Immune-related Gene Pairs Signature Research Articles

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.

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.

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 < 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 < 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.

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.

A Novel Immune-Gene Pair Signature Revealing the Tumor Microenvironment Features and Immunotherapy Prognosis of Muscle-Invasive Bladder Cancer.

Immunotherapy has been a milestone for muscle-invasive bladder cancer (MIBC), but only a small portion of patients can benefit from it. Therefore, it is crucial to develop a robust individualized immune-related signature of MIBC to identify patients potentially benefiting from immunotherapy. The current study identified patients from the Cancer Genome Atlas (TCGA) and immune genes from the ImmPort database, and used improved data analytical methods to build up a 45 immune-related gene pair signature, which could classify patients into high-risk and low-risk groups. The signature was then independently validated by a Gene Expression Omnibus (GEO) dataset and IMvigor210 data. The subsequent analysis confirmed the worse survival outcomes of the high-risk group in both training (p < 0.001) and validation cohorts (p = 0.018). A signature-based risk score was proven to be an independent risk factor of overall survival (p < 0.001) and could predict superior clinical net benefit compared to other clinical factors. The CIBERSORT algorithm revealed the low-risk group had increased CD8+ T cells plus memory-activated CD4+ T-cell infiltration. The low-risk group also had higher expression of PDCD1 (PD-1), CD40, and CD27, and lower expression of CD276 (B7-H3) and PDCD1LG2 (PD-L2). Importantly, IMvigor210 data indicated that the low-risk group had higher percentage of “inflamed” phenotype plus less “desert” phenotype, and the survival outcomes were significantly better for low-risk patients after immunotherapy (p = 0.014). In conclusion, we proposed a novel and promising prognostic immune-related gene pair (IRGP) signature of MIBC, which could provide us a panoramic view of the tumor immune microenvironment of MIBC and independently identify MIBC patients who might benefit from immunotherapy.

Development and Validation of an Immune-Related Gene Pairs Signature in Grade II/III Glioma.

BackgroundGliomas are prevalent primary intracerebral malignant tumors. Increasing evidence indicates an association between the immune signature and Grade II/III glioma prognosis. Thus, we aimed to develop an immune-related gene pair (IRGP) signature that can be used as a prognostic tool in Grade II/III glioma.MethodsThe gene expression levels and clinical information of Grade II/III glioma patients were collected from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. The TCGA data were randomly divided into a training cohort (n = 249) and a validation cohort (n = 162), and a CGGA dataset served as an external validation group (n = 605). IRGPs significantly associated with prognosis were selected by Cox regression. Gene set enrichment analysis and filtration were performed with the IRGPs.ResultsWithin a set of 1991 immune genes, 8 IRGPs including 15 unique genes that significantly affect survival constituted a gene signature. In the validation datasets, the IRGP signature significantly stratified patients with Grade II/III glioma into low- and high-risk groups (P < 0.001), and the IRGP index was found to be an independent prognostic factor through univariate and multivariate analyses (P < 0.05). Additionally, 26 functional pathways were identified through the intersection of Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) enrichment analysis.ConclusionThe IRGP signature demonstrated good prognostic value for Grade II/III gliomas, which may provide new insights into individual treatment for glioma patients. The IRGPs might function through the identified 26 functional pathways.

Prediction of Survival Outcome in Lower-Grade Glioma Using a Prognostic Signature with 33 Immune-Related Gene Pairs.

BackgroundLower-grade glioma (LGG) is one of the prevalent malignancies threatening human health, with considerable intrinsic heterogeneities in their biological behavior. Previous studies have revealed that the immune component is a key factor influencing the formation and development of malignancies. In this study, we aim to use a novel approach to develop a prognostic signature of immune-related gene pairs (IRGPs) to determine the survival outcome of patients with LGG.MethodsTranscriptomic profiles and clinical data for LGG were obtained from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases, and used as training and validation data sets, respectively. IRGPs influencing the overall survival (OS) of patients with LGG in the training data set were screened by performing univariate Cox regression analysis. Next, a prognostic IRGPs signature was constructed using least absolute shrinkage and selection operator (LASSO) regression. Finally, we cross-validated the two databases to verify the stability of the prognostic signature.ResultsA total of 33 IRGPs influencing prognosis of LGG in the training data set were included in the prognostic signature. Patients with high risk scores (RSs) in the training and validation data sets had a poorer OS than those with low RSs. Moreover, significant differences were observed in tumor-infiltrating immune cells (TICs) between high- and low-RS groups. Functional enrichment analyses results revealed that genes in the high-RS group were enriched in the immune-related activities and developmental processes.ConclusionThe prognostic signature containing 33 IRGPs has a significant correlation with OS and relative levels of immune cells associated with LGG. The results of the present study provide new insights into the prediction of survival outcome and therapeutic response of LGG.

Development and Verification of an Immune-Related Gene Pairs Prognostic Signature in Hepatocellular Carcinoma.

With the increasing prevalence of Hepatocellular carcinoma (HCC) and the poor prognosis of immunotherapy, reliable immune-related gene pairs (IRGPs) prognostic signature is required for personalized management and treatment of patients. Gene expression profiles and clinical information of HCC patients were obtained from the TCGA and ICGC databases. The IRGPs are constructed using immune-related genes (IRGs) with large variations. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct IRGPs signature. The IRGPs signature was verified through the ICGC cohort. 1,309 IRGPs were constructed from 90 IRGs with high variability. We obtained 50 IRGPs that were significantly connected to the prognosis and constructed a signature that included 17 IRGPs. In the TCGA and ICGC cohorts, patients were divided into high and low-risk patients by the IRGPs signature. The overall survival time of low-risk patients is longer than that of high-risk patients. After adjustment for clinical and pathological factors, multivariate analysis showed that the IRGPs signature is an independent prognostic factor. The Receiver operating characteristic (ROC) curve confirmed the accuracy of the signature. Besides, gene set enrichment analysis (GSEA) revealed that the signature is related to immune biological processes, and the immune microenvironment status is distinct in different risk patients. The proposed IRGPs signature can effectively assess the overall survival of HCC, and provide the relationship between the signature and the reactivity of immune checkpoint therapy and the sensitivity of targeted drugs, thereby providing new ideas for the diagnosis and treatment of the disease.

Development of immune gene pair-based signature predictive of prognosis and immunotherapy in esophageal cancer.

BackgroundEsophageal cancer (EC) is one of the deadliest solid malignancies, mainly consisting of esophageal squamous cell carcinoma (ESCC) and adenocarcinoma (EAC). Robust biomarkers that can improve patient risk stratification are needed to optimize cancer management. We sought to establish potent prognostic signatures with immune-related gene (IRG) pairs for ESCC and EAC.MethodsWe obtained differentially expressed IRGs by intersecting the Immunology Database and Analysis Portal (ImmPort) with the transcriptome data set of The Cancer Genome Atlas (TCGA)-ESCC and EAC cohorts. A novel rank-based pairwise comparison algorithm was applied to select effective IRG pairs (IRGPs), followed by constructing a prognostic IRGP signature via the least absolute shrinkage and selection operator (LASSO) regression model. We assessed the predictive power of the IRGP signatures on prognosis, tumor-infiltrating immune cells, and immune checkpoint inhibitor (ICI) efficacy in EC. Kaplan-Meier survival analysis and receiver operating characteristic curves (ROC) were used to evaluate the clinical significance of IRGPs. Univariate and multivariate Cox regression analyses were performed to investigate the association of overall survival (OS) with IRGPs and clinical characteristics.ResultsWe built a 19-IRGP signature for ESCC (n=75) and a 17-IRGP signature for EAC (n=78), with an area under the ROC curve (AUC) of 0.931 and 0.803, respectively. IRGP signature-derived risk scores stratified patients into low- and high-risk groups with significantly different OS in ESCC and EAC (P<0.001). Nomogram and decision curve analysis were used to evaluate the clinical relevance of the prognostic signatures, achieving a C-index of 0.973 in ESCC and 0.880 in EAC. The risk scores were associated with immune and ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) scores and the composition of immune cells in the tumor microenvironment. The association between risk score and human leukocyte antigens (HLAs), mismatch repair (MMR) genes, and immune checkpoint molecules demonstrated its predictive value for ICI response. Differential immune characteristics and predictive value of the risk score were observed in EAC.ConclusionsThe established immune signatures showed great promise in predicting prognosis, tumor immunogenicity, and immunotherapy response in ESCC and EAC.

Prognostic and Predictive Value of Immune-Related Gene Pair Signature in Primary Lower-Grade Glioma Patients.

Immune-related gene pairs (IRGPs) have been associated with prognosis in various cancer types, but few studies have examined their prognostic capabilities in glioma patients. Here, we gathered the gene expression and clinical profile data of primary lower-grade glioma (LGG) patients from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA, containing CGGAseq1 and CGGAseq2), the Gene Expression Omnibus (GEO: GSE16011), and Rembrandt datasets. In the TCGA dataset, univariate Cox regression was performed to detect overall survival (OS)-related IRGs, Lasso regression, and multivariate Cox regression were used to screen robust prognosis-related IRGs, and 19 IRGs were selected for the construction of an IRGP prognostic signature. All patients were allotted to high- and low-risk subgroups based on the TCGA dataset median value risk score. Validation analysis indicated that the IRGP signature returned a stable prognostic value among all datasets. Univariate and multivariate Cox regression analyses indicated that the IRG -signature could efficiently predict the prognosis of primary LGG patients. The IRGP-signature-based nomogram model was built, revealing the reliable ability of the IRGP signature to predict clinical prognosis. The single-sample gene set enrichment analysis (ssGSEA) suggested that high-risk samples contained higher numbers of immune cells but featured lower tumor purity than low-risk samples. Finally, we verified the prognostic ability of the IRGP signature using experiments performed in LGG cells. These results indicated that the IRGP signature could be regarded as a stable prognostic assessment predictor for identifying high-risk primary LGG patients.

Discovery and Verification of an Immune-Related Gene Pairs Signature for Predicting Prognosis in Head and Neck Squamous Cell Carcinoma.

The study of IRGPs to construct the prognostic signature in head and neck squamous cell carcinoma (HNSCC) has not yet elucidated. The objective of this study was to explore a novel model to predict the prognosis of HNSCC patients. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were set as training and validation cohorts, respectively. The least absolute shrinkage and selection operator (LASSO) and time-dependent ROC were employed to screen the highest frequency immune-related gene pairs (IRGPs) and their best cut-off value. Survival analysis, Cox regression analysis were applied to discover the effects of selected IRGPs signature on survival outcomes. The immune cell proportions were deconvoluted by the CIBERSORT method. After a couple of filtering, we obtained 22 highest frequency IRGPs. The overall survival time of HNSCC patients with a high score of IRGPs was shorter as compared to the ones with a low score in two independent datasets (P < 0.001). Six kinds of immune cells were found to be differentially distributed in the two different risk groups of HNSCC patients (P < 0.001). GO and GSEA analysis showed these differentially expressed genes enriched in multiple molecular functions. The new IRGPs signature probably confers a new insight into the prognosis prediction of HNSCC patients.

Identification and Verification of a 17 Immune-Related Gene Pair Prognostic Signature for Colon Cancer.

Background Colon cancer (CC) is a malignant tumor with a high incidence and poor prognosis. Accumulating evidence shows that the immune signature plays an important role in the tumorigenesis, progression, and prognosis of CC. Our study is aimed at establishing a novel robust immune-related gene pair signature for predicting the prognosis of CC. Methods Gene expression profiles and corresponding clinical information are obtained from two public data sets: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO, GSE39582). We screened out immune-related gene pairs (IRGPs) associated with prognosis in the discovery cohort. Lasso-Cox proportional hazard regression was used to develop the best prognostic signature model. According to this, the patients in the validation cohort were divided into high immune-risk group and low immune-risk group, and the prediction ability of the signature model was verified by survival analysis and independent prognostic analysis. Results A total of 17 IRGPs composed of 26 IRGs were used to construct a prognostic-related risk scoring model. This model accurately predicted the prognosis of CC patients, and the patients in the high immune-risk group indicated poor prognosis in the discovery cohort and validation cohort. Besides, whether in univariate or multivariate analysis, the IRGP signature was an independent prognostic factor. T cell CD4 memory resting in the low-risk group was significantly higher than that in the high-risk group. Functional analysis showed that the biological processes of the low-risk group included “TCA cycle” and “RNA degradation,” while the high-risk group was enriched in the “CAMs” and “focal adhesion” pathways. Conclusion We have successfully established a signature model composed of 17 IRGPs, which provides a novel idea to predict the prognosis of CC patients.

Identifying an immune-related gene-pair for prognosis prediction of metastatic colorectal cancer.

e15565 Background: Few robust predictive biomarkers have been applied in clinical practice due to the heterogeneity of metastatic colorectal cancer (mCRC) . Using the gene pair method, the absolute expression value of genes can be converted into the relative order of genes, which can minimize the influence of the sequencing platform difference and batch effects, and improve the robustness of the model. The main objective of this study was to establish an immune-related gene pairs signature (IRGPs) and evaluate the impact of the IRGPs in predicting the prognosis in mCRC. Methods: A total of 205 mCRC patients containing overall survival (OS) information from the training cohort ( n = 119) and validation cohort ( n = 86) were enrolled in this study. LASSO algorithm was used to select prognosis related gene pairs. Univariate and multivariate analyses were used to validate the prognostic value of the IRGPs. Gene sets enrichment analysis (GSEA) and immune infiltration analysis were used to explore the underlying biological mechanism. Results: An IRGPs signature containing 22 gene pairs was constructed, which could significantly separate patients of the training cohort ( n = 119) and validation cohort ( n = 86) into the low-risk and high-risk group with different outcomes. Multivariate analysis with clinical factors confirmed the independent prognostic value of IRGPs that higher IRGPs was associated with worse prognosis (training cohort: hazard ratio (HR) = 10.54[4.99-22.32], P &lt; 0.001; validation cohort: HR = 3.53[1.24-10.08], P = 0.012). GSEA showed that several metastasis and immune-related pathway including angiogenesis, TGF-β-signaling, epithelial-mesenchymal transition and inflammatory response were enriched in the high-risk group. Through further analysis of the immune factors, we found that the proportions of CD4+ memory T cell, regulatory T cell, and Myeloid dendritic cell were significantly higher in the low-risk group, while the infiltrations of the Macrophage (M0) and Neutrophil were significantly higher in the high-risk group. Conclusions: The IRGPs signature could predict the prognosis of mCRC patients. Further prospective validations are needed to confirm the clinical utility of IRGPs in the treatment decision.

Identification of an IRGP Signature to Predict Prognosis and Immunotherapeutic Efficiency in Bladder Cancer.

BackgroundIdentify immune-related gene pairs (IRGPs) signature related to the prognosis and immunotherapeutic efficiency for bladder cancer (BLCA) patients.Materials and MethodsOne RNA-seq dataset (The Cancer Genome Atlas Program) and two microarray datasets (GSE13507 and GSE31684) were included in this study. We defined these cohorts as training set to construct IRGPs and one immunotherapy microarray dataset as validation set. Identifying BLCA subclasses based on IRGPs by consensus clustering. The Lasso penalized Cox proportional hazards regression model was used to construct prognostic signature and potential molecular mechanisms were analyzed.ResultsThis signature can accurately predict the overall survival of BLCA patients and was verified in the immunotherapy validation set. IRGP-signatures can be used as independent prognostic risk factor in various clinical subgroups. Use the CIBERSORT algorithm to assess the abundance of infiltrating immune cells in each sample, and combine the results of the gene set enrichment analysis of a single sample to explore the differences in the immune microenvironment between IRPG signature groups. According to the results of GSVA, GSEA, and CIBERSORT algorithm, we found that IRGP is strikingly positive correlated with tumor microenvironment (TME) stromal cells infiltration, indicating that the poor prognosis and immunotherapy might be caused partly by enrichment of stromal cells. Finally, the results from the TIDE analysis revealed that IRGP could efficiently predict the response of immunotherapy in BLCA.ConclusionThe novel IRGP signature has a significant prognostic value for BLCA patients might facilitate personalized for immunotherapy.

An Immune-Related Gene Pairs Signature Predicts Prognosis and Immune Heterogeneity in Glioblastoma.

PurposeGlioblastoma is one of the most aggressive nervous system neoplasms. Immunotherapy represents a hot spot and has not been included in standard treatments of glioblastoma. So in this study, we aim to filtrate an immune-related gene pairs (IRGPs) signature for predicting survival and immune heterogeneity.MethodsWe used gene expression profiles and clinical information of glioblastoma patients in the TCGA and CGGA datasets, dividing into discovery and validation cohorts. IRGPs significantly correlative with prognosis were selected to conduct an IRGPs signature. Low and high risk groups were separated by this IRGPs signature. Univariate and multivariate cox analysis were adopted to check whether risk can be a independent prognostic factor. Immune heterogeneity between different risk groups was analyzed via immune infiltration and gene set enrichment analysis (GSEA). Some different expressed genes between groups were selected to determine their relationship with immune cells and immune checkpoints.ResultsWe found an IRGPs signature consisting of 5 IRGPs. Different risk based on IRGPs signature is a independent prognostic factor both in the discovery and validation cohorts. High risk group has some immune positive cells and more immune repressive cells than low risk group by means of immune infiltration. We discovered some pathways are more active in the high risk group, leading to immune suppression, drug resistance and tumor evasion. In two specific signaling, some genes are over expressed in high risk group and positive related to immune repressive cells and immune checkpoints, which indicate aggression and immunotherapy resistance.ConclusionWe identified a robust IRGPs signature to predict prognosis and immune heterogeneity in glioblastoma patients. Some potential targets and pathways need to be further researched to make different patients benefit from personalized immunotherapy.

An Immune-Related Gene Pairs Signature for Predicting Survival in Glioblastoma.

Background: Glioblastoma (GBM) is the frequently occurring and most aggressive form of brain tumors. In the study, we constructed an immune-related gene pairs (IRGPs) signature to predict overall survival (OS) in patients with GBM.Methods: We established IRGPs with immune-related gene (IRG) matrix from The Cancer Genome Atlas (TCGA) database (Training cohort). After screened by the univariate regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis, IRGPs were subjected to the multivariable Cox regression to develop an IRGP signature. Then, the predicting accuracy of the signature was assessed with the area under the receiver operating characteristic curve (AUC) and validated the result using the Chinese Glioma Genome Atlas (CGGA) database (Validation cohorts 1 and 2).Results: A 10-IRGP signature was established for predicting the OS of patients with GBM. The AUC for predicting 1-, 3-, and 5-year OS in Training cohort was 0.801, 0.901, and 0.964, respectively, in line with the AUC of Validation cohorts 1 and 2 [Validation cohort 1 (1 year: 0.763; 3 years: 0.786; and 5 years: 0.884); Validation cohort 2 (1 year: 0.745; 3 years: 0.989; and 5 years: 0.987)]. Moreover, survival analysis in three cohorts suggested that patients with low-risk GBM had better clinical outcomes than patients with high-risk GBM. The univariate and multivariable Cox regression demonstrated that the IRGPs signature was an independent prognostic factor.Conclusions: We developed a novel IRGPs signature for predicting OS in patients with GBM.

Development of an immune-related gene pairs signature for predicting clinical outcome in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the main pathological subtype of Non-small cell lung cancer. We downloaded the gene expression profile and immune-related gene set from the TCGA and ImmPort database, respectively, to establish immune-related gene pairs (IRGPs). Then, IRGPs were subjected to univariate Cox regression analysis, LASSO regression analysis, and multivariable Cox regression analysis to screen and develop an IRGPs signature. The receiver operating characteristic curve (ROC) was applied for evaluating the predicting accuracy of this signature by calculating the area under ROC (AUC) and data from the GEO set was used to validate this signature. The relationship of 22 tumor-infiltrating immune cells (TIICs) to the immune risk score was also investigated. An IRGPs signature with 8 IRGPs was constructed. The AUC for 1- and 3-year overall survival in the TCGA set was 0.867 and 0.870, respectively. Similar results were observed in the AUCs of GEO set 1, 2 and 3 (GEO set 1 [1-year: 0.819; 3-year: 0.803]; GEO set 2 [1-year: 0.834; 3-year: 0.870]; GEO set 3 [1-year: 0.955; 3-year: 0.827]). Survival analysis demonstrated high-risk LUAD patients exhibited poorer prognosis. The multivariable Cox regression indicated that the risk score was an independent prognostic factor. The immune risk score was highly associated with several TIICs (Plasma cells, memory B cells, resting memory CD4 T cells, and activated NK cells). We developed a novel IRGPs signature for predicting 1- and 3- year overall survival in LUAD, which would be helpful for prognosis assessment of LUAD.

Prediction of clinical prognosis in cutaneous melanoma using an immune-related gene pair signature

ABSTRACT Cutaneous melanoma (CM) is a malignant and aggressive skin cancer that is the leading cause of skin cancer-related deaths. Increasing evidence shows that immunity plays a vital role in the prognosis of CM. In this study, we developed an immune-related gene pair (IRGP) signature to predict the clinical prognosis of patients with CM. Immune-related genes from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were selected to construct the IRGPs, and patients with CM in these two cohorts were assigned to low- and high-risk subgroups. Moreover, we investigated the IRGPs and their individualized prognostic signatures using Kaplan-Meier survival analysis, univariate and multivariate Cox analyses, and analysis of immune cell infiltration in CM. A 41-IRGP signature was constructed from 2498 immune genes that could significantly predict the overall survival of patients with CM in both the TCGA and GEO cohorts. Immune infiltration analysis indicated that several immune cells, especially M1 macrophages and activated CD4 T cells, were significantly associated with the prognostic effect of the IRGP signature in patients with CM. Overall, the IRGP signature constructed in this study was useful for determining the prognosis of patients with CM and for providing further understanding of CM immunotherapy.