Tumor mutation load better predicts the prognosis of patients treated with immune checkpoint inhibitors in upper gastrointestinal cancers: A systematic review and meta-analysis.

BACKGROUND: Tumor mutational load (TML) has emerged as a potential biomarker for multiple solid tumors. However, data on its prognostic impact on upper gastrointestinal (UGI) cancer are limited. Therefore, the aim of this systematic review and meta-analysis was to assess the prognostic value of TML for the survival of patients with UGI cancer. METHOD: A comprehensive search of the PubMed, Embase, Cochrane Library, and Web of Science databases was conducted up to February 13, 2023. Eleven studies met our inclusion criteria. Hazard ratios (HRs) for progression-free survival and overall survival and their 95% confidence intervals (CIs) were calculated. Subsequently, the combined HR and its 95% CI were calculated for UGI tract cancers in the high and low TML groups. I2 statistics and p-values were used to evaluate heterogeneity. Publication bias, sensitivity, and subgroup analyses were performed to determine sources of heterogeneity. RESULTS: In total, 932 patients with UGI tract cancer from 11 publications were included. The high TML group treated with immunotherapy showed significantly improved overall survival (HR = 0.68; 95% CI: 0.53, 0.86; p = .001) and progression-free survival (HR = 0.74; 95% CI: 0.58, 0.95; p = .020) compared with the low TML group. CONCLUSION: Our study demonstrated that patients with UGI tumors and higher TML have a better prognosis with immunotherapy, suggesting that TML is a promising predictive biomarker for immunotherapy. REGISTRATION: The study protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO Registration No: CRD42023405596).

Identification of a ferroptosis-related gene signature predicting recurrence in stage II/III colorectal cancer based on machine learning algorithms.

Background: Colorectal cancer (CRC) is one of the most prevalent cancer types globally. A survival paradox exists due to the inherent heterogeneity in stage II/III CRC tumor biology. Ferroptosis is closely related to the progression of tumors, and ferroptosis-related genes can be used as a novel biomarker in predicting cancer prognosis. Methods: Ferroptosis-related genes were retrieved from the FerrDb and KEGG databases. A total of 1,397 samples were enrolled in our study from nine independent datasets, four of which were integrated as the training dataset to train and construct the model, and validated in the remaining datasets. We developed a machine learning framework with 83 combinations of 10 algorithms based on 10-fold cross-validation (CV) or bootstrap resampling algorithm to identify the most robust and stable model. C-indice and ROC analysis were performed to gauge its predictive accuracy and discrimination capabilities. Survival analysis was conducted followed by univariate and multivariate Cox regression analyses to evaluate the performance of identified signature. Results: The ferroptosis-related gene (FRG) signature was identified by the combination of Lasso and plsRcox and composed of 23 genes. The FRG signature presented better performance than common clinicopathological features (e.g., age and stage), molecular characteristics (e.g., BRAF mutation and microsatellite instability) and several published signatures in predicting the prognosis of the CRC. The signature was further stratified into a high-risk group and low-risk subgroup, where a high FRG signature indicated poor prognosis among all collected datasets. Sensitivity analysis showed the FRG signature remained a significant prognostic factor. Finally, we have developed a nomogram and a decision tree to enhance prognosis evaluation. Conclusion: The FRG signature enabled the accurate selection of high-risk stage II/III CRC population and helped optimize precision treatment to improve their clinical outcomes.

Machine learning-based identification of a novel prognosis-related long noncoding RNA signature for gastric cancer.

Gastric cancer (GC) is one of the most common malignancies with a poor prognosis. Immunotherapy has attracted much attention as a treatment for a wide range of cancers, including GC. However, not all patients respond to immunotherapy. New models are urgently needed to accurately predict the prognosis and the efficacy of immunotherapy in patients with GC. Long noncoding RNAs (lncRNAs) play crucial roles in the occurrence and progression of cancers. Recent studies have identified a variety of prognosis-related lncRNA signatures in multiple cancers. However, these studies have some limitations. In the present study, we developed an integrative analysis to screen risk prediction models using various feature selection methods, such as univariate and multivariate Cox regression, least absolute shrinkage and selection operator (LASSO), stepwise selection techniques, subset selection, and a combination of the aforementioned methods. We constructed a 9-lncRNA signature for predicting the prognosis of GC patients in The Cancer Genome Atlas (TCGA) cohort using a machine learning algorithm. After obtaining a risk model from the training cohort, we further validated the model for predicting the prognosis in the test cohort, the entire dataset and two external GEO datasets. Then we explored the roles of the risk model in predicting immune cell infiltration, immunotherapeutic responses and genomic mutations. The results revealed that this risk model held promise for predicting the prognostic outcomes and immunotherapeutic responses of GC patients. Our findings provide ideas for integrating multiple screening methods for risk modeling through machine learning algorithms.

Case Report: Abdominal Cocoon With Jejuno-Ileo-Colonic Fistula.

Introduction: Abdominal cocoon is a unique peritoneal disease that is frequently misdiagnosed. The occurrence of the abdominal cocoon with a jejuno-ileo-colonic fistula has not been previously reported. Case Presentation: We admitted a 41-year-old female patient with an abdominal cocoon and a jejuno-ileo-colonic fistula. She was admitted to our hospital for the following reasons: "the menstrual cycle is prolonged for half a year, and fatigue, palpitations, and shortness of breath for 2 months". On the morning of the 4th day of admission, the patient experienced sudden, severe, and intolerable abdominal pain after defecating. An emergency abdominal CT examination revealed intestinal obstruction. Surgery was performed, and the small intestine and colon were observed to be conglutinated and twisted into a mass surrounded by a fibrous membrane, and an enteroenteric fistula was observed between the jejunum, ileum, and sigmoid colon. We successfully relieved the intestinal obstruction and performed adhesiolysis. The patient was discharged from our hospital on the 6th postoperative day, then she recovered and was discharged from Feicheng People's Hospital after another 11 days of conservative treatment, and she recovered well-during the 2-month follow-up period. Conclusion: Abdominal cocoon coexisting with a jejuno-ileo-colonic fistula is very rare. During the process of abdominal cocoon treatment, the patient's medical history should be understood in detail before the operation, and the abdominal organs should be carefully evaluated during the operation to avoid missed diagnoses.

Transcriptome analysis confers a complex disease resistance network in wild rice Oryza meyeriana against Xanthomonas oryzae pv. oryzae.

Rice bacterial blight (BB), caused by Xanthomonas oryzae pv. oryzae (Xoo), is one of the devastating diseases of rice. It is well established that the wild rice Oryza meyeriana is immune to BB. In this study, the transcriptomic analysis was carried out by RNA sequencing of O. meyeriana leaves, inoculated with Xoo to understand the transcriptional responses and interaction between the host and pathogen. Totally, 57,313 unitranscripts were de novo assembled from 58.7 Gb clean reads and 14,143 unitranscripts were identified after Xoo inoculation. The significant metabolic pathways related to the disease resistance enriched by KEGG, were revealed to plant-pathogen interaction, phytohormone signaling, ubiquitin mediated proteolysis, and phenylpropanoid biosynthesis. Further, many disease resistance genes were also identified to be differentially expressed in response to Xoo infection. Conclusively, the present study indicated that the induced innate immunity comprise the basal defence frontier of O. meyeriana against Xoo infection. And then, the resistance genes are activated. Simultaneously, the other signaling transduction pathways like phytohormones and ubiquitin mediated proteolysis may contribute to the disease defence through modulation of the disease-related genes or pathways. This could be an useful information for further investigating the molecular mechanism associated with disease resistance in O. meyeriana.