DIRC1 (Disrupted In Renal Carcinoma 1)

Machine learning algorithms can be used to Identify key dysregulated genes/miRNAs involved in the pathogenesis of diseases, leading to the detection of patients in earlier stages. Our data also demonstrated the prognostic value of C1orf174 in colorectal cancer.

Our study highlights the potential of the FRLs signature as a prognostic tool and its implications for therapeutic strategies in MM.

Through drug sensitivity analysis, we screened four small molecules that might be beneficial for gastric cancer treatment. A prognostic model consisting of these six TRDELs was capable to predict the prognosis of gastric cancer patients.

Moreover, we analyzed immune status of different risk groups and found that STAD patients in low-risk group were more sensitive to immunotherapy. A prognostic risk assessment model for STAD using anoikis-related lncRNA genes was constructed here, which was proven to have high predictive accuracy and thus could offer a reference for prognostic evaluation and clinical treatment of STAD patients.

In short, we established a novel immune-related prognostic signature in LUCS, which has significant sensitivity and accuracy in predicting the prognosis of patients. Our research can guide the evaluation of the prognosis of LUSC patients in clinical, and the discovered immune-related genes can provide a theoretical basis for the discovery of new therapeutic targets.

RT-PCR showed that the expression of DIRC1 in the serum of Gastric Cancer patients was higher than healthy people (P = .027). DIRC1 expression was significantly correlated with poor survival and immune infiltrations in STAD, and it may be a promising prognostic biomarker in STAD.