Application of MFI-5 in severe complications and unfavorable outcomes after radical resection of colorectal cancer.

BACKGROUND: Frailty is considered a characteristic manifestation of physiological decline in multiple organ systems, which significantly increases the vulnerability of elderly individuals with colorectal cancer (CRC) and is associated with a poor prognosis. While studies have demonstrated that the 11-factor Modified Frailty Index (mFI-11) can effectively predict adverse outcomes following radical resection of CRC, there is a lack of research on the applicability of the 5-factor Modified Frailty Index (mFI-5) within this patient population. METHODS: In this retrospective analysis, we examined a cohort of CRC patients aged 65 years and above who had undergone radical resection. For each patient, we calculated their mFI-5 score, considering a score of ≥ 2 as an indication of frailty. We conducted univariate and multivariate analyses to assess the association between the mFI-5 and adverse outcomes as well as postoperative complications. RESULTS: Patients with an mFI-5 score ≥ 2 exhibited a significantly higher incidence of serious postoperative complications (53% vs. 30%; P = 0.001) and experienced a longer hospital stay [19.00 (15.00-24.50) vs. 17.00 (14.00-20.00); P < 0.05]. Notably, an mFI-5 score greater than 2 emerged as an independent risk factor for severe postoperative complications (odds ratio: 2.297; 95% confidence interval: 1.216 to 4.339; P = 0.01). Furthermore, the mFI-5 score displayed predictive capabilities for severe postoperative complications with an area under the receiver operating characteristic (ROC) curve of 0.629 (95% confidence interval: 0.551 to 0.707; P < 0.05). CONCLUSION: The mFI-5 demonstrates a high level of sensitivity in predicting serious complications, prolonged hospital stays, and mortality following radical resection of colorectal carcinoma. As a practical clinical assessment tool, the mFI-5 enables the identification of high-risk patients and facilitates preoperative optimization.

Computational Biology Predicts the Efficacy of Tumor Immune Checkpoint Blockade.

Tumor immunotherapy is considered as one of the most promising methods in cancer treatment in recent years. Immune checkpoint blockade (ICB) can activate immune cells to destroy tumors by relieving the inhibitory pathway of tumor cells to immune cells. In silico prediction of the ICB response is an important step toward achieving effective and personalized cancer immunotherapy. Although immune checkpoint inhibitors have shown exciting clinical effects in the treatment of many types of tumors, there are still some clinical problems in practical application, such as low response rate and large individualized differences. How to predict the efficacy of effective individualized immune checkpoint inhibitors for tumor patients based on specific biomarkers and computational models is one of the key issues in the immunotherapy of this kind of tumor. In our work, from the five levels of genome level, transcription level, epigenetic level, microbial taxonomy level, and the immune cell infiltration profile level, the biomarkers and in silico calculation methods that affect the efficacy of tumor immune checkpoint inhibitors are comprehensively summarized.