LncRNA DLEU2 contributes to Taxol resistance of gastric cancer cells through regulating the miR-30c-5p-LDHA axis.

Gastric cancer (GC) is a human malignancy which is associated with high mortality rate and poor prognosis. In addition to surgery, chemo- and radio-therapies are effective strategies against GC at advanced or metastatic stage. Taxol is a traditionally anti-cancer drug which is applied to various types of cancer. However, development of drug resistance limited the anti-cancer effects of Taxol. Currently, the biological roles and mechanisms of non-coding RNA DLEU2 in Taxol resistant GC remain unclear. This study reported that DLEU2 was significantly upregulated and miR-30c-5p was remarkedly downregulated in gastric tumours and cell lines. Silencing DLEU2 or overexpression of miR-30c-5p effectively increased the Taxol sensitivity of GC cells. Through bioinformatics analysis, RNA pull-down and luciferase assay, we demonstrated that DLEU2 sponged miR-30c-5p to block its expression in GC cells. Moreover, from the established Taxol resistant GC cell line, we detected remarkedly upregulated DLEU2 and downregulated miR-30c-5p expressions and significantly elevated glucose metabolism. Under low glucose condition, Taxol resistant cells were more susceptible to Taxol. In addition, we showed overexpression of miR-30c-5p blocked glucose metabolism through inhibiting the LDHA, a glucose metabolism key enzyme by direct targeting the 3'UTR of LDHA. Finally, rescue experiments validated that restoration of miR-30c-5p in DLEU2-overexpressing Taxol resistant GC cells effectively overcame the DLEU2-promoted Taxol resistance. In summary, this study uncovered new roles and molecular mechanisms of the lncRNA DLEU2-promoted Taxol resistance of gastric cancer cells, presenting the DLEU2-miR-30c-5p-LDHA-glucose metabolism axis a potentially therapeutic target for treatment of Taxol resistant GC.

Blood Biomarkers Panels for Screening of Colorectal Cancer and Adenoma on a Machine Learning-Assisted Detection Platform.

OBJECTIVE: A mini-invasive and good-compliance program is critical to broaden colorectal cancer (CRC) screening and reduce CRC-related mortality. Blood testing combined with imaging examination has been proved to be feasible on screen for multicancer and guide intervention. The study aims to construct a machine learning-assisted detection platform with available multi-targets for CRC and colorectal adenoma (CRA) screening. METHODS: This was a retrospective study that the blood test data from 204 CRCs, 384 CRAs, and 229 healthy controls was extracted. The classified models were constructed with 4 machine learning (ML) algorithms including support vector machine (SVM), random forest (RF), decision tree (DT), and eXtreme Gradient Boosting (XGB) based on the candidate biomarkers. The importance index was used by SHapely Adaptive exPlanations (SHAP) analysis to identify the dominant characteristics. The performance of classified models was evaluated. The most dominating features from the proposed panel were developed by logistic regression (LR) for identification CRC from control. RESULTS: The candidate biomarkers consisted of 26 multi-targets panel including CEA, AFP, and so on. Among the 4 models, the SVM classifier for CRA yields the best predictive performance (the area under the receiver operating curve, AUC: .925, sensitivity: .904, and specificity: .771). As for CRC classification, the RF model with 26 candidate biomarkers provided the best predictive parameters (AUC: .941, sensitivity: .902, and specificity: .912). Compared with CEA and CA199, the predictive performance was significantly improved. The streamlined model with 6 biomarkers for CRC also obtained a good performance (AUC: .946, sensitivity: .885, and specificity: .913). CONCLUSIONS: The predictive models consisting of 26 multi-targets panel would be used as a non-invasive, economical, and effective risk stratification platform, which was expected to be applied for auxiliary screening of CRA and CRC in clinical practice.