Immune-related Gene Pairs Research Articles

An immune-related gene pair signature predicts the prognosis and immunotherapeutic response in glioblastoma

BackgroundGlioblastoma (GBM) has the feature of aggressive growth and high rates of recurrence. Immunotherapy was not included in standard therapy for GBM due to lacking the predictive biomarkers. In the present study, we performed an immune-related gene pair (IRGP) signature to predict the prognosis and immunotherapy response of GBM. MethodsA total of 160 GBM patients from TCGA were included. ssGSEA was conducted to evaluate the immune infiltration level. Univariate Cox, LASSO regression analysis, ROC analysis, and Kaplan-Meier survival analysis were applied to construct and evaluate the risk model. Moreover, the association between immune infiltration and the risk score was assessed. Finally, the expression of immune checkpoints between different risk groups was explored. ResultsAccording to the normal/tumor, high-/low-immunity group, we identified 125 differentially expressed immune-related genes. Subsequently, a prognostic model including 22 IRGPs was established. The area under the ROC curve to predict 1, 3, and 5-year was 0.811, 0.958, and 0.99 respectively. According to the optimal cut-off value of the 3-year ROC curve, patients were classified into high- and low-risk groups. The Kaplan-Meier analysis result indicated that patients in the low-risk group have longer survival time. The risk score was an independent prognostic predictor (P < 0.001). Moreover, PDCD1 was positively correlated with the risk score (P < 0.01). We also found that patients with high PDCD1 expression had worse survival. ConclusionsThe IRGP signature was built to predict the prognosis of GBM patients. This signature can serve as a tool to predict the response to immunotherapy in GBM.

Identification of Immune-Related Gene Pair Signature to Predict Prognosis of Diffuse Large B-Cell Lymphoma Based on Bioinformatics Analyses

Since over one-third of DLBCL patients experience relapse or refractory after standard therapy, high-risk patients must be predicted. We developed a prognostic immune-related gene pairs (IRGPs) signature for DLBCL patients using bioinformatics analyses. This signature can predict the prognosis of these patients adequately, either alone or in combination with other clinical parameters. It hopes to improve the stratification and management of these patients for broad clinical applications.

Construction of immune-related gene pairs signature to predict the overall survival of multiple myeloma patients based on whole bone marrow gene expression profiling.

Multiple myeloma (MM) is a plasma cell dyscrasia that is characterized by the uncontrolled proliferation of malignant PCs in the bone marrow. Due to immunotherapy, attention has returned to the immune system in MM, and it appears necessary to identify biomarkers in this area. In this study, we created a prognostic model for MM using immune-related gene pairs (IRGPs), with the advantage that it is not affected by technical bias. After retrieving microarray data of MM patients, bioinformatics analyses like COX regression and least absolute shrinkage and selection operator (LASSO) were used to construct the signature. Then its prognostic value is assessed via time-dependent receiver operating characteristic (ROC) and the Kaplan-Meier (KM) analysis. We also used XCELL to examine the status of immune cell infiltration among MM patients. 6-IRGP signatures were developed and proved to predict MM prognosis with a P-value of 0.001 in the KM analysis. Moreover, the risk score was significantly associated with clinicopathological characteristics and was an independent prognostic factor. Of note, the combination of age and β2-microglobulin with risk score could improve the accuracy of determining patients' prognosis with the values of the area under the curve (AUC) of 0.73 in 5 years ROC curves. Our model was also associated with the distribution of immune cells. This novel signature, either alone or in combination with age and β2-microglobulin, showed a good prognostic predictive value and might be used to guide the management of MM patients in clinical practice.

Identification of qualitative characteristics of immunosuppression in sepsis based on immune-related genes and immune infiltration features

ObjectiveSepsis is linked to high morbidity and mortality rates. Consequently, early diagnosis is crucial for proper treatment, reducing hospitalization, and mortality rates. Additionally, over one-fifth of sepsis patients still face a risk of death. Hence, early diagnosis, and effective treatment play pivotal roles in enhancing the prognosis of patients with sepsis. MethodThe study analyzed whole blood data obtained from patients with sepsis and control samples sourced from three datasets (GSE57065, GSE69528, and GSE28750). Commonly dysregulated immune-related genes (IRGs) among these three datasets were identified. The differential characteristics of these common IRGs in the sepsis and control samples were assessed using the REO-based algorithm. Based on these differential characteristics, samples from eight Gene Expression Omnibus (GEO) databases (GSE57065, GSE69528, GSE28750, GSE65682, GSE69063, GSE95233, GSE131761, and GSE154918), along with three ArrayExpress databases (E-MTAB-4421, E-MTAB-4451, and E-MTAB-7581), were categorized and scored. The effectiveness of these differential characteristics in distinguishing sepsis samples from control samples was evaluated using the AUC value derived from the receiver operating characteristic curve (ROC) curve. Furthermore, the expression of IRGs was validated in peripheral blood samples obtained from patients with sepsis through qRT-PCR. ResultsAmong the three training datasets, a total of 84 common dysregulated immune-related genes (IRGs) were identified. Utilizing a within-sample relative expression ordering (REOs)-based algorithm to analyze these common IRGs, differential characteristics were observed in three reverse stable pairs (ELANE-RORA, IL18RAP-CD247, and IL1R1-CD28). In the eight GEO datasets, the expression of ELANE, IL18RAP, and IL1R1 demonstrated significant upregulation, while RORA, CD247, and CD28 expression exhibited notable downregulation during sepsis. These three pairs of immune-related marker genes displayed accuracies of 95.89% and 97.99% in distinguishing sepsis samples among the eight GEO datasets and the three independent ArrayExpress datasets, respectively. The area under the receiver operating characteristic curve ranged from 0.81 to 1.0. Additionally, among these three immune-related marker gene pairs, mRNA expression levels of ELANE and IL1R1 were upregulated, whereas the levels of CD247 and CD28 mRNA were downregulated in blood samples from patients with sepsis compared to normal controls. ConclusionThese three immune-related marker gene pairs exhibit high predictive performance for blood samples from patients with sepsis. They hold potential as valuable auxiliary clinical blood screening tools for sepsis.

Based on the prognosis model of immunogenes, the prognosis model was constructed to predict the invasion of immune genes and immune cells related to primary liver cancer and its experimental validation

BackgroundPrimary liver cancer (PLC) is a prevalent malignancy of the digestive system characterized by insidious symptom onset and a generally poor prognosis. Recent studies have highlighted a significant correlation between the initiation and prognosis of liver cancer and the immune function of PLC patients. PurposeRevealing the expression of PLC-related immune genes and the characteristics of immune cell infiltration provides assistance for the analysis of clinical pathological parameters and prognosis of PLC patients. MethodsPLC-related differentially expressed genes (DEGs) with a median absolute deviation (MAD > 0.5) were identified from TCGA and GEO databases. These DEGs were intersected with immune-related genes (IRGs) from the ImmPort database to obtain PLC-related IRGs. The method of constructing a prognostic model through immune-related gene pairs (IRGPs) is used to obtain IRGPs and conduct the selection of central immune genes. The central immune genes obtained from the selection of IRGPs are validated in PLC. Subsequently, the relative proportions of 22 types of immune cells in different immune risk groups are evaluated, and the differential characteristics of PLC-related immune cells are verified through animal experiments. ResultsThrough database screening and the construction of an IRGP prognosis model, 84 pairs of IRGPs (P < 0.001) were ultimately obtained. Analysis of these 84 IRGPs revealed 11 central immune genes related to PLC, showing differential expression in liver cancer tissues compared to normal liver tissues. Results from the CiberSort platform indicate differential expression of immune cells such as naive B cells, macrophages, and neutrophils in different immune risk groups. Animal experiments demonstrated altered immune cell proportions in H22 tumor-bearing mice, validating findings from peripheral blood and spleen homogenate analyses. ConclusionOur study successfully predicted and validated PLC-related IRGs and immune cells, suggesting their potential as prognostic indicators and therapeutic targets for PLC.

Identification of FLRT2 as a key prognostic gene through a comprehensive analysis of TMB and IRGPs in BLCA patients.

The tumor immune environment and immune-related genes are instrumental in the development, progression, and prognosis of bladder cancer (BLCA). This study sought to pinpoint key immune-related genes influencing BLCA prognosis and decipher their mechanisms of action. We analyzed differentially expressed genes (DEGs) between high- and low- tumor mutational burden (TMB) groups. Subsequently, we constructed a reliable prognostic model based on immune-related gene pairs (IRGPs) and analyzed DEGs between high- and low-risk groups. A total of 22 shared DEGs were identified across differential TMB and IRGPs-derived risk groups in BLCA patients. Through univariate Cox and multivariate Cox analyses, we highlighted five genes - FLRT2, NTRK2, CYTL1, ZNF683, PRSS41 - significantly correlated with BLCA patient prognosis. Notably, the FLRT2 gene emerged as an independent prognostic factor for BLCA, impacting patient prognosis via modulation of macrophage infiltration in immune microenvironment. Further investigation spotlighted methylation sites - cg25120290, cg02305242, and cg01832662 - as key regulators of FLRT2 expression. These findings identified pivotal prognostic genes in BLCA and illuminated the intricate mechanisms dictating patient prognosis. This study not only presents a novel prognostic marker but also carves out potential avenues for immunotherapy and targeted therapeutic strategies in BLCA. By demystifying the profound impact of immune-related genes and the tumor immune environment, this study augments the comprehension and prognostic management of bladder cancer.

A robust immune-related gene pairs signature for predicting the overall survival of esophageal cancer

BackgroundIdentifying reliable biomarkers could effectively predict esophagus carcinoma (EC) patients with poor prognosis. In this work, we constructed an immune-related gene pairs (IRGP) signature to evaluate the prognosis of EC.ResultsThe IRGP signature was trained by the TCGA cohort and validated by three GEO datasets, respectively. Cox regression model together with LASSO was applied to construct the overall survival (OS) associated IRGP. 21 IRGPs consisting of 38 immune-related genes were included in our signature, according to which patients were stratified into high- and low-risk groups. The results of Kaplan-Meier survival analyses indicated that high-risk EC patients had worse OS than low-risk group in the training set, meta-validation set and all independent validation datasets. After adjustment in multivariate Cox analyses, our signature continued to be an independent prognostic factor of EC and the signature-based nomogram could effectively predict the prognosis of EC sufferers. Besides, Gene Ontology analysis revealed this signature is related to immunity. ‘CIBERSORT’ analysis revealed the infiltration levels of plasma cells and activated CD4 memory T cells in two risk groups were significantly different. Ultimately, we validated the expression levels of six selected genes from IRGP index in KYSE-150 and KYSE-450.ConclusionsThis IRGP signature could be applied to select EC patients with high mortality risk, thereby improving prospects for the treatment of EC.

A novel prognostic signatures based on metastasis- and immune-related gene pairs for colorectal cancer.

Metastasis remains the leading cause of mortality in patients diagnosed with colorectal cancer (CRC). The pivotal contribution of the immune microenvironment in the initiation and progression of CRC metastasis has gained significant attention. A total of 453 CRC patients from The Cancer Genome Atlas (TCGA) were included as the training set, and GSE39582, GSE17536, GSE29621, GSE71187 were included as the validation set. The single-sample gene set enrichment analysis (ssGSEA) was performed to assess the immune infiltration of patients. Least absolute shrinkage and selection operator (LASSO) regression analysis, Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier analysis were used to construct and validate risk models based on R package. CTSW and FABP4-knockout CRC cells were constructed via CRISPR-Cas9 system. Western-blot and Transwell assay were utilized to explore the role of fatty acid binding protein 4 (FABP4) / cathepsin W (CTSW) in CRC metastasis and immunity. Based on the normal/tumor, high-/low-immune cell infiltration, and metastatic/non-metastatic group, we identified 161 differentially expressed genes. After random assignment and LASSO regression analysis, a prognostic model containing 3 metastasis- and immune-related gene pairs was constructed and represented good prognostic prediction efficiency in the training set and 4 independent CRC cohorts. According to this model, we clustered patients and found that the high-risk group was associated with stage, T and M stage. In addition, the high-risk group also shown higher immune infiltration and high sensitivity to PARP inhibitors. Further, FABP4 and CTSW derived from the constitutive model were identified to be involved in metastasis and immunity of CRC. In conclusion, a validated prognosis predictive model for CRC was constructed. CTSW and FABP4 are potential targets for CRC treatment.

Construction and Validation of a Novel Immune-Related Gene Pairs-Based Prognostic Model in Lung Adenocarcinoma.

Focus on immune-related gene pairs (IRGPs) and develop a prognostic model to predict the prognosis of patients with lung adenocarcinoma (LUAD). First, the LUAD patient dataset was downloaded from The Cancer Genome Atlas database, and paired analysis of immune-related genes was subsequently conducted. Then, LASSO regression was used to screen prognostic IRGPs for building a risk prediction model. Meanwhile, the Gene Expression Omnibus database was used for external validation of the model. Next, the clinical predictive power of IRGPs features was assessed by uni-multivariate Cox regression analysis, the infiltration of key immune cells in high and low IRGPs risk groups was analyzed with CIBERSORT, quanTIseq, and Timer, and the key pathways enriched for IRGPs were assessed using the Kyoto Encyclopedia of Genes and Genomes. Finally, the expression and related functions of key immune cells and genes were verified by immunofluorescence and cell experiments of tissue samples. It was revealed that the risk score of 19 IRGPs could be used as accurate indicators to evaluate the prognosis of LUAD patients, and the risk score was mainly related to T cell infiltration based on CIBERSORT analysis. Two genes of IRGPs, IL6, and CCL2, were found to be closely associated with the expression of PD-1/PD-L1 and the function of T-cells. Depending on the results of tissue immunofluorescence, IL6, CCL2, and T cells were highly expressed in the LUAD tissues of patients. Furthermore, IL6 and CCL2 were positively correlated with the expression of T cells. Besides, qRT-PCR assay in four different LUAD cells proved that IL6 and CCL2 were positively correlated with the expression of PD-L1 (P < .001). Based on 19 IRGPs, an effective prognosis model was established to predict the prognosis of LUAD patients. In addition, IL6 and CCL2 are closely related to the function of T-cells.

Insulin-like growth factor 2 receptor is a key immune-related gene that is correlated with a poor prognosis in patients with triple-negative breast cancer: A bioinformatics analysis

Immunotherapy plays an important role in the treatment of triple-negative breast cancer (TNBC). This study aimed to identify immune-related genes that are associated with the prognosis of patients with TNBC as possible targets of immunotherapy, alongside their related tumor-infiltrating lymphocytes (TILs). The clinical data and gene expression profiles of patients with breast cancer were extracted from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and divided into training (n = 1,053) and verification (n = 508) groups. CIBERSORT was used to predict the differences in immune cell infiltration in patient subsets that were stratified according to risk. Gene Ontology (GO) enrichment analysis was used to identify pathways associated with immune-related genes in patient subsets that were stratified according to risk. The clinical data and insulin-like growth factor 2 receptor (IGF2R) expression profiles of patients with breast cancer were extracted from METABRIC. The expression of IGF2R and TILs were evaluated in a cohort containing 282 untreated patients with TNBC. The correlations of IGF2R expression, TILs, and clinicopathological parameters with patient prognosis were analyzed in the whole cohort. The prognostic model, which was composed of 26 immune-related gene pairs, significantly distinguished between high- and low-risk patients. Univariate and multivariate analyses indicated that the model was an independent prognostic factor for breast cancer. Among the identified genes, the expression of IGF2R significantly distinguished between high- and low-risk patients in TCGA (P = 0.008) and in METABRIC patients (P < 0.001). The expression of IGF2R was significantly associated with clinical risk factors such as TNBC, estrogen receptor (ER)-negative expression, human epidermal growth factor receptor 2 (HER2)-positive expression, and age ≤60 years old in METABRIC patients. In addition, the patients with IGF2R-positive expression had lower disease-free survival (DFS) rates than those with IGF2R-negative expression in the TNBC cohort (67.8% vs. 78.5%, P = 0.023). IGF2R expression also was significantly negatively correlated with TILs, particularly with CD8+ TILs and CD19+ TILs in the cohort of patients with TNBC. IGF2R can be used as an indicator of a poor prognosis in patients with TNBC and as a potential target and research direction for TNBC immunotherapy in the future.

An immuno-score signature of tumor immune microenvironment predicts clinical outcomes in locally advanced rectal cancer.

Background and purposeAccumulating evidence indicates that neoadjuvant chemoradiotherapy(nCRT) success has an immune-associated constituent in locally advanced rectal cancer (LARC). The immune-associated configuration of the tumor microenvironment associated with responses to treatment was explored in LARC in this study.Material and methodsA novel analytic framework was developed based on within-sample relative expression orderings for identifying tumor immune-associated gene pairs and identified an immuno-score signature from bulk transcriptome profiling analysis of 200 LARC patients. And sequencing and microarray analysis of gene expression was conducted to investigate the association between the signature and response to nCRT, immunotherapy, and cell function of CD4 and CD8. The results were validated using 111 pretreated samples from publicly available datasets in multiple aspects and survival analyses.ResultsThe immuno-score signature of 18 immune-related gene pairs (referred to as IPS) was validated on bulk microarray and RNA-Seq data. According to the model’s immune score, LARC patients were divided into high- and low-score groups. The patients with high-score were greater sensitivity to nCRT and immunotherapy, gaining a significantly improved prognosis. In addition, the immune-score gene pair signature was associated with type I anti-tumor T cell responses, positive regulators of T cell functions, and chromosomal instability while reflecting differences between CD8+ T cell subtypes.ConclusionThe immuno-score signature underlines a key role of tumor immune components in nCRT response, and predicts the prognosis of LARC patients as well.

Risk predictive model based on three immune-related gene pairs to assess prognosis and therapeutic sensitivity for hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) as a common tumor has a poor prognosis. Recently, a combination of atezolizumab and bevacizumab has been recommended as the preferred regimen for advanced HCC. However, the overall response rate of this therapy is low. There is an urgent need to identify sensitive individuals for this precise therapy among HCC patients.MethodsThe Wilcox test was used to screen the differentially expressed immune-related genes by combining the TCGA cohort and the Immunology Database. Univariate and multivariate Cox regression analysis were used to screen the immune gene pairs concerning prognosis. A predictive model was constructed using LASSO Cox regression analysis, and correlation analysis was conducted between the signature and clinical characteristics. ICGC cohort and GSE14520 were applied for external validations of the predictive risk model. The relationship between immune cell infiltration, TMB, MSI, therapeutic sensitivity of immune checkpoint inhibitors, targeted drugs, and the risk model were assessed by bioinformatics analysis in HCC patients.ResultsA risk predictive model consisting of 3 immune-related gene pairs was constructed and the risk score was proved as an independent prognostic factor for HCC patients combining the TCGA cohort. This predictive model exhibited a positive correlation with tumor size (p < 0.01) and tumor stage (TNM) (p < 0.001) in the chi-square test. The predictive power was verified by external validations (ICGC and GSE14520). The risk score clearly correlated with immune cell infiltration, MSI, immune checkpoints, and markers of angiogenesis.ConclusionsOur research established a risk predictive model based on 3 immune-related gene pairs and explored its relationship with immune characteristics, which might help to assess the prognosis and treatment sensitivity to immune and targeted therapy of HCC patients.

A signature of immune-related gene pairs (IRGPs) for risk stratification and prognosis of oral cancer patients

BackgroundWith low response to present immunotherapy, it is imperative to identify new immune-related biomarkers for more effective immunotherapies for oral cancer.MethodsRNA profiles for 390 oral cancer patients and 32 normal samples were downloaded from The Cancer Genome Atlas (TCGA) database and differentially expressed genes (DEGs) were analyzed. Immune genesets from ImmPort repository were overlapped with DEGs. After implementing univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, key immune-related gene pairs (IRGPs) among the overlapped DEGs for predicting the survival risk were obtained. Then, the cutoff of risk score was calculated by the receiver operating characteristic (ROC) curve to stratify oral cancer patients into high and low-risk groups. Multivariate Cox analysis was used to analyze independent prognostic indicators for oral cancer. Besides, infiltration of immune cells, functional annotation, and mutation analysis of IRGPs were conducted. Biological functions correlated with IRGPs were enriched by Gene Set Enrichment Analysis (GSEA) method.ResultsWe identified 698 differentially expressed genes (DEGs) in response to oral cancer. 17 IRGPs among the DEGs were identified and integrated into a risk score model. Patients in the high-risk group have a significantly worse prognosis than those in the low-risk group in both training (P<0.001) and test (P=0.019) cohorts. Meanwhile, the IRGP model was identified as an independent prognostic factor for oral cancer. Different infiltration patterns of immune cells were found between the high- and low-risk groups that more types of T and B cells were enriched in the low-risk group. More immune-related signaling pathways were highly enriched in the low-risk group and Tenascin C (TNC) was the most frequently mutated gene. We have developed a novel 17-IRGPs signature for risk stratification and prognostic prediction of oral cancer.ConclusionOur study provides a foundation for improved immunotherapy and prognosis and is beneficial to the individualized management of oral cancer patients.

Development and validation of an individualized immune prognostic signature in colorectal cancer.

e15531 Background: Colorectal cancer (CRC) is the third most common cause of cancer mortality worldwide with more than 1.85 million cases and 850,000 deaths annually. Although the outcome of CRC patients has improved significantly with the recent implementation of annual screening programs, reliable prognostic biomarkers are still needed due to the disease heterogeneity. Accumulating evidence revealed an association between immune signature and CRC prognosis. Therefore, we aim to build a robust immune-related gene pairs (IRGPs) signature that can evaluate the prognosis for CRC patients. Methods: Gene expression profiles and complete clinical information of CRC patients were collected from seven public cohorts, divided into the training cohort (TCGA cohort, N = 460) and six independent validation cohorts (GSE17536, GSE39582, GSE30378, GSE41258, GSE71187 and GSE87211, N = 1,151). Gene expression level in a specific sample or profile underwent pairwise comparison to generate a score for each IRGP. An IRGP score of 1 was assigned if IRG 1 was less than IRG 2; otherwise, the IRGP score was 0. Prognostic IRGPs were selected using log-rank test and LASSO Cox proportional hazards regression model. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were used to examine model predictive performance in the validation cohort. Results: Within 942 immune-related genes that were expressed in all the cohorts, a prognostic immune signature of 80 gene pairs consisting of 101 unique genes was constructed which was significantly associated with patients overall survival in the training dataset (HR, 4.53 [2.89-7.09]; P &lt; 0.0001). In the validation cohorts, the IRGPs signature significantly stratified patients into high-risk and low-risk groups in terms of prognosis and was prognostic in multivariate analyses (HR, 3.08 [2.03-4.65]; P &lt; 0.0001). Biological processes that were dramatically involved in cancer immunology, such as cell chemotaxis and leukocyte migration, were enriched among genes in the IRGPs signature. To further improve accuracy, we combined tumor stage and IRGPs score to fit a Cox proportional hazards regression model using the training dataset and derived an immune-clinical prognostic index (ICPI) as (0.53633 × stage) + (1.31369 × IRGPs score). Significantly improved estimation of survival was achieved by ICPI relative to IRGPs score (mean C-index, 0.81 vs 0.79; P = 0 .014). Conclusions: We developed a robust IRGPs signature for estimating prognosis in CRC patients, providing new insights into the identification of CRC patients with a high risk of mortality. Prospective studies are needed to further validate its analytical accuracy and to test its clinical utility in individualized management of CRC patients.

Identification of an immune-related gene pair signature in breast cancer.

BackgroundAlthough breast cancer outcome has improved significantly with the recent use of molecularly targeted agents, reliable prognostic signatures are still unavailable because of tumor heterogeneity. Immune processes play an important role in tumor progression. Therefore, the aim of this study was to construct a prognostic signature based on immune-related genes (IRGs).MethodsClinical information and gene expression of 3,496 patients were extracted from eight public data sets. A total of 2,498 IRGs associated to 17 immune processes were downloaded from the ImmPort database. RNA sequencing (RNAseq) datasets [The Cancer Genome Atlas (TCGA) and GSE96058] were used as the training set (n=2,736) and all microarray datasets were used as validation set (n=760). IRGs related to prognosis were screened out from the training set and used to construct gene pairs. The Cox regression model was used, based on the immune-related gene pairs (IRGPs). The risk score of each patient was calculated and patients were stratified into high- and low-risk groups according to the optimal threshold of the risk score. Immune cell infiltration was evaluated between both groups.ResultsAmong the 129 prognostic-related immune genes, 8,256 IRGPs were constructed. After screening, 89 IRGPs, including 86 unique IRGs, were used in the prognostic prediction model. Patients in the high-risk group exhibited a significantly poorer overall survival (OS) both in the training set [hazard ratio (HR): 5.9, 95% confidence interval (CI): 4.61–7.54] and validation set (HR: 1.52, 95% CI: 1.16–1.98) compared to the low-risk group. In addition, patients in the high-risk group showed a significantly lower infiltration of CD8+ T cells than patients in the low-risk group.ConclusionsAn independent IRGP signature was constructed. Through pairwise comparison of a set of genes, the OS of patients could be predicted. This method avoids the impact of the batch effect caused by different sequencing platforms and has a promising application prospect.

Immune Subtype Profiling and Establishment of Prognostic Immune-Related lncRNA Pairs in Human Ovarian Cancer.

This study collected immune-related genes (IRGs) and used gene expression data from TCGA database to construct a molecular subtype of ovarian cancer (OV) based on immune-related lncRNA gene pairs (IRLnc_GPs). The relationships between molecular subtypes and prognosis and clinical characteristics were further explored. IRGs were acquired from the ImmPort database, and round-robin pairing of immune-related lncRNAs was performed. The NMF algorithm was used to identify molecular subtypes, and the immune score of a single sample was calculated through ESTIMATE, TIMER, ssGSEA, MCPcounter, and CIBERSORT. The relationship between molecular subtypes and immune microenvironments was identified. A hypergeometric test was used to test the lncRNA pairs among the OV molecular subtypes (C1 and C2 subtypes). The BH method was used to screen the different lncRNA pairs, and a predictive risk model was constructed and verified. Finally, correlation analysis between the risk model, immune checkpoint genes, and chemotherapy drugs was carried out. Based on IRLnc_GP to classify 373 OV samples of TCGA, the samples were divided into two subtypes, and the prognosis between the subtypes showed significant differences. The C1 subtype with a poor prognosis was more related to the pathways of tumor occurrence and development. We identified 180 differential lncRNA pairs between subtypes and constructed a prognostic risk model based on 8 IRLnc_GPs. In the independent dataset, the distribution of subtypes in functional modules was different and highly repeatable. There were significant differences in the molecular and clinical characteristics of the subtypes and the drug sensitivity of immunotherapy/chemotherapy. In conclusion, the risk model established based on IRLnc_GP can better evaluate the prognosis of OV samples and can also assess the effects of different drug treatments in the high- and low-risk groups, providing new insights and ideas for the treatment of OV.

A novel signature based on immune-related gene pairs and clinical features to predict prognosis and treatment effect in "driver gene negative" lung adenocarcinoma.

ObjectiveExamining the role of immune‐related genes (IRGs) in “driver gene negative” lung adenocarcinoma (LUAD) may provide new ideas for the treatment and study for this LUAD subgroup. We aimed to find the hub immune‐related gene pairs can stratify the risk of “driver‐gene‐negative” LUAD.Materials and MethodsIRGs were identified according to ImmPort database based on RNA sequencing results of tumors and normal tissues from 46 patients with “driver gene negative” LUAD at The First Affiliated Hospital of Sun Yat‐sen University and cyclically singly paired as immune‐related gene pairs (IRGPs). Multivariate Cox analysis was used to construct an immune risk model and a prognostic nomogram combining was also been developed. Immune microenvironment landscape described by CIBERSORT and drug sensitivity calculated by pRRophetic algorithm were used to explore possible treatment improvements.ResultsA novel immune risk model with 5‐IRGPs (CD1A|CXCL135, CD1A|S100A7L2, IFNA7|CMTM2, IFNA7|CSF3, CAMP|TFR2) can accurately distinguish patients in the high‐ and low‐risk groups. Risk score act as an independent prognostic factor and is related to clinical stage. There are significant differences in tumor immune microenvironment and PD‐1/PD‐L1/CTLA‐4 expression between groups. The low‐risk patient may benefit more from the commonly used chemotherapy regimens such as gemcitabine and paclitaxel.ConclusionThis study constructed 5‐IRGPs as a reliable prognostic tool and may represent genes pairs that are potential rationale for choice of treatment for “driver gene negative” LUAD.

An Immune-Related Gene Pair Index Predicts Clinical Response and Survival Outcome of Immune Checkpoint Inhibitors in Melanoma.

The durable responses and favorable long-term outcomes are limited to a proportion of advanced melanoma patients treated with immune checkpoint inhibitors (ICI). Considering the critical role of antitumor immunity status in the regulation of ICI therapy responsiveness, we focused on the immune-related gene profiles and aimed to develop an individualized immune signature for predicting the benefit of ICI therapy. During the discovery phase, we integrated three published datasets of metastatic melanoma treated with anti-PD-1 (n = 120) and established an immune-related gene pair index (IRGPI) for patient classification. The IRGPI was constructed based on 31 immune-related gene pairs (IRGPs) consisting of 51 immune-related genes (IRGs). The ROC curve analysis was performed to evaluate the predictive accuracy of IRGPI with AUC = 0.854. Then, we retrospectively collected one anti-PD-1 therapy dataset of metastatic melanoma (n = 55) from Peking University Cancer Hospital (PUCH) and performed the whole-transcriptome RNA sequencing. Combined with another published dataset of metastatic melanoma received anti-CTLA-4 (VanAllen15; n = 42), we further validated the prediction accuracy of IRGPI for ICI therapy in two datasets (PUCH and VanAllen15) with AUCs of 0.737 and 0.767, respectively. Notably, the survival analyses revealed that higher IRGPI conferred poor survival outcomes in both the discovery and validation datasets. Moreover, correlation analyses of IRGPI with the immune cell infiltration and biological functions indicated that IRGPI may be an indicator of the immune status of the tumor microenvironment (TME). These findings demonstrated that IRGPI might serve as a novel marker for treating of melanoma with ICI, which needs to be validated in prospective clinical trials.

Identification and validation of an immune-related gene pairs signature for three urologic cancers.

Reliable biomarkers are needed to recognize urologic cancer patients at high risk for recurrence. In this study, we built a novel immune-related gene pairs signature to simultaneously predict recurrence for three urologic cancers. We gathered 14 publicly available gene expression profiles including bladder, prostate and kidney cancer. A total of 2,700 samples were classified into the training set (n = 1,622) and validation set (n = 1,078). The 25 immune-related gene pairs signature consisting of 41 unique genes was developed by the least absolute shrinkage and selection operator regression analysis and Cox regression model. The signature stratified patients into high- and low-risk groups with significantly different relapse-free survival in the meta-training set and its subpopulations, and was an independent prognostic factor of urologic cancers. This signature showed a robust ability in the meta-validation and multiple independent validation cohorts. Immune and inflammatory response, chemotaxis and cytokine activity were enriched with genes relevant to the signature. A significantly higher infiltration level of M1 macrophages was found in the high-risk group versus the low-risk group. In conclusion, our signature is a promising prognostic biomarker for predicting relapse-free survival in patients with urologic cancer.

A 25 Immune-Related Gene Pair Signature Predicts Overall Survival in Cervical Cancer.

Mounting evidence suggests that the tumor microenvironment plays an important role in the occurrence and development of cancer, with immune system dysfunction being closely related to malignant cancers. We aimed to screen immune-related genes (IRGs) to generate an IRG pair (IRGP)-based prognostic signature for cervical cancer (CC). Datasets were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases and used as training and validation cohorts, respectively. Using the ImmPort database, IRGs in control and CC samples were compared, and differentially expressed genes were identified to construct an IRGP prognostic signature. Based on this analysis, 25 IRGPs were identified as important factors for the prognosis of CC. Univariate and multivariate Cox regression analyses further showed that the IRGP signature was an independent prognostic factor of overall survival. In summary, we successfully constructed an IRGP prognostic signature of CC, providing insights into immunotherapy for CC.