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Abstract Psychiatric disorders are severe health challenges that exert a heavy public burden. Air pollution has been widely reported as related to psychiatric disorder risk, but their casual association and pathological mechanism remained unclear. Herein, we systematically investigated the large genome-wide association studies (6 cohorts with 1,357,645 samples), single-cell RNA (26 samples with 157,488 cells), and bulk-RNAseq (1595 samples) datasets to reveal the genetic causality and biological link between four air pollutants and nine psychiatric disorders. As a result, we identified ten positive genetic correlations between air pollution and psychiatric disorders. Besides, PM2.5 and NO2 presented significant causal effects on schizophrenia risk which was robust with adjustment of potential confounders. Besides, transcriptome-wide association studies identified the shared genes between PM2.5/NO2 and schizophrenia. We then discovered a schizophrenia-derived inhibitory neuron subtype with highly expressed shared genes and abnormal synaptic and metabolic pathways by scRNA analyses and confirmed their abnormal level and correlations with the shared genes in schizophrenia patients in a large RNA-seq cohort. Comprehensively, we discovered robust genetic causality between PM2.5, NO2, and schizophrenia and identified an abnormal inhibitory neuron subtype that links schizophrenia pathology and PM2.5/NO2 exposure. These discoveries highlight the schizophrenia risk under air pollutants exposure and provide novel mechanical insights into schizophrenia pathology, contributing to pollutant-related schizophrenia risk control and therapeutic strategies development. Graphical Abstract

Abstract Triple-negative breast cancer (TNBC) is a relatively aggressive breast cancer subtype due to tumor relapse, drug resistance, and multi-organ metastatic properties. Identifying reliable biomarkers to predict prognosis and precisely guide TNBC immunotherapy is still an unmet clinical need. To address this issue, we successfully constructed a novel 25 machine learning (ML) algorithms-based immune infiltrating cell (IIC) associated signature of TNBC (MLIIC), achieved by multiple transcriptome data of purified immune cells, TNBC cell lines, and TNBC entities. The TSI index was employed to determine IIC-RNAs that were accompanied by an expression pattern of upregulation in immune cells and downregulation in TNBC cells. LassoLR, Boruta, Xgboost, SVM, RF, and Pamr were utilized for further obtaining the optimal IIC-RNAs. Following univariate Cox regression analysis, LassoCox, CoxBoost, and RSF were utilized for the dimensionality reduction of IIC-RNAs from a prognostic perspective. RSF, Ranger, ObliqueRSF, Rpart, CoxPH, SurvivalSVM, CoxBoost, GlmBoost, SuperPC, StepwiseCox, Enet, LassoCox, CForest, Akritas, BlackBoost, PlsRcox, SurvReg, GBM, and CTree were used for determining the most potent MLIIC signature. Consequently, this MLIIC signature was correlated significantly with survival status validated by four independent TNBC cohorts. Also, the MLIIC signature had a superior predictive capability for TNBC prognosis, compared with 148 previously reported signatures. In addition, MLIIC signature scores developed by immunofluorescent staining of tissue arrays from TNBC patients showed a substantial prognostic value. In TNBC immunotherapy, the low MLIIC profile demonstrated significant immune-responsive efficacy in a dataset of multiple cancer types. MLIIC signature could also predict m6A epigenetic regulation which controls T cell homeostasis. Therefore, this well-established MLIIC signature is a robust predictive indicator for TNBC prognosis and the benefit of immunotherapy, thus providing an efficient tool for combating TNBC.

BackgroundAir pollution poses a major threat to human health by causing various illnesses, such as cardiovascular diseases. While plenty of research indicates a correlation between air pollution and hypertension, a definitive answer has yet to be found.MethodsOur analyses were performed using the Genome-wide association study (GWAS) of exposure to air pollutants from UKB (PM2.5, PM10, NO2, and NOX; n = 423,796 to 456,380), essential hypertension from FinnGen (42,857 cases and 162,837 controls) and from UKB (54,358 cases and 408,652 controls) as a validated cohort. Univariable and multivariable Mendelian randomization (MR) were conducted to investigate the causal relationship between air pollutants and essential hypertension. Body mass index (BMI), alcohol intake frequency, and the number of cigarettes previously smoked daily were included in multivariable MRs (MVMRs) as potential mediators/confounders.ResultsOur findings suggested that higher levels of both PM2.5 (OR [95%CI] per 1 SD increase in predicted exposure = 1.24 [1.02–1.53], p = 3.46E-02 from Finn; OR [95%CI] = 1.04 [1.02–1.06], p = 7.58E-05 from UKB) and PM10 (OR [95%CI] = 1.24 [1.02–1.53], p = 3.46E-02 from Finn; OR [95%CI] = 1.04 [1.02–1.06], p = 7.58E-05 from UKB) were linked to an increased risk for essential hypertension. Even though we used MVMR to adjust for the impacts of smoking and drinking on the relationship between PM2.5 exposure and essential hypertension risks, our findings suggested that although there was a direct positive connection between them, it is not present after adjusting BMI (OR [95%CI] = 1.05 [0.87–1.27], p = 6.17E-01). Based on the study, higher exposure to PM2.5 and PM10 increases the chances of developing essential hypertension, and this influence could occur through mediation by BMI.ConclusionExposure to both PM2.5 and PM10 is thought to have a causal relationship with essential hypertension. Those impacted by substantial levels of air pollution require more significant consideration for their cardiovascular health.

Background: Tertiary lymphoid structure (TLS) is a unique organ that carries out tumor cell elimination at tumor sites. It is continuously stimulated by inflammatory tumor signals and has been found to augment immunotherapy response. However, the detailed mechanisms behind it still need to be defined. Methods: To explore and grasp the whole picture of TLS from a pan-cancer view, we collected nine TLS-related genes from previous studies. We performed a comprehensive analysis of 9637 samples across 33 tumor types accessed from The Cancer Genome Atlas (TCGA) database. EdU, Transwell, and flow cytometry were performed on the feature gene PTGDS in U251 cells. The regulatory role of PTGDS on PD-L1 expression and macrophage polarization was verified. Results: Alteration analysis showed that mutations of TLS-related genes were widespread and relatively high. Clustering analysis based on the expression of these nine genes obtained two distinct clusters, with high EIF1AY and PTGDS in cluster 2 and better overall survival in cluster 1. To distinguish the two clusters, we utilized six machine learning algorithms and filtrated EIF1AY, PTGDS, SKAP1, and RBP5 as the characteristic genes, among which the former two genes were proved to be hazardous. PTGDS was found to regulate PD-L1 expression and also promoted the proliferation and migration of U251 cells. The knockdown of PTGDS could reduce the migration of macrophages and inhibit the polarization of macrophages into M2-phenotype. In addition, we established a TLS score to demonstrate patients’ TLS activity. The low TLS-score group overlapped with cluster 1 and displayed a better prognosis. Besides, the low TLS-score group was related to better immunotherapy responses. The HE staining of histopathological sections confirmed that the low TLS-score group exhibited higher infiltration of immune cells. Conclusion: This study reveals broad molecular, tumorigenic, and immunogenic signatures for further functional and therapeutic studies of tertiary lymphoid structure. The TLS score we established effectively predicted immunotherapy response and patients’ survival. Its future application and combination await more research.

Gliomas cause high mortality around the world. The metabolic pattern of the tumor was previously suggested to be associated with the patient's survival outcome and immune activity. Yet, this relationship in glioma remains unknown. This study systematically evaluated the immune landscape in different phenotypes classified by metabolic‐related pathways of 3068 glioma samples and 33 glioblastoma single‐cell sequencing samples. Machine learning prediction analysis of microarray with R (pamr) was used for validating clustering results. A total of 5842 pan‐cancer samples were used for external validation of the metabolic clusters. Cell Counting Kit‐8 (CCK8) assay, cell clone assay, EdU assay, wound healing assay, Transwell assay, and co‐culture assay were performed to verify the distinction in molecular characteristics among metabolic clusters. Metabolomics and RNA sequencing were performed on HS683 and U251 cells to annotate potential hyaluronic acid (HA)‐mediated pathways. Three distinct metabolic phenotypes were identified. Metabolic cluster 1 correlated with a high number of immune infiltrating cells and poor survival of glioma patients. Metabolic clusters were proved with different levels of the macrophage markers CD68 and CD163 by multiplex immunofluorescence staining. Glioma cells from other metabolic clusters also expressed various levels of HA. HA was further found to mediate glioma proliferation, progression, and invasion. Moreover, HA potentially promoted macrophage recruitment and M2 polarization through the IL‐1/CHI3L1 and TGF‐b/CHI3L1 axes. HA also regulated the expression of PD‐L1. This work revealed the significant connection between metabolic patterns, especially HA, and tumor immune infiltration in gliomas.

Abstract CD47 performs a vital function in cancer therapy by binding to different SIRPα, thrombospondin 1, and integrin. However, its role in tumor immunity and its correlation with prognosis among many cancer types remain unknown. The raw mRNA expression data of CD47 in cancer patients was downloaded from TCGA and GTEx datasets. The protein expression of CD47 was detected using a microarray. Kaplan Meier analysis and forest plot were performed to compare the effects of high and low expression of CD47 on overall survival in different cancers. In addition, the correlations between CD47 expression and immune cell infiltration, stromal components, immune checkpoint genes, tumor mutational burden (TMB), and microsatellite instability (MSI) were analyzed from the public database. The gene function was determined by Gene Set Enrichment Analysis (GSEA). The expressions of CD47 in CHOL, COAD, ESCA, HNSC, KIRC, STAD, and THCA were higher compared with normal tissues. Elevated expression of CD47 predicted poor prognosis in ACC, KICH, KIRP, LGG, PAAD and UCEC. CD47 expression was strongly associated with immune infiltrating cells among KICH, KIRP, LGG, and PAAD. In addition, significant positive correlations with most immune checkpoint genes including PDCD 1 (PD-1), CD274 (PD-L1), CTLA4 in BLCA, DLBC, KICH, KIRC, LUAD, LUSC, PAAD, PCPG, SKCM, STAD, UCEC, and UVM was noted for the expression of CD47. GSEA analysis demonstrated that CD47 was a key regulator in metabolism-related pathways. These findings provide novel evidence that CD47 could be utilized as a promising prognostic biomarker and combination treatment target in various cancers.

IntroductionGlioblastoma is a malignant brain tumor with poor prognosis. Lactate is the main product of tumor cells, and its secretion may relate to immunocytes’ activation. However, its role in glioblastoma is poorly understood. MethodsThis work performed bulk RNA-seq analysis and single cell RNA-seq analysis to explore the role of lactate in glioblastoma progression. Over 1400 glioblastoma samples were grouped into different clusters according to their expression and the results were validated with our own data, the xiangya cohort. Immunocytes infiltration analysis, immunogram and the map of immune checkpoint genes’ expression were applied to analyze the potential connection between the lactate level with tumor immune microenvironment. Furthermore, machine learning algorithms and cell-cell interaction algorithm were introduced to reveal the connection of tumor cells with immunocytes. By co-culturing CD8 T cells with tumor cells, and performing immunohistochemistry on Xiangya cohort samples further validated results from previous analysis.DiscussionIn this work, lactate is proved that contributes to glioblastoma immune suppressive microenvironment. High level of lactate in tumor microenvironment can affect CD8 T cells’ migration and infiltration ratio in glioblastoma. To step further, potential compounds that targets to samples from different groups were also predicted for future exploration.

Gliomas, originating from the glial cells, are the most lethal type of primary tumors in the central nervous system. Standard treatments like surgery have not significantly improved the prognosis of glioblastoma patients. Recently, immune therapy has become a novel and effective option. As a conserved group of transcriptional regulators, the Sry-type HMG box (SOX) family has been proved to have a correlation with numerous diseases. Based on the large-scale machine learning, we found that the SOX family, with significant immune characteristics and genomic profiles, can be divided into two distinct clusters in gliomas, among which SOX10 was identified as an excellent immune regulator of macrophage in gliomas. The high expression of SOX10 is related to a shorter OS in LGG, HGG, and pan-cancer groups but benefited from the immunotherapy. It turned out in single-cell sequencing that SOX10 is high in neurons, M1 macrophages, and neural stem cells. Also, macrophages are found to be elevated in the SOX10 high-expression group. SOX10 has a positive correlation with macrophage cytokine production and negative regulation of macrophages’ chemotaxis and migration. In conclusion, our study demonstrates the outstanding cluster ability of the SOX family, indicating that SOX10 is an immune regulator of macrophage in gliomas, which can be an effective target for glioma immunotherapy.

Immunogenic cell death (ICD) is a type of cell death that leads to the regulation and activation of the immune response, which is marked by the exposure and delivery of damage‐associated molecular patterns (DAMPs) in the tumor microenvironment. Accumulating evidence has revealed the significance of ICD-related genes in tumor progression and therapeutic response. In this study, we obtained two ICD-related clusters for glioblastoma (GBM) by applying consensus clustering, and further constructed a risk signature on account of the prognostic ICD genes. Based on the risk signature, we found that higher risk scores were associated with worse patient prognosis. Besides, the results illustrated that ferroptosis regulators/markers were highly enriched the high-risk group, and ferroptosis were correlated with cytokine signaling pathway and other immune-related pathways. We also discovered that high-risk scores were correlated to specific immune infiltration patterns and good response to immune checkpoint blockade (ICB) treatment. In conclusion, our study highlights the significance of ICD-related genes as prognostic biomarkers and immune response indicators in GBM. And the risk signature integrating prognostic genes possessed significant potential value to predict the prognosis of patients and the efficacy of ICB treatment.

Summary: Microenvironment cells (MCs) play a critical role in tumor proliferation, progression, and metastasis. However, it has not been adequately addressed whether MCs could be used as a reliable prognostic marker in glioblastoma (GBM). In the current study, the cell pair (CP) score was constructed in 1137 GBM samples based on the cell pair algorithm and Gaussian finite mixture model (GMM) and was verified in 73 GBM samples from the Xiangya cohort. CP score predicted GBM patients’ survival and response to anti-PD-1 treatment with high sensitivity. Macrophage markers CD68 and CD163 were co-expressed with pericyte markers MCAM and MG2. Pericyte could mediate the infiltration, migration, and M2 type polarization of macrophages by MCAM. The CP score was a valuable tool for predicting survival outcomes and guiding immunotherapy for GBM patients. Cell pair pericyte/macrophage and gene pair CD163/MCAM were biologically significant in the tumor microenvironment of GBM.

The aberrant protein disulfide isomerase A5 (PDIA5) expression was relevant to the poor prognosis of patients with human cancers. However, its relationship with the epigenetic and genetic alterations and its effect on tumor immunity is still lacking. In the present study, we comprehensively analyzed the immune infiltration role of PDIA5 in human cancers based on large-scale bioinformatics analyses and in vitro experiments. Obvious DNA methylation and moderate alteration frequency of PDIA5 were observed in human cancers. The expression level of PDIA5 was significantly correlated with infiltrated immune cells, immune pathways, and other immune signatures. We found that cancer cells and macrophages exhibited high PDIA5 expression in human cancers using the single-cell RNA sequencing analysis. We also demonstrated the interaction between PDIA5 and immune cells in glioblastoma multiforme (GBM). Multiplex immunofluorescence staining showed the upregulated expression level of PDIA5 and the increased number of M2 macrophage markers-CD163 positive cells in pan-cancer samples. Notably, PDIA5 silencing resulted in upregulated expression of PD-L1 and SPP1 in U251 cells. Silencing of PDIA5 in hepG2 cells, U251 cells, and PC3 cells contributed to a decline in their ability of proliferation, clone formation, and invasion and inhibited the migration of cocultured M2 macrophages. Additionally, PDIA5 also displayed predictive value in the immunotherapy response of both murine and human cancer cohorts. Overall, our findings indicated that PDIA5 might be a potential target for immunotherapies in cancers.

BackgroundJMJD8 has recently been identified as a cancer-related gene, but current studies provide limited information. We aimed to clarify its roles and the potential mechanisms in pan-cancer.MethodsPan-cancer bulk sequencing data and online web tools were applied to analyze JMJD8’s correlations with prognosis, genome instability, cancer stemness, DNA repair, and immune infiltration. Moreover, single-cell datasets, SpatialDB database, and multiple fluorescence staining were used to validate the association between JMJD8 expression and M2 macrophages. Further, we utilized ROCplotter and cMap web tool to analyze the therapeutic responses and screened JMJD8-targeted compounds, respectively, and we used AlphaFold2 and Discovery Studio to conduct JMJD8 homology modeling and molecular docking.ResultsWe first noticed that JMJD8 was an oncogene in many cancer types. High JMJD8 was associated with lower genome stability. We then found that high JMJD8 correlated with high expression of mismatch repair genes, stemness, homologous repair gene signature in more than 9 cancers. ESTIMATE and cytokine analyses results presented JMJD8’s association with immunosuppression. Also, immune checkpoint CD276 was positively relevant to JMJD8. Subsequently, we validated JMJD8 as the M2 macrophage marker and showed its connection with other immunosuppressive cells and CD8+ T-cell depression. Finally, potential JMJD8-targeted drugs were screened out and docked to JMJD8 protein.ConclusionWe found that JMJD8 was a novel oncogene, and it correlated with immunosuppression and DNA repair. JMJD8 was highly associated with immune checkpoint CD276 and was an M2 macrophage biomarker in many cancers. This study will reveal JMJD8’s roles in pan-cancer and its potential as a novel therapeutic target.

BackgroundLung adenocarcinoma (LUAD) is a fatal malignancy in the world. Growing evidence demonstrated that autophagy-related genes regulated the immune cell infiltration and correlated with the prognosis of LUAD. However, the autophagy-based signature that can predict the prognosis and the efficiency of checkpoint immunotherapy in LUAD patients is yet to be discovered.MethodsWe used conventional autophagy-related genes to screen candidates for signature construction in TCGA cohort and 9 GEO datasets (tumor samples, n=2181; normal samples, n=419). An autophagy-based signature was constructed, its correlation with the prognosis and the immune infiltration of LUAD patients was explored. The prognostic value of the autophagy-based signature was validated in an independent cohort with 70 LUAD patients. Single-cell sequencing data was used to further characterize the various immunological patterns in tumors with different signature levels. Moreover, the predictive value of autophagy-based signature in PD-1 immunotherapy was explored in the IMvigor210 dataset. At last, the protective role of DRAM1 in LUAD was validated by in vitro experiments.ResultsAfter screening autophagy-related gene candidates, a signature composed by CCR2, ITGB1, and DRAM1 was established with the ATscore in each sample. Further analyses showed that the ATscore was significantly associated with immune cell infiltration and low ATscore indicated poor prognosis. Meanwhile, the prognostic value of ATscore was validated in our independent LUAD cohort. GSEA analyses and single-cell sequencing analyses revealed that ATscore was associated with the immunological status of LUAD tumors, and ATscore could predict the efficacy of PD-1 immunotherapy. Moreover, in vitro experiments demonstrated that the inhibition of DRAM1 suppressed the proliferation and migration capacity of LUAD cells.ConclusionOur study identified a new autophagy-based signature that can predict the prognosis of LUAD patients, and this ATscore has potential applicative value in the checkpoint therapy efficiency prediction.

Increasing evidence has demonstrated that RING finger (RNF) proteins played a vital role in cellular and physiological processes and various diseases. However, the function of RNF proteins in low-grade glioma (LGG) remains unknown. In this study, 138 RNF family members revealed their role in LGG. The TCGA database was used as the training cohort; two CGGA databases and GSE108474 were selected as external validation cohorts. Patients were grouped into cluster 1 and cluster 2, both in the training and validation cohorts, using consensus clustering analysis. The prognosis of patients in cluster 1 is significantly better than that in cluster 2. Meanwhile, biofunction prediction was further introduced to explore the potential mechanisms that led to differences in survival outcomes. Patients in Cluster 2 showed more complicated immunocytes infiltration and highly immunosuppressive features than cluster 1. Enrichment pathways such as negative regulation of mast cell activation, DNA replication, mismatch repair, Th17 cell differentiation, antigen processing and presentation, dendritic cell antigen processing and presentation, dendritic cell differentiation were also enriched in cluster 2 patients. For the last, the main contributors were distinguished by employing a machine learning algorithm. A lot of targeted and small molecule drugs that are sensitive to patients in cluster 2 were predicted. Importantly, we discovered TRIM8, DTX2, and TRAF5 as the most vital contributors from the RNF family, which were related to immune infiltration in LGG tumor immune landscape. In this study, we demonstrated the predicted role of RNF proteins in LGG. In addition, we found out three markers among RNF proteins that are closely related to the immune aspects of LGG, which might serve as novel therapeutic targets for immunotherapy in the future.

Lower-grade glioma (LGG) is one of the most common primary tumor types in adults. The chemokine-like factor (CKLF)-like Marvel transmembrane domain-containing (CMTM) family is widely expressed in the immune system and can modulate tumor progression. However, the role of the CMTM family in LGG remains unknown. A total of 508 LGG patients from The Cancer Genome Atlas (TCGA) database were used as a training cohort, and 155 LGG patients from the Chinese Glioma Genome Atlas (CGGA) array database, 142 LGG patients from the CGGA RNA-sequencing database, and 168 LGG patients from the GSE108474 database were used as the validation cohorts. Patients were subdivided into two groups using consensus clustering. The ENET algorithm was applied to build a scoring model based on the cluster model. Finally, ESTIMATE, CIBERSORT, and xCell algorithms were performed to define the tumor immune landscape. The expression levels of the CMTM family genes were associated with glioma grades and isocitrate dehydrogenase (IDH) status. Patients in cluster 2 and the high-risk score group exhibited a poor prognosis and were enriched with higher grade, wild-type IDH (IDH-WT), 1p19q non-codeletion, MGMT promoter unmethylation, and IDH-WT subtype. Patients in cluster 1 and low-risk score group were associated with high tumor purity and reduced immune cell infiltration. Enrichment pathways analysis indicated that several essential pathways involved in tumor progression were associated with the expression of CMTM family genes. Importantly, PD-1, PD-L1, and PD-L2 expression levels were increased in cluster 2 and high-risk groups. Therefore, the CMTM family contributes to LGG progression through modulating tumor immune landscape.

Gliomas are malignant tumors that originate from the central nervous system. The aldehyde dehydrogenase family has been documented to affect cancer progression; however, its role in gliomas remains largely unexplored. Bulk RNA-seq analysis and single-cell RNA-Seq analysis were performed to explore the role of the aldehyde dehydrogenases family in gliomas. Training cohort contained The Cancer Genome Atlas data, while data from Chinese Glioma Genome Atlas and Gene Expression Omnibus were set as validation cohorts. Our scoring system based on the aldehyde dehydrogenases family suggested that high-scoring samples were associated with worse survival outcomes. The enrichment score of pathways were calculated by AUCell to substantiate the biofunction prediction results that the aldehyde dehydrogenases family affected glioma progression by modulating tumor cell proliferation, migration, and immune landscape. Tumor immune landscape was mapped from high-scoring samples. Moreover, ALDH3B1 and ALDH16A1, two main contributors of the scoring system, could affect glioblastoma cell proliferation and migration by inducing cell-cycle arrest and the epithelial-mesenchymal transition. Taken together, the aldehyde dehydrogenases family could play a significant role in the tumor immune landscape and could be used to predict patient prognosis. ALDH3B1 and ALDH16A1 could influence tumor cell proliferation and migration.

Gliomas are aggressive tumors in the central nervous system and glioblastoma is the most malignant type. Ferroptosis is a programmed cell death that can modulate tumor resistance to therapy and the components of tumor microenvironment. However, the relationship between ferroptosis, tumor immune landscape, and glioblastoma progression is still elusive. In this work, data from bulk RNA-seq analysis, single cell RNA-seq analysis, and our own data (the Xiangya cohort) are integrated to reveal their relationships. A scoring system is constructed according to ferroptosis related gene expression, and high scoring samples resistant to ferroptosis and show worse survival outcome than low scoring samples. Notably, most of the high scoring samples are aggressive glioblastoma subtype, mesenchymal, and classical, by calculating RNA velocity. Cross-talk between high scoring glioblastoma cells and immunocytes are explored by R package ‘celltalker’. Ligand–receptor pairs like the TRAIL or TWEAK signaling pathway are identified as novel bridges implying how ferroptosis modulate immunocytes’ function and shape tumor microenvironment. Critically, potential drugs target to high scoring samples are predicted, namely, SNX2112, AZ628, and bortezomib and five compounds from the CellMiner database. Taken together, ferroptosis associates with glioblastoma aggressiveness, cross-talk with immunocytes and offer novel chemotherapy strategy.

Hypoxia and angiogenesis are the leading causes of tumor progression, and their strong correlation has been discovered in many cancers. However, their collective function’s prognostic and biological roles were not reported in gastric cancer. Hence, we aimed to investigate the effects of hypoxia and angiogenesis on gastric cancer via sequencing data. This study used weighted gene coexpression network analysis and random forest regression to build a hypoxia-angiogenesis-related model (HARM) via the TCGA-STAD lncRNA data. It estimated the HARM’s correlation with clinical features and its accuracy for survival prediction. Sequential functional analyses were conducted to investigate its biological role, and we next sought the immune landscape status and immunological function variation by ESTIMATE score calculation and GSVA, respectively. Seven different algorithms were conducted to assess the immunocyte infiltration, and TIDE score and immune checkpoint levels were compared between the high- and low-HARM groups. As a result, we found that HARM predicted patient survival with high accuracy and was correlated with higher stages of gastric cancer. Various cancer-associated pathways and macrophage-related regulations were upregulated in the high-HRAM group. The high-HARM group harbored higher immune levels, and M2 macrophages and cancer-associated fibroblasts were particularly highly unfiltered. Furthermore, globally upregulated immune checkpoints and higher TIDE scores were observed in the high-HARM group. Finally, we filtered eight drugs with lower IC50 in the high-HARM group as potential drugs for the HARM-targeted therapy. We believe this study opens up novel perspectives into the interaction between hypoxia-angiogenesis and immunosuppression and will provide novel insights for gastric cancer immunotherapy.

Gliomas are the most common and lethal primary malignant tumor of the brain. Routine treatment including surgical resection, chemotherapy, and radiotherapy produced limited therapeutic effect, while immunotherapy targeting the glioma microenvironment has offered a novel therapeutic option. PDIA5 protein is the member of PDI family, which is highly expressed in glioma and participates in glioma progression. Based on large-scale bioinformatics analysis, we discovered that PDIA5 expression level is upregulated in aggressive gliomas, with high PDIA5 expression predicting poor clinical outcomes. We also observed positive correlation between PDIA5 and immune infiltrating cells, immune related pathways, inflammatory activities, and other immune checkpoint members. Patients with high PDIA5 high-expression benefited from immunotherapies. Additionally, immunohistochemistry revealed that PDIA5 and macrophage biomarker CD68 were upregulated in high-grade gliomas, and patients with low PDIA5 level experienced favorable outcomes among 33 glioma patients. Single cell RNA sequencing exhibited that PDIA5 was in high level presenting in neoplastic cells and macrophages. Cell transfection and co-culture of glioma cells and macrophages revealed that PDIA5 in tumor cells mediated macrophages exhausting. Altogether, our findings indicate that PDIA5 overexpression is associated with immune infiltration in gliomas, and may be a promising therapeutic target for glioma immunotherapy.