Identification of biomarkers of hepatocellular carcinoma gene prognosis based on the immune-related lncRNA signature of transcriptome data.

BACKGROUND: Long non-coding RNAs (lncRNAs) are well established to have an important role in cancer. The goal of this research was to investigate the prognostic usefulness of putative immune-related lncRNAs in hepatocellular carcinoma (HCC). METHODS: The developed lncRNA signature was validated using 343 HCC patients from The Cancer Genome Atlas (TCGA) and 81 samples from Gene Expression Omnibus (GEO). Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) analysis were used to analyze immune-related lncRNAs for HCC prognosis. Patients in the low-risk group survived substantially longer than those in the high-risk group (P < 0.05). The discovered signal might be a useful prognostic factor for predicting patient survival. Overall survival predicted some clinical net improvements, according to the nomogram. Numerous enrichment approaches (including gene set enrichment analysis) were utilized to investigate the underlying mechanisms. RESULTS: Drug metabolism, mTOR, and p53 signaling pathways were associated with high-risk groups. When the expression of lncRNA PRRT3-AS1 was silenced in HepG2 cells, the proliferation, migration, and invasion abilities of HepG2 cells were decreased, and apoptosis was enhanced. In the supernatant from HepG2 cells with PRRT3-AS1 knockdown, the anti-inflammatory factors IL-10 and TGF-1 were induced, whereas the pro-inflammatory factors IL-1ß, TNF-α, and IL-6 were reduced (P < 0.05). After PRRT3-AS1 knockdown, the protein expression of CD24, THY1, LYN, CD47, and TRAF2 in HepG2 cells was attenuated (P < 0.05). CONCLUSION: The discovery of five immune-related lncRNA signatures has significant therapeutic significance for predicting patient prognosis and directing personalized treatment for patients with HCC, which requires additional prospective confirmation.

TREM-1 as a potential prognostic biomarker associated with immune infiltration in clear cell renal cell carcinoma.

BACKGROUND: The tumor immune microenvironment plays a crucial role in the efficacy of various therapeutics. However, their correlation is not yet completely understood in Clear cell renal cell carcinoma (ccRCC). This study aimed to investigate the potential of TREM-1 as a potential novel biomarker for ccRCC. METHODS: We constructed a ccRCC immune prognostic signature. The clinical characteristics, the status of the tumor microenvironment, and immune infiltration were analyzed through the ESTIMATE and CIBERSORT algorithms for the hub gene, while the Gene Set Enrichment Analysis and PPI analysis were performed to predict the function of the hub gene. Immunohistochemical staining was used to detect the expression of TREM-1 in renal clear cell carcinoma tissues. RESULTS: The CIBERSORT and ESTIMATE algorithms revealed that TREM-1 was correlated with the infiltration of 12 types of immune cells. Therefore, it was determined that TREM-1 was involved in numerous classical pathways in the immune response via GSEA analysis. In Immunohistochemical staining, we found that the expression of TREM-1 was significantly upregulated with increasing tumor grade in renal clear cell carcinoma, and elevated TREM-1 expression was associated with poor prognosis. CONCLUSIONS: The results suggest that TREM-1 may act as an implicit novel prognostic biomarker in ccRCC that could be utilized to facilitate immunotherapeutic strategy.

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

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

An immune-related prognostic signature associated with immune landscape and therapeutic responses in gastric cancer.

Immune-related genes (IRGs) have attracted attention in recent years as therapeutic targets in various tumors. However, the role of IRGs in gastric cancer (GC) has not been clearly elucidated. This study presents a comprehensive analysis exploring the clinical, molecular, immune, and drug response features characterizing the IRGs in GC. Data were acquired from the TCGA and GEO databases. The Cox regression analyses were performed to develop a prognostic risk signature. The genetic variants, immune infiltration, and drug responses associated with the risk signature were explored using bioinformatics methods. Lastly, the expression of the IRS was verified by qRT-PCR in cell lines. In this manner, an immune-related signature (IRS) was established based on 8 IRGs. According to the IRS, patients were divided into the low-risk group (LRG) and high-risk group (HRG). Compared with the HRG, the LRG was characterized by a better prognosis, high genomic instability, more CD8+ T cell infiltration, greater sensitivity to chemotherapeutic drugs, and greater likelihood of benefiting from the immunotherapy. Moreover, the expression result showed good consistency between the qRT-PCR and TCGA cohort. Our findings provide insights into the specific clinical and immune features underlying the IRS, which may be important for patient treatment.

Immune-related RNA signature predicts outcome of PD-1 inhibitor-combined GEMCIS therapy in advanced intrahepatic cholangiocarcinoma.

Background: Immune checkpoint inhibitor (ICI)-combined chemotherapy in advanced intrahepatic cholangiocarcinoma has been proved to have more efficacy in a series of clinical trials. However, whether the tumor microenvironment (TME) plays a vital role in immune-combined therapy has not been rigorously evaluated. Methods: Firstly, we assayed the immunogenic properties of GEM-based chemotherapy. Then, 12 ICC patients treated with PD-1 inhibitor (sintilimab) combined with gemcitabine and cisplatin (GemCis) from a phase 2 clinical trial (ChiCTR2000036652) were included and their immune-related gene expression profiles were analyzed using RNA from baseline tumor samples. Immune-related signature correlating with clinical outcome was identified according to the 12 ICC patients, and its predictive value was validated in an ICC cohort with 26 patients. Multiplexed immunofluorescence (mIF) and flow cytometry (FCM) analysis were performed to evaluate the immune-related molecules with therapeutic outcomes. Results: GEM-based chemotherapy induced immunogenic cell death of cholangiocarcinoma cells, together with increased CD274 expression. In an ICC cohort, we found that upregulation of immune-checkpoint molecules and immune response-related pathways were significantly related to better clinical outcome. On the contrary, baseline immune-cell proportions in tumor tissues did not show any correlation with clinical benefit between responders and non-responders. Immune-related signature (including six genes) correlating with clinical outcome was identified according to the 12 ICC patients, and its predictive value was validated in a small ICC cohort with 26 patients. Conclusion: Immune-related RNA signature predicts the outcome of PD-1 inhibitor-combined GEMCIS therapy in advanced intrahepatic cholangiocarcinoma, which could be tested as a biomarker for immune-chemotherapy in the future.

An immune-related signature that to improve prognosis prediction of breast cancer.

Although the classic molecular subtype of breast cancer (BRCA) has been widely used in clinical diagnosis, as a highly heterogeneous malignant tumor, the classic scheme is not enough to accurately predict the prognosis of breast cancer patients. Immune cells in the tumor microenvironment (TME) are thought to play a paramount role in tumor development and driving poor prognosis. In this study, we aimed to develop a TME-associated, immune-related signature to improve prognosis prediction of BRCA. BRCA_OURS enriched transcriptomic RNA sequencing (RNA-seq) of tumor tissue was acquired from 43 breast cancer patients before any treatment. On the immune gene profiles of 43 patients from BRCA_OURS and 932 BRCA patients from The Cancer Genome Atlas (TCGA), we identified a robust immune-related signature including one positive coefficients gene (IL-10) and other 9 genes (C14orf79, C1orf168, C1orf226, CELSR2, FABP7, FGFBP1, KLRB1, PLEKHO1, and RAC2), of which the negative coefficients suggesting higher expression were correlated with better prognosis. Based on the expression of these genes, patients were grouped into the high- and low-risk group with significant overall survival (OS) (P<0.0001). The high-risk group was likely to have inferior outcomes related to several important cancer-associated pathways, including mobilizing more Golgi vesicle-mediated transport and intensive DNA double-strand breaking, which are closely related to the infiltration of immune cells and holds the key for further growing and metastasizing. Collectively, our results highlight that the immunological value within BRCA is an essential determinant of prognostic factor. Our signature may provide an effective risk stratification tool for clinical prognosis assessment of patients with BRCA.

A novel comprehensive immune-related gene signature as a promising survival predictor for the patients with head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC), the most frequent subtype of head and neck cancer, continues to have a poor prognosis with no improvement. The TNM stage is not satisfactory for individualized prognostic assessment and it does not predict response to therapy. In the present study, we downloaded the gene expression profiles from TCGA database to establish a training set and GEO database for a validation set. In the training set, we developed an 10 immune-related genes signature which had superior predictive value compared with TNM stage. A nomogram including clinical characteristics was also constructed for accurate prediction. Furthermore, it was determined that our prognostic signature might act as an independent factor for predicting the survival of HNSCC patients. As for the immune microenvironment, our results showed higher immune checkpoint expression (CLTA-4 and PD-1) in low-risk group which might reflect a positive immunotherapy response. Thus, our signature not only provided a promising biomarker for survival prediction, but might be evaluated as an indicator for personalized immunotherapy in patients with HNSCC.

Tumor Expression Profile Analysis Developed and Validated a Prognostic Model Based on Immune-Related Genes in Bladder Cancer.

Background: Bladder cancer (BLCA) ranks 10th in incidence among malignant tumors and 6th in incidence among malignant tumors in males. With the application of immune therapy, the overall survival (OS) rate of BLCA patients has greatly improved, but the 5-year survival rate of BLCA patients is still low. Furthermore, not every BLCA patient benefits from immunotherapy, and there are a limited number of biomarkers for predicting the immunotherapy response. Therefore, novel biomarkers for predicting the immunotherapy response and prognosis of BLCA are urgently needed. Methods: The RNA sequencing (RNA-seq) data, clinical data and gene annotation files for The Cancer Genome Atlas (TCGA) BLCA cohort were extracted from the University of California, Santa Cruz (UCSC) Xena Browser. The BLCA datasets GSE31684 and GSE32894 from the Gene Expression Omnibus (GEO) database were extracted for external validation. Immune-related genes were extracted from InnateDB. Significant differentially expressed genes (DEGs) were identified using the R package "limma," and Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for the DEGs were performed using R package "clusterProfiler." Least absolute shrinkage and selection operator (LASSO) regression analysis were used to construct the signature model. The infiltration level of each immune cell type was estimated using the single-sample gene set enrichment analysis (ssGSEA) algorithm. The performance of the model was evaluated with receiver operating characteristic (ROC) curves and calibration curves. Results: In total, 1,040 immune-related DEGs were identified, and eight signature genes were selected to construct a model using LASSO regression analysis. The risk score of BLCA patients based on the signature model was negatively correlated with OS and the immunotherapy response. The ROC curve for OS revealed that the model had good accuracy. The calibration curve showed good agreement between the predictions and actual observations. Conclusions: Herein, we constructed an immune-related eight-gene signature that could be a potential biomarker to predict the immunotherapy response and prognosis of BLCA patients.

A Novel Model of Tumor-Infiltrating B Lymphocyte Specific RNA-Binding Protein-Related Genes With Potential Prognostic Value and Therapeutic Targets in Multiple Myeloma.

Background: RNA-binding proteins (RBPs) act as important regulators in the progression of tumors. However, their role in the tumorigenesis and prognostic assessment in multiple myeloma (MM), a B-cell hematological cancer, remains elusive. Thus, the current study was designed to explore a novel prognostic B-cell-specific RBP signature and the underlying molecular mechanisms. Methods: Data used in the current study were obtained from the Gene Expression Omnibus (GEO) database. Significantly upregulated RBPs in B cells were defined as B cell-specific RBPs. The biological functions of B-cell-specific RBPs were analyzed by the cluster Profiler package. Univariate and multivariate regressions were performed to identify robust prognostic B-cell specific RBP signatures, followed by the construction of the risk classification model. Gene set enrichment analysis (GSEA)-identified pathways were enriched in stratified groups. The microenvironment of the low- and high-risk groups was analyzed by single-sample GSEA (ssGSEA). Moreover, the correlations among the risk score and differentially expressed immune checkpoints or differentially distributed immune cells were calculated. The drug sensitivity of the low- and high-risk groups was assessed via Genomics of Drug Sensitivity in Cancer by the pRRophetic algorithm. In addition, we utilized a GEO dataset involving patients with MM receiving bortezomib therapy to estimate the treatment response between different groups. Results: A total of 56 B-cell-specific RBPs were identified, which were mainly enriched in ribonucleoprotein complex biogenesis and the ribosome pathway. ADAR, FASTKD1 and SNRPD3 were identified as prognostic B-cell specific RBP signatures in MM. The risk model was constructed based on ADAR, FASTKD1 and SNRPD3. Receiver operating characteristic (ROC) curves revealed the good predictive capacity of the risk model. A nomogram based on the risk score and other independent prognostic factors exhibited excellent performance in predicting the overall survival of MM patients. GSEA showed enrichment of the Notch signaling pathway and mRNA cis-splicing via spliceosomes in the high-risk group. Moreover, we found that the infiltration of diverse immune cell subtypes and the expression of CD274, CD276, CTLA4 and VTCN1 were significantly different between the two groups. In addition, the IC50 values of 11 drugs were higher in the low-risk group. Patients in the low-risk group exhibited a higher complete response rate to bortezomib therapy. Conclusion: Our study identified novel prognostic B-cell-specific RBP biomarkers in MM and constructed a unique risk model for predicting MM outcomes. Moreover, we explored the immune-related mechanisms of B cell-specific RBPs in regulating MM. Our findings could pave the way for developing novel therapeutic strategies to improve the prognosis of MM patients.

Identification of an immune-related signature for the prognosis of uveal melanoma.

AIM: To construct an immune-related prognostic signature (IPS) that can distinguish and predict prognosis in uveal melanoma (UM). METHODS: The transcriptomic data and clinicopathological information of 80 UM patients were extracted from the TCGA database. These patients were randomly assigned to a training and a testing set. RESULTS: Lasso Cox analysis was performed for the prognostic immune-related genes to develop an IPS for UM in the training set. The signature was validated in both the testing set and entire cohort. We confirmed the prognostic value of our IPS in distinct subgroups and found its association with the T stage and basal diameter of the tumor. Tumor Immune Estimation Resource database analysis revealed that the IPS was negatively correlated with the infiltration of neutrophils and dendritic cells, but positively correlated with the infiltration level of CD8+ T cells. In addition, we demonstrated that immune checkpoints containing PD-1, CTLA-4, IDO, and TIGIT were moderately associated with the IPS. CONCLUSION: This is the first study to develop and validate an immune-related signature with the ability of predicting prognosis for UM patients. Further studies are needed to validate its prediction accuracy.

A 14 immune-related gene signature predicts clinical outcomes of kidney renal clear cell carcinoma.

Kidney renal clear cell carcinoma (KIRC) is the leading cause of kidney cancer-related deaths. Currently, there are no studies in tumor immunology investigating the use of signatures as a predictor of overall survival in KIRC patients. Our study attempts to establish an immune-related gene risk signature to predict clinical outcomes in KIRC. A total of 528 patients from The Cancer Genome Atlas (TCGA) database were included in our analysis and randomly divided into training (n = 315) and testing sets (n = 213). We collected 1,534 immune-related genes from the Immunology Database and Analysis Portal as candidates to construct our signature. LASSO-COX was used to find gene models with the highest predictive ability. We used survival and Cox analysis to test the model's independent prognostic ability. Univariate analysis identified 650 immune-related genes with prognostic abilities. After 1,000 iterations, we choose 14 of the most frequent and stable immune-related genes as our signature. We found that the signature was associated with M stage, T stage, and pathological staging. More importantly, the signature can independently predict clinical prognosis in KIRC patients. Gene Set Enrichment Analysis (GSEA) showed an association between our signature and critical metabolism pathways. Our research established a model based upon 14 immune-related genes that predicted the prognosis of KIRC patients based on tumor immune microenvironments.

Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in Lower-Grade Glioma.

OBJECTIVE: Despite several clinicopathological factors being integrated as prognostic biomarkers, the individual variants and risk stratification have not been fully elucidated in lower grade glioma (LGG). With the prevalence of gene expression profiling in LGG, and based on the critical role of the immune microenvironment, the aim of our study was to develop an immune-related signature for risk stratification and prognosis prediction in LGG. METHODS: RNA-sequencing data from The Cancer Genome Atlas (TCGA), Genome Tissue Expression (GTEx), and Chinese Glioma Genome Atlas (CGGA) were used. Immune-related genes were obtained from the Immunology Database and Analysis Portal (ImmPort). Univariate, multivariate cox regression, and Lasso regression were employed to identify differentially expressed immune-related genes (DEGs) and establish the signature. A nomogram was constructed, and its performance was evaluated by Harrell's concordance index (C-index), receiver operating characteristic (ROC), and calibration curves. Relationships between the risk score and tumor-infiltrating immune cell abundances were evaluated using CIBERSORTx and TIMER. RESULTS: Noted, 277 immune-related DEGs were identified. Consecutively, 6 immune genes (CANX, HSPA1B, KLRC2, PSMC6, RFXAP, and TAP1) were identified as risk signature and Kaplan-Meier curve, ROC curve, and risk plot verified its performance in TCGA and CGGA datasets. Univariate and multivariate Cox regression indicated that the risk group was an independent predictor in primary LGG. The prognostic signature showed fair accuracy for 3- and 5-year overall survival in both internal (TCGA) and external (CGGA) validation cohorts. However, predictive performance was poor in the recurrent LGG cohort. The CIBERSORTx algorithm revealed that naïve CD4+ T cells were significant higher in low-risk group. Conversely, the infiltration levels of M1-type macrophages, M2-type macrophages, and CD8+T cells were significant higher in high-risk group in both TCGA and CGGA cohorts. CONCLUSION: The present study constructed a robust six immune-related gene signature and established a prognostic nomogram effective in risk stratification and prediction of overall survival in primary LGG.