Prognostic Immune Genes Research Articles

The identification of a N6-methyladenosin-modifed immune pattern to predict immunotherapy response and survival in urothelial carcinoma.

Dysregulation of the immune system and N6-methyladenosine (m6A) contribute to immune therapy resistance and cancer progression in urothelial carcinoma (UC). This study aims to identify immune-related molecules, that are m6A-modified, and that are associated with tumor progression, poor prognosis, and immunotherapy response. We identified prognostic immune genes (PIGs) using Cox analysis and random survival forest variable hunting algorithm (RSF-VH) on immune genes retrieved from the Immunology Database and Analysis Portal database (ImmPort). The RM2Target database and MeRIP-seq analysis, combined with a hypergeometric test, assessed m6A methylation in these PIGs. We analyzed the correlation between the immune pattern and prognosis, as well as their association with clinical factors in multiple datasets. Moreover, we explored the interplay between immune patterns, tumor immune cell infiltration, and m6A regulators. 28 PIGs were identified, of which the 10 most significant were termed methylated prognostic immune genes (MPIGs). These MPIGs were used to create an immune pattern score. Kaplan-Meier and Cox analyses indicated this pattern as an independent risk factor for UC. We observed significant associations between the immune pattern, tumor progression, and immune cell infiltration. Differential expression analysis showed correlations with m6A regulators expression. This immune pattern proved effective in predicting immunotherapy response in UC in real-world settings. The study identified a m6A-modified immune pattern in UC, offering prognostic and therapeutic response predictions. This emphasizes that immune genes may influence tumor immune status and progression through m6A modifications.

Identification of a cisplatin resistant-based prognostic immune related gene signature in MIBC

Cisplatin resistance plays a significant role in the dismal prognosis and progression of muscle-invasive bladder cancer (MIBC). However, the strategies to predict prognosis and cisplatin resistance are inefficient, and it remains unclear whether cisplatin resistance is associated with tumor immunity. In this study, we integrated the transcriptional data from cisplatin-resistant cell lines and a TCGA-MIBC cohort to establish cisplatin-resistance-related cluster classification and a cisplatin-resistance-related gene risk score (CRRGRS). Kaplan–Meier survival curves showed that compared with those in low CRRGRS group, MIBC patients belonging to high CRRGRS group had worse prognosis in TCGA-MIBC cohort and external GEO cohorts. Meanwhile, CRRGRS was able to help forecast chemotherapy and immunotherapy response of MIBC patients in the TGCA cohort and IMvigor210 cohort. Moreover, compared with the low CRRGRS group, the high CRRGS group possessed a relatively immunosuppressive “cold tumor” phenotype with a higher tumor immune dysfunction and exclusion (TIDE) score, ESTIMATE score, stromal score and immune score and a lower immunophenoscore (IPS) score. The upregulated expression levels of immune checkpoint genes, including PD-1, PD-L1 and CTLA4, in the high CRRGRS group also further indicated that a relative immunosuppressive tumor microenvironment may exist in MIBC patients belonging to high CRRGRS group. In addition, we integrated CRRGRS and clinical characteristics with prognostic value to develop a nomogram, which could help forecast overall survival of MIBC patients. Furthermore, DIAPH3 was identified as a regulator of proliferation and cisplatin resistance in MIBC. The expression of DIAPH3 was increased in cisplatin-resistant cell lines and chemotherapy-unsensitive people. Further mechanism exploration revealed that DIAPH3 facilitated tumor proliferation and cisplatin resistance by regulating the NF-kB and epithelial-mesenchymal transition (EMT) pathways. In conclusion, the comprehensive investigations of CRRGRS increased the understanding of cisplatin resistance and provided promising insights to restrain tumor growth and overcome chemoresistance by targeting DIAPH3.

Abstract 872: Development of a nomogram and prognostic model based on immune-related genes in kidney renal papillary cell carcinoma

Abstract Background: Kidney renal papillary cell carcinoma (KIRP) is frequently associated with an unfavorable prognosis for affected individuals. Unfortunately, there has been insufficient exploration in search for a reliable prognosis signature and predictive indicators to forecast outcomes for KIRP patients. Thus, we have used bioinformatics of existing transcriptomic and health data to identify survival-related and prognostic immune genes. Methods: Processed RNA-seq FPKM data of 289 KIRP and 32 adjacent normal KIRP tissues were downloaded from the TCGA data portal. We downloaded 2499 immune-related genes via the Immunology Database and Analysis portal database and used the Cistrome database to identify and extract 318 total transcription factors (TFs). The differentially expressed immune-related genes and TFs were detected using the Wilcoxon test method. R was used to generate heatmaps and volcano plots. R was also used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Multivariate Cox regression analysis was implemented to investigate the prognostic value of differentially expressed immune-related genes used to construct the predictive model and nomogram. Kaplan-Meier analysis was performed to compare overall survival. We used Tumor Immune Estimation Resource (TIMER) and the Human Protein Atlas database (HPA) to validate expression and explore associations between a prognostic model and immune cell infiltration. Statistical analyses were performed using R; a p-value < 0.05 was considered to be significant. Results: A total of 368 immune-related genes and 60 TFs were identified as differentially expressed in KIRP tissues compared with normal tissues. Of the 368, 23 were found to be related to overall survival. GO and KEGG analysis suggested that these prognostic immune-related genes mainly participated in the ERK1 and ERK2 cascades, Rap1 signaling pathway, and the PI3K-Akt signaling pathway - pathways shown to be significantly correlated with the development of cancer. Of these 23 genes, 9 (GRN, PDGFRB, APOH, BIRC5, CCL19, RETN, HTR3A, PTGER1, TRAV39) were identified from Cox regression to be statistically significant prognostic-related genes. Survival analysis showed that a model based on these 9 prognostic-related genes has high predictive performance. Our constructed nomogram has good predictive power and clinical practicability. Immunohistochemistry results show that APOH, BIRC5, CCL19 and GRN were significantly increased in kidney cancer. B cells and CD4+ T cells were positively correlated with risk score model. Conclusion: We were able to create a prognostic model based on 9 immune-related genes correlated with overall survival in KIRP. We hope that this work helps to provide some insight into therapeutic approaches and prognostic predictors of KIRP. Citation Format: Adrian Lim, Mouad Edderkaoui, Yi Zhang, Stephen J. Pandol, Yan Ou. Development of a nomogram and prognostic model based on immune-related genes in kidney renal papillary cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 872.

Exploring Prognostic Immune Microenvironment-Related Genes in Head and Neck Squamous Cell Carcinoma from the TCGA Database.

Purpose: Head and neck squamous cell carcinoma (HNSCC) has a high rate of local and distant metastases. In tumor tissues, the interaction between tumor cells and the tumor microenvironment (TME) is closely related to cancer development and prognosis. Therefore, screening for TME-related genes in HNSCC is crucial for understanding metastatic patterns. Methods: Our research relied mainly on a novel algorithm called Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE). Fragments Per Kilobase of exon model per Million mapped fragments (FPKM) data and HNSCC clinical data were obtained from the TCGA database, and the purity of HNSCC tissue and the features of stromal and immune cell infiltration were determined. Furthermore, differentially expressed genes (DEGs) were screened based on immune, stromal, and ESTIMATE scores, and their protein-protein interaction (PPI) networks and ClueGO functions were evaluated. Finally, the expression profiles of DEGs related to immunity in HNSCC were determined. Differential gene expression was verified in the highly invasive oral cancer cell lines (SCC-25, CAL-27, and FaDu) and oral cancer tissues. Results: Our analysis found that both the immune and ESTIMATE scores were significantly associated with the prognosis of HNSCC. Moreover, cross-validation using the Venn algorithm revealed that 433 genes were significantly upregulated, and 394 genes were significantly downregulated. All DEGs were associated with both ESTIMATE and immune scores. The enrichment of cytokine-cytokine receptor interactions and chemokine signaling pathways was observed using pathway enrichment analyses. We initially screened 25 genes after analyzing the key sub-networks of the PPI network. Survival analysis revealed the significance of CCR4, CXCR3, P2RY14, CCR2, CCR8, and CCL19 in relation to survival and their association with immune infiltration-related metastasis in HNSCC. Conclusions: The expression profiles of relevant TME-related genes were screened following stromal and immune cell scoring using ESTIMATE, and DEGs associated with survival were identified. These TME-related gene markers offer valuable utility as both prognostic indicators and markers denoting metastatic traits in HNSCC.

Integrated analysis of tumour-derived exosome-related immune genes to predict progression and immune status of hepatocellular carcinoma

Tumour-derived exosomes (TDEs) play an important role in tumourigenesis and progression by regulating components in the tumour microenvironment (TME), however, the role of TDE-related immune genes in hepatocellular carcinoma is not fully known. We systematically analysed TDE genes from ExoCarta and immune genes from Immport,Machine learning ultimately identified eight TDE-related prognostic immune genes and used them as the basis for constructing a risk model, which was constructed to better predict patients with hepatocellular carcinoma (HCC) compared with published prognostic models. There were significant differences between the high and low risk groups in terms of biological functioning. Low-risk group were more sensitive to immunotherapy, the sensitivity to oxaliplatin and cisplatin differed between the high- and low-risk groups, and knockout of the core gene RAC1 limited the malignant biological behaviour of hepatocellular carcinoma cells. In conclusion, TIRGs are effective in predicting the prognosis of patients with hepatocellular carcinoma and provide a new perspective on immunotherapy and chemotherapy for patients.

Abstract 3467: GRWD5769: A first-in-class inhibitor of ERAP1, generating novel cancer antigens to drive de novo anti-tumor T cell responses

Abstract Immune checkpoint inhibitors (ICI) have changed the cancer treatment paradigm, yet significant unmet need remains. Clinical data shows that increasing antigen presentation diversity, genomic instability, tumor mutational burden and HLA diversity, as well as preventing permanent exhaustion of pre-existing cytotoxic T cells, caused by chronic cancer antigen stimulation, are all factors that improve clinical response to ICI. Additionally, increasing presentation of cancer-specific target peptides on cancer cells will enhance T cell receptor (TCR)-mediated therapeutics such as ImmTACs or TCR-Engineered T cells. Endoplasmic reticulum aminopeptidase 1 (ERAP1) trims peptides loaded into classical and non-classical MHC Class I. In cancer, knockout or inhibition of ERAP1 changes a proportion of the antigen repertoire, generating and upregulating cancer antigens. This leads to the activation of de novo anti-tumor T cell responses, causing tumor growth inhibition and bypassing T cell exhaustion. We report the preclinical development, characterization and mechanistic analysis of the first-in-class ERAP1 inhibitor, GRWD5769. Extensive analysis of the immunopeptidomes of diverse cancer cell lines robustly showed that GRWD5769 modulates the cancer-related antigen repertoire across genotypes and cancer-type backgrounds. Novel or upregulated neoantigens generated by ERAP1 inhibition are conserved across cancer cell types and genetic backgrounds. ERAP1 inhibition in combination with an ICI (anti-PD1 mAb) diversified the TCR repertoire, upregulated prognostic immune gene markers in the tumor, including markers for recently activated (thus non-exhausted) T cells, and drove infiltration of T cells into syngeneic mouse model tumors. This combination is efficacious across syngeneic models, including different mouse strains and the effects of ERAP1 inhibition on the T cell response correlate with efficacy. Further, ex vivo human T cell co-cultures with cancer cell lines demonstrate that novel and upregulated neoantigens generated by ERAP1 inhibition drive tumor cell killing. In addition, these studies showed enhanced tumor cell killing when an ImmTAC was used in combination with ERAP1 inhibition to enhance presentation of the cancer-specific target peptide. In conclusion, the first-in-class, ERAP1 inhibitor clinical candidate, GRWD5769, drives novel anti-tumor T cell responses through neoantigen creation and circumventing T cell exhaustion. GRWD5769 has demonstrated a good safety profile in GLP toxicology studies and robust proof of mechanism and proof of principle biomarkers have been developed to provide a clear path to establish the activity and efficacy of GRWD5769 in patients in 2023. Citation Format: Andrew Leishman, Wayne Paes, Emma Sparrow, Ana Ribeiro, Michael Cundell, Milos Aleksic, Michael P. Pinggera, Jessica Sette, Kate Anderton, Nicola Ternette, Juliet Morgan, Jason J. Shiers, Martin Quibell, Peter I. Joyce. GRWD5769: A first-in-class inhibitor of ERAP1, generating novel cancer antigens to drive de novo anti-tumor T cell responses [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3467.

Role of gene signature regulation in tumor immune microenvironment on the mechanism of uveal melanoma metastasis.

Uveal melanoma (UM) is a rare but deadly cancer. The main cause of death from UM is liver metastasis. Though the metastasis mechanism remains unclear, it is closely related to the immune microenvironment and gene expression. This study aimed to identify the prognostic genes in primary and metastatic UM and their relationship with the immune microenvironment. Primary and metastatic UM data from the GEO database included GSE22138 and GSE44295 datasets. Kaplan-Meier analysis, Cox regression models, and ROC analysis were applied to screen genes in GSE22138. TIMER2.0 was employed to analyze the immune microenvironment from gene expression. Prognostic immune gene correlation was tested by Spearman. The results were validated in the independent dataset of cohort GSE44295. Metastasis and primary differential gene analysis showed 107 significantly different genes associated with prognosis, and 11 of them were immune-related. ROC analysis demonstrated that our signature was predictive for UM prognosis (AUC > 0.8). Neutrophil and myeloid dendritic cells were closely associated with metastasis with scores that significantly divided patients into high-risk and low-risk groups (log-rank p< 0.05). Of these 11 genes, FABP5 and SHC4 were significantly associated with neutrophils in metastatic tumors, while ROBO1 expression was significantly correlated with myeloid dendritic cells in the primary tumors. The present study constructed an 11-gene signature and established a model for risk stratification and prediction of overall survival in metastatic UM. Since FABP5 and SHC4 are related to neutrophil infiltration in metastatic UM, FABP5 and neutrophil regulation might be crucial in metastatic UM.

An immune genes signature for predicting mortality in sepsis patients.

A growing body of evidence indicates that the immune system plays a central role in sepsis. By analyzing immune genes, we sought to establish a robust gene signature and develop a nomogram that could predict mortality in patients with sepsis. Herein, data were extracted from the Gene Expression Omnibus and Biological Information Database of Sepsis (BIDOS) databases. We enrolled 479 participants with complete survival data using the GSE65682 dataset, and grouped them randomly into training (n = 240) and internal validation (n = 239) sets based on a 1:1 proportion. GSE95233 was set as the external validation dataset (n=51). We validated the expression and prognostic value of the immune genes using the BIDOS database. We established a prognostic immune genes signature (including ADRB2, CTSG, CX3CR1, CXCR6, IL4R, LTB, and TMSB10) via LASSO and Cox regression analyses in the training set. Based on the training and validation sets, the Receiver Operating Characteristic curves and Kaplan-Meier analysis revealed that the immune risk signature has good predictive power in predicting sepsis mortality risk. The external validation cases also showed that mortality rates in the high-risk group were higher than those in the low-risk group. Subsequently, a nomogram integrating the combined immune risk score and other clinical features was developed. Finally, a web-based calculator was built to facilitate a convenient clinical application of the nomogram. In summary, the signature based on the immune gene holds potential as a novel prognostic predictor for sepsis.

Immune landscape and prognostic index for pancreatic cancer based on TCGA database and in vivo validation

The immunotherapy efficacy on pancreatic cancer remains unsatisfactory. Therefore, it is still necessary to further clarify the pancreatic immune cell infiltration and search for immune-related prognostic indicators. We analyzed the 135 pancreatic cancer patients’ data retrieved from the TCGA database for the immune cell infiltration, tumor microenvironment score and the correlation of the immune cells, followed by identification of prognostic immune clusters and genes clusters. The R language was used for the immune score calculation, and immune cells proportion related survival differences identification. The function of immune cells was verified through datasets in the GEO database and in vivo experiments. The results showed that M0 Macrophages had negative relations to CD8 + T cells and immune scores. There were differences in median survival in ICI clusters, gene clusters, and immune score groups (p < 0.05). M0 macrophages accounted for more than 9.8%, indicating a poor prognosis, while T cells accounted for more than 9.2%, indicating a good prognosis. In vivo results showed that M0 macrophages promote pancreatic cancer growth. Elimination of M0 macrophages may be a hopeful strategy against pancreatic cancer.

Identification of immune related gene signature for predicting prognosis of cholangiocarcinoma patients.

To identify the gene subtypes related to immune cells of cholangiocarcinoma and construct an immune score model to predict the immunotherapy efficacy and prognosis for cholangiocarcinoma. Based on principal component analysis (PCA) algorithm, The Cancer Genome Atlas (TCGA)-cholangiocarcinoma, GSE107943 and E-MTAB-6389 datasets were combined as Joint data. Immune genes were downloaded from ImmPort. Univariate Cox survival analysis filtered prognostically associated immune genes, which would identify immune-related subtypes of cholangiocarcinoma. Least absolute shrinkage and selection operator (LASSO) further screened immune genes with prognosis values, and tumor immune score was calculated for patients with cholangiocarcinoma after the combination of the three datasets. Kaplan-Meier curve analysis determined the optimal cut-off value, which was applied for dividing cholangiocarcinoma patients into low and high immune score group. To explore the differences in tumor microenvironment and immunotherapy between immune cell-related subtypes and immune score groups of cholangiocarcinoma. 34 prognostic immune genes and three immunocell-related subtypes with statistically significant prognosis (IC1, IC2 and IC3) were identified. Among them, IC1 and IC3 showed higher immune cell infiltration, and IC3 may be more suitable for immunotherapy and chemotherapy. 10 immune genes with prognostic significance were screened by LASSO regression analysis, and a tumor immune score model was constructed. Kaplan-Meier (KM) and receiver operating characteristic (ROC) analysis showed that RiskScore had excellent prognostic prediction ability. Immunohistochemical analysis showed that 6 gene (NLRX1, AKT1, CSRP1, LEP, MUC4 and SEMA4B) of 10 genes were abnormal expressions between cancer and paracancer tissue. Immune cells infiltration in high immune score group was generally increased, and it was more suitable for chemotherapy. In GSE112366-Crohn's disease dataset, 6 of 10 immune genes had expression differences between Crohn's disease and healthy control. The area under ROC obtained 0.671 based on 10-immune gene signature. Moreover, the model had a sound performance in Crohn's disease. The prediction of tumor immune score model in predicting immune microenvironment, immunotherapy and chemotherapy in patients with cholangiocarcinoma has shown its potential for indicating the effect of immunotherapy on patients with cholangiocarcinoma.

A meta-validated immune infiltration-related gene model predicts prognosis and immunotherapy sensitivity in HNSCC

BackgroundTumor microenvironment (TME) is of great importance to regulate the initiation and advance of cancer. The immune infiltration patterns of TME have been considered to impact the prognosis and immunotherapy sensitivity in Head and Neck squamous cell carcinoma (HNSCC). Whereas, specific molecular targets and cell components involved in the HNSCC tumor microenvironment remain a twilight zone.MethodsImmune scores of TCGA-HNSCC patients were calculated via ESTIMATE algorithm, followed by weighted gene co-expression network analysis (WGCNA) to filter immune infiltration-related gene modules. Univariate, the least absolute shrinkage and selection operator (LASSO), and multivariate cox regression were applied to construct the prognostic model. The predictive capacity was validated by meta-analysis including external dataset GSE65858, GSE41613 and GSE686. Model candidate genes were verified at mRNA and protein levels using public database and independent specimens of immunohistochemistry. Immunotherapy-treated cohort GSE159067, TIDE and CIBERSORT were used to evaluate the features of immunotherapy responsiveness and immune infiltration in HNSCC.ResultsImmune microenvironment was significantly associated with the prognosis of HNSCC patients. Total 277 immune infiltration-related genes were filtered by WGCNA and involved in various immune processes. Cox regression identified nine prognostic immune infiltration-related genes (MORF4L2, CTSL1, TBC1D2, C5orf15, LIPA, WIPF1, CXCL13, TMEM173, ISG20) to build a risk score. Most candidate genes were highly expressed in HNSCC tissues at mRNA and protein levels. Survival meta-analysis illustrated high prognostic accuracy of the model in the discovery cohort and validation cohort. Higher proportion of progression-free outcomes, lower TIDE scores and higher expression levels of immune checkpoint genes indicated enhanced immunotherapy responsiveness in low-risk patients. Decreased memory B cells, CD8+ T cells, follicular helper T cells, regulatory T cells, and increased activated dendritic cells and activated mast cells were identified as crucial immune cells in the TME of high-risk patients.ConclusionsThe immune infiltration-related gene model was well-qualified and provided novel biomarkers for the prognosis of HNSCC.

Identification of a prognostic six-immune-gene signature and a nomogram model for uveal melanoma

BackgroundTo identify an immune-related prognostic signature and find potential therapeutic targets for uveal melanoma.MethodsThe RNA-sequencing data obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. The prognostic six-immune-gene signature was constructed through least absolute shrinkage and selection operator and multi-variate Cox regression analyses. Functional enrichment analysis and single sample GSEA were carried out. In addition, a nomogram model established by integrating clinical variables and this signature risk score was also constructed and evaluated.ResultsWe obtained 130 prognostic immune genes, and six of them were selected to construct a prognostic signature in the TCGA uveal melanoma dataset. Patients were classified into high-risk and low-risk groups according to a median risk score of this signature. High-risk group patients had poorer overall survival in comparison to the patients in the low-risk group (p < 0.001). These findings were further validated in two external GEO datasets. A nomogram model proved to be a good classifier for uveal melanoma by combining this signature. Both functional enrichment analysis and single sample GSEA analysis verified that this signature was truly correlated with immune system. In addition, in vitro cell experiments results demonstrated the consistent trend of our computational findings.ConclusionOur newly identified six-immune-gene signature and a nomogram model could be used as meaningful prognostic biomarkers, which might provide uveal melanoma patients with individualized clinical prognosis prediction and potential novel treatment targets.

FCGR2C: An emerging immune gene for predicting sepsis outcome.

Sepsis is a life-threatening disease associated with immunosuppression. Immunosuppression could ultimately increase sepsis mortality. This study aimed to identify the prognostic biomarkers related to immunity in sepsis. Public datasets of sepsis downloaded from the Gene Expression Omnibus (GEO) database were divided into the discovery cohort and the first validation cohort. We used R software to screen differentially expressed genes (DEGs) and analyzed DEGs' functional enrichment in the discovery dataset. Immune-related genes (IRGs) were filtered from the GeneCards website. A Lasso regression model was used to screen candidate prognostic genes from the intersection of DEGs and IRGs. Then, the candidate prognostic genes with significant differences were identified as prognostic genes in the first validation cohort. We further validated the expression of the prognostic genes in the second validation cohort of 81 septic patients recruited from our hospital. In addition, we used four immune infiltration methods (MCP-counter, ssGSEA, ImmuCellAI, and CIBERSORT) to analyze immune cell composition in sepsis. We also explored the correlation between the prognostic biomarker and immune cells. First, 140 genes were identified as prognostic-related immune genes from the intersection of DEGs and IRGs. We screened 18 candidate prognostic genes in the discovery cohort with the lasso regression model. Second, in the first validation cohort, we identified 4 genes (CFHR2, FCGR2C, GFI1, and TICAM1) as prognostic immune genes. Subsequently, we found that FCGR2C was the only gene differentially expressed between survivors and non-survivors in 81 septic patients. In the discovery and first validation cohorts, the AUC values of FCGR2C were 0.73 and 0.67, respectively. FCGR2C (AUC=0.84) had more value than SOFA (AUC=0.80) and APACHE II (AUC=0.69) in evaluating the prognosis of septic patients in our recruitment cohort. Moreover, FCGR2C may be closely related to many immune cells and functions, such as B cells, NK cells, neutrophils, cytolytic activity, and inflammatory promotion. Finally, enrichment analysis showed that FCGR2C was enriched in the phagosome signaling pathway. FCGR2C could be an immune biomarker associated with prognosis, which may be a new direction of immunotherapy to reduce sepsis mortality.

Prognostic characteristics of immune subtypes associated with acute myeloid leukemia and their identification in cell subsets based on single-cell sequencing analysis.

Immune genes play an important role in the development and progression of acute myeloid leukemia (AML). However, the role of immune genes in the prognosis and microenvironment of AML remains unclear. In this study, we analyzed 151 AML patients in the TCGA database for relevant immune cell infiltration. AML patients were divided into high and low immune cell infiltration clusters based on ssGSEA results. Immune-related pathways, AML pathways and glucose metabolism pathways were enriched in the high immune cell infiltration cluster. Then we screened the differential immune genes between the two immune cell infiltration clusters. Nine prognostic immune genes were finally identified in the train set by LASSO-Cox regression. We constructed a model in the train set based on the nine prognostic immune genes and validated the predictive capability in the test set. The areas under the ROC curve of the train set and the test set for ROC at 1, 3, 5 years were 0.807, 0.813, 0.815, and 0.731, 0.745, 0.830, respectively. The areas under ROC curve of external validation set in 1, 3, and 5 years were 0.564, 0.619, and 0.614, respectively. People with high risk scores accompanied by high TMB had been detected with the worst prognosis. Single-cell sequencing analysis revealed the expression of prognostic genes in AML cell subsets and pseudo-time analysis described the differentiation trajectory of cell subsets. In conclusion, our results reveal the characteristics of immune microenvironment and cell subsets of AML, while it still needs to be confirmed in larger samples studies. The prognosis model constructed with nine key immune genes can provide a new method to assess the prognosis of AML patients.

Development of a Novel Prognostic Model of Glioblastoma Based on m6A-Associated Immune Genes and Identification of a New Biomarker.

BackgroundAccumulating evidence shows that m6A regulates oncogene and tumor suppressor gene expression, thus playing a dual role in cancer. Likewise, there is a close relationship between the immune system and tumor development and progression. However, for glioblastoma, m6A-associated immunological markers remain to be identified.MethodsWe obtained gene expression, mutation, and clinical data on glioblastoma from The Cancer Genome Atlas and Chinese Glioma Genome Atlas databases. Next, we performed univariate COX–least absolute shrinkage and selection operator (LASSO)–multivariate COX regression analyses to establish a prognostic gene signature and develop a corresponding dynamic nomogram application. We then carried out a clustering analysis twice to categorize all samples according to their m6A-regulating and m6A-associated immune gene expression levels (high, medium, and low) and calculated their m6A score. Finally, we performed quantitative reverse transcription-polymerase chain reaction, cell counting kit-8, cell stemness detection, cell migration, and apoptosis detection in vitro assays to determine the biological role of CD81 in glioblastoma cells.ResultsOur glioblastoma risk score model had extremely high prediction efficacy, with the area under the receiver operating characteristic curve reaching 0.9. The web version of the dynamic nomogram application allows rapid and accurate calculation of patients’ survival odds. Survival curves and Sankey diagrams indicated that the high-m6A score group corresponded to the groups expressing medium and low m6A-regulating gene levels and high m6A-associated prognostic immune gene levels. Moreover, these groups displayed lower survival rates and higher immune infiltration. Based on the gene set enrichment analysis, the pathophysiological mechanism may be related to the activation of the immunosuppressive function and related signaling pathways. Moreover, the risk score model allowed us to perform immunotherapy benefit assessment. Finally, silencing CD81 in vitro significantly suppressed proliferation, stemness, and migration and facilitated apoptosis in glioblastoma cells.ConclusionWe developed an accurate and efficient prognostic model. Furthermore, the correlation analysis of different stratification methods with tumor microenvironment provided a basis for further pathophysiological mechanism exploration. Finally, CD81 may serve as a diagnostic and prognostic biomarker in glioblastoma.

Identification of a Novel Risk Model: A Five-Gene Prognostic Signature for Pancreatic Cancer.

Objective Biomarkers for pancreatic cancer (PCa) prognosis provide evidence for improving the survival outcome of this disease. This study aimed to identify a prognostic risk model based on gene expression profiling of microarray bioinformatics analysis. Methods Prognostic immune genes in the TCGA-PAAD cohort were identified using the univariate Cox regression and Kaplan–Meier survival analysis. Multivariate Cox regression (stepAIC) was used to identify prognostic genes from the top 20 hub genes in the protein-protein interaction (PPI) network. A prognostic risk model was established and its performance in predicting the overall survival in PCa was validated in GSE62452. Gene mutations and infiltration immune cells in PCa tumors were analyzed using online databases. Results Univariate Cox regression and Kaplan–Meier survival analyses identified 128 prognostic genes. Multivariate Cox regression (stepAIC) identified five prognostic genes (PLCG1, MET, TNFSF10, CXCL9, and TLR3) out of the 20 hub genes in the PPI network. A prognostic risk model was established using the signature of five genes. This model had moderate to high accuracies (AUC > 0.700) in predicting 3-year and 5-year overall survival in TCGA and GSE62452 cohorts. The Kaplan–Meier survival analysis showed that high-risk scores were correlated with poor survival outcomes in PCa (p < 0.05). Also, mutations in the five genes were related to poor survival. The five genes were related to multiple immune cells. Conclusions The prognostic risk model was significantly correlated with the survival in PCa patients. This model modulated PCa tumor progression and prognosis by regulating immune cell infiltration.

CGB5, INHBA and TRAJ19 Hold Prognostic Potential as Immune Genes for Patients with Gastric Cancer.

Gastric cancer (GC) seriously threatens people's health and life quality worldwide. The current study sought to explore prognostic immune genes and their regulatory network in GC. First, expression data in GC and normal samples were analyzed based on bioinformatics analysis. Immune-related genes were identified and confirmed with univariate/multivariate Cox analysis and receiver-operating characteristic curve. The upstream transcription factors of immune genes were subsequently predicted, and their regulatory network was constructed. GC and adjacent normal tissues were obtained from 76 patients with GC to determine the expression patterns of immune genes and their correlation with overall prognosis. CD8+ T-cell infiltration of patients with high or low risk was detected by means of immunohistochemistry. Bioinformatics analysis highlighted 3689 differentially expressed genes in GC, including 87 immune genes, 8 of which were significantly associated with patient survival. CGB5 and INHBA were high-risk genes, while TRAJ19 was identified as a low-risk gene, all of which were found to be regulated by 11 different transcription factors. Furthermore, CGB5 and INHBA exhibited negative correlation with the prognosis of GC patients; however, TRAJ19 was positively correlated with GC patient prognosis. The incidence of lymph node metastasis was higher, the pathological stage was advanced and the infiltrated CD8+ T cells were fewer in the high-risk GC group. Overall, our findings identified the key roles of CGB5, INHBA and TRAJ19 in prognosis GC patients, serving as an important gene set for prognostic prediction.

Identification of immune infiltration-related genes as prognostic indicators for hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) has a high degree of malignancy and a poor prognosis. Immune infiltration-related genes have shown good predictive value in the prognosis of many solid tumours. In this study, we established and verified prognostic biomarkers consisting of immune infiltration-related genes in HCC. Gene expression data and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. Differential gene expression analysis, univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression algorithm were used to screen prognostic immune infiltration-related genes and to construct a risk scoring model. Kaplan-Meier (KM) survival plots and receiver operating characteristic (ROC) curve analysis were used to evaluate the prognostic performance of the risk scoring model in the TCGA-HCC cohort. In addition, a nomogram model with a risk score was established, and its predictive performance was verified by ROC analysis and calibration plot analysis in the TCGA-HCC cohort. Gene set enrichment analysis (GSEA) identified pathways and biological processes that may be enriched in the high-risk group. Finally, immune infiltration analysis was used to explore the characteristics of the tumour microenvironment related to the risk score. We identified 17 immune infiltration-related genes with prognostic value and constructed a risk scoring model. ROC analysis showed that the risk scoring model can accurately predict the 1-year, 3-year, and 5-year overall survival (OS) of HCC patients in the TCGA-HCC cohort. KM analysis showed that the OS of the high-risk group was significantly lower than that of the low-risk group (P < 0.001). The nomogram model effectively predicted the OS of HCC patients in the TCGA-HCC cohort. GSEA indicated that the immune infiltration-related genes may be involved in biological processes such as amino acid and lipid metabolism, matrisome and small molecule transportation, immune system regulation, and hepatitis virus infection. Immune infiltration analysis showed that the level of immune cell infiltration in the high-risk group was low, and the risk score was negatively correlated with infiltrating immune cells. Our prognostic model based on immune infiltration-related genes in HCC could help the prognostic assessment of HCC patients and provide potential targets for HCC inhibition.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12885-022-09587-0.

Screening of prognostic immune genes and establishment of prognostic model of cervical adenocarcinoma based on bioinformatics analysis.

BackgroundThere is currently a lack of clinical models to accurately predict the prognosis of cervical adenocarcinoma. This study aimed to explore the correlation between immune genes and the prognosis of cervical adenocarcinoma patients, and establish a prognostic model.MethodsTranscriptome sequencing data sets and clinical data sets of cervical adenocarcinoma samples were downloaded from the Gene Expression Omnibus (GEO) database. Information about the immune gene was obtained from the ImmPort database. Differentially expressed genes and differentially expressed immune genes were screened in cervical adenocarcinoma tissue and normal cervical group by edgeR package. Differentially expressed immune genes were screened for prognosis-related immune genes by Cox analysis. Taking the immune genes related to prognosis as variables, a prognosis prediction model was established by multivariate Cox regression analysis. Kaplan-Meier analysis and a receiver operating characteristic (ROC) curve were used to test the effectiveness of the model. According to the clinical information and risk score, univariate multivariate Cox analyses were used to screen the independent prognostic risk factors of cervical adenocarcinoma.ResultsCXCL9 was an independent prognostic factor of cervical adenocarcinoma [hazard ratio (HR) =0.63; P=0.025]. CGB5 (HR =1.22; P=0.034), CXCL12 (HR =1.33; P=0.023), PTX3 (HR =1.53; P=0.024), and CXCL10 (HR =2.31; P=0.031) were prognostic risk factors for cervical adenocarcinoma. The risk score was calculated as follows: risk score = (0.005 × CXCL10) + (0.076 × CGB5) + (0.061 × CXCL12) + (0.034 × PTX3) + (−0.004 × CXCL9). The prognosis of the low-risk score group was better than that of the high-risk score group (P=0.035). The area under the ROC curve (AUC) of the risk score was 0.713, and the predictive power was good. Multivariate Cox analysis showed that N stage (HR =1.34; P=0.035) and risk score (HR =1.37; P<0.001) were independent risk factors for the prognosis of cervical adenocarcinoma (HR >1; P<0.001).ConclusionsIn this study, an immune gene prognosis prediction model for cervical adenocarcinoma was established based on the GEO and ImmPort databases. The prediction performance of the model is good, and the prognosis of patients can be evaluated according to the gene expression, which has clinical practicability.

Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system

ObjectivesImmune microenvironment was closely related to the occurrence and progression of colorectal cancer (CRC). The objective of the current research was to develop and verify a Machine learning survival predictive system for CRC based on immune gene expression data and machine learning algorithms.MethodsThe current study performed differentially expressed analyses between normal tissues and tumor tissues. Univariate Cox regression was used to screen prognostic markers for CRC. Prognostic immune genes and transcription factors were used to construct an immune-related regulatory network. Three machine learning algorithms were used to create an Machine learning survival predictive system for CRC. Concordance indexes, calibration curves, and Brier scores were used to evaluate the performance of prognostic model.ResultsTwenty immune genes (BCL2L12, FKBP10, XKRX, WFS1, TESC, CCR7, SPACA3, LY6G6C, L1CAM, OSM, EXTL1, LY6D, FCRL5, MYEOV, FOXD1, REG3G, HAPLN1, MAOB, TNFSF11, and AMIGO3) were recognized as independent risk factors for CRC. A prognostic nomogram was developed based on the previous immune genes. Concordance indexes were 0.852, 0.778, and 0.818 for 1-, 3- and 5-year survival. This prognostic model could discriminate high risk patients with poor prognosis from low risk patients with favorable prognosis.ConclusionsThe current study identified twenty prognostic immune genes for CRC patients and constructed an immune-related regulatory network. Based on three machine learning algorithms, the current research provided three individual mortality predictive curves. The Machine learning survival predictive system was available at: https://zhangzhiqiao8.shinyapps.io/Artificial_Intelligence_Survival_Prediction_for_CRC_B1005_1/, which was valuable for individualized treatment decision before surgery.