Intratumoral microbiome is associated with gastric cancer prognosis and therapy efficacy.

The role of the intratumoral microbiome in gastric cancer (GC) has not been comprehensively assessed. Here, we explored the relationship between the microbial community and GC prognosis and therapy efficacy. Several cancer-associated microbial characteristics were identified, including increased α-diversity, differential ß-diversity, and decreased Helicobacter pylori abundance. After adjusting for clinical features, prognostic analysis revealed 2 phyla, 14 genera, and 5 species associated with the overall survival of patients with GC. Additionally, 2 phyla, 14 genera, and 6 species were associated with adjuvant chemotherapy (ACT) efficacy in patients with stage II - III GC. Furthermore, we classified GC microbiome structures into three microbial subtypes (MS1, MS2 and MS3) with distinguishing features. The MS1 subtype exhibited high immune activity and enrichment of microbiota related to immunotherapy and butyric acid-producing, as well as potential benefits in immunotherapy. MS2 featured the highest α-diversity and activation of the TFF pathway, MS3 was characterized by epithelial-mesenchymal transition and was associated with poor prognosis and reduced ACT efficacy. Collectively, the results of this study provide valuable insights into the microbial characteristics associated with GC prognosis and therapy efficacy.

Transcriptome profiling of patient-derived tumor xenografts suggests novel extracellular matrix-related signatures for gastric cancer prognosis prediction.

BACKGROUND: A major obstacle to the development of personalized therapies for gastric cancer (GC) is the prevalent heterogeneity at the intra-tumor, intra-patient, and inter-patient levels. Although the pathological stage and histological subtype diagnosis can approximately predict prognosis, GC heterogeneity is rarely considered. The extracellular matrix (ECM), a major component of the tumor microenvironment (TME), extensively interacts with tumor and immune cells, providing a possible proxy to investigate GC heterogeneity. However, ECM consists of numerous protein components, and there are no suitable models to screen ECM-related genes contributing to tumor growth and prognosis. We constructed patient-derived tumor xenograft (PDTX) models to obtain robust ECM-related transcriptomic signatures to improve GC prognosis prediction and therapy design. METHODS: One hundred twenty two primary GC tumor tissues were collected to construct PDTX models. The tumorigenesis rate and its relationship with GC prognosis were investigated. Transcriptome profiling was performed for PDTX-originating tumors, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to extract prognostic ECM signatures and establish PDTX tumorigenicity-related gene (PTG) scores. The predictive ability of the PTG score was validated using two independent cohorts. Finally, we combined PTG score, age, and pathological stage information to establish a robust nomogram for GC prognosis prediction. RESULTS: We found that PDTX tumorigenicity indicated a poor prognosis in patients with GC, even at the same pathological stage. Transcriptome profiling of PDTX-originating GC tissues and corresponding normal controls identified 383 differentially expressed genes, with enrichment of ECM-related genes. A robust prognosis prediction model using the PTG score showed robust performance in two validation cohorts. A high PTG score was associated with elevated M2 polarized macrophage and cancer-associated fibroblast infiltration. Finally, combining the PTG score with age and TNM stage resulted in a more effective prognostic model than age or TNM stage alone. CONCLUSIONS: We found that ECM-related signatures may contribute to PDTX tumorigenesis and indicate a poor prognosis in GC. A feasible survival prediction model was built based on the PTG score, which was associated with immune cell infiltration. Together with patient ages and pathological TNM stages, PTG score could be a new approach for GC prognosis prediction.

PTPRD/PTPRT mutation correlates to treatment outcomes of immunotherapy and immune landscape in pan-cancers.

Objective: PTPRD and PTPRT are phosphatases of the JAK-STAT pathway related to immunotherapy. However, the role and mechanism of PTPRD and PTPRT mutations in multiple cancers remains unclear. Methods: Clinical data and PTPRD/PTPRT mutation information from 12 cohorts were collected and classified as a discovery cohort and three validation cohorts. The association between PTPRD/PTPRT mutations and immunotherapeutic efficacy was analyzed. Then, the association between PTPRD/PTPRT mutation and immune profiles was analyzed using The Cancer Genome Atlas (TCGA) cohort. Results: A total of 2,392 patients across 20 cancer types were included in this study. Our results showed that patients harboring PTPRD/PTPRT mutation, especially co-mutations, had a significantly elevated response rate to immunotherapy in multiple cancers. Patients with PTPRD/PTPRT mutation had a higher objective response rate (ORR) (P=0.002), longer overall survival (OS) (P=0.005) and progression-free survival (PFS) (P=0.038). Importantly, the above findings were further verified in validation cohorts. In addition, we found that the PTPRD/PTPRT co-mutations (co-mut) subgroup exhibited an immune-activated phenotype, the wild-type subgroup tended to have an immune-desert phenotype, and the uni-mutation (uni-mut) subgroup might have an immune-mixed phenotype. Our further analyses suggested that combining programmed cell death ligand 1 (PD-L1) expression and PTPRD/PTPRT mutation can be used to screen patients who may benefit from immunotherapy. Conclusions: PTPRD/PTPRT mutation could serve as a potential predictive biomarker for cancer immunotherapy.

TRIM28 promotes the escape of gastric cancer cells from immune surveillance by increasing PD-L1 abundance.

Immune checkpoint blockade (ICB) offers a new opportunity for treatment for gastric cancer (G.C.). Understanding the upstream regulation of immune checkpoints is crucial to further improve the efficacy of ICB therapy. Herein, using the CRISPR-Cas9-based genome-wide screening, we identified TRIM28 as one of the most significant regulators of PD-L1, a checkpoint protein, in G.C. cells. Mechanistically, TRIM28 directly binds to and stabilizes PD-L1 by inhibiting PD-L1 ubiquitination and promoting PD-L1 SUMOylation. Furthermore, TRIM28 facilitates K63 polyubiquitination of TBK1, activating TBK1-IRF1 and TBK1-mTOR pathways, resulting in enhanced PD-L1 transcription. It was found that TRIM28 was positively correlated with PD-L1 in G.C. cells. Moreover, high TRIM28 expression suggests poor survival in a cohort of 466 patients with G.C., and this observation is consistent while analyzing data from publicly available databases. Ectopic TRIM28 expression facilitated tumor growth, increased PD-L1 expression, and suppressed T cell activation in mice. Administration of the PD-L1 or TBK1 inhibitor significantly alleviated the TRIM28-induced tumor progression. Furthermore, combining the TBK1 inhibitor with CTLA4 immune checkpoint blockade has synergistic effects on G.C., and provides a novel strategy for G.C. therapy.

Piceatannol enhances Beclin-1 activity to suppress tumor progression and its combination therapy strategy with everolimus in gastric cancer.

The effects and regulation of Beclin-1-an autophagy-related protein-have not been fully defined, however, a negative correlation has been reported between Beclin-1 expression and carcinogenesis. Meanwhile, no compound has been shown to directly inhibit its activity. Here, we evaluate piceatannol, a naturally occurring polyphenolic compound, as a potential targeting agonist of Beclin-1, to assess its efficacy as an antitumor agent against gastric cancer. More specifically, we determine the effects of piceatannol treatment on cell viability using a monitoring system and colony forming assay. Piceatannol was found to efficiently inhibit the proliferation of several human gastric cancer cell lines. Autophagic flux is increased by piceatannol treatment, and correlates with inhibition of cell proliferation and colony formation. Additionally, microscale thermophoresis and surface plasmon resonance results show a direct interaction between piceatannol and Beclin-1, which reduces the phosphorylation activity of Beclin-1 at the Ser-295 site. Notably, piceatannol impairs the binding of Beclin-1 to Bcl-2 and enhances the recruitment of binding of UV radiation resistance-associated gene protein, which further triggers Beclin-1-dependent autophagy signaling. An increase in autophagic activity via treatment with the mTOR inhibitor, everolimus, effectively sensitizes piceatannol-induced antitumor effects. Xenograft models confirmed that piceatannol inhibits tumor development and elicits a potent synergistic effect with everolimus in vivo. Taken together, the findings of this study strongly support the application of combinatorial piceatannol and everolimus therapy in future clinical trials for gastric cancer patients.

A Novel Classification and Scoring Method Based on Immune-Related Transcription Factor Regulation Patterns in Gastric Cancer.

Background: Transcription factors (TFs) play a crucial role in tumorigenesis and anti-tumor immunity. However, the potential role of large-scale transcription factor regulation patterns in the progression in gastric cancer (GC) is unknown. Methods: We comprehensively assessed the relevance of immune-related TF (IRTF) regulation patterns in anti-tumor immunity and immunotherapy in 1,136 gastric cancer (GC) patients, and evaluated the IRTF score based on IRTF regulation patterns using random forests. Results: Two distinct IRTF regulation patterns were identified, which demonstrating the distinct characteristics in clinical phenotypes, tumor immune microenvironment (TIME), immunogenicity and prognosis in GC. Subsequently, the IRTF score was established to quantify the IRTF regulation pattern for each GC patient. Analysis of large conventional therapy cohorts showed low IRTF score was associated with a better prognosis. In addition, analysis of multiple immunotherapy cohorts showed low IRTF score was also linked to enhanced response to immunotherapy. Conclusion: TF regulation patterns were found to play an important role in the complex immune regulatory relationships in GC. Evaluation of the IRTF regulation patterns in patients will enhance our understanding of immune specificities, and thus, provide effective strategies for personalized therapy.

Novel prognostic marker LINC00205 promotes tumorigenesis and metastasis by competitively suppressing miRNA-26a in gastric cancer.

Rapid proliferation and metastasis of gastric cancer (GC) resulted in a poor prognosis in the clinic. Previous studies elucidated that long non-coding RNA (LncRNA) LINC00205 was upregulated in various tumors and participated in tumor progression. The aim of our study was to investigate the regulating role of LINC00205 in tumorigenesis and metastasis of GC. Both public datasets and our data showed that the LINC00205 was highly expressed in GC tissues and several cell lines. Notably, GC patients with high level of LINC00205 had a poor prognosis in our cohort. Mechanistically, knockdown of LINC00205 by shRNAs suppressed GC cells proliferation, migration, invasion remarkably, and induced cell cycle arrest. Based on bioinformatics prediction, we found that LINC00205 might act as a competitive endogenous RNA (ceRNA) through targeting miR-26a. The level of miR-26a had negatively correlated with LINC00205 expression and was decreased among GC cell lines, tissues, and serum samples. Our results for the first time confirmed that miR-26a was a direct target of LINC00205 and might have the potential to become a plasma marker for clinical tumor diagnosis. Indeed, LINC00205 knockdown resulted in the dramatic promotion of miR-26a expression as well as inhibition of miR-26a potential downstream targets, such as HMGA2, EZH2, and USP15. These targets were essential for cell survival and epithelial-mesenchymal transition. Importantly, LINC00205 was able to remodel the miR-26a-mediated downstream silence, which identified a new mechanism of malignant transformation of GC cells. In conclusion, this study revealed the regulating role of the LINC00205/miR-26a axis in GC progression and provided a new potential therapeutic strategy for GC treatment.

Identification of prognostic factors and construction of a nomogram for patients with relapse/refractory adult-onset Still's disease.

OBJECTIVES: This study aimed to identify the risk factors for relapse/refractory adult-onset Still's disease (AOSD) and to construct and validate a prognostic nomogram for predicting the individual risk of relapse/refractory disease. METHOD: A total of 174 patients were included in our study. Univariate and multivariate logistic regression analyses were used to identify relapse/refractory-associated factors, which were used to construct nomograms. Receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA) were used to assess the predictive ability of the nomograms. RESULTS: Univariate and multivariate logistic analyses showed that age, fever, disease duration, platelet count, serum ferritin level, and erythrocyte sedimentation rate were independent unfavourable factors for relapse/refractory AOSD (p < 0.05). We constructed a 6-factor nomogram based on univariate and multivariate logistic analyses. ROC analysis indicated that the area under the curve of the 6-factor nomogram in the training set and test set was 0.765 and 0.714, respectively. In addition, the calibration curves showed excellent prediction accuracy, and DCA showed superior net benefit in the 6-factor nomograms. Moreover, we evaluated the predictive effectiveness of our nomogram in females and young adults. The results showed that our 6-factor nomogram has the same predictive ability in both subgroups. CONCLUSIONS: Novel nomograms based on clinical characteristics were developed and may be applied to help predict the individual risk of poor prognosis of patients. Key Points • Logistic regression was used to identify risk factors for relapse/refractory adult-onset Still's disease. • We then constructed a nomogram for predicting disease risk. • ROC analysis, calibration curves, and DCA all showed that the nomogram exerted good prediction ability in both the training set and test set. • The nomogram has the same predictive ability in both female and young adult subgroups.

Dermatomyositis: immunological landscape, biomarkers, and potential candidate drugs.

INTRODUCTION: Dermatomyositis (DM) is a rare inflammatory disease characterized by the invasion of the skin and muscles. Environmental, genetic, and immunological factors contribute to disease pathology. To date, no bioinformatics studies have been conducted on the potential pathogenic genes and immune cell infiltration in DM. Therefore, we aimed to identify differentially expressed genes (DEGs) and immune cells, as well as potential pathogenic genes and immune characteristics, which may be useful for the diagnosis and treatment of DM. METHOD: GSE1551, GSE5370, GSE39454, and GSE48280 from Gene Expression Omnibus were included in our study. Limma, ClusterProfiler, and Kyoto Encyclopedia of Genes and Genomes were used to identify DEGs, Gene Ontology (GO), and perform pathway analyses, respectively. Cytoscape was used to construct the protein-protein interaction (PPI) network. Small-molecule drugs were identified using a connectivity map (CMap), and the TIMER database was used to identify infiltrating cells. RESULTS: DEG analysis identified 12 downregulated and 163 upregulated genes. GO analysis showed that DEGs were enriched in immune-related pathways. Ten hub genes were identified from the PPI network. Additionally, CMap analysis showed that caffeic acid, sulfaphenazole, molindone, tiabendazole, and bacitracin were potential small-molecule drugs with therapeutic significance. We identified eight immune cells with differential infiltration in patients with DM and controls. Finally, we constructed a powerful diagnostic model based on memory B cells, M1, and M2 macrophages. CONCLUSIONS: This study explored the potential molecular mechanism and immunological landscape of DM and may guide future research and treatment of DM. KEY POINTS: • We explored the molecular mechanism and immunological landscape of dermatomyositis. • GO analysis showed that DEGs were enriched in immune-related pathways. • We predicted small-molecular drugs with potential therapeutic significance based on bioanalytical techniques. • We identified six immune cells with differential infiltration in patients with DM and controls.

Eight immune-related genes predict survival outcomes and immune characteristics in breast cancer.

Advancements in immunotherapy have improved our understanding of the immune characteristics of breast cancer. Here, we analyzed gene expression profiles and clinical data obtained from The Cancer Genome Atlas database to identify genes that were differentially expressed between breast tumor tissues and normal breast tissues. Comparisons with the Immunology Database and Analysis Portal (ImmPort) indicated that many of the identified differentially expressed genes were immune-related. Risk scores calculated based on an eight-gene signature constructed from these immune-related genes predicted both overall survival and relapse-free survival outcomes in breast cancer patients. The predictive value of the eight-gene signature was validated in different breast cancer subtypes using external datasets. Associations between risk score and breast cancer immune characteristics were also identified; invitro experiments using breast cancer cell lines confirmed those associations. Thus, the novel eight-gene signature described here accurately predicts breast cancer survival outcomes as well as immune checkpoint expression and immune cell infiltration processes.

Prognostic nomogram for overall survival in upper aerodigestive tract extranodal natural killer/T-cell lymphoma, nasal type, stages IE and IIE: A SEER-based study.

The present study aimed to develop a widely accepted prognostic nomogram for stage IE and IIE extranodal natural killer/T-cell lymphoma (ENKTCL) of the upper aerodigestive tract by using the Surveillance, Epidemiology, and End Results program database. A total of 396 patients with ENKTCL were included in the present study and were divided into training (n=280) and validation (n=116) cohorts. The Kaplan-Meier method and Cox regression model were used to evaluate the prognostic value of multiple clinical parameters on overall survival. The C-index and calibration curves were both used to determine the predictive and discriminatory capacities of the nomogram. In the training cohort, multivariate analysis demonstrated that age, primary site, radiation therapy and stage were independent prognostic factors. Nomograms with a C-index of 0.717 in the training cohort and a C-index of 0.737 in the validation cohort were developed. The calibration curves reported excellent consistency between predicted and real survival in patients with ENKTCL. In addition, a subgroup analysis of 264 patients who were receiving chemotherapy revealed that based on chemotherapy, supplementation with radiation therapy was significantly beneficial to patients survival. In conclusion, the present study demonstrated that this prognostic model may serve as a novel tool for improving prediction of survival outcomes and may therefore be used in clinical applications.

B7-CD28 gene family expression is associated with prognostic and immunological characteristics of diffuse large B-cell lymphoma.

The B7-CD28 gene family plays a key role in regulating cellular immunity and is closely related to tumorigenesis and immune evasion. Here, we explored associations between clinical and immune features and B7-CD28 gene family expression in Gene Expression Omnibus (GEO) datasets representing 1812 diffuse large B-cell lymphoma (DLBCL) patients. This included 414 in the GSE10846 training cohort and 470 and 928 patients in the GSE31312 and GSE117556 validation cohorts, respectively. Four survival-associated genes identified in the GSE10846 cohort by univariate Cox analysis were incorporated into a multivariate analysis, ultimately establishing a three-gene risk signature. Risk scores assigned based on expression of these genes were validated by Kaplan­Meier and multivariable Cox analyses in the remaining datasets and in important clinical subsets. High-risk patients had shorter overall survival and, in some cases, progression-free survival than low-risk patients. Additionally, expression of programmed cell death 1 (PD-1) and programmed death ligand 1 (PD-L1), as well as several other important immune checkpoint genes, differed between high-risk and low-risk patients, as did the proportions of various immune-infiltrating cells. Finally, further analysis confirmed that these B7-CD28 genes play important roles in immune responses altered in DLBCL.

Clinical features and prognostic factors of primary bone marrow lymphoma.

BACKGROUND: Primary bone marrow lymphoma (PBML) is a very uncommon neoplasm originally arising in the bone marrow system, and the most common pathological type is diffuse large B-cell lymphoma. PATIENTS AND METHODS: To describe the clinical characteristics of PBML and evaluate the risk factors related to prognosis, we recruited and studied 66 patients from our center and the current published literature. Various symptoms are present at the onset of PBML, the most important of which is cytopenia, followed by fever. Forty-seven of these patients were included in our analysis. RESULTS: Univariate analysis suggested that B symptoms (P=0.024), a low serum platelet level (<75×109/L; P=0.032), an elevated serum LDH level (P=0.039), and not achieving a complete response (CR) following initial therapy (P=0.007) are associated with worse outcomes. Multivariate analysis showed that only a low serum platelet level (<75×109/L), B symptoms, and not achieving a CR following initial therapy are independent factors for prognosis. In addition, intensive regimens appear to be beneficial for prognosis. CONCLUSION: PBML is a lymphoma with special clinical features, and its recognition is important for establishing a definitive prognosis model and searching for appropriate therapy.