Immune Microenvironment Features Research Articles

Comprehensive multi-omics integration uncovers mitochondrial gene signatures for prognosis and personalized therapy in lung adenocarcinoma

The therapeutic efficacy of lung adenocarcinoma (LUAD), the most prevalent histological subtype of primary lung cancer, remains inadequate, with accurate prognostic assessment posing significant challenges. This study sought to elucidate the prognostic significance of mitochondrial-related genes in LUAD through an integrative multi-omics approach, aimed at developing personalized therapeutic strategies. Utilizing transcriptomic and single-cell RNA sequencing (scRNA-seq) data, alongside clinical information from publicly available databases, we first applied dimensionality reduction and clustering techniques to the LUAD single-cell dataset, focusing on the subclassification of fibroblasts, epithelial cells, and T cells. Mitochondrial-related prognostic genes were subsequently identified using TCGA-LUAD data, and LUAD cases were stratified into distinct molecular subtypes through consensus clustering, allowing for the exploration of gene expression profiles and clinical feature distributions across subtypes. By leveraging an ensemble of machine learning algorithms, we developed an Artificial Intelligence-Derived Prognostic Signature (AIDPS) model based on mitochondrial-related genes and validated its prognostic accuracy across multiple independent datasets. The AIDPS model demonstrated robust predictive power for LUAD patient outcomes, revealing significant differences in responses to immunotherapy and chemotherapy, as well as survival outcomes between risk groups. Furthermore, we conducted comprehensive analyses of tumor mutation burden (TMB), immune microenvironment characteristics, and genome-wide association study (GWAS) data, providing additional insights into the mechanistic roles of mitochondrial-related genes in LUAD pathogenesis. This study not only offers a novel approach to improving prognostic assessments in LUAD but also establishes a strong foundation for the development of personalized therapeutic interventions.

Association of glutaminase expression with immune-suppressive tumor microenvironment, clinicopathologic features, and clinical outcomes in endometrial cancer

ObjectiveIncreased glutamine metabolism by cancer cells via upregulation of the drug-targetable enzyme glutaminase may contribute to an immune-suppressive tumor microenvironment. Inhibiting glutamine metabolism can not only suppress tumor growth, but...

Integrated machine learning and Mendelian randomization reveal PALMD as a prognostic biomarker for nonspecific orbital inflammation

Background: Nonspecific Orbital Inflammation (NSOI) remains a perplexing enigma among proliferative inflammatory disorders. Its etiology is idiopathic, characterized by distinctive and polymorphous lymphoid infiltration within the orbital region. Preliminary investigations suggest that PALMD localizes within the cytosol, potentially playing a crucial role in cellular processes, including plasma membrane dynamics and myogenic differentiation. The potential of PALMD as a biomarker for NSOI warrants meticulous exploration. Methods: PALMD was identified through the intersection analysis of common DEGs from datasets GSE58331 and GSE105149 from the GEO database, alongside immune-related gene lists from the ImmPort database, using Lasso regression and SVM-RFE analysis. GSEA and GSVA were conducted with gene sets co-expressed with PALMD. To further investigate the correlation between PALMD and immune-related biological processes, the CIBERSORT algorithm and ESTIMATE method were employed to evaluate immune microenvironment characteristics of each sample. The expression levels of PALMD were subsequently validated using GSE105149. Results: Among the 314 DEGs identified, several showed significant differences. Lasso and SVM-RFE algorithms pinpointed 15 hub genes. Functional analysis of PALMD emphasized its involvement in cell-cell adhesion, leukocyte migration, and leukocyte-mediated immunity. Enrichment analysis revealed that gene sets positively correlated with PALMD were enriched in immune-related pathways. Immune infiltration analysis indicated that resting dendritic cells, resting mast cells, activated NK cells, and plasma cells positively associate with PALMD expression. Conversely, naive B cells, activated dendritic cells, M0 and M1 macrophages, activated mast cells, activated CD4 memory T cells, and naive CD4 T cells showed a negative correlation with PALMD expression. PALMD demonstrated significant diagnostic potential in differentiating NSOI. Conclusions: This study identifies PALMD as a potential biomarker linked to NSOI, providing insights into its pathogenesis and offering new avenues for tracking disease progression.

Construction and validation of a histone-related gene signature for the diagnosis of endometriosis.

Endometriosis is a common chronic disease in childbearing women and a major cause of infertility. Our study aimed to identify and validate a novel gene signature for diagnosing endometriosis based on histone-related genes (HRGs), and to investigate their biological functions in endometriosis. RNA sequence data were downloaded from the Gene Expression Omnibus database, and HRGs were retrieved from the GeneCards database. We identified differentially expressed genes using the limma package, and constructed a diagnostic model using the rms package. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were performed for visualization, annotation, and integrated discovery. Subsequently, we validated the model using the recall and decision curve analysis (DCA). Additionally, we analyzed the immune microenvironment features using CIBERSORT. A total of 18 differentially expressed HRGs were identified in patients with endometriosis compared with controls. GO and KEGG enrichment was mainly in spindle organization, positive regulation of the cell cycle process, progesterone-mediated oocyte maturation, and cellular senescence and cell cycle. We obtained a signature of four HRGs (JUNB, FRY, LMNB1, and SPAG1). DCA revealed that the diagnostic model benefits patients with endometriosis, regardless of the incidence. CIBERSORT analysis showed that the number of plasma cells increased significantly in endometriosis samples from all four datasets. Our findings provide novel insights into the function of HRGs in the development of endometriosis and identify a new signature of four HRGs that may serve as valuable diagnostic markers and therapeutic targets for this disease.

P4.07E.06 Distinct Genomic and Immune Microenvironment Features of Solid or Micropapillary Predominant Subtype in Stage I Lung Adenocarcinomas

P4.07E.06 Distinct Genomic and Immune Microenvironment Features of Solid or Micropapillary Predominant Subtype in Stage I Lung Adenocarcinomas

SMARCA4 mutations and expression in lung adenocarcinoma: prognostic significance and impact on the immunotherapy response.

The switch/sucrose non-fermenting (SWI/SNF) complex family includes important chromatin-remodeling factors that are frequently mutated in lung adenocarcinoma (LUAD). However, the role of one family member, SMARCA4, in LUAD prognosis and immunotherapy sensitivity remains unclear. In the present study, 6745 LUAD samples from the cBioPortal database were used to analyze the relationships between SMARCA4 mutations and patient prognoses and clinical characteristics. Additionally, we examined the correlation between SMARCA4 mutations and prognosis in patients treated with immunotherapy using two immune-related datasets. SMARCA4 mutations and low expression were associated with shorter survival, and mutations were associated with a high tumor mutational burden and high microsatellite instability. SMARCA4 mutations were accompanied by KRAS, KEAP1, TP53 and STK11 mutations. No significant difference was observed in the immunotherapy response between patients with and without SMARCA4 mutations. When KRAS or STK11 mutations were present, immunotherapy effectiveness was poorer; however, when both SMARCA4 and TP53 mutations were present, immunotherapy was more effective. Furthermore, low SMARCA4 expression predicted a higher immunophenoscore, and SMARCA4 expression was correlated with certain immune microenvironment features. Taken together, our results suggest that SMARCA4 mutations and low expression might be associated with poor LUAD prognosis. Additionally, immunotherapy efficacy in patients with SMARCA4 mutations depended on the co-mutant genes. Thus, SMARCA4 could be an important factor to be considered for LUAD diagnosis and treatment.

Multi-omics features of immunogenic cell death in gastric cancer identified by combining single-cell sequencing analysis and machine learning

Gastric cancer (GC) is a prevalent malignancy with high mortality rates. Immunogenic cell death (ICD) is a unique form of programmed cell death that is closely linked to antitumor immunity and plays a critical role in modulating the tumor microenvironment (TME). Nevertheless, elucidating the precise effect of ICD on GC remains a challenging endeavour. ICD-related genes were identified in single-cell sequencing datasets and bulk transcriptome sequencing datasets via the AddModuleScore function, weighted gene co-expression network (WGCNA), and differential expression analysis. A robust signature associated with ICD was constructed using a machine learning computational framework incorporating 101 algorithms. Furthermore, multiomics analysis, including single-cell sequencing analysis, bulk transcriptomic analysis, and proteomics analysis, was conducted to verify the correlation of these hub genes with the immune microenvironment features of GC and with GC invasion and metastasis. We screened 59 genes associated with ICD and developed a robust ICD-related gene signature (ICDRS) via a machine learning computational framework that integrates 101 different algorithms. Furthermore, we identified five key hub genes (SMAP2, TNFAIP8, LBH, TXNIP, and PIK3IP1) from the ICDRS. Through single-cell analysis of GC tumor s, we confirmed the strong correlations of the hub genes with immune microenvironment features. Among these five genes, LBH exhibited the most significant associations with a poor prognosis and with the invasion and metastasis of GC. Finally, our findings were validated through immunohistochemical staining of a large clinical sample set, and the results further supported that LBH promotes GC cell invasion by activating the epithelial–mesenchymal transition (EMT) pathway.

Phosphorus Metabolism-Related Genes Serve as Novel Biomarkers for Predicting Prognosis in Bladder Cancer: A Bioinformatics Analysis.

Phosphorus metabolism might be associated with tumor initiation and progression. We aimed to screen out the phosphorus metabolism genes related to bladder cancer and construct a clinical prognosis model. The dataset used for the analysis was obtained from TCGA database. GO and KEGG enrichment analyses were subsequently applied to differentially expressed genes. Consensus clustering was utilized, and different clusters of the tumor immune microenvironment and other features were compared. The phosphorus metabolism-related genes involved in prognosis were screened out by univariate Cox regression, LASSO regression and multivariate Cox regression analysis, and a nomogram was constructed. The performance of the nomogram was validated using TCGA dataset and the GEO dataset, respectively. Overall, 405 phosphorus metabolism-related differentially expressed genes from TCGA database were identified, which were associated with phosphorylation, cell proliferation, leukocyte activation, and signaling pathways. Two clusters were obtained by consistent clustering. After tumor immune microenvironment analysis, significant differences in immune cell infiltration between cluster 1 and cluster 2 were found. Four phosphorus metabolism-related genes (LIME1, LRP8, SPDYA, and MST1R) were associated with the prognosis of bladder cancer (BLCA) patients. We built a prognostic model and visualized the model as a nomogram. Calibration curves demonstrated the performance of this nomogram, in agreement with that shown by the ROC curves. We successfully identified four phosphorus metabolism-related genes associated with prognosis, providing potential targets for biomarkers and therapeutics. A nomogram based on these genes was developed. Nevertheless, this study is based on bioinformatics, and experimental validation remains essential.

A spatiotemporal comparative analysis on tumor immune microenvironment characteristics between neoadjuvant chemotherapy and preoperative immunotherapy for ESCC

The average five-year survival rate for esophageal cancer, a common malignant tumor of the digestive system, is barely 20%. The majority of esophageal squamous cell carcinoma (ESCC) patients had already progressed to a locally advanced or even advanced stage at initial diagnosis, making routine surgery ineffective. Chemotherapy and immunotherapy are important neoadjuvant treatments for ESCC, however, it remains unknown how treatment will affect the immunological microenvironment, especially at the spatial level. Here, we presented the TME characters of ESCC from the temporal and spatial dimensions using scRNA-seq and ST, investigated the changes of immune cell clusters in the TME under neoadjuvant chemotherapy and preoperative immunotherapy, and explored the potential mechanisms. It was found that compared with chemotherapy, immunotherapy combined with chemotherapy increased the level of T cell proliferation, partially restored the function of exhausted T cells, induced the expansion of specific exhausted CD8 T cells, increased the production of dendritic cells (DCs), and supported the immune hot microenvironment of the tumor. We also found that CD52 and ID3 have potential as biomarkers of ESCC. Particularly, CD52 may be served as a predictor of the efficacy to screen the advantaged population of different regimens. Through multiple pathways, CAF2 and CAF5’s antigen-presenting role affected the other fibroblast clusters, resulting in malignant transformation. We analyzed the immune microenvironment differences between the two regimens to provide a more thorough description of the ESCC microenvironment profile and serve as a foundation for customized neoadjuvant treatment of ESCC.

Anoikis-Related Long Non-Coding RNA Signatures to Predict Prognosis and Immune Infiltration of Gastric Cancer.

Anoikis is a distinct type of programmed cell death and a unique mechanism for tumor progress. However, its exact function in gastric cancer (GC) remains unknown. This study aims to investigate the function of anoikis-related lncRNA (ar-lncRNA) in the prognosis of GC and its immunological infiltration. The ar-lncRNAs were derived from RNA sequencing data and associated clinical information obtained from The Cancer Genome Atlas. Pearson correlation analysis, differential screening, LASSO and Cox regression were utilized to identify the typical ar-lncRNAs with prognostic significance, and the corresponding risk model was constructed, respectively. Comprehensive methods were employed to assess the clinical characteristics of the prediction model, ensuring the accuracy of the prediction results. Further analysis was conducted on the relationship between immune microenvironment and risk features, and sensitivity predictions were made about anticancer medicines. A risk model was built according to seven selected ar-lncRNAs. The model was validated and the calibration plots were highly consistent in validating nomogram predictions. Further analyses revealed the great accuracy of the model and its ability to serve as a stand-alone GC prognostic factor. We subsequently disclosed that high-risk groups display significant enrichment in pathways related to tumors and the immune system. Additionally, in tumor immunoassays, notable variations in immune infiltrates and checkpoints were noted between different risk groups. This study proposes, for the first time, that prognostic signatures of ar-lncRNA can be established in GC. These signatures accurately predict the prognosis of GC and offer potential biomarkers, suggesting new avenues for basic research, prognosis prediction and personalized diagnosis and treatment of GC.

Identifying epithelial-mesenchymal transition-related genes as prognostic biomarkers and therapeutic targets of hepatocellular carcinoma by integrated analysis of single-cell and bulk-RNA sequencing data.

Hepatocellular carcinoma (HCC) remains one of the most lethal cancers globally. Patients with advanced HCC tend to have poor prognoses and shortened survival. Recently, data from bulk RNA sequencing have been employed to discover prognostic markers for various cancers. However, they fall short in precisely identifying core molecular and cellular activities within tumor cells. In our present study, we combined bulk-RNA sequencing (bulk RNA-seq) data with single-cell RNA sequencing (scRNA-seq) to develop a prognostic model for HCC. The goal of our research is to uncover new biomarkers and enhance the accuracy of HCC prognosis prediction. Integrating single-cell sequencing data with transcriptomics were used to identify epithelial-mesenchymal transition (EMT)-related genes (ERGs) implicated in HCC progression and their clinical significance was elucidated. Utilizing marker genes derived from core cells and ERGs, we constructed a prognostic model using univariate Cox analysis, exploring a multitude of algorithmic combinations, and further refining it through multivariate Cox analysis. Additionally, we conducted an in-depth investigation into the disparities in clinicopathological features, immune microenvironment composition, immune checkpoint expression, and chemotherapeutic drug sensitivity profiles between high- and low-risk patient cohorts. We developed a prognostic model predicated on the expression profiles of eight signature genes, namely HSP90AA1, CIRBP, CCR7, S100A9, ADAM17, ENG, PGF, and INPP4B, aiming at predicting overall survival (OS) outcomes. Notably, patients classified with high-risk scores exhibited a propensity towards diminished OS rates, heightened frequencies of stage III-IV disease, increased tumor mutational burden (TMB), augmented immune cell infiltration, and diminished responsiveness to immunotherapeutic interventions. This study presented a novel prognostic model for predicting the survival of HCC patients by integrating scRNA-seq and bulk RNA-seq data. The risk score emerges as a promising independent prognostic factor, showing a correlation with the immune microenvironment and clinicopathological features. It provided new clinical tools for predicting prognosis and aided future research into the pathogenesis of HCC.

Molecular subtypes based on immunologic and epithelial-mesenchymal transition gene sets reveal tumor immune microenvironment characteristics and implications for immunotherapy of patients with glioma

The tumor immune microenvironment (TIME) significantly influences cancer progression and treatment. This study sought to uncover novel TIME-related glioma biomarkers to advance antitumor immunotherapies by integrating data from sequencing of bulk RNA as well as scRNA. Immunologic and epithelial-mesenchymal transition (EMT) characteristics were used to classify glioma patients into two immune subtypes (ISs) and two EMT subtypes (ESs). Patients in IS1 and ES1, characterized by high immune infiltration and low stemness scores, exhibited poor clinical outcomes and limited responsiveness to immunotherapy. A new risk signature was developed using 16 genes and validated in independent glioma cohorts. Among these, HAVCR2, IL18, LAGLS9, and PTPN6 emerged as hub genes, with IL18 identified as a potential independent indicator. The upregulation of IL18 in high-grade gliomas and U-251 MG cells aligned with bioinformatics analysis. These insights deepen the understanding of TIME-related mechanisms in glioma and highlight potential therapeutic targets, offering a theoretical foundation for effective antitumor immunotherapies in glioma.

WDR77 in Pan-Cancer: Revealing expression patterns, genetic insights, and functional roles across diverse tumor types, with a spotlight on colorectal cancer

ObjectiveDespite its involvement in regulating various cellular functions, the expression and role of WD repeat-containing protein 77 (WDR77) in cancer remain elusive. This study aims to explore the expression and potential roles of WDR77 across multiple cancers, with a particular focus on its relevance in colorectal cancer (CRC). MethodsWe obtained WDR77 RNA-seq data, mutations, CNVs, and DNA methylation data from the TCGA, GTEx, and GEO databases to investigate its expression patterns and prognostic value. Additionally, we examined the correlation between WDR77 expression and somatic mutations, copy number variations, DNA methylation, and mRNA modifications. We utilized GSVA, GSEA algorithms, and CRISPR KO data from the Dependency Map database to explore WDR77′s potential biological functions. The association between WDR77 and the tumor immune microenvironment was investigated using ESTIMATE and IOBR algorithms. Finally, we assessed WDR77 expression in CRC and its impact on cell proliferation through qRT-PCR, Western blotting, immunohistochemistry, CCK8, colony formation, and EdU assays. ResultsWDR77 was upregulated in various tumors and correlated with poor patient prognosis. Its high expression positively correlated with pathways related to cell proliferation and negatively correlated with immune-related pathways. In CRC, WDR77 expression was associated with specific clinical features, genomic alterations, and immune microenvironment characteristics. Experimental validation confirmed upregulated WDR77 expression in CRC tissues and cells, with WDR77 knockdown significantly inhibiting CRC cell proliferation. ConclusionWDR77 holds potential as an oncogene and biological marker in various cancers, particularly CRC.

Prognostic significance of HS2ST1 expression in patients with hepatocellular carcinoma

BackgroundHeparan sulfate 2-O-sulfotransferase 1 (HS2ST1) catalyzes the sulfation of glucuronic acid residues in heparan sulfate proteoglycans, enabling these proteoglycans to interact with numerous ligands within tumor microenvironments. However, the prognostic role of HS2ST1 expression in cancer remains unclear.ObjectiveThis investigated HS2ST1 expression levels and their prognostic significance in various cancer types, demonstrated the prognostic value of HS2ST1 expression in hepatocellular carcinoma (HCC) patients, and identified molecular signatures associated with HS2ST1 expression.MethodsHS2ST1 expression and patient survival data from The Cancer Genome Atlas (TCGA) datasets were analyzed using the Gene Expression Profiling Interactive Analysis (GEPIA) portal. We obtained gene expression and clinicopathological information on HCC patients from the TCGA and the Japan and France International Cancer Genome Consortium (ICGC) databases and performed survival analyses. We also examined relevant protein networks, differentially expressed genes, gene set enrichments, and tumor immune microenvironment features associated with HS2ST1 expression.ResultsHS2ST1 exhibited higher expression in eight tumor types compared with normal tissues and was associated with poor prognoses in five tumors, including HCC. HS2ST1 status correlated with poor prognosis in two ICGC HCC cohorts. Elevated HS2ST1 expression in HCC tumors was associated with signaling pathways involved in cell cycle progression, protein secretion, and mTORC1 signaling. Moreover, HS2ST1 expression levels were inversely correlated with immune cell infiltration in the tumor microenvironment.ConclusionOur study elucidates the prognostic significance of HS2ST1 expression in HCC patients and provides insights into the potential roles of HS2ST1 in signaling pathways and the tumor microenvironment.

Hypoxia-driven lncRNA CTD-2510F5.4: a potential player in hepatocellular carcinoma's prognostic stratification, cellular behavior, tumor microenvironment, and therapeutic response.

Hepatocellular carcinoma (HCC) is a highly aggressive cancer with limited therapeutic options. Hypoxia is a common feature of the tumor microenvironment that reportedly promotes tumorigenesis. Long non-coding RNAs (lncRNAs) are a class of regulatory molecules with diverse functions in cancer biology. This study aimed to identify hypoxia-induced lncRNAs associated with HCC and evaluate their potential as prognostic and therapeutic biomarkers. We employed microarray and The Cancer Genome Atlas (TCGA) data to identify hypoxia-induced lncRNAs in HCC. Subsequently, we focused on CTD-2510F5.4, a candidate lncRNA, and predicted its functional roles in HCC using Gene Ontology (GO) and Guilt-by-Association (GBA) analyses. We validated its expression under hypoxia in Huh7 and HepG2 cells using RT-PCR. Functional assays, including CCK8, wound-healing, and transwell assays, were performed to assess the effects of CTD-2510F5.4 overexpression on HCC cell proliferation, invasion, and metastasis potential. Furthermore, we investigated the association between CTD-2510F5.4 expression and patient prognosis, tumor mutation signature, immune microenvironment characteristics, and therapeutic response to different treatment modalities. Our data demonstrated a significant upregulation of CTD-2510F5.4 expression in response to hypoxia. Functional enrichment analyses revealed the involvement of CTD-2510F5.4 in cell cycle regulation, E2F targets, G2M checkpoint control, and MYC signaling pathways. Functionally, CTD-2510F5.4 overexpression promoted HCC cell proliferation, invasion, and metastasis. Patients with high CTD-2510F5.4 expression exhibited a worse prognosis, a higher prevalence of TP53 mutations, increased infiltration by immunosuppressive regulatory T cells, elevated expression of immune checkpoint molecules, and higher TIDE scores indicative of immune dysfunction and exclusion. Notably, patients with low CTD-2510F5.4 expression displayed greater sensitivity to immunotherapy and antiangiogenic therapy, while those with high expression responded better to chemotherapy. Our findings suggest that CTD-2510F5.4 plays a critical role in HCC progression and immune modulation. Its potential as a prognostic biomarker and a predictor of therapeutic response warrants further investigation for personalized treatment strategies in HCC patients.

Glycolysis-related genes predict prognosis and indicate immune microenvironment features in gastric cancer.

Gastric cancer (GC) is a major contributor to cancer-related mortality. Glycolysis plays a pivotal role in tumor microenvironment (TME) reprogramming. In this research, the functions of glycolysis-associated genes (GRGs) were evaluated to predict the outcome and reveal the characteristics of the immune microenvironment in individuals with stomach cancer. The Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) cohort provided gene expression and clinical data for gastric cancer (GC) patients, which were further authenticated using datasets sourced from the Gene Expression Omnibus (GEO). By referencing the Molecular Signatures Database (MSigDB), a total of 326 GRGs were pinpointed. The various subtypes of GC were outlined through consensus clustering, derived from the expression patterns of these GRGs. Utilizing multivariate Cox regression analysis, a multigene risk score model was formulated. Both the CIBERSORT and ESTIMATE algorithms played a pivotal role in assessing the immune microenvironment. To delve into the biological functions of the key genes, wound healing, transwell invasion, and MTT assays were conducted. Based on the expression patterns of GRGs, patients were categorized into two distinct groups: the metabolic subtype, designated as cluster A, and the immune subtype, labeled as cluster B. Patients belonging to cluster B exhibited a poorer prognosis. A prognostic risk score model, formulated upon the expression levels of six key GRGs - ME1, PLOD2, NUP50, CXCR4, SLC35A3, and SRD35A3 - emerged as a viable tool for predicting patient outcomes. The downregulation of CXCR4 notably diminished the glycolytic capacity of gastric cancer (GC) cells, alongside their migratory, invasive, and proliferative capabilities. Intriguingly, despite the adverse prognostic implications associated with both the immune subtype (cluster B) and the high-risk cohort, these groups exhibited a favorable immune microenvironment coupled with elevated expression of immune checkpoint genes. Our investigations revealed a positive correlation between high CXCR4 expression and low ME1 expression with the infiltration of CD8+ T cells, as well as an enhanced responsiveness to treatment with an anti-PD-1 immune checkpoint inhibitor. In this study, we discovered that the expression profiles of GRGs hold the potential to forecast the prognosis of gastric cancer (GC) patients, thereby possibly aiding in clinical treatment decision-making.

DNA mismatch repair defect and intratumor heterogeneous deficiency differently impact immune responses in diffuse large B-cell lymphoma

ABSTRACT Deficient (d) DNA mismatch repair (MMR) is a biomarker predictive of better response to PD-1 blockade immunotherapy in solid tumors. dMMR can be caused by mutations in MMR genes or by protein inactivation, which can be detected by sequencing and immunohistochemistry, respectively. To investigate the role of dMMR in diffuse large B-cell lymphoma (DLBCL), MMR gene mutations and expression of MSH6, MSH2, MLH1, and PMS2 proteins were evaluated by targeted next-generation sequencing and immunohistochemistry in a large cohort of DLBCL patients treated with standard chemoimmunotherapy, and correlated with the tumor immune microenvironment characteristics quantified by fluorescent multiplex immunohistochemistry and gene-expression profiling. The results showed that genetic dMMR was infrequent in DLBCL and was significantly associated with increased cancer gene mutations and favorable immune microenvironment, but not prognostic impact. Phenotypic dMMR was also infrequent, and MMR proteins were commonly expressed in DLBCL. However, intratumor heterogeneity existed, and increased DLBCL cells with phenotypic dMMR correlated with significantly increased T cells and PD-1+ T cells, higher average nearest neighbor distance between T cells and PAX5+ cells, upregulated immune gene signatures, LE4 and LE7 ecotypes and their underlying Ecotyper-defined cell states, suggesting the possibility that increased T cells targeted only tumor cell subsets with dMMR. Only in patients with MYC¯ DLBCL, high MSH6/PMS2 expression showed significant adverse prognostic effects. This study shows the immunologic and prognostic effects of genetic/phenotypic dMMR in DLBCL, and raises a question on whether DLBCL-infiltrating PD-1+ T cells target only tumor subclones, relevant for the efficacy of PD-1 blockade immunotherapy in DLBCL.

Immune predictors of response to immune checkpoint inhibitors in mismatch repair-deficient endometrial cancer

BackgroundPatients with mismatch repair-deficient (MMRd) endometrial cancer (EC) can derive great benefit from immune checkpoint inhibitors (ICI). However not all responses and predictors of primary resistance are lacking.MethodsWe compared the...

Constructing an immune-related prognostic signature for predicting prognosis and immune response in hepatocellular carcinoma

BackgroundCurrently, there are few studies on immune-related prognostic analysis of hepatocellular carcinoma (HCC). Our aim was to establish an immune-correlated prognostic model for HCC. MethodsImmune-associated cells were obtained from the scRNA-seq dataset (GSE149614) of HCC. Differentially expressed genes between normal and tumor cells from immune-associated cells and the immune-related genes from the ImmPort database were used to identify immune-related differentially expressed genes (IRDEGs). Subsequently, the risk model was established in the TCGA-LIHC cohort (n = 438) from the Cancer Genome Atlas (TCGA) database by using Kaplan-Meier (K-M) survival curve, univariate/multivariate Cox regression analysis. Subsequently, we further analyzed tumor immune microenvironment characteristics, somatic mutation, immune checkpoint and its ligand expression levels between high- and low-risk groups, as well as drug sensitivity prediction. ICGC cohort was set as the validation cohort. TCGA-LIHC cohort and three independent the Gene Expression Omnibus (GEO) datasets (GSE54236, GSE14520, and GSE64041) was used to verify IRDEGs expression, as well as PCR assays using clinical samples. ResultsThe IRDEGs was composed of 4 genes, namely B2M, SPP1, PPIA, and HRG. The 438 HCC patients were divided into high- and low-risk group. The high-risk group was associated with poor prognosis, including higher T stage, advanced pathological stages, less immune cell infiltration, higher TP53 mutation rate, the high expression of CTLA4 and HAVCR2. Besides, high-risk populations benefit from most chemotherapy drugs. Similarly, the performance of the risk model was validated in the ICGC. All four datasets (TCGA-LIHC cohort, GSE54236, GSE14520, and GSE64041) and clinical q-PCR results demonstrated that, compared with normal samples, the expressions of B2M and HRG were lower in tumor samples, and the expression of SPP1 was higher. ConclusionIn summary, the immune-related prognostic signature had a good predictive performance on prognosis and immunotherapy for HCC patients.

A heterogeneous tumor immune microenvironment of uncommon epidermal growth factor receptor mutant non-small cell lung cancer.

Common epidermal growth factor receptor (EGFR) mutations are usually not considered for immunotherapy in non-small cell lung cancer (NSCLC) due to poor efficacy. However, whether uncommon EGFR mutations are suitable for immunotherapy has not been thoroughly studied. Thus, we explored the tumor immune microenvironment (TME) features in uncommon EGFR mutant NSCLC. In this study, a total of 41 patients with EGFR mutations were included, the majority (85.4%) of whom were stage I. Among them, 22 patients harbored common mutations, while 19 patients presented with uncommon mutations. Compared with common mutations, uncommon mutations exhibited more infiltrating T cells and fewer M2 macrophages, upregulated expression of antigen processing and a presentation pathway. Unsupervised clustering based on the mIF profile identified two classes with heterogeneous TME in uncommon mutations. Class 1 featured the absence of PD-1+ cytotoxic T cell infiltration, and class 2 displayed a hotter TME because of the downregulated expression of hypoxia (p < 0.001), oxidative phosphorylation (p = 0.009), and transforming growth factor beta signaling (p = 0.01) pathways as well as increased expression of CTLA4 (p = 0.001) and PDCD1 (p = 0.004). The association of CTLA4 and PDCD1 with TME profiles was validated in a TCGA lung adenocarcinoma cohort with uncommon EGFR mutations. Our study reveals the distinct and heterogeneous TME features in uncommon EGFR mutant NSCLC.