Hui He

TPX2 Serves as a Cancer Susceptibility Gene and Is Closely Associated with the Poor Prognosis of Endometrial Cancer

Background. Endometrial cancer (EC) is a common tumor of the genital tract that affects the female reproductive system but with only limited treatment options. We aimed to discover new prognostic biomarkers for EC. Methods. We used mRNA-seq data to detect differentially expressed genes (DEGs) between EC and control tissues. Detailed clinicopathological information was collected, and changes in the mRNA and protein levels of hub DEGs were analyzed in EC. Copy number variation (CNV) was also evaluated for its association with the pathogenesis of EC. Gene set enrichment analysis (GSEA) was conducted to enrich significant pathways driven by the hub genes. Cox regression analysis was used to select variables to create a nomogram. The nomogram was calibrated by applying the concordance index (C-index), and net benefits of the nomogram at different threshold probabilities were quantified using decision curve analysis (DCA). Results. Differential expression analysis identified 24 DEGs as potential risk factors for EC. Survival analysis revealed that TPX2 expression was related to worsening overall survival in patients with advanced EC. A high CNV was associated with the overexpression of TPX2; this suggested that modifications in the cell-cycle pathway might be crucial in the advancement of EC. Moreover, an individualized nomogram was developed for TPX2 incorporating clinical factors; this was also evaluated for its ability to predict EC. Calibration and DCA analyses confirmed the robustness and clinical usefulness of the nomogram. Conclusion. We offer novel insights into the pathogenesis and molecular mechanisms of EC. The overexpression of TPX2 was related to a poorer prognosis and could serve as a biomarker for predicting prognostic outcomes in EC patients.

Overexpression of SMC4 predicts a poor prognosis in cervical cancer and facilitates cancer cell malignancy phenotype by activating NF-κB pathway

Cervical cancer is one of the leading female malignancy tumors worldwide. Structural maintenance of chromosomes 4 (SMC4), a member of the SMC family, is associated with cancer pathogenesis and progression. However, the role of SMC4 in cervical cancer is still unclear. In the study, SMC4 was increased in cervical cancer tissues compared with adjacent normal tissues. The SMC4 knockdown and overexpression were performed in cervical cancer cells. SMC4 knockdown inhibited cell proliferation, colony formation, cell migration and invasion, and suppressed epithelial-mesenchymal transition (EMT). Conversely, SMC4 overexpression exerted opposite effects. Moreover, SMC4 knockdown down-regulated stem cell markers, reduced the capacity of spheroid formation and inactivated NF-κB pathway. SMC4 overexpression contributed to stem cell markers, and stimulated spheroid formation and NF-κB pathway activation. Additionally, BAY11-7082 (an NF-κB inhibitor) alleviated the SMC4-mediated the effects in cervical cancer cells. In conclusion, these findings demonstrated that SMC4 overexpression facilitated the development of cervical cancer cells by activating NF-κBpathway, which provides a new therapeutic target for patients with cervical cancer.

Construction of Metabolic Molecular Classification and Immune Characteristics for the Prognosis Prediction of Ovarian Cancer

Background. Ovarian cancer (OC) is a malignant tumor that seriously threatens women’s health. Molecular classification based on metabolic genes can reflect the deeper characteristics of ovarian cancer and provide support for prognostic evaluation and the guidance of individualized treatment. Method. The metabolic subtypes were determined by consensus clustering and CDF. We used the ssGSEA method to calculate the IFNγ score of each patient. The CIBERSORT method was used to evaluate the score distribution and differential expression of 22 immune cells, and LDA was applied to establish a subtype classification feature index. The Kaplan-Meier and ROC curves were generated to validate the prognostic performance of metabolic subtypes in different cohorts. WGCNA was used to screen the coexpression modules associated with metabolic genes. Results. We obtained three metabolic subtypes (MC1, MC2, and MC3). MC2 had the best prognosis, and MC1 and MC3 had poor prognoses. Consistently, MC2 subtype had higher T cell lytic activity and lower angiogenesis, IFNγ, T cell dysfunction, and rejection scores. TIDE analysis showed that MC2 patients were more likely to benefit from immunotherapy; MC1 patients were more sensitive to immune checkpoint inhibitors and traditional chemotherapy drugs. The multiclass AUCs based on the RNASeq and GSE cohorts were 0.93 and 0.84, respectively. Finally, we screened 11 potential gene markers related to the metabolic characteristic index that could be used to indicate the prognosis of OC. Conclusion. Molecular subtypes related to metabolism are crucial to comprehensively understand the molecular pathological characteristics related to metabolism for OC development, explore reliable markers for prognosis, improve the OC staging system, and guide personalized treatment.