Combining pathological risk factors and T, N staging to optimize the assessment for risk stratification and prognostication in low-risk stage III colon cancer.

BACKGROUND: This study aimed to investigate the combined pathological risk factors (PRFs) to stratify low-risk (pT1-3N1) stage III colon cancer (CC), providing a basis for individualized treatment in the future. PATIENTS AND METHODS: PRFs for low-risk stage III CC were identified using COX model. Low-risk stage III CC was risk-grouped combining with PRFs, and survival analysis were performed using Kaplan-Meier. The Surveillance, Epidemiology, and End Results (SEER) databases was used for external validation. RESULTS: Nine hundred sixty-two stage III CC patients were included with 634 (65.9%) as low risk and 328 (34.1%) as high risk. Poor differentiation (OS: P = 0.048; DFS: P = 0.011), perineural invasion (OS: P = 0.003; DFS: P < 0.001) and tumor deposits (OS: P = 0.012; DFS: P = 0.003) were identified as PRFs. The prognosis of low-risk CC combined with 2 PRFs (OS: HR = 3.871, 95%CI, 2.004-7.479, P < 0.001; DFS: HR = 3.479, 95%CI, 2.158-5.610, P < 0.001) or 3 PRFs (OS: HR = 5.915, 95%CI, 1.953-17.420, P = 0.002; DFS: HR = 5.915, 95%CI, 2.623-13.335, P < 0.001) was similar to that of high-risk CC (OS: HR = 3.927, 95%CI, 2.317-6.656, P < 0.001; DFS: HR = 4.132, 95%CI, 2.858-5.974, P < 0.001). In the SEER database, 18,547 CC patients were enrolled with 10,023 (54.0%) as low risk and 8524 (46.0%) as high risk. Low-risk CC combined with 2 PRFs (OS: HR = 1.857, 95%CI, 1.613-2.139, P < 0.001) was similar to that of high-risk CC without PRFs (HR = 1.876, 95%CI, 1.731-2.033, P < 0.001). CONCLUSION: Combined PRFs improved the risk stratification of low-risk stage III CC, which could reduce the incidence of undertreatment and guide adjuvant chemotherapy.

Effects of the Oat Hay Feeding Method and Compound Probiotic Supplementation on the Growth, Antioxidant Capacity, Immunity, and Rumen Bacteria Community of Dairy Calves.

This study aimed to investigate the effects of the oat hay feeding method and compound probiotics (CMP) on the growth, health, serum antioxidant and immune indicators, rumen fermentation, and bacteria community of dairy calves from 3 to 5 months of age. Forty-eight female Holstein calves (80 ± 7 days of age, 93.71 ± 5.33 kg BW) were selected and randomly divided into four groups. A 2 × 2 factorial design was adopted for the experiment, with the factors of the oat hay feeding method (fed as free-choice or 16.7% in the diet) and compound probiotics (CMP) inclusion (0.15% or 0%) in the pelleted starter. The results showed that, compared with giving oat hay as free-choice, feeding a diet of 16.7% oat hay increased the pelleted starter intake at 1-84 d (p < 0.05), with an average daily gain (ADG) at 61-84 d (p = 0.02); adding CMP to the pelleted starter did not significantly affect body weight, and reduced the fecal index (p < 0.05). Feeding 16.7% oat hay increased the concentration of IgA, IgG, and IgM (p < 0.01), while adding CMP increased the catalase (p < 0.01) and decreased the concentration of malondialdehyde (p < 0.01) in serum. Feeding 16.7% oat hay increased the ruminal concentration of propionic acid (p < 0.05) and isobutyric acid (p = 0.08), and decreased the ruminal pH (p = 0.08), the concentration of acetic acid (p < 0.05), and the ratio of acetic acid to propionic acid (p < 0.01). Feeding 16.7% oat hay reduced the relative abundance of ruminal Firmicutes, Unidentified-Bacteria, Actinobacteria, Prevotella, NK4A214-group, Olsenella, and Actinobacteriota (p < 0.05); adding CMP increased the relative abundance of ruminal Prevotella, Rikenellaceae-RC9-gut-group, Ruminococcus, NK4A214-group, and Ruminococcus (p < 0.05), and decreased the abundance of Desulfobacterora, Prevotella-7, and Erysipelotricaceae-UCG-002 (p < 0.05). In conclusion, feeding a diet of 16.7% oat hay increased the pelleted starter intake and average daily gain, while slightly reducing the ruminal pH values; adding CMP to the pelleted starter resulted in reduced diarrhea incidence, increased serum antioxidant capacity and immunity, as well as ruminal richness and diversity of microorganisms in dairy calves from 3 to 5 months of age.

Construction and Validation of a Novel Prognosis Model in Colon Cancer Based on Cuproptosis-Related Long Non-Coding RNAs.

Colon cancer (CC) is one of the most common (6%) malignancies and leading cause of cancer-associated death (more than 0.5 million) worldwide, which demands reliable prognostic biomarkers. Cuproptosis is a novel modality of regulated cell death triggered by the accumulation of intracellular copper. LncRNAs have been reported as prognostic signatures in different types of tumors. However, the correlation between cuproptosis-related lncRNAs (CRLs) and CC remains unclear. Data of CC patients were downloaded from public databases. The prognosis-associated CRLs were identified by co-expression analysis and univariate Cox. Least absolute shrinkage and selection operator were utilized to construct the CRLs-based prognostic signature in silico for CC patients. CRLs level was validated in human CC cell lines and patient tissues. ROC curve and Kaplan-Meier curve results revealed that high CRLs-risk score was associated with poor prognosis in CC patients. Moreover, the nomogram revealed that this model possessed a steady prognostic prediction capability with C-index as 0.68. More importantly, CC patients with high CRLs-risk score were more sensitive to eight targeted therapy drugs. The prognostic prediction power of the CRLs-risk score was further confirmed by cell lines, tissues and two independent CC cohorts. This study constructed a novel ten-CRLs-based prognosis model for CC patients. The CRLs-risk score is expected to serve as a promising prognostic biomarker and predict targeted therapy response in CC patients.

A Novel m7G-Related Gene Signature Predicts the Prognosis of Colon Cancer.

Colon cancer (CC), one of the most common malignancies worldwide, lacks an effective prognostic prediction biomarker. N7-methylguanosine (m7G) methylation is a common RNA modification type and has been proven to influence tumorigenesis. However, the correlation between m7G-related genes and CC remains unclear. The gene expression levels and clinical information of CC patients were downloaded from public databases. Twenty-nine m7G-related genes were obtained from the published literature. Via unsupervised clustering based on the expression levels of m7G-related genes, CC patients were divided into three m7G clusters. Based on differentially expressed genes (DEGs) from the above three groups, CC patients were further divided into three gene clusters. The m7G score, a prognostic model, was established using principal component analysis (PCA) based on 15 prognosis-associated m7G genes. KM curve analysis demonstrated that the overall survival rate was remarkably higher in the high-m7G score group, which was much more significant in advanced CC patients as confirmed by subgroup analysis. Correlation analysis indicated that the m7G score was associated with tumor mutational burden (TMB), PD-L1 expression, immune infiltration, and drug sensitivity. The expression level of prognosis-related m7G genes was further confirmed in human CC cell lines and samples. This study established an m7G gene-based prognostic model (m7G score), which demonstrated the important roles of m7G-related genes during CC initiation and progression. The m7G score could be a practical biomarker to predict immunotherapy response and prognosis in CC patients.