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Colorectal cancer (CRC) has the third highest incidence and the second highest mortality in the world, which seriously affects human health, while current treatments methods for CRC, including systemic therapy, preoperative radiotherapy, and surgical local excision, still have poor survival rates for patients with metastatic disease, making it critical to develop new strategies for treating CRC. In this article, we found that the gut microbiota can modulate the signaling pathways of cancer cells through direct contact with tumor cells, generate inflammatory responses and oxidative stress through interactions between the innate and adaptive immune systems, and produce diverse metabolic combinations to trigger specific immune responses and promote the initiation of systemic type I interferon (IFN-I) and anti-viral immunity. In addition, oncolytic virus-mediated immunotherapy for regulating oncolytic virus can directly lyse tumor cells, induce the immune activity of the body, interact with interferon, inhibit the anti-viral effect of IFN-I, and enhance the anti-tumor effect of IFN-II. Interferon plays an important role in the anti-tumor process. We put forward that exploring the effects of intestinal flora and oncolytic virus on interferon to treat CRC is a promising therapeutic option.

Combining Fourier transform infrared spectroscopy (FTIR) with endoscopy, it is expected that noninvasive, rapid detection of colorectal cancer can be performed in vivo in the future. In this study, Fourier transform infrared spectra were collected from 88 endoscopic biopsy colorectal tissue samples (41 colitis and 47 cancers). A new method, viz., entropy weight local-hyperplane k-nearest-neighbor (EWHK), which is an improved version of K-local hyperplane distance nearest-neighbor (HKNN), is proposed for tissue classification. In order to avoid limiting high dimensions and small values of the nearest neighbor, the new EWHK method calculates feature weights based on information entropy. The average results of the random classification showed that the EWHK classifier for differentiating cancer from colitis samples produced a sensitivity of 81.38% and a specificity of 92.69%.

Background Colorectal cancer remains a serious public health problem due to the poor prognosis. In the present study, we attempted to develop and validate a prognostic signature to predict the individual mortality risk in colorectal cancer patients. Materials and Methods The original study datasets were downloaded from The Cancer Genome Atlas database. The present study finally included 424 colorectal cancer patients with wholly gene expression information and overall survival information. Results A nine-lncRNA prognostic signature was built through univariate and multivariate Cox proportional regression model. Time-dependent receiver operating characteristic curves in model cohort demonstrated that the Harrell’s concordance indexes of nine-lncRNA prognostic signature were 0.768 (95% CI [0.717–0.819]), 0.778 (95% CI [0.727–0.829]) and 0.870 (95% CI [0.819–0.921]) for 1-year, 3-year and 5-year overall survival respectively. In validation cohort, the Harrell’s concordance indexes of nine-lncRNA prognostic signature were 0.761 (95% CI [0.710–0.812]), 0.801 (95% CI [0.750–0.852]) and 0.883 (95% CI [0.832–0.934]) for 1-year, 3-year and 5-year overall survival respectively. According to the median of nine-lncRNA prognostic signature score in model cohort, 424 CRC patients could be stratified into high risk group (n = 212) and low risk group (n = 212). Kaplan–Meier survival curves showed that the overall survival rate of high risk group was significantly lower than that of low risk group (P