Immune-related Gene Signature Research Articles

HLA-A+ tertiary lymphoid structures with reactivated tumor infiltrating lymphocytes are associated with a positive immunotherapy response in esophageal squamous cell carcinoma.

Immune checkpoint blockade (ICB) therapy provides remarkable clinical benefits for multiple cancer types. However, the overall response rate to ICB therapy remains low in esophageal squamous cell carcinoma (ESCC). This study aimed to identify biomarkers of ICB therapy for ESCC and interrogate its potential clinical relevance. We investigated gene expression in 42 treatment-naïve ESCC tumor tissues and identified differentially expressed genes, tumor-infiltrating lymphocytes and immune-related genes signatures associated with differential immunotherapy responses. We systematically assessed the tumor microenvironment using the NanoString GeoMx digital spatial profiler, single-cell RNA-seq and multiplex immunohistochemistry in ESCC. Finally, we evaluated the associations between HLA-A-positive tertiary lymphoid structures (TLSs) and patients' responses to ICB in 60 ESCC patients. Tumor infiltrating B lymphocytes and several immune-related gene signatures, such as the antigen presenting machinery (APM) signature, are significantly elevated in ICB treatment responders. Multiplex immunohistochemistry identified the presence of HLA-A+ TLSs and showed that TLS-resident cells increasingly express HLA-A as TLSs mature. Most TLS-resident HLA-A+ cells are tumor-infiltrating T (TIL-T) or tumor-infiltrating B (TIL-B) lymphocytes. Digital spatial profiling of spatially distinct TIL-T lymphocytes and single-cell RNA-seq data from 60 ESCC tumor tissues revealed that CXCL13-expressing exhausted TIL-Ts inside TLSs are reactivated with elevated expression of the APM signature as TLSs mature. Finally, we demonstrated that HLA-A+ TLSs and their major cellular components, TIL-Ts and TIL-Bs, are associated with a clinical benefit from ICB treatment for ESCC. HLA-A+ TLSs are present in ESCC tumor tissues. TLS-resident TIL-Ts with elevated expression of the APM signature may be reactivated. HLA-A+ TLSs and their major cellular components, TIL-Ts and TIL-Bs, may serve as biomarkers for ICB-treated ESCC patients.

Development and verification of a manganese metabolism- and immune-related genes signature for prediction of prognosis and immune landscape in gastric cancer

Development and verification of a manganese metabolism- and immune-related genes signature for prediction of prognosis and immune landscape in gastric cancer

Abstract CT029: Chemotherapy-free neoadjuvant regimen with durvalumab, trastuzumab and pertuzumab (DTP) in HER2-enriched early breast cancer: A prospective, open-label phase II trial

Abstract Background: High risk HER2-positive early breast cancer (EBC) patients are traditionally treated with neoadjuvant chemotherapy in combination with HER2-directed therapy. De-escalation approaches are being studied as benefits of chemotherapy in the HER2-enriched subset of EBC, but they have been inconclusive thus far. Preclinical studies suggest synergistic effect of immune checkpoint blockade (ICB) and HER2-directed therapy. This study hypothesizes that biologically directed HER-2 targeted therapy with Trastuzumab (T) and Pertuzumab (P) together with ICB (Durvalumab, D) will obviate the need of chemotherapy in HER2-enriched EBC patients. Methods: In this single arm, open-label phase II study, previously untreated, stage I-III, ER/PR negative, HER2-enriched breast cancer (BluePrint®, Agendia) patients were treated with D (1120mg IV, q3w), T (8mg/kg IV loading dose, 6 mg/kg IV q3w), and P (840 mg IV loading dose, 420 mg IV q3w) for 6 cycles. Response was assessed at end of 6 cycles with breast MRI and possible biopsy if residual disease was present. Responders (MRI and/or biopsy negative for residual cancer) proceeded to surgery, while patients with biopsy-proven residual disease were offered salvage standard chemotherapy (TCHP) before surgery. Primary end-point was pathological response rate defined as residual cancer burden (RCB)-0 and RCB-1. Patients who achieved RCB 0/1 received adjuvant DTP for 1 year. Results: A total of 51 patients were screened; 39 patients have been enrolled and received at least one cycle of treatment. Of these, 35 patients are evaluable for pathologic response, and 2 patients are still receiving treatment. One patient died of unrelated myocardial infarction, and one patient was lost to follow-up. The median age was 55 years (range 29-85). Thirty-one (79.5%) patients were white; 4 (10.3%) African American, and 4 (10.3%) Asian. Twenty-nine (74.3%) patients had >=T2 tumors; 21 (53.8%) patients had N1/N2/N3 disease. Six patients (15.3%) had biopsy-proven residual disease after DTP therapy, and they received salvage standard neoadjuvant TCHP chemotherapy. The combination was well tolerated; 5 (18.8%) patients had G3-4 adverse events. Twenty-seven of thirty-five patients (77.1%) had pathologic RCB 0/1 (RCB0, 20/35; RCB1 7/35), of whom only 3 patients had received salvage chemotherapy. Correlative studies, including tumor-infiltrating lymphocytes, immune-related gene signatures and PD-L1 expression are underway. Conclusions: Chemotherapy-free neoadjuvant regimen with DTP in HER2-enriched EBC showed high pathologic response rates comparable to that with chemotherapy. This DTP regimen may provide an effective, relatively non-toxic, and biologically driven alternative to standard of care chemotherapy in this HER2-enriched subset of EBC. Citation Format: Jian Guan, Kai Sun, Dharamvir Jain, Sunil Mathur, Hanh Mai, Polly Niravath, Jenny Chang. Chemotherapy-free neoadjuvant regimen with durvalumab, trastuzumab and pertuzumab (DTP) in HER2-enriched early breast cancer: A prospective, open-label phase II trial [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr CT029.

Identification of an immune-related gene signature for predicting prognosis and immunotherapy efficacy in liver cancer via cell-cell communication.

Liver cancer is one of the deadliest malignant tumors worldwide. Immunotherapy has provided hope to patients with advanced liver cancer, but only a small fraction of patients benefit from this treatment due to individual differences. Identifying immune-related gene signatures in liver cancer patients not only aids physicians in cancer diagnosis but also offers personalized treatment strategies, thereby improving patient survival rates. Although several methods have been developed to predict the prognosis and immunotherapeutic efficacy in patients with liver cancer, the impact of cell-cell interactions in the tumor microenvironment has not been adequately considered. To identify immune-related gene signals for predicting liver cancer prognosis and immunotherapy efficacy. Cell grouping and cell-cell communication analysis were performed on single-cell RNA-sequencing data to identify highly active cell groups in immune-related pathways. Highly active immune cells were identified by intersecting the highly active cell groups with B cells and T cells. The significantly differentially expressed genes between highly active immune cells and other cells were subsequently selected as features, and a least absolute shrinkage and selection operator (LASSO) regression model was constructed to screen for diagnostic-related features. Fourteen genes that were selected more than 5 times in 10 LASSO regression experiments were included in a multivariable Cox regression model. Finally, 3 genes (stathmin 1, cofilin 1, and C-C chemokine ligand 5) significantly associated with survival were identified and used to construct an immune-related gene signature. The immune-related gene signature composed of stathmin 1, cofilin 1, and C-C chemokine ligand 5 was identified through cell-cell communication. The effectiveness of the identified gene signature was validated based on experimental results of predictive immunotherapy response, tumor mutation burden analysis, immune cell infiltration analysis, survival analysis, and expression analysis. The findings suggest that the identified gene signature may contribute to a deeper understanding of the activity patterns of immune cells in the liver tumor microenvironment, providing insights for personalized treatment strategies.

Construction of an immune-related gene signature for overall survival prediction and immune infiltration in gastric cancer.

Treatment options for patients with gastric cancer (GC) continue to improve, but the overall prognosis is poor. The use of PD-1 inhibitors has also brought benefits to patients with advanced GC and has gradually become the new standard treatment option at present, and there is an urgent need to identify valuable biomarkers to classify patients with different characteristics into subgroups. To determined the effects of differentially expressed immune-related genes (DEIRGs) on the development, prognosis, tumor microenvironment (TME), and treatment response among GC patients with the expectation of providing new biomarkers for personalized treatment of GC populations. Gene expression data and clinical pathologic information were downloaded from The Cancer Genome Atlas (TCGA), and immune-related genes (IRGs) were searched from ImmPort. DEIRGs were extracted from the intersection of the differentially-expressed genes (DEGs) and IRGs lists. The enrichment pathways of key genes were obtained by analyzing the Kyoto Encyclopedia of Genes and Genomes (KEGGs) and Gene Ontology (GO) databases. To identify genes associated with prognosis, a tumor risk score model based on DEIRGs was constructed using Least Absolute Shrinkage and Selection Operator and multivariate Cox regression. The tumor risk score was divided into high- and low-risk groups. The entire cohort was randomly divided into a 2:1 training cohort and a test cohort for internal validation to assess the feasibility of the risk model. The infiltration of immune cells was obtained using 'CIBERSORT,' and the infiltration of immune subgroups in high- and low-risk groups was analyzed. The GC immune score data were obtained and the difference in immune scores between the two groups was analyzed. We collected 412 GC and 36 adjacent tissue samples, and identified 3627 DEGs and 1311 IRGs. A total of 482 DEIRGs were obtained. GO analysis showed that DEIRGs were mainly distributed in immunoglobulin complexes, receptor ligand activity, and signaling receptor activators. KEGG pathway analysis showed that the top three DEIRGs enrichment types were cytokine-cytokine receptors, neuroactive ligand receptor interactions, and viral protein interactions. We ultimately obtained an immune-related signature based on 10 genes, including 9 risk genes (LCN1, LEAP2, TMSB15A mRNA, DEFB126, PI15, IGHD3-16, IGLV3-22, CGB5, and GLP2R) and 1 protective gene (LGR6). Kaplan-Meier survival analysis, receiver operating characteristic curve analysis, and risk curves confirmed that the risk model had good predictive ability. Multivariate COX analysis showed that age, stage, and risk score were independent prognostic factors for patients with GC. Meanwhile, patients in the low-risk group had higher tumor mutation burden and immunophenotype, which can be used to predict the immune checkpoint inhibitor response. Both cytotoxic T lymphocyte antigen4+ and programmed death 1+ patients with lower risk scores were more sensitive to immunotherapy. In this study a new prognostic model consisting of 10 DEIRGs was constructed based on the TME. By providing risk factor analysis and prognostic information, our risk model can provide new directions for immunotherapy in GC patients.

Identification of an immune-related signature as a prognostic classifier for patients with early-stage head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is the most common type and accounts for 90% of all head and neck cancer cases. Despite advances in early diagnosis and treatment strategies-chemotherapy, surgical resection, and radiotherapy-5-year survival remains grim. For patients with early-stage HNSCC, accurately predicting clinical outcomes is challenging. Considering the pivotal role of the immune system in HNSCC, we developed a reliable immune-related gene signature (IRGS) and explored its predictive accuracy in patients with early-stage HNSCC. We examined immune gene expression profiles and clinical information from 230 early-stage HNSCC specimens, including 100 cases from The Cancer Genome Atlas (TCGA), 49 cases from the Gene Expression Omnibus (GEO; GSE65858), and 81 cases from an independent clinical cohort. The prognostic signature was constructed using Kaplan-Meier analysis and the least absolute shrinkage and selection operator (LASSO) Cox algorithm. We also explored the IRGS-related biological pathways and immune landscape using bioinformatics analysis. A nine-immune-gene signature was generated to significantly stratify patients into high and low-risk groups. High risk patients exhibited shorter survival time [hazard ratio (HR) =13.795, 95% confidence interval (CI): 3.275-58.109, P<0.001]. The signature demonstrated robust prognostic ability in the training and validation sets and could independently predict overall survival (OS) and relapse-free survival (RFS). Subsequently, the receiver operating characteristic (ROC) curve and C-index confirmed the signature's predictive accuracy compared to clinical parameters. Additionally, cases classified as low risk showed more immune cell infiltration than high-risk cases. Our novel IRGS is a reliable and robust classifier for accurate patient stratification and prognostic evaluation. Future studies will attempt to affirm the signature's clinical application to early-stage HNSCC.

Comprehensive analyses of m1A regulator-mediated modification patterns determining prognosis in lower-grade glioma (running title: m1A in LGG)

N1-methyladenosine (m1A) modification is a crucial post-transcriptional regulatory mechanism of messenger RNA (mRNA) in living organisms. Few studies have focused on analysis of m1A regulators in lower-grade gliomas (LGG). We employed the Nonnegative Matrix Factorization (NMF) technique on The Cancer Genome Atlas (TCGA) dataset to categorize LGG patients into 2 groups. These groups exhibited substantial disparities in terms of both overall survival (OS) and levels of infiltrating immune cells. We collected the significantly differentially expressed immune-related genes between the 2 clusters, and performed LASSO regression analysis to obtain m1AScores, and established an m1A-related immune-related gene signature (m1A-RIGS). Next, we categorized all patients with LGG into high- and low-risk subgroups, predictive significance of m1AScore was confirmed by conducting univariate/multivariate Cox regression analyses. Additionally, we confirmed variations in immune-related cells and ssGSEA and among the high-/low-risk subcategories in the TCGA dataset. Finally, our study characterized the effects of MSR1 and BIRC5 on LGG cells utilizing Edu assay and flow cytometry to explore the effects of modulation of these genes on glioma. The results of this study suggested that m1A-RIGS may be an excellent prognostic indicator for patients with LGG, and could also promote development of novel immune-based treatment strategies for LGG. Additionally, BIRC5 and MSR1 may be potential therapeutic targets for LGG.

Identification of immune-related gene signature for non-small cell lung cancer patients with immune checkpoint inhibitors

BackgroundThe utilization of immune checkpoint inhibitors (ICIs) has become the established protocol for treating advanced non-small cell lung cancer (NSCLC). This work aimed to identify the immune-related gene signature that can predict the prognosis of NSCLC patients receiving ICI treatment. MethodsThe ImmPort database was queried to obtain a list of immune-related genes (IRGs). Differentially expressed IRGs in NSCLC patients were identified using the TCGA database. RNA-seq data and clinical information from NSCLC patients receiving immunotherapy were obtained from the GEO database (GSE93157 and ////). A gene signature was generated through multivariate Cox and LASSO regression analyses. The prognostic value and function of this gene signature were thoroughly investigated using comprehensive bioinformatics analyses. ResultsA total of 6 prognostic-related genes were identified from 617 differentially expressed genes, and two prognostic-related differentially expressed genes (CAMP and IL17A) were determined to construct gene signature. Our gene signature demonstrated superior performance compared to other clinicopathological parameters in predicting the prognosis of NSCLC patients receiving immunotherapy, with an area under the ROC curve (AUC) of 0.812. Furthermore, immune infiltration analysis indicated that the high-risk group was enriched with resting CD4 T cell memory, while the low-risk group showed a "hot" tumor microenvironment that promotes anti-tumor immunity in NSCLC patients. ConclusionGene signatures based on immune-related genes exhibited excellent indicator performance of prognosis and immune infiltration, which has the potential to be an effective biomarker for NSCLC with ICI treatment.

A novel immune-related gene signature for diagnosis and potential immunotherapy of microsatellite stable endometrial carcinoma

An immune-related gene signature (IRGS) was established to better understand the molecular and immunologic characteristics of microsatellite instable (MSI) and microsatellite stable (MSS) endometrial carcinoma (EC), and provide potential immunotherapy directions for MSS patients. Top 20 immune-related hub genes were screened by weight gene coexpression network analysis (WGCNA), and an IRGS was further established through Cox regression analysis. The molecular and immune characteristics were clarified in IRGS high and low risk groups. Expression and MS status validation of the IRGS were conducted through quantitative real-time Polymerase Chain Reaction (rt-qPCR) and immunohistochemistry (IHC) analysis. The IRGS includes 2 oncogenes (AGTR1 and HTR3C) and 2 tumor suppressor genes (CD3E and SERPIND1). Patients in IRGS high-risk group were more with MSS status, higher tumor grade, later FIGO stage, serous histology and elder ages compared with IRGS low-risk group (P < 0.05). Besides, patients in MSS group were more FIGO stages II–IV (42.7% vs. 26%), serous histology (35.7% vs. 5.3%) and with higher IRGS risk score (1.51 ± 3.11 vs. 1.02 ± 0.67) (P < 0.05) than patients in MSI group. Furthermore, patients in IRGS high-risk group had higher tumor purity, more Macrophages M1 and Macrophages M2 infiltrating, higher proportion of Macrophages M2 and Dendritic cells activated, lower proportion of T cells regulatory (Tregs), lower tumor mutation burden (TMB). Correspondingly, subjects in IRGS low-risk group had higher immunphenoscores than IRGS high-risk group. The relative mRNA level of AGTR1 and HTR3C were gradually increase, while CD3E and SERPIND1 were reversed in rt-qPCR. Through IHC experiments, AGTR1(69.2% vs 30%, P = 0.074) and HTR3C (76.9% vs 30%, P = 0.024) had higher positive staining rates in ECs than non-ECs. While SERPIND1 (84.6% vs 20%, P = 0.003) and CD3E (61.5% vs 40%, P = 0.000) had higher positive staining rates in non-ECs. IRGS is a potential diagnostic and prognostic biomarker for EC. IRGS low risk group might benefit from immune checkpoint inhibitors, while IRGS high risk group deserve other potential immunotherapy.

Comparison of immune-related gene signatures and immune infiltration features in early- and late-onset preeclampsia.

Preeclampsia, a severe pregnancy syndrome, is widely accepted divided into early- and late-onset preeclampsia (EOPE and LOPE) based on the onset time of preeclampsia, with distinct pathophysiological origins. However, the molecular mechanism especially immune-related mechanisms for EOPE and LOPE is currently obscure. In the present study, we focused on placental immune alterations between EOPE and LOPE and search for immune-related biomarkers that could potentially serve as potential therapeutic targets through bioinformatic analysis. The gene expression profiling data was obtained from the Gene Expression Omnibus database. ESTIMATE algorithm and Gene Set Enrichment Analysis were employed to evaluate the immune status. The intersection of differentially expressed genes in GSE74341 series and immune-related genes set screened differentially expressed immune-related genes. Protein-protein interaction network and random forest were used to identify hub genes with a validation by a quantitative real-time PCR. Kyoto Encyclopedia of Genes and Genomes pathways, Gene Ontology and gene set variation analysis were utilized to conduct biological function and pathway enrichment analyses. Single-sample gene set enrichment analysis and CIBERSORTx tools were employed to calculate the immune cell infiltration score. Correlation analyses were evaluated by Pearson correlation analysis. Hub genes-miRNA network was performed by the NetworkAnalyst online tool. Immune score and stromal score were all lower in EOPE samples. The immune system-related gene set was significantly downregulated in EOPE compared to LOPE samples. Four hub differentially expressed immune-related genes (IL15, GZMB, IL1B and CXCL12) were identified based on a protein-protein interaction network and random forest. Quantitative real-time polymerase chain reaction validated the lower expression levels of four hub genes in EOPE compared to LOPE samples. Immune cell infiltration analysis found that innate and adaptive immune cells were apparent lacking in EOPE samples compared to LOPE samples. Cytokine-cytokine receptor, para-inflammation, major histocompatibility complex class I and T cell co-stimulation pathways were significantly deficient and highly correlated with hub genes. We constructed a hub genes-miRNA regulatory network, revealing the correlation between hub genes and hsa-miR-374a-5p, hsa-miR-203a-3p, hsa-miR-128-3p, hsa-miR-155-3p, hsa-miR-129-2-3p and hsa-miR-7-5p. The innate and adaptive immune systems were severely impaired in placentas of EOPE. Four immune-related genes (IL15, GZMB, IL1B and CXCL12) were closely correlated with immune-related pathogenesis of EOPE. The result of our study may provide a new basis for discriminating between EOPE and LOPE and acknowledging the role of the immune landscape in the eventual interference and tailored treatment of EOPE.

Deciphering Immune-Related Gene Signatures in Diabetic Retinopathy: Insights from In Silico Analysis and In vitro Experiment.

Diabetes retinopathy (DR) is one of the most common microvascular consequences of diabetes, and the economic burden is increasing. Our aim is to decipher the relevant mechanisms of immune-related gene features in DR and explore biomarkers targeting DR. Provide a basis for the treatment and prevention of DR. The immune infiltration enrichment score of DR patients was evaluated from the single- cell RNA sequencing dataset, and the samples were divided into low immune subgroups and high immune subgroups based on this result. Through weighted gene correlation network analysis, differentially expressed genes (DEGs) between two subgroups were identified and crossed with genes with the strongest immune association, resulting in significant key genes. Then divide the DR individuals into two immune related differentially expressed gene (IDEG) clusters, A and B. Submit cross DEGs between two clusters through Gene Set Enrichment Analysis (GSEA) to further explore their functions. A protein-protein interaction (PPI) network of IDEG was established to further identify central genes associated with DR. Use the discovered central genes to predict the regulatory network involved in the pathogenesis of DR. Then, the role of the identified hub gene in the pathogenesis of DR was further studied through in vitro experiments. We found that the immune scores of DR and control groups were different, and 27 IDEGs were found in the DR subgroup. Compared with cluster A, the proportion of cytotoxic lymphocytes, B lineage, monocyte lineage, and fibroblasts in DR patients in cluster B is significantly enriched. GSEA indicates that these genes are associated with T cell activation, regulation of immune response processes, lymphocyte-mediated immunity, TNF signaling pathway, and other signaling pathways. The PPI network subsequently identified 10 hub genes in DR, including SIGLEC10, RGS10, PENK, FGD2, LILRA6, CIITA, EGR2, SIGLEC7, LILRB1, and CD300LB. The upstream regulatory network and lncRNA miRNA mRNA ceRNA network of these hub genes were ultimately constructed. The discovery and identification of these genes will provide biomarkers for targeted prediction and treatment of DR. By integrating bioinformatics analysis and in vitro experiments, we have identified a set of central genes, indicating that these genes can serve as potential biomarkers for DR, which may be promising targets for future DR immunotherapy interventions.

Identification of the prognostic immune subtype in copy-number high endometrial cancer.

The TCGA molecular subtype of endometrial cancer (EC) is widely applied, among which the copy-number high (CNH) subtype has the poorest prognosis. However, the heterogeneity of this subtype remains elusive. In this study, we aimed to identify heterogeneous immune subtypes in CNH EC and explore their prognostic significance. We collected 60 CNH EC cases in the TCGA database and performed unsupervised cluster analysis based on the enrichment scores of immune-related gene signatures to identify immune subtypes. We described their immune characteristics and prognoses and conducted differential gene analysis and lasso regression to identify a prognostic biomarker, GZMM. For experimental validation, we performed immunohistochemical staining of GZMM in 39 p53-positive EC surgical samples. We defined two immune subtypes, immune-hot (IH) and immune-cold (IC), which differed in immune cell infiltration, cytokine and chemokine expression and prognosis. The IH subtype has significantly stronger immune activation than the IC subtype, showing a significant infiltration of immune effector cells and high expression of relevant chemokines, with better prognosis. Moreover, the immunohistochemical staining of GZMM in a cohort of 39 p53-positive EC surgical samples confirmed GZMM as a unique prognostic biomarker, with high expression in both tumor cells and lymphocytes predicting a better prognosis. Our study revealed heterogeneous immune subtypes in CNH EC and identified GZMM as a prognostic biomarker. The stratified classification strategy combining molecular and immune subtypes provides valuable insights for future clinical practice.

Identification and Validation of the Glycolysis and Immune-related Gene Signature for Prognosis in Colorectal Cancer.

Glycolysis has a role in regulating the tumor immune microenvironment. However, the functions and clinical role for facilitating the prognosis prediction of colorectal cancer (CRC) based on glycolysis and immune-related genes remain to be identified. Genes associated with glycolysis and immunity (GI) were identified from established databases (MSigDB and ImmPort). The TCGA (training cohort) and GSE39582 (validation cohort) datasets were used. Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were applied for model construction. The prognostic power of the GI signature was examined by multivariate Cox regression analysis. The correlations between the GI signature, immune cell infiltration and immune checkpoint blockade (ICB) genes were analyzed. To further validate the identified gene signature, quantitative RT-PCR was performed. Cell proliferation assays were conducted for CCK8 detection. A new GI model was constructed, and this signature may serve as an independent prognostic biomarker in CRC. The GI signature remained an effective tool for predicting prognosis among each clinical subgroup. This signature was related to immune cell infiltration of myeloid dendritic cells, cancer-associated fibroblasts (CAFs), CD4+ and CD8+ T cells and response to the ICB immunotherapy-related genes IDO1, BTLA, PD-L1 and PD-L2. In addition, our findings showed that PMM2, IL20RB, and NTF4 exhibited high expression levels in CRC. The upregulation of these genes resulted in the promotion of the proliferation of CRC cells. This novel prognostic signature contributed to CRC risk stratification and survival prediction based on glycolysis and immune status.

A new immune-related gene signature predicts the prognosis and immune escape of bladder cancer.

The biological roles of immune-related genes (IRGs) in bladder cancer (BC) need to be further elucidated. To elucidate the predictive value of IRGs for prognosis and immune escape in BC. We comprehensively analyzed the transcriptomic and clinical information of 430 cases, including 19 normal and 411 BC patients from the TCGA database, and verified 165 BC cases in the GSE13507 dataset. The risk model was constructed based on IRGs by applying LASSO Cox regression and exploring the relationship between the risk score and prognosis, gene mutations, and immune escape in BC patients. We identified 4 survival-related genes (PSMC1, RAC3, ROBO2 and ITGB3) among 6,196 IRGs in both the TCGA and GES13507 datasets,, which were used to establish a gene risk model by applying LASSO Cox regression. The results showed that the high-risk (HR) group was closely associated with poor survival or advanced pathological stage of BC. Furthermore, the risk score was found to be an independent risk factor for prognosis of BC patients. In addition, high-risk individuals showed a greater prevalence of TP53 mutations lower CD8+ T-cell and NK cell infiltration, higher Treg cell infiltration, higher expression of PD-L1, and higher immune exclusion scores than those in the low-risk (LR) group. Finally, the experimental verification shows that the model construction gene, especially PMSC1, plays an important role in the growth and metastasis of bladder cancer. These evidences revealed the vital role of IRGs in predicting prognosis, TP53 mutation and immune escape in BC patients.

A novel prognostic predictor of immune microenvironment and therapeutic response in clear cell renal cell carcinoma based on angiogenesis–immune-related gene signature

BackgroundClear cell renal cell carcinoma (ccRCC), the most common type of RCC, typically produces no symptoms initially. Patients with ccRCC are at increased risk of developing advanced metastatic disease due to the absence of dependable and effective prognostic biomarkers. Therefore, it is particularly urgent to find optimal stratification of patients with ccRCC to distinguish the clinical benefits of different malignant degrees. Angiogenesis has a profound impact on the malignant behavior of renal cancer cells, and anti-angiogenic drugs have been applied to metastatic renal cancer patients. Moreover, immune function dysregulation is also a significant factor in tumorigenesis. We aim to construct a predictive model that combines angiogenesis and immune-related genes (AIRGs) to aid clinicians in predicting ccRCC prognosis. MethodsWe gathered transcriptome and clinicopathology data from two datasets, the E-MTAB-1980 dataset and the Cancer Genome Atlas (TCGA). We utilized consensus clustering to find new molecular subgroups. A predictive model for the prognosis of angiogenesis-immune-associated genes (AIRGs) was conducted by the lasso and multivariate Cox regression analysis. The signature's predictive ability was then tested in different datasets. Meticulous scrutiny and comprehensive assessment were undertaken, both internally and externally, to establish the prognostic model. Analyses of immunogenomics were carried out to examine the relationship between risk scores and clinical/immune features, including immune cell infiltration, genomic alterations, and response to targeted and immunotherapy therapy. ResultsOur prognostic signature, comprising 4 AIRGs, stood as an independent prognostic factor for ccRCC, while risk scores emerged as a novel indicator for forecasting overall survival. Risk scores exhibited significant associations with various immunophenotypic factors, such as oncogenic pathways, antitumor response, different immune cell infiltration, antitumor immunity, and response to targeted and immunotherapy therapy. ConclusionsAIRGs-based prognostic prediction model could effectively predict immunotherapy responses and survival outcomes of ccRCC.

Prediction of immune infiltration and prognosis for patients with gastric cancer based on the immune-related genes signature

ObjectiveThe immune microenvironment influenced clinical outcomes and treatment response of gastric cancer (GC) patients. Though thousands of immune-related genes (IRGs) have been identified, their effects on GC are not fully understood. The objective of the study is to analyze the correlations between the expression and effect of IRGs and clinical outcomes. Moreover, we evaluate the efficacy and value of utilizing the immune-related genes signature as a prognosis prediction model for GC patients. MethodsWe identified the differentially expressed IRGs and systematically analyzed their functions. We constructed a novel GC prognostic signature and a new nomogram, Moreover, we explored the infiltrated immune cell types in the immune microenvironment and discussed the genetic variation in GC IRGs. ResultsEight IRGs, including CCL15, MSR1, GNAI1, NR3C1, ITGAV, NMB, AEN, and TGFBR1 were identified. Based on the prognostic signature, GC patients were distinguished into two subtype groups. As verified in multiple datasets, the prognostic signature exhibited good performance in predicting the prognosis (AUC = 0.803, P-value <0.001) and revealed the different clinical features and infiltrated immune cell types in the immune microenvironment. ConclusionsIn summary, we found that IRGs contributed to GC prognosis prediction and constructed an IRGs-based GC prognostic signature, which could serve as an effective prognostic stratification tool.

Metabolic and immune-related gene signatures: Predictive stratification and prognostic implications in gastric cancer.

Gastric cancer, marked by its heterogeneous nature, showcases various molecular subtypes and clinical trajectories. This research delves into the significance of metabolic and immune-driven pathways in gastric cancer, constructing a prognostic signature derived from differentially expressed metabolic and immune-correlated genes (DE-MIGs). Metabolic and immune-associated gene were sourced from the GeneCards database. Differential expression analysis on the TCGA-STAD dataset was executed using the limma package, unveiling 51 DE-MIGs that underwent functional enrichment scrutiny. The LASSO Cox regression methodology guided the creation of the prognostic signature, and individual patient risk scores were determined. Assessment tools like CIBERSORT, ESTIMATE and ssGSEA were deployed to study the immune microenvironment, while mutation profiles, genomic stability, resistance to chemotherapy and immunotherapy responsiveness were scrutinized across distinct signature categorizations. Among the identified DE-MIGs, 26 were significantly tied to the overall survival of gastric cancer patients. The developed prognostic signature proficiently differentiated patients into high-risk and low-risk cohorts, with the latter showing markedly better outcomes. The study underscored the centrality of the immune microenvironment in influencing gastric cancer outcomes. Key pathways such as TGF-Beta, TP53 and NRF2 dominated the high-risk group, whereas the LRTK-RAS and WNT pathways characterized the low-risk group. Interestingly, the low-risk segment also manifested a heightened tumor mutation burden and enhanced susceptibility to immunotherapy. Our findings introduce a pivotal prognostic signature, rooted in DE-MIGs, that effectively segregates gastric cancer patients into distinct risk-based segments. Insights into the influential role of the immune microenvironment in gastric cancer progression pave the way for more refined therapeutic interventions.

Highly potent, orally active novel small-molecule HPK1 inhibitor DS21150768 induces anti-tumor responses in multiple syngeneic tumor mouse models

Augmenting T-cell activity is a promising approach to enhance the efficacy of cancer immunotherapy treatment. Hematopoietic progenitor kinase 1 (HPK1) is predominantly expressed in immune cells and negatively regulates T-cell receptor signaling. It is reported that inhibition of the kinase function of HPK1 results in tumor growth suppression by enhancing cancer immunity. Thus, developing HPK1 inhibitors has attracted considerable attention as a future cancer immunotherapy approach. However, despite recent progress in HPK1 biology and pharmacology, various challenges still remain, such as developing HPK1 inhibitors with favorable pharmacological profiles and identifying tumor characteristics that can be applied to define susceptibility to HPK1 inhibition. Here, we present the identification and pharmacological evaluation of DS21150768, a potent small-molecule HPK1 inhibitor with a novel chemical scaffold. DS21150768 shows remarkable inhibition of HPK1 kinase activity, and in vitro studies demonstrated its potent activity to enhance T-cell function. DS21150768 is orally bioavailable and shows sustained plasma exposure, which leads to enhanced cytokine responses in vivo. We conducted a comparison of the anti-tumor efficacy of DS21150768 alone or in combination with anti-PD-1 antibody in 12 different mouse cancer cell models, and observed that the treatments suppressed tumor growth in multiple models. Furthermore, Gene Set Enrichment Analysis demonstrated significant enrichment of immune-related gene signatures in the tumor models responsive to DS21150768 treatment. Our results provide a path forward for the future development of HPK1 inhibitors and fundamental insights into biomarkers of HPK1-targeted therapy.

A novel immune-related gene signature for predicting immunotherapy outcomes and survival in clear cell renal cell carcinoma

Clear cell renal carcinoma (ccRCC) is one of the most common cancers worldwide. In this study, a new model of immune-related genes was developed to predict the overall survival and immunotherapy efficacy in patients with ccRCC. Immune-related genes were obtained from the ImmPort database. Clinical data and transcriptomics of ccRCC samples were downloaded from GSE29609 and The Cancer Genome Atlas. An immune-related gene-based prognostic model (IRGPM) was developed using the least absolute shrinkage and selection operator regression algorithm and multivariate Cox regression. The reliability of the developed models was evaluated by Kaplan–Meier survival curves and time-dependent receiver operating characteristic curves. Furthermore, we constructed a nomogram based on the IRGPM and multiple clinicopathological factors, along with a calibration curve to examine the predictive power of the nomogram. Overall, this study investigated the association of IRGPM with immunotherapeutic efficacy, immune checkpoints, and immune cell infiltration. Eleven IRGs based on 528 ccRCC samples significantly associated with survival were used to construct the IRGPM. Remarkably, the IRGPM, which consists of 11 hub genes (SAA1, IL4, PLAUR, PLXNB3, ANGPTL3, AMH, KLRC2, NR3C2, KL, CSF2, and SEMA3G), was found to predict the survival of ccRCC patients accurately. The calibration curve revealed that the nomogram developed with the IRGPM showed high predictive performance for the survival probability of ccRCC patients. Moreover, the IRGPM subgroups showed different levels of immune checkpoints and immune cell infiltration in patients with ccRCC. IRGPM might be a promising biomarker of immunotherapeutic responses in patients with ccRCC. Overall, the established IRGPM was valuable for predicting survival, reflecting the immunotherapy response and immune microenvironment in patients with ccRCC.

Identification of tryptophan metabolism- and immune-related genes signature and prediction of immune infiltration landscape in bladder urothelial carcinoma.

Tryptophan metabolism is indirectly involved in immune tolerance and promotes response to anticancer drugs. However, the mechanisms underlying tryptophan metabolism and immune landscape in bladder urothelial carcinoma (BLCA) are not fully understood. A BLCA dataset containing 406 tumor samples with clinical survival information and 19 normal samples were obtained from the Cancer Genome Atlas database. The validation set, GSE32894, contained 223 BLCA tumor samples with survival information, and the single-cell dataset, GSE135337, included seven BLCA tumor samples; both were obtained from the gene expression omnibus database. Univariate and multivariate Cox regression analyses were conducted to evaluate clinical parameters and risk scores. Immune infiltration and checkpoint analyses were performed to explore the immune landscape of BLCA. Single-cell analysis was conducted to further identify the roles of model genes in BLCA. Finally, NAMPT expression in BLCA and adjacent tissues was detected using RT-qPCR, CCK-8 and Transwell assays were conducted to determine the role of NAMPT in BLCA cells. Six crossover genes (TDO2, ACAT1, IDO1, KMO, KYNU, and NAMPT) were identified by overlap analysis of tryptophan metabolism-related genes, immune-related genes, and differentially expressed genes (DEGs). Three biomarkers, NAMPT, IDO1, and ACAT1, were identified using Cox regression analysis. Accordingly, a tryptophan metabolism- and immune-related gene risk model was constructed, and the patients were divided into high- and low-risk groups. There were significant differences in the clinical parameters, prognosis, immune infiltration, and immunotherapy response between the risk groups. RT-qPCR revealed that NAMPT was upregulated in BLCA samples. Knocking down NAMPT significantly inhibited BLCA cell proliferation, migration, and invasion. In our study, we constructed a tryptophan metabolism- and immune-related gene risk model based on three biomarkers, namely NAMPT, IDO1, and ACAT1, that were significantly associated with the progression and immune landscape of BLCA. The risk model could effectively predict patient prognosis and immunotherapy response and can guide individualized immunotherapy.