Background: Colorectal cancer (CRC) is one of the most common cancers of the gastrointestinal tract and ranks third in cancer-related deaths worldwide. This study was conducted to identify novel biomarkers related to the pathogenesis of CRC based upon a bioinformatics analysis, and further verify the biomarkers in clinical tumor samples and CRC cell lines. Methods: A series of bioinformatics analyses were performed using datasets from NCBI-GEO and constructed a protein–protein interaction (PPI) network. This analysis enabled the identification of Hub genes, for which the mRNA expression and overall survival of CRC patients data distribution was explored in The Cancer Genome Atlas (TCGA) colon cancer and rectal cancer (COADREAD) database. Furthermore, the differential expression of HCAR3 and INLS5 was validated in clinical tumor samples by Real-time quantitative PCR analysis, western blotting analysis, and immunohistochemistry analysis. Finally, CRC cells over-expressing INSL5 were constructed and used for CCK8, cell cycle, and cell apoptosis validation assays in vitro. Results: A total of 286 differentially expressed genes (DEGs) were screened, including 64 genes with increased expression and 143 genes with decreased expression in 2 CRC database, from which 10 key genes were identified: CXCL1, HCAR3, CXCL6, CXCL8, CXCL2, CXCL5, PPY, SST, INSL5, and NPY1R. Among these genes, HCAR3 and INSL5 had not previously been explored and were further verified in vitro. Conclusions: HCAR3 expression was higher in CRC tissues and associated with better overall survival of CRC patients. INSL5 expression in normal tissue was higher than that in tumor tissue and its high expression was associated with a better prognosis for CRC. The overexpression of INSL5 significantly inhibited the proliferation and promoted the shearing of PARP of CRC cells. This integrated bioinformatics study presented 10 key hub genes associated with CRC. HCAR3 and INSL5 were expressed in tumor tissue and these were associated with poor survival and warrant further studies as potential therapeutic targets.

Radiotherapy is one of the main treatments for cervical cancer. Early cervical cancer is usually considered postoperative radiotherapy alone. Radiotherapy combined with cisplatin is the standard treatment for locally advanced cervical cancer (LACC), but sometimes the disease will relapse within a short time after the end of treatment. Tumor recurrence is usually related to the inherent radiation resistance of the tumor, mainly involving cell proliferation, apoptosis, DNA repair, tumor microenvironment, tumor metabolism, and stem cells. In the past few decades, the mechanism of radiotherapy resistance of cervical cancer has been extensively studied, but due to its complex process, the specific mechanism of radiotherapy resistance of cervical cancer is still not fully understood. In this review, we discuss the current status of radiotherapy resistance in cervical cancer and the possible mechanisms of radiotherapy resistance, and provide favorable therapeutic targets for improving radiotherapy sensitivity. In conclusion, this article describes the importance of understanding the pathway and target of radioresistance for cervical cancer to promote the development of effective radiotherapy sensitizers.

Ovarian cancer is a heterogeneous disease with different molecular phenotypes. We performed molecular typing of ovarian cancer using cell differentiation trajectory analysis and proposed a prognostic risk scoring model. Using the copy number variation provided by inferCNV, we identified malignant tumor cells. Then, ovarian cancer samples were divided into four subtypes based on differentiation-related genes (DRGs). There were significant differences in survival rates, clinical features, tumor microenvironment scores, and the expression levels of ICGs among the subtypes. Based on nine DRGs, a prognostic risk score model was generated (AUC at 1 year: 0.749; 3 years: 0.651). Then we obtained a nomogram of the prognostic variable combination, including risk scores and clinicopathological characteristics, and predicted the 1-, 3- and 5-year overall survival. Finally, we explored some issues of immune escape using the established risk model. Our study demonstrates the significant influence of cell differentiation on predicting prognosis in OV patients and provides new insights for OV treatment and potential immunotherapeutic strategies.