Identification of Comprehensive Biomarkers in Patients With Mismatch Repair-Deficient Colon Adenocarcinoma Based on Parallel Multiomics.

Immunocheckpoint inhibitors have shown impressive efficacy in patients with colon cancer and other types of solid tumor that are mismatch repair-deficient (dMMR). Currently, PCR-capillary electrophoresis is one of the mainstream detection methods for dMMR, but its accuracy is still limited by germline mismatch repair (MMR) mutations, the functional redundancy of the MMR system, and abnormal methylation of MutL Homolog 1 promoter. Therefore, this study aimed to develop new biomarkers for dMMR based on artificial intelligence (AI) and pathologic images, which may help to improve the detection accuracy. To screen for the differential expression genes (DEGs) in dMMR patients and validate their diagnostic and prognostic efficiency, we used the expression profile data from the Cancer Genome Atlas (TCGA). The results showed that the expression of Immunoglobulin Lambda Joining 3 in dMMR patients was significantly downregulated and negatively correlated with the prognosis. Meanwhile, our diagnostic models based on pathologic image features showed good performance with area under the curves (AUCs) of 0.73, 0.86, and 0.81 in the training, test, and external validation sets (Jiangsu Traditional Chinese Medicine Hospital cohort). Based on gene expression and pathologic characteristics, we developed an effective prognosis model for dMMR patients through multiple Cox regression analysis (with AUC values of 0.88, 0.89, and 0.88 at 1-, 3-, and 5-year intervals, respectively). In conclusion, our results showed that Immunoglobulin Lambda Joining 3 and nucleus shape-related parameters (such as nuclear texture, nuclear eccentricity, nuclear size, and nuclear pixel intensity) were independent diagnostic and prognostic factors, suggesting that they could be used as new biomarkers for dMMR patients.

Evaluation of laboratory and environmental exposure systems for protein modification upon gas pollutants and environmental factors.

Chemical modifications of proteins induced by ambient ozone (O3) and nitrogen oxides (NOx) are of public health concerns due to their potential to trigger respiratory diseases. The laboratory and environmental exposure systems have been widely used to investigate their relevant mechanism in the atmosphere. Using bovine serum albumin (BSA) as a model protein, we evaluated the two systems and aimed to reduce the uncertainties of both the reactants and products in the corresponding kinetic study. In the laboratory simulation system, the generated gaseous pollutants showed negligible losses. Ten layers of BSA were coated on the flow tube with protein extraction recovery of 87.4%. For environmental exposure experiment, quartz fiber filter was selected as the upper filter with low gaseous O3 (8.0%) and NO2 (1.7%) losses, and cellulose acetate filter was appropriate for the lower filter with protein extraction efficiency of 95.2%. The protein degradation process was observed without the exposure to atmospheric oxidants and contributed to the loss of protein monomer mass fractions, while environmental factors (e.g., molecular oxygen and ultraviolet) may cause greater protein monomer losses. Based on the evaluation, the study exemplarily applied the two systems to protein modification and both showed that O3 promotes the protein oligomerization and nitration, while increased temperature can accelerate the oligomerization and increased relative humidity can inhibit the nitration in the environmental exposure samples. The developed laboratory and environmental systems are suitable for studying protein modifications formed under different atmospheric conditions. A combination of the two will further reveal the actual mechanism of protein modifications.

Occupational noise and genetic variants in stress hormone biosynthesis-based genes and rates of blood lipid changes in China: A five-year longitudinal study.

Lipid profiles are influenced by both noise and genetic variants. However, little is known about the associations of occupational noise and genetic variants with age-related changes in blood lipids, a crucial event in the initiation and evolution of atherosclerotic cardiovascular diseases. We aimed to evaluate the associations of blood lipid change rates with occupational noise and genetic variants in stress hormone biosynthesis-based genes. This cohort was established in 2012 and 2013 and was followed up until 2017. A total of 952 participants were included in the final analysis and all of them were categorized to two groups, the exposed group and control group, according to the exposed noise levels in their working area. Single nucleotide polymorphisms (SNPs) in stress hormone biosynthesis-based genes were genotyped. Five physical examinations were conducted from 2012 to 2017 and lipid measurements were repeated five times. The estimated annual changes (EACs) of blood lipid were calculated as the difference in blood lipid levels between any 2 adjacent examinations divided by their time interval (year). The generalized estimating equations for repeated measures analyses with exchangeable correlation structures were used to evaluate the influence of exposing to noise (versus being a control) and the SNPs mentioned above on the EACs of blood lipids. We found that the participants experienced accelerated age-related decline in high-density lipoprotein cholesterol (HDL-C) levels as they were exposed to noise (ß = -0.38, 95% confidence interval (CI), -0.66 to -0.10, P = 0.007), after adjusting for work duration, gender, smoking, alcohol consumption, and pack-years. This trend was only found in participants with COMT-rs165815 TT genotype (ß = -1.19, 95% CI, -1.80 to -0.58, P < 0.001), but not in those with the CC or CT genotypes. The interaction of noise exposure and rs165815 was marginally significant (Pinteraction = 0.010) after multiple adjustments. Compared with DDC-rs11978267 AA genotype carriers, participants carrying rs11978267 GG genotype had decreased EAC of triglycerides (TG) (ß = -5.06, 95% CI, -9.07 to -1.05, P = 0.013). Participants carrying DBH-rs4740203 CC genotype had increased EAC of total cholesterol (TC) (ß = 1.19, 95% CI, 0.06 to 2.33, P = 0.039). However, these findings were not statistically significant after multiple adjustments. These results indicated that Occupational noise exposure was associated with accelerated age-related decreases in HDL-C levels, and the COMT-rs165815 genotype appeared to modify the effect of noise exposure on HDL-C changes among the occupational population.

An oncolytic virus delivering tumor-irrelevant bystander T cell epitopes induces anti-tumor immunity and potentiates cancer immunotherapy.

Tumor-specific T cells are crucial in anti-tumor immunity and act as targets for cancer immunotherapies. However, these cells are numerically scarce and functionally exhausted in the tumor microenvironment (TME), leading to inefficacious immunotherapies in most patients with cancer. By contrast, emerging evidence suggested that tumor-irrelevant bystander T (TBYS) cells are abundant and preserve functional memory properties in the TME. To leverage TBYS cells in the TME to eliminate tumor cells, we engineered oncolytic virus (OV) encoding TBYS epitopes (OV-BYTE) to redirect the antigen specificity of tumor cells to pre-existing TBYS cells, leading to effective tumor inhibition in multiple preclinical models. Mechanistically, OV-BYTE induced epitope spreading of tumor antigens to elicit more diverse tumor-specific T cell responses. Remarkably, the OV-BYTE strategy targeting human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific T cell memory efficiently inhibited tumor progression in a human tumor cell-derived xenograft model, providing important insights into the improvement of cancer immunotherapies in a large population with a history of SARS-CoV-2 infection or coronavirus disease 2019 (COVID-19) vaccination.