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Background: The high heterogeneity of ovarian cancer (OC) brings great difficulties to its early diagnosis and prognostic forecast. It is an urgent need to establish a prognostic model of OC based on clinicopathological features and genomics. Methods: We identified hypoxia-related differentially expressed genes (DEGs) between OC tissues from The Cancer Genome Atlas (TCGA) and normal tissues from the Genotype-Tissue Expression (GTEx). LASSO Cox regression analysis was applied for building a prognostic model in the TCGA-GTEx cohorts, and its predictive value was validated in the GEO-OC cohort. Functional enrichment analysis was performed to investigate the underlying mechanisms. By constructing a hypoxia model of SKOV3 cell line and applying qRT-PCR, we investigated the relationship between hypoxia with two novel genes in the prognostic model (ISG20 and ANGPTL4). Results: Twelve prognostic hypoxia-related DEGs were identified and nine of them were selected to establish a prognostic model. OC patients were stratified into two risk groups, and the high-risk group showed reduced survival time compared to the low-risk group upon survival analysis. Univariate and multivariate Cox regression analysis demonstrated that the risk score acted as an independent risk factor for overall survival. The biological function of the identified prognostic hypoxia-related gene signature was involved in immune cells infiltration. Low expression of ISG20 was observed in the CoCl2-mimicked hypoxic SKOV3 cell line and negatively correlated with HIF-1α. Conclusion: Our findings showed that this hypoxia-related gene signature can serve as a satisfactory prognostic classifier for OC and will be beneficial to the research and development of targeted therapeutic strategies.

Background Infertility is associated with the alteration of the seminal microbiome. However, how the onset of dysbiosis remains controversial and the involvement of host factors remains elusive. This study investigates the alterations of the seminal microbiome in male infertility and examines the association and function of DEFB119, a reproductive-tract-specific host antimicrobial peptide, on the seminal microbiome and male fertility. Results We analyzed the seminal microbiome by 16S rRNA sequencing in 30 individuals with normal sperm parameters and 58 male-factor infertile patients. While we observed comparable genera, diversity and evenness of bacterial communities, a marked decrease in the modularity of the metacommunities was observed in patients with abnormal spermiogram. Analysis of the protein level of DEFB119 by ELISA revealed a marked elevation of DEFB119 in a subpopulation of male-infertile patients. Elevated seminal DEFB119 was associated with a decrease in the observed genera, diversity and evenness of bacterial communities and further distortion of the metacommunities. Mediation analysis suggests the involvement of elevated DEFB119 and dysbiosis of the seminal microbiome in mediating the abnormalities in the spermiogram. Functional experiments showed that recombinant DEFB119 significantly decrease the progressive motility of sperm in patients with abnormal spermiogram. Moreover, DEFB119 demonstrated species-specific antimicrobial activity against common seminal and non-seminal species. Conclusions Male infertility is associated with distorted bacterial networks. Elevation of the DEFB119 level in seminal plasma stratifies male infertile patients with dysbiosis of the seminal microbiome. Both elevated DEFB119 and dysbiosis contribute to the abnormal spermiogram. Our work identifies an important host factor that mediates the host-microbiome interaction and stratifies the seminal microbiome associated with male infertility. These results may lead to a new diagnostic method for male infertility and regimens for formulating the microbiome in the reproductive tract and other organ systems.

Chimeric antigen receptor engineered T cells (CARTs) for HCC and other solid tumors are not as effective as they are for blood cancers. CARTs may lose function inside tumors due to persistent antigen engagement. The aims of this study are to develop low-affinity monoclonal antibodies (mAbs) and low-avidity CARTs for HCC and to test the hypothesis that low-avidity CARTs can resist exhaustion and maintain functions in solid tumors, generating durable antitumor effects.New human glypican-3 (hGPC3) mAbs were developed from immunized mice. We obtained three hGPC3-specific mAbs that stained HCC tumors, but not the adjacent normal liver tissues. One of them, 8F8, bound an epitope close to that of GC33, the frequently used high-affinity mAb, but with approximately 17-fold lower affinity. We then compared the 8F8 CARTs to GC33 CARTs for their in vitro function and in vivo antitumor effects. In vitro, low-avidity 8F8 CARTs killed both hGPC3The low-avidity 8F8-BBz CART resists exhaustion and apoptosis inside tumor lesions, demonstrating a greater therapeutic potential than high-avidity CARTs.

Aim The study aimed to identifying critical long non-coding RNAs (lncRNAs) and constructed a prognostic signature to optimize prognosis predication of patients with Stomach Adenocarcinoma (STAD). Background STAD is a common malignant tumor with high metastasis rate and low survival rate. LncRNAs participate in the regulation process of epithelial-mesenchymal transition (EMT) and development of STAD. Methods RNAseq data was obtained from TCGA-STAD, while 200 EMT-associated genes (EAGs) from ‘HALLMARK_EPITHELIAL_MESENCHYMA-L _TRANSITION’ gene set. Differentially expressed EAGs and EMT-associated lncRNAs (EALs) were identified. Moreover, Lasso Cox regression analysis was used to construct a signature of differentially expressed EALs, and univariate and multivariate analyses, Kaplan-Meier analysis, receiver operating characteristic curve (ROC) analysis and nomogram were conducted to predict its prognostic value. Enrichment functional analysis was performed. Quantitative Real-Time PCR (qRT-PCR) was used to determine lncRNAs expressions in cell lines. Results A total of 52 differentially expressed EAGs and 320 EALs were identified in this study. Meanwhile, 16 EALs was used to construct the signature, and further analysis indicated that it had high prognostic value for STAD patients. Enrichment functional analysis revealed the signature was correlated to tumor immunity in STAD. Moreover, three novel EALs expressions were confirmed in cell lines. Conclusion A novel survival signature was established to predict and evaluate prognosis of STAD patients.

As the prognosis of early gastric cancer (EGC) is significantly better than that of advanced gastric cancer (AGC), the development of biomarkers to monitor the progression of chronic atrophic gastritis (CAG) to gastric cancer (GC) is essential.Stomach tissue miRNA and mRNA sequences from patients with chronic non-atrophic gastritis (CNAG), CAG, precancerous lesions of gastric cancer (PLGC), and GC were analyzed. A publicly available GC-related miRNA microarray dataset was obtained from the Gene Expression Omnibus database. Spearman's correlation and differential gene analyses, and clinical validation were used to identify novel miRNAs correlating with CAG progression to GC. miRNA targets were predicted using weighted gene co-expression analysis and databases. A dual-luciferase reporter assay was performed to check for direct interaction between miR-196a-5p and ACER2. The CCK-8 and wound healing assays, and flow cytometry were performed to evaluate cell proliferation, migration, and apoptosis.miR-196a-5p was correlated with CAG progression to GC. Overexpression of miR-196a-5p promoted GC cell proliferation and migration and inhibited apoptosis, whereas suppression of miR-196a-5p exerted the opposite effect. Based on the prediction and luciferase assays, ACER2 was identified as the target of miR-196a-5p. ACER2 was downregulated in GC cell lines. Knockdown of ACER2 increased GC cell proliferation rates and migration ability and inhibited apoptosis, while ACER2 overexpression led to the opposite effect.miR-196a-5p correlated with CAG progression to GC and induced malignant biological behaviors of GC cells by targeting ACER2, providing a novel monitoring biomarker and target for GC prevention.

Xiaotao Jiang,1,2 Fan Liu,2,3 Peng Liu,1,2 Yanhua Yan,1,2 Shaoyang Lan,1 Kunhai Zhuang,1,4 Yufeng Liu,5 Kailin Jiang,1,2 Yuancheng Huang,1,2 Kechao Nie,1,2 Zhihua Zheng,1,2 Jinglin Pan,6 Junhui Zheng,1,2 Fengbin Liu,1,4 Shijie Xu,5 Peiwu Li,1 Yi Wen1 1Department of Gastroenterology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, People’s Republic of China; 2First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, People’s Republic of China; 3Zhaoqing Hospital of Chinese Medicine Affiliated to Southern Medical University, Zhaoqing, Guangdong, People’s Republic of China; 4Baiyun Hospital of the First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, People’s Republic of China; 5Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, People’s Republic of China; 6Department of Gastroenterology, Hainan Provincial Hospital of Traditional Chinese Medicine, Haikou, Hainan, People’s Republic of ChinaCorrespondence: Peiwu Li; Yi Wen Email doctorlipw2@163.com; 421491922@qq.comObjective: We aimed to build a ferroptosis-based classifier to characterize the molecular features of gastric cancers (GC) and investigate the relationship between different ferroptosis patterns and GC tumor microenvironment (TME).Methods: Based on the genomic and clinical information from TCGA portal and GEO database, non-negative matrix factorization (NMF) was used to identify ferroptosis subtypes in GC patients. In order to estimate the ferroptosis levels, we established ferroptosis subtype score (FSS) to quantify ferroptosis patterns and ferroptosis potential index (FPI) by principal component analysis (PCA). The correlations of different ferroptosis patterns with TME cell-infiltrating characteristics (including immune cell infiltration, immune checkpoints expression levels, tumor mutational burden (TMB) and immunotherapy response) were systematically analyzed.Results: Two ferroptosis subtypes, C1 (with lower FSS) and C2 (with higher FSS), were determined. C2 displayed a significantly lower FPI than C1. Besides, C2 was associated with diffuse subtype while C1 with intestinal subtype. As for TME characteristics, C2 was in accordance with the immune-excluded phenotype as it showed more active immune and stromal activities but lower TMB, less probability of immunotherapy response and poorer prognosis. C1 was linked to immune-inflamed phenotype as it had lower stromal activities but increased neoantigen load, enhanced response to immunotherapy and relatively better prognosis.Conclusion: The systematic assessment of ferroptosis patterns and ferroptosis levels presented in our study implied that ferroptosis serves as an important factor in the formation of TME, which may expand the understanding of TME and provide a novel perspective for the development of targeted immunotherapeutic strategies for GC patients.Keywords: ferroptosis, gastric cancer, immune cell infiltration, tumor microenvironment, immunotherapy

Yanhua Yan,1 Kechao Nie,2 Junhui Zheng,1 Xiaotao Jiang,1 Yuancheng Huang,1 Zhihua Zheng,1 Yi Wen,3 Peiwu Li3 1The First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, 510000, People’s Republic of China; 2Department of Integrated Traditional Chinese & Western Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan, 410011, People’s Republic of China; 3Department of Gastroenterology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510000, People’s Republic of ChinaCorrespondence: Peiwu Li; Yi Wen Email doctorlipw@gzucm.edu.cn; 421491922@qq.comBackground: Stomach adenocarcinoma (STAD) is the most common gastrointestinal cancer and is associated with high mortality worldwide. Endothelin receptor type A (EDNRA) is associated with guanine-nucleotide-binding (G) proteins and plays important roles in cellular processes and various diseases.Purpose: To investigate the prognosis value of EDNRA expression and its correlation with immune infiltrates in patients with STAD.Methods: The association between clinical characteristics and EDNRA expression in STAD was analyzed using the Wilcoxon signed-rank test and logistic regression. The Kaplan–Meier plotter analysis and Cox regression were constructed to evaluate the influence of EDNRA on prognosis, and a receiver operating characteristic (ROC) curve and nomogram were constructed. Gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA) were conducted to analyze the correlation between EDNRA and immune infiltrates. In addition, Oncomine, TIMER databases and qRT-PCR of STAD cell lines were used to verify the EDNRA expression in STAD.Results: Our results revealed that EDNRA expression was significantly higher in patients with STAD than normal gastric tissues, and the results have been confirmed by RT-qPCR. KM-plotter analysis revealed that patients with STAD had shorter OS, FP, and PPS (P< 0.001). Multivariate Cox analysis further confirmed that high EDNRA expression was an independent risk factor for OS in patients with STAD. Moreover, other clinicopathologic features were related with worse prognosis in STAD, including age, lymph nodes metastases and primary outcome. More importantly, ROC analysis also confirmed the diagnostic value, and a prognostic nomogram involving age, T, M, N classification, pathologic stage, residual tumor and EDNRA was constructed. GSEA revealed that high EDNRA expression was correlated with immunoregulatory interactions between lymphoid and non lymphoid cells pathways, natural killer cell activation involved in immune response, interleukin 1 receptor binding and pathways in cancer, and ssGSEA showed that EDNRA is correlated with macrophages and NK cells.Conclusion: Collectively, EDNRA can be an independent prognostic biomarker and correlated with immune infiltration in stomach adenocarcinoma.Keywords: EDNRA, STAD, bioinformatics, prognosis

Background. Gastric cancer (GC), an extremely aggressive tumor with a very different prognosis, is the third leading cause of cancer-related mortality. We aimed to construct a ferroptosis-related prognostic model that can be distinguished prognostically. Methods. The gene expression and the clinical data of GC patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO). The ferroptosis-related genes were obtained from the FerrDb. Using the “limma” R package and univariate Cox analysis, ferroptosis-related genes with differential expression and prognostic value were identified in the TCGA cohort. Last absolute shrinkage and selection operator (LASSO) Cox regression was applied to shrink ferroptosis-related predictors and construct a prognostic model. Functional enrichment, ESTIMATE algorithm, and single-sample gene set enrichment analysis (ssGSEA) were applied for exploring the potential mechanism. GC patients from the GEO cohort were used for validation. Results. An 8-gene prognostic model was constructed and stratified GC patients from TCGA and meta-GEO cohort into high-risk groups or low-risk groups. GC patients in high-risk groups have significantly poorer OS compared with those in low-risk groups. The risk score was identified as an independent predictor for OS. Functional analysis revealed that the risk score was mainly associated with the biological function of extracellular matrix (ECM) organization and tumor immunity. Conclusion. In conclusion, the ferroptosis-related model can be utilized for the clinical prognostic prediction in GC.

AIM Hepatocellular carcinoma (HCC) is a common malignancy associated with a poor prognosis due to difficulties in reliably estimating overall survival (OS). MicroRNAs (miRNAs) play critical roles in HCC initiation, progression, and metastasis and are highly correlated with patient prognosis. Thus, miRNA-based risk signatures and nomograms are urgently required for predicting OS in patients with HCC. METHODS We constructed a 13-miRNA-based signature and prognostic nomogram using 408 HCC samples and 58 normal tissues with miRNA sequencing data and clinical data from 323 patients downloaded from The Cancer Genome Atlas. A total of 195 patients were assigned as the internal validation cohort for verification and testing. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis was applied to investigate pathway enrichment for the signature. RESULTS We identified and validated a 13-miRNA risk signature highly associating with the OS of HCC patients. The signature showed good performances by calculating C-index, area under the curve, and calibration curves. After verification and testing using an internal validation cohort, the results yielded a miRNA-based signature and a prognostic nomogram with reliable predictive accuracy. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis indicated that various genes and multiple pathways were closely related to the mechanisms of HCC proliferation and metastasis. CONCLUSION We successfully identified a 13-miRNA-based signature and prognostic nomogram that are capable of predicting OS in patients with HCC.

Hepatitis B virus (HBV)-specific T cells play a critical role in determining the outcome of HBV infection. However, T cell response induced by predominant Ag in chronic infection is hardly detectable owing to the lack of a suitable assay. We herein established an optimized method to enumerate HBV-specific T cells and assessed the association between HBV surface Ag (HBsAg) and HBV DNA. Sixty chronic HBV infection patients were enrolled. HBV-specific T cells were expanded by using overlapping peptide pools covering the entire sequence of HBV genotypes B and C. IFN-γ-producing HBV-specific T cells were detected by a cultured enzyme-linked immunospot (ELISPOT) assay, ex vivo ELISPOT assay, or flow cytometry staining. The association between HBV-specific T cells and serum levels of HBsAg and HBV DNA were analyzed. Cultured ELISPOT assay had a higher sensitivity than ex vivo ELISPOT in the detection of HBV-specific T cells. Moreover, consistent results were acquired by flow cytometry analysis and cultured ELISPOT assay, but the latter required only a limited number of cells for detection. Interestingly, HBV core peptide pool induced a robust HBV-specific T cell response in patients with lower levels of HBV DNA and HBsAg. Specifically, the frequency of HBV core Ag-specific IFN-γ+ spot-forming cells was inversely correlated with serum levels of HBV DNA and HBsAg. An optimized cultured ELISPOT assay reveals the association between HBV core Ag-induced T cell response and HBV control; this method may favor the investigation of HBV-specific T cell in chronic HBV infection.

Recently, interactive character control models based on neural network have become a hot research topic in computer graphics and motion synthesis. A real-time interactive character control model with two substructures called gated neural network is proposed in this paper. In the first part of the model, a gated network is used to calculate the expert weights based on the user’s input parameters and the character’s current pose dynamically. Mixture of experts is used in choosing different control strategies for user input. The character posture is controlled by selecting mode-adjustment or phase-adjustment. In the second part, a simple neural network is used to adjust the character’s state through additional input parameters on different landscapes and to synthesize the final character motion. Experimental results show that the model can improve the performance of character control.

The ongoing global pandemic of coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and significantly impacts the world economy and daily life. Symptoms of COVID-19 range from asymptomatic to fever, dyspnoea, acute respiratory distress and multiple organ failure. Critical cases often occur in the elderly and patients with pre-existing conditions. By binding to the angiotensin-converting enzyme 2 receptor, SARS-CoV-2 can enter and replicate in the host cell, exerting a cytotoxic effect and causing local and systemic inflammation. Currently, there is no specific treatment for COVID-19, and immunotherapy has consistently attracted attention because of its essential role in boosting host immunity to the virus and reducing overwhelming inflammation. In this review, we summarise the immunopathogenic features of COVID-19 and highlight recent advances in immunotherapy to illuminate ideas for the development of new potential therapies.

Objectives: The purpose of this study was to investigate the role of 13 m5C-related regulators in colon adenocarcinoma (COAD) and determine their prognostic value.Main Methods: Gene expression and clinicopathological data were obtained from The Cancer Genome Atlas (TCGA) datasets. The expression of m5C-related regulators were analyzed with clinicopathological characteristics and alterations within m5C-related regulators. Subsequently, different subtypes of patients with COAD were identified. Then, the prognostic value of m5C-related regulators in COAD were confirmed via univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. The prognostic value of risk scores was evaluated using the Kaplan-Meier method, receiver operating characteristic (ROC) curves, and univariate and multivariate regression analyses. Additionally, Gene Set Enrichment Analysisc (GSEA), Kyoto Encyclopedia of Genes and Genomes c (KEGG) pathways, and Gene Ontologyc (GO) analysis were performed for biological functional analysis.Results: m5C-related regulators were found to be differentially expressed in COAD with different clinicopathological features. We observed a high alteration frequency in these genes, which were significantly correlated with their mRNA expression levels. Two clusters with different prognostic features were identified. Based on two independent prognostic m5C-related regulators (NSUN6 and ALYREF), a risk signature with good predictive significance was constructed. Univariate and multivariate Cox regression analyses suggested that the risk score was an independent prognostic factor. Biological processes and pathways associated with cancer, immune response, and RNA processing were identified.Conclusion: We revealed the genetic signatures and prognostic values of m5C-related regulators in COAD. Together, this has improved our understanding of m5C RNA modification and provided novel insights to identify predictive biomarkers and develop molecular targeted therapy for COAD.

ObjectivesThe purpose of this study was to investigate the role of m6A-related lncRNAs in gastric adenocarcinoma (STAD) and to determine their prognostic value.MethodsGene expression and clinicopathological data were obtained from The Cancer Genome Atlas (TCGA) database. Correlation analysis and univariate Cox regression analysis were conducted to identify m6A-related prognostic lncRNAs. Subsequently, different clusters of patients with STAD were identified via consensus clustering analysis, and a prognostic signature was established by least absolute shrinkage and selection operator (LASSO) Cox regression analyses. The clinicopathological characteristics, tumor microenvironment (TME), immune checkpoint genes (ICGs) expression, and the response to immune checkpoint inhibitors (ICIs) in different clusters and subgroups were explored. The prognostic value of the prognostic signature was evaluated using the Kaplan-Meier method, receiver operating characteristic curves, and univariate and multivariate regression analyses. Additionally, Gene Set Enrichment Analysis (GSEA), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Gene Ontology (GO) analysis were performed for biological functional analysis.ResultsTwo clusters based on 19 m6A-related lncRNAs were identified, and a prognostic signature comprising 14 m6A-related lncRNAs was constructed, which had significant value in predicting the OS of patients with STAD, clinicopathological characteristics, TME, ICGs expression, and the response to ICIs. Biological processes and pathways associated with cancer and immune response were identified.ConclusionsWe revealed the role and prognostic value of m6A-related lncRNAs in STAD. Together, our finding refreshed the understanding of m6A-related lncRNAs and provided novel insights to identify predictive biomarkers and immunotherapy targets for STAD.

Purpose: The purpose of this study was to investigate the role of m6A-related lncRNAs in colon adenocarcinoma (COAD) and determine their prognostic value.Material and methods: Gene expression and clinicopathological data were obtained from The Cancer Genome Atlas database. Correlation and univariate Cox regression analysis were conducted to identify m6A-related prognostic lncRNAs. A prognostic signature was established via least absolute shrinkage and selection operator (LASSO) Cox regression analyses. The prognostic value of risk scores was evaluated using the Kaplan-Meier method, receiver operating characteristic curves, and univariate and multivariate regression analyses. Whether the prognostic model could serve as a prognostic indicator for overall survival (OS) in subgroups of patients with different clinical characteristics were explored. Next, We established a competing endogenous RNA network. Gene Set Enrichment Analysis, Kyoto Encyclopedia of Genes and Genomes pathway, and Gene Ontology analysis were performed for biological functional analysis.Results: 36 lncRNAs that were highly correlated with OS of patients were identified. A prognostic signature comprising 11 m6A-related lncRNAs was constructed, which had significant value in predicting the OS of patients . Univariate and Multivariate Cox regression analyses suggested that the risk score was an independent prognostic factor. This m6A-related lncRNA prognostic model could serve as a prognostic indicator for OS in subgroups of patients with different clinical characteristics. Biological processes and pathways associated with cancer were identified.Conclusion: We revealed the role and prognostic value of m6A-related lncRNAs in COAD. Our finding refreshed the understanding of m6A-related lncRNAs and provided novel insights to identify predictive biomarkers and develop targeted therapy for COAD.

Cancer immunotherapy is a new and promising option for cancer treatment. Unlike traditional chemo- and radiotherapy, immunotherapy actives host immune system to attack malignancies, and this potentially offers long-term protection from recurrence with less toxicity in comparison to conventional chemo- and radiation therapy. In adoptive CD8+ T cell therapy (ACT), large numbers of tumor-specific T cells are sourced from patients and expanded in vitro and infused back to patients. T cells can be expanded from naturally-induced tumor-specific CD8+ T cells isolated from tumor infiltrating lymphocytes (TIL) or genetically-modified autologous circulating CD8+ T cells. The engineered T cells expressed tumor-specific antigen receptors including chimeric antigen receptors (CARs) and T cell receptors (TCRs), prepared from cultured B and T cell clones, respectively. The most successful ACT, anti-CD19 chimeric antigen receptor T (CAR-T) cell therapy directed against B cell lymphoma, is already approved for use based on evidence of efficacy. Efficacy of solid tumors is not yet forthcoming. This review summarizes current technology developments using ACT in clinical trials. In this review, differences between various ACT approaches are discussed. Furthermore, resistance factors in the tumor microenvironment are also considered, as are immune related adverse effects, critical clinic monitoring parameters and potential mitigation approaches.

Background and aim This study aims to systematically analyze the effect of long-term therapy with proton pump inhibitors (PPIs) on the risk of gastric cancer. Methods PubMed, EMBASE, Cochrane Library, China National Knowledge Infrastructure (CNKI), and China biomedical literature database (CBM) were searched for studies before February 2019. We evaluated the quality of the included articles through the Newcastle-Ottawa Scale and gathered relevant data to calculate the pooled odds ratio (OR) through Stata14.0. Results Seven relevant articles conformed to the inclusion criteria; 943 070 patients were included. The pooled OR was 2.50; 95% CI (1.74, 3.85); the subgroup analysis results showed that patients who had used PPIs for more than 36 months were most likely to develop gastric cancer, and an increased risk was observed among patients after Helicobacter pylori eradication. Noncardia gastric cancer was more likely to develop. Conclusions Long-term use of PPIs can possibly increase the risk of gastric cancer even among patients after H. pylori eradication; in particular, for noncardia gastric cancer, the risk increases with longer durations of PPI use. Due to the limited number of studies, more high-quality studies are required to be designed.

The prognostic prediction of hepatocellular carcinoma (HCC) is still challenging. Immune cells play a crucial role in tumor initiation, progression, and drug resistance. However, prognostic value of immune-related genes in HCC remains to be further clarified. In this study, the mRNA expression profiles and corresponding clinical information of HCC patients were downloaded from public databases. Then, we estimated the abundance of immune cells and identified the differentially infiltrated and prognostic immune cells. The weighted gene co-expression network analysis (WGCNA) was performed to identify immune-related genes in TCGA cohort and GEO cohort. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied to establish a risk-scoring model in the TCGA cohort. HCC patients from the GSE14520 datasets were utilized for risk model validation. Our results found that high level of dendritic cell (DC) infiltration was associated with poor prognosis. Over half of the DC-related genes (58.2%) were robustly differentially expressed between HCC and normal specimens in the TCGA cohort. 17 differentially expressed genes (DEGs) were found to be significantly associated with overall survival (OS) by univariate Cox regression analysis. A 12-gene risk-scoring model was established to evaluate the prognosis of HCC. The high-risk group exhibits significantly lower OS rate of HCC patients than the low-risk group. The risk-scoring model shows benign predictive capacity in both GEO dataset and TCGA dataset. The 12-gene risk-scoring model may independently perform prognostic value for HCC patients. Receiver operating characteristic (ROC) curve analysis of the risk-scoring model in GEO cohort and TCGA cohort performed well in predicting OS. Taken together, the 12-gene risk-scoring model could provide prognostic and potentially predictive information for HCC. SDC3, NCF2, BTN3A3, and WARS were noticed as a novel prognostic factor for HCC.

Background & Aims: Gastric cancer is the common malignancies from cancer worldwide. Endoscopy is currently the most effective method to detect early gastric cancer (EGC). However, endoscopy is not infallible and EGC can be missed during endoscopy. Artificial intelligence (AI)-assisted endoscopic diagnosis is a recent hot spot of research. We aimed to quantify the diagnostic value of AI-assisted endoscopy in diagnosing EGC. Method: The PubMed, MEDLINE, Embase and the Cochrane Library Databases were searched for articles on AI-assisted endoscopy application in EGC diagnosis. The pooled sensitivity, specificity, and area under the curve (AUC) were calculated, and the endoscopists' diagnostic value was evaluated for comparison. The subgroup was set according to endoscopy modality, and number of training images. A funnel plot was delineated to estimate the publication bias. Result: 16 studies were included in this study. We indicated that the application of AI in endoscopic detection of EGC achieved an AUC of 0.96 (95% CI, 0.94-0.97), a sensitivity of 86% (95% CI, 77-92%), and a specificity of 93% (95% CI, 89-96%). In AI-assisted EGC depth diagnosis, the AUC was 0.82(95% CI, 0.78-0.85), and the pooled sensitivity and specificity was 0.72(95% CI, 0.58-0.82) and 0.79(95% CI, 0.56-0.92). The funnel plot showed no publication bias. Conclusion: The AI applications for EGC diagnosis seemed to be more accurate than the endoscopists. AI assisted EGC diagnosis was more accurate than experts. More prospective studies are needed to make AI-aided EGC diagnosis universal in clinical practice.

Dermatomyositis is an autoimmune disease characterized by severe symmetrical muscle dysfunction and pain. This study was aimed at discovering vital hub genes and potential molecular pathways of DM through bioinformatics analysis, which contributes to identifying potential diagnostic or therapeutic biomarkers and targets. In this study, a total of 915 DEGs in DM samples including 167 upregulated genes and 748 downregulated genes were screened out by the limma package based on the GSE142807 dataset from the Gene Expression Omnibus (GEO) database. Furthermore, the results of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis indicated that these downregulated genes were highly associated with the immune-related biological processes and pathways. Therefore, 41 genes closely related to DM were extracted for further study based on the subcluster analysis through the Molecular Complex Detection (MCODE) software plugin in Cytoscape. Ultimately, 10 hub genes (including ISG15, DDX58, IFIT3, CXCL10, and STAT1) were identified as the potential candidate biomarkers and targets. Besides, we found that the identified hub genes directly or indirectly communicated with each other via molecular signaling pathways on the protein and transcription level. In general, under the guidance of bioinformatics analysis, 10 vital hub genes and molecular mechanisms in DM were identified and the expression of proinflammatory factors and interferon family proteins and genes showed high association with DM, which might help provide a theoretical foundation for the development of point-to-point targeted therapy in the future treatment of DM.

Background & Aims Chimeric antigen receptor engineered T cells (CARTs) for hepatocellular carcinoma (HCC) and other solid tumors are not as effective as they are for blood cancers. CARTs may lose function inside tumors due to persistent antigen engagement. The aims of this study are to develop low-affinity monoclonal antibodies (mAb) and low-avidity CARTs for HCC and to test the hypothesis that low-avidity CARTs can resist exhaustion and maintain functions in solid tumors, generating durable antitumor effects. Methods and results New human glypican-3 (hGPC3) mAbs were developed from immunized mice. We obtained 3 hGPC3-specific mAbs that stained HCC tumors, but not the adjacent normal liver tissues. One of them, 8F8, bound an epitope close to that of GC33, the frequently used high-affinity mAb, but with ~17 fold lower affinity. We then compared the 8F8 CARTs to GC33 CARTs for their in vitro function and in vivo antitumor effects. In vitro, low-avidity 8F8 CARTs killed both hGPC3high and hGPC3low HCC tumor cells to the same extent as high-avidity GC33 CARTs. 8F8 CARTs expanded and persisted to a greater extent than GC33 CARTs, resulting in durable responses against HCC xenografts. Importantly, compared to GC33 CARTs, there were 5 folds more of 8F8-BBz CARTs in the tumor mass for a longer period of time. Remarkably, the tumor infiltrating 8F8 CARTs were less exhausted and apoptotic, and more functional than GC33 CARTs. Conclusion The novel low-avidity 8F8-BBz CART resists exhaustion and apoptosis inside tumor lesions, demonstrating a greater therapeutic potential than high-avidity CARTs.

Background: Polycystic ovary syndrome (PCOS) is a common endocrine disease in women that seriously interferes with patient's metabolic and reproductive functions. The current diagnostic criteria for PCOS are expert-based and still disputed. Previous studies have identified changes in DNA methylation in peripheral blood of women with PCOS, but their diagnostic potential for PCOS remains to be studied. Objective: The present study aimed to identify potential methylation biomarkers for the diagnosis of PCOS in blood. Methods: Methylation profiling of peripheral blood was downloaded from a public database, Gene Expression Omnibus (GEO), including 30 PCOS patients (diagnosed with the revised 2003 Rotterdam consensus criteria) and 30 age-matched healthy women recruited from Centre of Reproductive Medicine, Linyi People’s Hospital, Shandong, China. Weighted gene co-expression network analysis (WGCNA) was utilized to identify PCOS-related co-methylation CpG sites (co- MPs). Functional enrichment analysis was performed on the localized genes of PCOS-related co- MPs. The least absolute shrinkage and selection operator (LASSO) regression was used to screen out CpG methylation signatures for PCOS diagnosis, and receiver operating characteristic (ROC) analysis was conducted to evaluate their diagnostic accuracy. To assess the accuracy of the combination of the investigated indicators, multivariate ROC analysis was performed on the predicted probability values obtained using binary logistic regression on the methylation levels of selected CpGs. Results: Seven co-methylation modules were obtained, among which the turquoise module is the most relevant to PCOS, containing 194 co-MPs. The genes that these co-MPs located in were mainly associated with the immune-related pathway. According to LASSO regression, three Co- MPs (cg23464743, cg06834912, cg00103771) were identified as potential diagnostic biomarkers of PCOS. ROC analysis showed an AUC (area under curve) of 0.7556 (sensitivity 60.0%, specificity 83.3%) for cg23464743, 0.7822 (sensitivity 70.0%, specificity 80.0%) for cg06834912, and 0.7611 (sensitivity 63.3%, specificity 83.3%) for cg00103771. The diagnostic accuracy of the combination of these 3 indicators presented to be higher than any single one of them, with the AUC of 0.8378 (sensitivity 73.3%, specificity 93.3%). Conclusion: The combination of 3 CpG methylation signatures in blood was identified with a good diagnostic accuracy for PCOS, which may bring new insight into the development of PCOS diagnostic markers in the future.

Background: With the coming of immunotherapy era, immunotherapy is gradually playing a vital role in the treatment of gastric cancer (GC). However, immune microenvironment in gastric precancerous lesions (GPL) and early gastric cancer (EGC) still remain largely unknown. Methods: From the Gene Expression Omnibus (GEO), data of three GPL-related gene expression profiles (GSE55696, GSE87666 and GSE130823) and three GC data sets with clinical information (GSE66229, GSE15459 and GSE34942) were downloaded. Three GC data were consolidated as a GC meta-GEO cohort. RNA sequencing data of 375 stomach adenocarcinoma (STAD) samples with clinical information from The Cancer Genome Atlas (TCGA) and 175 stomach normal controls (NC) from Genotype-Tissue Expression (GTEx) datasets were obtained from the UCSC Xena browser, which were merged as a STAD TCGA-GTEx cohort. The abundance of immune cells in above datasets were estimated using Immune Cell Abundance Identifier (ImmuCellAI) algorithm. Firstly, key immune cells associated with GPL progression to EGC were identified using one‐way analysis of variance (ANOVA) test as well as Spearman’s correlation test in two GPL and EGC related datasets (GSE55696 and GSE87666). Then, weighted gene co-expression analysis (WGCNA) and pathway enrichment were adopted to identify hub gene co-expression network. Candidate hub genes were identified based on network parameters. Combining expression comparison and prognosis analysis in STAD TCGA-GTEx and GC meta-GEO cohort, Genes with significant difference between GC and NC and prognostic significance were identified as real hub genes. Correlation between real hub genes and key immune cells was evaluated using Pearson’s correlation test. The pattern of key immune cells infiltration and hub genes expression as well as their correlation during GPL progression to EGC were validated in an independent cohort GSE130823. The correlation was also verified in the GC datasets (STAD TCGA-GTEx and GC meta-GEO cohort).Results: Combining with GSE55696 and GSE87666 cohorts, NKT cell was found gradually decreased with GPL progression and negatively correlated with tumorigenesis significantly. It was identified as the key immune cell associated with GPL progression to EGC based on one-way ANOVA test and Spearman’s correlation test. Further verification indicated that it was significantly downregulated in GC in meta-GEO cohort and STAD TCGA-GTEx cohort. According to the results of WGCNA and KEGG pathway enrichment, green modules in GSE55696 and GSE87666 cohorts were considered as hub modules as they were negatively associated with NKT cell infiltration at a significant level and their overlapping genes were significantly enriched in immune-related pathways. In further screening, CXCR4 was found to be significantly upregulated in GC and had a poor prognosis, which was determined as the real hub gene. CXCR4 expression was found increased with GPL progression, positively correlated with tumorigenesis and negatively correlated with NKT cell infiltration significantly. The pattern of NKT cell infiltration and CXCR4 expression as well as their relationship stay consistent in the independent GPL cohort GSE130823. The negative correlation of CXCR4 with NKT cell infiltration was also confirmed in GC datasets (GC meta-GEO cohort and STAD TCGA-GTEx cohort).Conclusion: CXCR4 and NKT cell are possible to serve as biomarkers in monitoring GPL progression to EGC. Besides, CXCR4 may be involved in regulating NKT cell infiltration during GPL progression to EGC, which may provide a new immunotherapeutic target.

Increasing evidence suggests that aberrant long noncoding (lnc) RNA expression plays a vital role in gastric cancer (GC) initiation and progression. Thus, we aimed to develop a lncRNA-based risk signature and nomogram to predict overall survival (OS) for patients with GC. Our primary cohort was composed of 341 patients with clinical and lncRNA expression data in The Cancer Genome Atlas stomach adenocarcinoma (TCGA STAD), the internal validation cohort was composed of 172 randomly assigned patients, and the external validation cohort was composed of 300 patients from GSE62254 dataset. A risk signature and nomogram were developed for the primary cohort and validated on the validation cohorts. Furthermore, gene set enrichment analysis (GSEA) was used to investigate the pathway enrichment for the risk signature. The expression patterns of several lncRNAs were also investigated in clinical samples from 10 GC patients. We identified and validated a 14-lncRNA signature highly associated with the OS of patients with GC, which performed well on evaluation with C-index, area under the curve, and calibration curves. In addition, univariate and multivariate Cox regression analyses indicated that the lncRNA signature was an independent predictive factor for GC patients. Therefore, a nomogram incorporating lncRNA signature and clinical factors was constructed to predict OS for patients with GC in primary cohort that suggested powerful predictive values for survival in the TCGA cohort and the other two validation cohorts. In addition, GSEA indicated that the identified lncRNAs may regulate the autophagy pathway, affecting tumorigenesis and prognosis of patients with GC. Experimental validation demonstrated that the expression of lncRNAs showed the same trend both in our clinical samples and STAD dataset. These results suggest that both risk signature and nomogram were effective prognostic indicators for patients with GC.

Increasing evidence indicates that TP53 mutation impacts the patients' prognosis by regulating the gastric cancer (GC) immunophenotype. An immune prognostic signature (IPS) was constructed based on TP53 status. The effects of the IPS on the immune microenvironment of GC were analyzed. We also constructed a nomogram integrating the IPS and other clinical factors. An IPS was constructed in the TCGA cohort and validated in the meta-GEO cohort. TP53 mutation resulted in the downregulation of the immune response in GC. Concretely, high-risk patients were characterized by increased monocyte, macrophage M0 and T cell follicular helper infiltration; increased stromal score, ESTIMATE score and immune score; higher TIM3 and BTLA expression; and decreased dendritic cell and T cell CD4 memory-activated infiltration and tumor purity. The nomogram also showed good predictive performance. These results suggest that the IPS is an effective prognostic indicator for GC patients, which might provide a theoretical foundation for immunotherapy.

Background Increasing evidence suggests that the lncRNA-miRNA-mRNA regulatory network is highly correlated with gastric cancer (GC) development. However, a prognosis-associated lncRNA-miRNA-mRNA network remains to be identified in GC. Methods Differentially expressed genes (DEGs) were screened by integrating 6 microarray datasets using the RRA method. Hub genes were identified by analysing their degrees in a PPI (protein–protein interaction) network. Upstream miRNAs and lncRNAs of hub genes were predicted by miRTarBase and miRNet, respectively. Key genes, miRNAs and lncRNAs were identified by evaluating their expression and prognosis in GEPIA and Kaplan-Meier plotter, respectively. A key lncRNA-miRNA-mRNA network was constructed in Cytoscape, and the correlations were analysed in the ENCORI database. We also evaluated the mRNA expression of ceRNA axes in the TIMER and Oncomine databases and their correlation with prognosis in GC patients with different clinical features using Kaplan-Meier plotter. In addition, correlations between mRNA and immune infiltrating cells in GC were investigated by the TIMER database. Finally, several experiments were conducted to verify our analyses. Results Forty-two upregulated and 86 downregulated DEGs were obtained from the “RRA” integrated analysis. Eight of the 20 hub genes were identified as key genes by analysing their expression and prognosis. Seventeen miRNAs were predicted to target key genes, and low expression of 4 miRNAs suggested poor outcome in GC. Furthermore, 155 lncRNAs were predicted to target 4 key miRNAs, and only 5 lncRNAs were highly expressed, suggesting poor outcomes in patients with GC. Then, the H19-miR-29a-3p-COL1A2 axis was constructed by correlation analysis. In addition, COL1A2 was positively correlated with lymphatic metastasis, immune infiltrating cell levels, markers of monocytes, tumour-associated macrophages (TAMs), and M2 macrophages but not M1 macrophages in GC. The experimental results revealed that the H19-miR-29a-3p-COL1A2 axis may promote macrophage polarization from M1 to M2 in GC. Conclusions A novel lncRNA-miRNA-mRNA axis was identified and may be involved in regulating immune cell infiltration and macrophage polarization, which may provide new treatment strategies for GC.

Background: Human glypican-3 (hGPC3) is a protein highly expressed in hepatocellular carcinoma (HCC) but limited in normal tissues, making it an ideal target for immunotherapy. The adoptive transfer of hGPC3-specific chimeric antigen receptor T (CAR-T) cells for HCC treatment has been conducted in clinical trials. Due to the rigid construction, conventional CAR-T cells have some intrinsic limitations, like uncontrollable overactivation and inducing severe cytokine release syndrome. Methods: We redesigned the hGPC3-specific CAR by splitting the traditional CAR into two parts. By using coculturing assays and a xenograft mouse model, the in vitro and in vivo cytotoxicity and cytokine release of the split anti-hGPC3 CAR-T cells were evaluated against various HCC cell lines and compared with conventional CAR-T cells. Results: In vitro data demonstrated that split anti-hGPC3 CAR-T cells could recognize and lyse hGPC3+ HepG2 and Huh7 cells in a dose-dependent manner. Impressively, split anti-hGPC3 CAR-T cells produced and released a significantly lower amount of proinflammatory cytokines, including IFN-γ, TNF-α, IL-6, and GM-CSF, than conventional CAR-T cells. When injected into immunodeficient mice inoculated subcutaneously with HepG2 cells, our split anti-hGPC3 CAR-T cells could suppress HCC tumor growth, but released significantly lower levels of cytokines than conventional CAR-T cells. Conclusions: We describe here for the first time the use of split anti-hGPC3 CAR-T cells to treat HCC; split anti-hGPC3 CAR-T cells could suppress tumor growth and reduce cytokine release, and represent a more versatile and safer alternative to conventional CAR-T cells treatment.