Tumor-secreted FGF21 acts as an immune suppressor by rewiring cholesterol metabolism of CD8+T cells.

Tumors employ diverse strategies for immune evasion. Unraveling the mechanisms by which tumors suppress anti-tumor immunity facilitates the development of immunotherapies. Here, we have identified tumor-secreted fibroblast growth factor 21 (FGF21) as a pivotal immune suppressor. FGF21 is upregulated in multiple types of tumors and promotes tumor progression. Tumor-secreted FGF21 significantly disrupts anti-tumor immunity by rewiring cholesterol metabolism of CD8+T cells. Mechanistically, FGF21 sustains the hyperactivation of AKT-mTORC1-sterol regulatory-element-binding protein 1 (SREBP1) signal axis in the activated CD8+T cells, resulting in the augment of cholesterol biosynthesis and T cell exhaustion. FGF21 knockdown or blockade using a neutralizing antibody normalizes AKT-mTORC1 signaling and reduces excessive cholesterol accumulation in CD8+T cells, thus restoring CD8+T cytotoxic function and robustly suppressing tumor growth. Our findings reveal FGF21 as a "secreted immune checkpoint" that hampers anti-tumor immunity, suggesting that inhibiting FGF21 could be a valuable strategy to enhance the cancer immunotherapy efficacy.

Analysis and application of RNA binding protein gene pairs to predict the prognosis of gastric cancer.

Background: RNA-binding proteins (RBPs) are closely related to tumors, but little is known about the mechanism of RBPs in tumorigenesis and progression of gastric cancer (GC). As genes do not usually act alone in the pathway deregulation, gene pair combinations are more likely to become stable and accurate biomarkers. The purpose of our research is to establish a novel signature based on RBP gene pairs to predict the prognosis of gastric cancer patients. Methods: We downloaded genetic and clinical information from the TCGA and GEO database. TCGA and GSE13911 were used for screening differentially expressed genes (DEGs). The RBP genes were gathered from previous studies and employed to screen out DE-RBP genes after intersecting with DEGs. Samples were classified according to the relative expression of each pair of DE-RBP genes. The univariate Cox regression analysis and random forest were used to identify hub gene pairs to construct signature for predicting the prognosis of gastric cancer. Time-dependent ROC curves and KM survival curves were performed to evaluate the signature. GSEA was performed in TCGA training cohort and GSE62254 testing cohort to analyze enrichment pathways. Finally, the influence of these gene pairs on the prognosis of GC patients was further elucidated respectively through the combination of high and low expression of the two genes in each hub gene pair. Results: We screened out 6 hub RBP gene pairs (COL5A2/FEN1, POP1/GFRA1, EXO1/PLEKHS1, SLC39A10/CHI3L1, MMP7/PPP1R1 B and SLC5A6/BYSL) to predict the prognosis of patients with gastric cancer. Using the optimal cut-off value to divide patients into high-risk and low-risk groups in the training and testing cohort, we found that the overall survival (OS) of the low-risk group was higher than that of the high-risk group (P < 0.05). The area under the ROC curves for 1, 3, and 5 years were (0.659, 0.744, 0.758) and (0.624, 0.650, 0.653) in two cohorts. Univariate and multivariate Cox regression analysis showed that 6 RBP gene pairs signature were independent prognostic factors for gastric cancer (P < 0.05). In addition, the prognostic survival analysis showed that COL5A2-high/FEN1-low, POP1-low/GFRA1-high, EXO1-low/PLEKHS1-low,SLC39A10-high/CHI3L1-low, MMP7-high/PPP1R1 B-low, SLC5A6-low/BYSL-low had worse OS (P < 0.05). And the gene correlation analysis showed that there was no obvious correlation between the genes in each gene pairs except SLC5A6/BYSL and POP1/GFRA1. Finally, GSEA analysis showed that the high-risk group was enriched in tumor migration, invasion and growth-related pathways. Conclusion: Our study identified a novel 6 RBP gene pairs signature to predict the prognosis of gastric cancer patients and provide potential targets for clinical gene therapy.

The TGF-ß/Smad2/3 signaling pathway is involved in Musashi2-induced invasion and metastasis of colorectal cancer.

To identify Musashi2 as an effective biomarker regulated by the TGF-ß/Smad2/3 signaling pathway for the precise diagnosis and treatment of colorectal cancer (CRC) through bioinformatic tools and experimental verification. The Cancer Genome Atlas, Timer, and Kaplan-Meier analyses were performed to clarify the expression of Musashi2 and its influence on the prognosis of CRC. Transforming growth factor beta 1 (TGF-ß1) was used to activate the TGF-ß/Smad2/3 signaling pathway to identify whether it could regulate the expression and function of Musashi2. Western blot analysis and quantitative PCR analyses were conducted to verify the expression of Musashi2. Cell counting kit-8 (CCK8), EdU, wound healing, and Transwell assays were conducted to reveal the role of Musashi2 in the proliferation, migration, and invasion of CRC. Musashi2 was upregulated in CRC and promoted proliferation and metastasis. TGF-ß1 increased the expression of Musashi2, while the antagonist inducer of type II TGF-ß receptor degradation-1 (ITD-1) decreased the expression. CCK8 and EdU assays demonstrated that inhibition of Musashi2 or use of ITD-1 lowered proliferation ability. The Transwell and wound healing assays showed that the migration and invasion abilities of CRC cells could be regulated by Musashi2. The above functions could be enhanced by TGF-ß1 by activating the TGF-ß/Smad2/3 signaling pathway and reversed by ITD-1. A positive correlation was found between Musashi2 and the TGF-ß/Smad2/3 signaling pathway. TGF-ß1 activates the TGF-ß/Smad2/3 pathway to stimulate the expression of Musashi2, which promotes the progression of CRC. Musashi2 might become a target gene for the development of new antitumor drugs.

TGF-ß Pathways Stratify Colorectal Cancer into Two Subtypes with Distinct Cartilage Oligomeric Matrix Protein (COMP) Expression-Related Characteristics.

BACKGROUND: Colorectal cancers (CRCs) continue to be the leading cause of cancer-related deaths worldwide. The exact landscape of the molecular features of TGF-ß pathway-inducing CRCs remains uncharacterized. METHODS: Unsupervised hierarchical clustering was performed to stratify samples into two clusters based on the differences in TGF-ß pathways. Weighted gene co-expression network analysis was applied to identify the key gene modules mediating the different characteristics between two subtypes. An algorithm integrating the least absolute shrinkage and selection operator (LASSO), XGBoost, and random forest regression was performed to narrow down the candidate genes. Further bioinformatic analyses were performed focusing on COMP-related immune infiltration and functions. RESULTS: The integrated machine learning algorithm identified COMP as the hub gene, which exhibited a significant predictive value for two subtypes with an area under the curve (AUC) value equaling 0.91. Further bioinformatic analysis revealed that COMP was significantly upregulated in various cancers, especially in advanced CRCs, and regulated the immune infiltration, especially M2 macrophages and cancer-associated fibroblasts in CRCs. CONCLUSIONS: Comprehensive immune analysis and experimental validation demonstrate that COMP is a reliable signature for subtype prediction. Our results could provide a new point for TGFß-targeted anticancer drugs and contribute to guiding clinical decision making for CRC patients.

Clinicopathological characteristics and prognosis of gastric signet ring cell carcinoma.

BACKGROUND: The clinicopathological features and prognosis of gastric signet ring cell carcinoma (GSRC) remain controversial, particularly with regard to sensitivity to postoperative adjuvant therapy. AIM: To compare the pathological features of GSRC with those of gastric adenocarcinoma of different degrees of differentiation and the differences in survival prognosis between the different disease processes. METHODS: By screening gastric cancer patients from 2010 to 2015 in the database of Surveillance, Epidemiology and End Results, and collecting the clinicopathological and prognostic data of gastric cancer patients who underwent surgery from January 2014 to December 2016 in the Second Affiliated Hospital of Nanchang University, we analyzed the general pathological characteristics of GSRC by the chi-square test. Univariate and multivariate analyses were conducted to compare the factors affecting the survival and prognosis of early and advanced gastric adenocarcinoma. The Kaplan-Meier curves were plotted to reveal the survival difference between early and advanced GSRC and different differentiated types of gastric adenocarcinoma. The prognosis model of advanced GSRC was established with R software, and the area under curve (AUC) and C-index were used to assess the accuracy of the model. RESULTS: Analysis of pathological features revealed that signet ring-cell carcinoma (SRC) was more frequently seen in younger (< 60 years), female, and White patients compared to non-SRC patients. SRC was less commonly associated with early gastric cancer (EGC) (23.60% vs 39.10%), lower N0 (38.61% vs 61.03%), and larger tumour sizes > 5 cm (31.15% vs 27.10%) compared to the differentiated type, while the opposite was true compared to the undifferentiated type. Survival prognostic analysis found no significant difference in the prognosis of SRC patients among EGC patients. In contrast, among advanced gastric cancer (AGC) patients, the prognosis of SRC patients was correlated with age, race, tumour size, AJCC stage, T-stage, and postoperative adjuvant therapy. The predictive model showed that the 3-year AUC was 0.787, 5-year AUC was 0.806, and C-index was 0.766. Compared to non-SRC patients, patients with SRC had a better prognosis in EGC [hazard ratio (HR): 0.626, 95% confidence interval (CI): 0.427-0.919, P < 0.05] and a worse prognosis in AGC (HR: 1.139, 95%CI: 1.030-1.258, P < 0.05). When non-SRC was divided into differentiated and undifferentiated types for comparison, it was found that in EGC, SRC had a better prognosis than differentiated and undifferentiated types, while there was no significant difference between differentiated and undifferentiated types. In AGC, there was no significant difference in prognosis between SRC and undifferentiated types, both of which were worse than differentiated types. A prognostic analysis of postoperative adjuvant therapy for SRC in patients with AGC revealed that adjuvant postoperative radiotherapy or chemotherapy significantly improved patient survival (34.6% and 36.2% vs 18.6%, P < 0.05). CONCLUSION: The prognosis of SRC is better than that of undifferentiated type, especially in EGC, and its prognosis is even better than that of differentiated type. SRC patients can benefit from early detection, surgical resection, and aggressive adjuvant therapy.

The Hypoxic Landscape Stratifies Gastric Cancer Into 3 Subtypes With Distinct M6a Methylation and Tumor Microenvironment Infiltration Characteristics.

The interaction between hypoxia and RNA N6-methyladenosine (m6A) is an emerging focus of investigation. However, alterations in m6A modifications at distinct hypoxia levels remain uncharacterized in gastric cancer (GC). Unsupervised hierarchical clustering was performed to stratify samples into different clusters. Differentially expressed gene analysis, univariate Cox proportional hazards regression analysis, and hazard ratio calculations were used to establish an m6A score to quantify m6A regulator modification patterns. After using an algorithm integrating Least absolute shrinkage and selection operator (LASSO) and bootstrapping, we identified the best candidate predictive genes. Thence, we established an m6A-related hypoxia pathway gene prognostic signature and built a nomogram to evaluate its predictive ability. The area under the curve (AUC) value of the nomogram was 0.811, which was higher than that of the risk score (AUC=0.695) and stage (AUC=0.779), suggesting a high credibility of the nomogram. Furthermore, the clinical response of anti-PD-1/CTLA-4 immunotherapy between high- and low-risk patients showed a significant difference. Our study successfully explored a brand-new GC pathological classification based on hypoxia pathway genes and the quantification of m6A modification patterns. Comprehensive immune analysis and validation demonstrated that hypoxia clusters were reliable, and our signature could provide a new approach for clinical decision-making and immunotherapeutic strategies for GC patients.

Identification of key genes controlling cancer stem cell characteristics in gastric cancer.

BACKGROUND: Self-renewal of gastric cancer stem cells (GCSCs) is considered to be the underlying cause of the metastasis, drug resistance, and recurrence of gastric cancer (GC). AIM: To characterize the expression of stem cell-related genes in GC. METHODS: RNA sequencing results and clinical data for gastric adenoma and adenocarcinoma samples were obtained from The Cancer Genome Atlas database, and the results of the GC mRNA expression-based stemness index (mRNAsi) were analyzed. Weighted gene coexpression network analysis was then used to find modules of interest and their key genes. Survival analysis of key genes was performed using the online tool Kaplan-Meier Plotter, and the online database Oncomine was used to assess the expression of key genes in GC. RESULTS: mRNAsi was significantly upregulated in GC tissues compared to normal gastric tissues (P < 0.0001). A total of 16 modules were obtained from the gene coexpression network; the brown module was most positively correlated with mRNAsi. Sixteen key genes (BUB1, BUB1B, NCAPH, KIF14, RACGAP1, RAD54L, TPX2, KIF15, KIF18B, CENPF, TTK, KIF4A, SGOL2, PLK4, XRCC2, and C1orf112) were identified in the brown module. The functional and pathway enrichment analyses showed that the key genes were significantly enriched in the spindle cellular component, the sister chromatid segregation biological process, the motor activity molecular function, and the cell cycle and homologous recombination pathways. Survival analysis and Oncomine analysis revealed that the prognosis of patients with GC and the expression of three genes (RAD54L, TPX2, and XRCC2) were consistently related. CONCLUSION: Sixteen key genes are primarily associated with stem cell self-renewal and cell proliferation characteristics. RAD54L, TPX2, and XRCC2 are the most likely therapeutic targets for inhibiting the stemness characteristics of GC cells.

Molecular Subtypes and CD4+ Memory T Cell-Based Signature Associated With Clinical Outcomes in Gastric Cancer.

BACKGROUND: CD4+ memory T cells are an important component of the tumor microenvironment (TME) and affect tumor occurrence and progression. Nevertheless, there has been no systematic analysis of the effect of CD4+ memory T cells in gastric cancer (GC). METHODS: Three datasets obtained from microarray and the corresponding clinical data of GC patients were retrieved and downloaded from the Gene Expression Omnibus (GEO) database. We uploaded the normalize gene expression data with standard annotation to the CIBERSORT web portal for evaluating the proportion of immune cells in the GC samples. The WGCNA was performed to identify the modules the CD4+ memory T cell related module (CD4+ MTRM) which was most significantly associated with CD4+ memory T cell. Univariate Cox analysis was used to screen prognostic CD4+ memory T cell-related genes (CD4+ MTRGs) in CD4+ MTRM. LASSO analysis and multivariate Cox analysis were then performed to construct a prognostic gene signature whose effect was evaluated by Kaplan-Meier curves and receiver operating characteristic (ROC), Harrell's concordance index (C-index), and decision curve analyses (DCA). A prognostic nomogram was finally established based on the CD4+ MTRGs. RESULT: We observed that a high abundance of CD4+ memory T cells was associated with better survival in GC patients. CD4+ MTRM was used to stratify GC patients into three clusters by unsupervised clustering analysis and ten CD4+ MTRGs were identified. Overall survival, five immune checkpoint genes and 17 types of immunocytes were observed to be significantly different among the three clusters. A ten-CD4+ MTRG signature was constructed to predict GC patient prognosis. The ten-CD4+ MTRG signature could divide GC patients into high- and low-risk groups with distinct OS rates. Multivariate Cox analysis suggested that the ten-CD4+ MTRG signature was an independent risk factor in GC. A nomogram incorporating this signature and clinical variables was established, and the C-index was 0.73 (95% CI: 0.697-0.763). Calibration curves and DCA presented high credibility for the OS nomogram. CONCLUSION: We identified three molecule subtypes, ten CD4+ MTRGs, and generated a prognostic nomogram that reliably predicts OS in GC. These findings have implications for precise prognosis prediction and individualized targeted therapy.