Inferences of carboplatin response-related signature by integrating multiomics data in ovarian serous cystadenocarcinoma.

Platinum-based chemotherapy, especially carboplatin, is the primary measure to treat patients with ovarian cancer (OC). However, OC patients still have an adverse prognosis due to emergency of chemotherapy resistance. Ovarian serous cystadenocarcinoma (OSC) is the most common histological subtype of OC. Therefore, identifying the key factors that affect chemotherapy resistance and searching novel treatments had become a top priority. In this study, we analyzed carboplatin response-related mRNA, miRNA, DNA methylation, and alternative splicing (AS) and established a drug-resistant signature for carboplatin in OSC. This drug-resistant signature was obviously higher in resistant group than in non-resistant group and had accuracy predictive performance, which demonstrated that this signature could be considered as a superior indicator for OSC patients with carboplatin resistance. Furthermore, we selected three potential small molecule drugs including liranaftate, siguazodan, and tramiprostate to inhibit carboplatin resistance of OSC. In addition, we also identified ZINC00000205417, ZINC00000140928, and ZINC00021908260 were potential small molecule compounds for SLC17A7 based on Molecular Operating Environment (MOE) virtual screening. Finally, we confirmed the drug-like properties of these small molecule drugs via evaluating absorption, distribution, metabolism, elimination, and toxicity (ADMET) property. In summary, the signature could be used as biomarker for carboplatin resistance and small molecule drugs targeting these genes could improve clinical treatment for OSC in the future.

Development of alternative splicing signature in lung squamous cell carcinoma.

Increasing evidence demonstrated that alternative splicing (AS) plays a vital role in tumorigenesis and clinical outcome of patient. However, systematical analysis of AS in lung squamous cell carcinoma (LUSC) is lacking and greatly necessary. Thus, this study was to systematically estimate the function of AS events served as prognostic indicators in LUSC. Among 31,345 mRNA AS events in 9633 genes, we detected 1996 AS in 1409 genes which have significant connection with overall survival (OS) of LUSC patients. Then, prognostic model based on seven types of AS events was established and we further constructed a combined prognostic model. The Kaplan-Meier curve results suggested that seven types of AS signatures and the combined prognostic model could exhibit robust performance in predicting prognosis. Patients in the high-risk group had significantly shorter OS than those in the low-risk group. The ROC showed all prognostic models had high accuracy and powerful predictive performance with different AUC ranging from 0.837 to 0.978. Moreover, the combined prognostic model had highest performance in risk stratification and predictive accuracy than single prognostic models and had higher accuracy than other mRNA model. Finally, a significant correlation network between survival-related AS genes and prognostic splicing factors (SFs) was established. In conclusion, our study provided several potential prognostic AS models and constructed splicing network between AS and SFs in LUSC, which could be used as potential indicators and treatment targets for LUSC patients.