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Simple Summary: Cost-effective, Hepatocellular carcinoma (HCC) risk-based surveillance strategies should be established after achieving sustained virologic response (SVR). Circulating microRNAs are considered stable serum markers for early cancer diagnosis and prognosis and treatment response prediction. The aim of our longitudinal follow-up study was to assess whether Let-7 family members can predict HCC risk in CHC patients. We assessed the sera of 54 patients with CHC who developed HCC and 173 patients with CHC who did not develop HCC after antiviral therapy. Cox's regression model revealed the independent role of let-7i as an effective surrogate biomarker for predicting HCC development in patients with CHC. HCC, a leading cause of cancer-related mortality, is diagnosed at advanced stages. Although antiviral therapy has reduced the risk of HCC among chronic hepatitis C (CHC) patients, the risk of HCC remains, thus, highlighting the unmet need for continuous surveillance. Therefore, stable and cost-effective biomarkers, such as circulating microRNAs, must be identified. We aimed to clarify whether serum levels of the Let-7 family can predict HCC risk in patients with CHC using univariate and multivariate Cox's proportional hazards model. We analyzed the sera of 54 patients with CHC who developed HCC after antiviral therapy and compared the data with those of 173 patients without HCC development. The Let-7 family (except for let-7c) exhibited significant negative correlations with the fibrosis score (r = −0.2736 to −0.34, p = 0.0002 to <0.0001). After Cox's regression model was used to adjust for age, sex, HCV genotype, and FIB-4 ≥ 3.25, patients with CHC with let-7i median ≥ −1.696 (adjusted hazard ratio [aHR] = 0.31, 95% CI: 0.08–0.94, p = 0.0372) in the sustained virologic response (SVR) groups and ≥−1.696 (aHR = 0.09, 95% CI: 0.08–0.94, p = 0.0022) in the non-SVR group were less likely to develop HCC. Thus, circulating let-7i can be used for early CHC surveillance in patients with HCC risk after antiviral treatment. [ABSTRACT FROM AUTHOR]

Simple Summary: Using the covariate-adjusted tensor classification in the high-dimension (CATCH) model, we integrated adjusted radiomics-based CT images into RNA immune genomic expression data to achieve the accurate classification of recurrent CRC. The correlation between radiomic features and immune gene expression identifies potential therapeutic targets in CRC. We provide individualized cancer therapeutic strategies based on adjusted radiomic features in recurrent stage III CRC. To evaluate whether adjusted computed tomography (CT) scan image-based radiomics combined with immune genomic expression can achieve accurate stratification of cancer recurrence and identify potential therapeutic targets in stage III colorectal cancer (CRC), this cohort study enrolled 71 patients with postoperative stage III CRC. Based on preoperative CT scans, radiomic features were extracted and selected to build pixel image data using covariate-adjusted tensor classification in the high-dimension (CATCH) model. The differentially expressed RNA genes, as radiomic covariates, were identified by cancer recurrence. Predictive models were built using the pixel image and immune genomic expression factors, and the area under the curve (AUC) and F1 score were used to evaluate their performance. Significantly adjusted radiomic features were selected to predict recurrence. The association between the significantly adjusted radiomic features and immune gene expression was also investigated. Overall, 1037 radiomic features were converted into 33 × 32-pixel image data. Thirty differentially expressed genes were identified. We performed 100 iterations of 3-fold cross-validation to evaluate the performance of the CATCH model, which showed a high sensitivity of 0.66 and an F1 score of 0.69. The area under the curve (AUC) was 0.56. Overall, ten adjusted radiomic features were significantly associated with cancer recurrence in the CATCH model. All of these methods are texture-associated radiomics. Compared with non-adjusted radiomics, 7 out of 10 adjusted radiomic features influenced recurrence-free survival. The adjusted radiomic features were positively associated with PECAM1, PRDM1, AIF1, IL10, ISG20, and TLR8 expression. We provide individualized cancer therapeutic strategies based on adjusted radiomic features in recurrent stage III CRC. Adjusted CT scan image-based radiomics with immune genomic expression covariates using the CATCH model can efficiently predict cancer recurrence. The correlation between adjusted radiomic features and immune genomic expression can provide biological relevance and individualized therapeutic targets. [ABSTRACT FROM AUTHOR]