Overexpression of COX7A1 Promotes the Resistance of Gastric Cancer to Oxaliplatin and Weakens the Efficacy of Immunotherapy.

Gastric cancer (GC) is one of the most common clinical malignant tumors worldwide, with high morbidity and mortality. Presently, the overall response rate to immunotherapy is low, and current methods for predicting the prognosis of GC are not optimal. Therefore, novel biomarkers with accuracy, efficiency, stability, performance ratio, and wide clinical application are needed. Based on public data sets, the chemotherapy cohort and immunotherapy cohort from Sun Yat-sen University Cancer Center, a series of bioinformatics analyses, such as differential expression analysis, survival analysis, drug sensitivity prediction, enrichment analysis, tumor immune dysfunction and exclusion analysis, single-sample gene set enrichment analysis, stemness index calculation, and immune cell infiltration analysis, were performed for screening and preliminary exploration. Immunohistochemical staining and in vitro experiments were performed for further verification. Overexpression of COX7A1 promoted the resistance of GC cells to Oxaliplatin. COX7A1 may induce immune escape by regulating the number of fibroblasts and their cellular communication with immune cells. In summary, measuring the expression levels of COX7A1 in the clinic may be useful in predicting the prognosis of GC patients, the degree of chemotherapy resistance, and the efficacy of immunotherapy.

Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancer.

BACKGROUND: Only a subset of patients with gastric cancer experience long-term benefits from immune checkpoint inhibitors (ICIs). Currently, there is a deficiency in precise predictive biomarkers for ICI efficacy. The aim of this study was to develop and validate a pathomics-driven ensemble model for predicting the response to ICIs in gastric cancer, using H&E-stained whole slide images (WSI). METHODS: This multicenter study retrospectively collected and analyzed H&E-stained WSIs and clinical data from 584 patients with gastric cancer. An ensemble model, integrating four classifiers: least absolute shrinkage and selection operator, k-nearest neighbors, decision trees, and random forests, was developed and validated using pathomics features, with the objective of predicting the therapeutic efficacy of immune checkpoint inhibition. Model performance was evaluated using metrics including the area under the curve (AUC), sensitivity, and specificity. Additionally, SHAP (SHapley Additive exPlanations) analysis was used to explain the model's predicted values as the sum of the attribution values for each input feature. Pathogenomics analysis was employed to explain the molecular mechanisms underlying the model's predictions. RESULTS: Our pathomics-driven ensemble model effectively stratified the response to ICIs in training cohort (AUC 0.985 (95% CI 0.971 to 0.999)), which was further validated in internal validation cohort (AUC 0.921 (95% CI 0.839 to 0.999)), as well as in external validation cohort 1 (AUC 0.914 (95% CI 0.837 to 0.990)), and external validation cohort 2 (0.927 (95% CI 0.802 to 0.999)). The univariate Cox regression analysis revealed that the prediction signature of pathomics-driven ensemble model was a prognostic factor for progression-free survival in patients with gastric cancer who underwent immunotherapy (p<0.001, HR 0.35 (95% CI 0.24 to 0.50)), and remained an independent predictor after multivariable Cox regression adjusted for clinicopathological variables, (including sex, age, carcinoembryonic antigen, carbohydrate antigen 19-9, therapy regime, line of therapy, differentiation, location and programmed death ligand 1 (PD-L1) expression in all patients (p<0.001, HR 0.34 (95% CI 0.24 to 0.50)). Pathogenomics analysis suggested that the ensemble model is driven by molecular-level immune, cancer, metabolism-related pathways, and was correlated with the immune-related characteristics, including immune score, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data score, and tumor purity. CONCLUSIONS: Our pathomics-driven ensemble model exhibited high accuracy and robustness in predicting the response to ICIs using WSIs. Therefore, it could serve as a novel and valuable tool to facilitate precision immunotherapy.

Construction of a gene model related to the prognosis of patients with gastric cancer receiving immunotherapy and exploration of COX7A1 gene function.

BACKGROUND: GC is a highly heterogeneous tumor with different responses to immunotherapy, and the positive response depends on the unique interaction between the tumor and the tumor microenvironment (TME). However, the currently available methods for prognostic prediction are not satisfactory. Therefore, this study aims to construct a novel model that integrates relevant gene sets to predict the clinical efficacy of immunotherapy and the prognosis of GC patients based on machine learning. METHODS: Seven GC datasets were collected from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database and literature sources. Based on the immunotherapy cohort, we first obtained a list of immunotherapy related genes through differential expression analysis. Then, Cox regression analysis was applied to divide these genes with prognostic significancy into protective and risky types. Then, the Single Sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to score the two categories of gene sets separately, and the scores differences between the two gene sets were used as the basis for constructing the prognostic model. Subsequently, Weighted Correlation Network Analysis (WGCNA) and Cytoscape were applied to further screen the gene sets of the constructed model, and finally COX7A1 was selected for the exploration and prediction of the relationship between the clinical efficacy of immunotherapy for GC. The correlation between COX7A1 and immune cell infiltration, drug sensitivity scoring, and immunohistochemical staining were performed to initially understand the potential role of COX7A1 in the development and progression of GC. Finally, the differential expression of COX7A1 was verified in those GC patients receiving immunotherapy. RESULTS: First, 47 protective genes and 408 risky genes were obtained, and the ssGSEA algorithm was applied for model construction, showing good prognostic discrimination ability. In addition, the patients with high model scores showed higher TMB and MSI levels, and lower tumor heterogeneity scores. Then, it is found that the COX7A1 expressions in GC tissues were significantly lower than those in their corresponding paracancerous tissues. Meanwhile, the patients with high COX7A1 expression showed higher probability of cancer invasion, worse clinical efficacy of immunotherapy, worse overall survival (OS) and worse disease-free survival (DFS). CONCLUSIONS: The ssGSEA score we constructed can serve as a biomarker for GC patients and provide important guidance for individualized treatment. In addition, the COX7A1 gene can accurately distinguish the prognosis of GC patients and predict the clinical efficacy of immunotherapy for GC patients.

Novel staging for gastric neuroendocrine neoplasms by incorporating the WHO grading into the TNM staging system.

BACKGROUND: The 8th tumor-node-metastasis (TNM) classification of the American Joint Committee on Cancer (AJCC) can be used to estimate the prognosis of gastric neuroendocrine tumor (gNET) and gastric neuroendocrine carcinoma (gNEC) patients but not gastric neuroendocrine neoplasms (gNENs). METHODS: First, in the SEER (training) dataset, a TNMG system was built by combining the WHO G grade (G1-4; NEC grouped into G4) with the 8th AJCC T (T1-4), N (N0-1), and M (M0-1) stage, which was then validated in a Chinese (validation) cohort. RESULTS: In all, 2245 gNENs cases from the training dataset and 280 cases from the validation dataset were eligible. The T stage, M stage, and G grade were independent prognostic factors for OS in both datasets (all p < 0.05). The TNMG staging system demonstrated better C-index for predicting OS than the 8th AJCC TNM staging system in both the training (0.87, 95%CI: 0.86-0.88 vs. 0.79, 95%CI: 0.77-0.81) and validation (0.77, 95%CI: 0.73-0.80 vs. 0.75, 95%CI: 0.71-0.79) datasets. The AUC of the 3-year OS for the TNMG staging system was 0.936 and 0.817 in the SEER and validation dataset, respectively; higher than those of the 8th AJCC system (vs. 0.843 and 0.779, respectively). DCA revealed that compared with the 8th AJCC TNM staging system, the TNMG staging system demonstrated superior net prognostic benefit in both the training and validation datasets. CONCLUSIONS: The proposed TNMG staging system could more accurately predict the 3- and 5-year OS rate of gNENs patients than the 8th AJCC TNM staging system.

Improved photoelectric performance of all-inorganic perovskite through different additives for green light-emitting diodes.

The exceptional optical and electronic properties of all-inorganic cesium lead bromide (CsPbBr3) perovskite make it an ideal new optoelectronic material, but low surface coverage limits its performance. The morphological characteristics of thin films have a great influence on the performance of perovskite light emitting diodes, especially at low coverage, and an inhomogeneous surface will lead to current leakage. To tackle this problem, the widespread adoption of composite layers including polymers poly(ethylene oxide) (PEO) and organic insulating poly(vinylpyrrolidone) (PVP) and all-inorganic perovskites is an effective way to increase the surface coverage and uniformity of perovskite films and improve the performance of perovskite light emitting devices. In our work, the perovskite thin films are investigated by using PEO and PVP dual additives, and the optimized CsPbBr3-PEO-PVP LED with maximum luminance, current efficiency, and external quantum efficiency of 2353 cd m-2 (at 7.2 V), 2.14 cd A-1 (at 6.5 V) and 0.85% (at 6.5 V) was obtained. This work indicates that the method of using additives is not only the key to enhancing the quality of perovskite thin film, but also the key to achieving a higher performance perovskite LED.