Specifically, the C1 subtype had an unfavorable prognosis, which was associated with the activation of the TGF-beta signaling pathway, focal adhesion, and PI3K-Akt signaling pathway. 875 differentially expressed genes were screened, and 8 key genes were finally retained by the dimension reduction analysis. An 8-gene signature was established as an independent factor predictive of the prognosis of cervical cancer. The signature performance was even stronger when combined with N stage. Conclusion. Based on invasion-related genes, the present study categorized two cervical cancer subtypes with distinct TME characteristics and established an 8-gene marker that can accurately and independently predict the prognosis of cervical cancer.