Identification of a four immune-related genes signature based on an immunogenomic landscape analysis of clear cell renal cell carcinoma

Renal clear cell carcinoma (ccRCC) is the most common type of renal cell carcinoma, which has strong immunogenicity. A comprehensive study of the role of immune-related genes (IRGs) in ccRCC is of great significance in finding ccRCC treatment targets and improving patient prognosis. In this study, we comprehensively analyzed the expression of IRGs in ccRCC based on The Cancer Genome Atlas datasets. The mechanism of differentially expressed IRGs in ccRCC was analyzed by bioinformatics. In addition, Cox regression analysis was used to screen prognostic related IRGs from differentially expressed IRGs. We also identified a four IRGs signature consisting of four IRGs (CXCL2, SEMA3G, PDGFD, and UCN) through lasso regression and multivariate Cox regression analysis. Further analysis results showed that the four IRGs signature could effectively predict the prognosis of patients with ccRCC, and its predictive power is independent of other clinical factors. In addition, the correlation analysis of immune cell infiltration showed that this four IRGs signature could effectively reflect the level of immune cell infiltration of ccRCC. We also found that the expression of immune checkpoint genes CTLA-4, LAG3, and PD-1 in the high-risk group was higher than that in the low-risk group. Our research revealed the role of IRGs in ccRCC, and developed a four IRGs signature that could be used to evaluate the prognosis of patients with ccRCC, which will help to develop personalized treatment strategies for patients with ccRCC and improve their prognosis. In addition, these four IRGs may be effective therapeutic targets for ccRCC.     

Identification of a prognostic signature based on immune-related genes in bladder cancer

Bladder cancer (BLCA) has a high incidence and recurrence rate, and the effect of immunotherapy varies from person to person. Immune-related genes (IRGs) have been shown to be associated with immunotherapy and prognosis in many other cancers, but their role in immunogenic BLCA is less well defined. In this study, we constructed an eight-IRG risk model, which demonstrated strong prognostic and immunotherapeutic predictive power. The signature was significantly related to tumor clinicopathological characteristics, tumor class, immune cell infiltration and mutation status. Additionally, a nomogram containing the risk score and other potential risk factors could effectively predict the long-term overall survival probability of BLCA patients. The enriched mechanisms identified by gene set enrichment analysis suggested that the reason why this signature can accurately distinguish high- and low-risk populations may be closely related to the different degrees of innate immune response and T cell activation in different patients.     

Development and validation of LRP1B mutation-associated prognostic model for hepatocellular carcinoma

Purpose: To develop a lipoprotein receptor-related protein 1B (LRP1B) gene mutation-based prognostic model for hepatocellular carcinoma (HCC) patients risk prediction. Methods: The LRP1B gene mutation rate was calculated from HCC patient samples. Meanwhile, differentially expressed genes according to LRP1B mutant were screened out for prognostic model establishment. Based on this innovative model, HCC patients were categorized into high- and low-risk groups. The immune status including immune cell infiltration ratio and checkpoints have been explored in two groups. The functions of LRP1B and risk factors in the model were verified using both in vivo and in vitro experiments. Results: It could be demonstrated that LRP1B was a potential negative predictor for HCC patients prognosis with high mutation frequency. The functions of LRP1B were verified with ELISA and Quantitative Real-time PCR method based on clinic-recruited HCC participants. Eleven genes displayed significant differences according to LRP1B status, which could better predict HCC patient prognosis. The functions of these genes were examined using HCC cell line HCCLM3, suggesting they played a pivotal role in determining HCC cell proliferation and apoptosis. From the immune cell infiltration ratio analysis, there was a significant difference in the infiltration degree of seven types of immune cells and two immune checkpoints between high- and low-risk HCC patients. Conclusion: The present study hypothesized a potential prognostic biomarker and developed a novel LRP1B mutation-associated prognostic model for HCC, which provided a systematic reference for future understanding of clinical research.     

Identification of immunogenic cell death-associated subtypes and characterization of the tumor microenvironment in endometrial cancer

Immunogenic cell death (ICD) is one of the mechanisms regulating cell death, which activates adaptive immunity in immunocompetent hosts and is associated with tumor progression, prognosis and therapeutic response. Endometrial cancer (EC) is one of the most common malignancies of the female genital tract, and the potential role of immunogenic cell death-related genes (IRGs) in the tumor microenvironment (TME) remains unclear. We describe the variation of IRGs and assess the expression patterns in EC samples from The Cancer Genome Atlas and Gene Expression Omnibus cohorts. Based on the expression of 34 IRGs, we identified two different ICD-related clusters and subsequently differentially expressed genes between the two ICD-related clusters were used for the identification of two ICD gene clusters. We identified the clusters and found that alterations in the multilayer IRG were associated with patient prognosis and TME cell infiltration characteristics. On this basis, ICD score risk scores were calculated, and ICD signatures were constructed and validated for their predictive power in EC patients. To help clinicians better apply the ICD signature, an accurate nomogram was constructed. The low ICD risk group was characterized by high microsatellite instability, high tumor mutational load, high IPS score and stronger immune activation. Our comprehensive analysis of IRGs in EC patients suggested a potential role in the tumor immune interstitial microenvironment, clinicopathological features and prognosis. These findings may improve our understanding of the role of ICDs, and provide a new basis for assessing prognosis and developing more effective immunotherapeutic strategies in EC.     

Heat shock protein family A member 8 is a prognostic marker for bladder cancer: Evidences based on experiments and machine learning

Heat shock protein member 8 (HSPA8) is one of the most abundant chaperones in eukaryotic cells, but its biological roles in bladder cancer (BC) are largely unclear. First, we observed that HSPA8 was abundant in both cell lines and tissues of BC, and the HSPA8-high group had poorer T stages and overall survival (OS) than the HSPA8-low group in the TCGA patients. Next, when we knocked down HSPA8 in BC cells, the growth and migration abilities were significantly decreased, the apoptosis rates were significantly increased, and the Ki67 fluorescence intensity was decreased in BC cells. Moreover, caspase 3 was significantly decreased with overexpression of HSPA8 in BC cells. After that, a machine learning prognostic model was created based on the expression of HSPA8 by applying LASSO Cox regression in TCGA and GEO patients. The model indicated that the low-risk (LR) group with BC had better tumour stages, lymphovascular invasion, and OS than the high-risk (HR) group. Additionally, the risk score was demonstrated to be an independent risk factor for the prognosis of BC by univariate and multivariate Cox analyses. Moreover, the HR group showed a greater rate of TP53 mutations and was mostly enriched in the ECM-receptor interaction pathway than the LR group. Importantly, lower CD8⁺ T-cell and NK cell infiltration, higher immune exclusion scores, higher expression of PD-L1 and CTLA4 and poorer immune checkpoint therapy effects were found in the HR group. These findings demonstrated how crucial HSPA8 plays a role in determining the prognosis of bladder cancer.     

Prediction of overall survival and response to immune checkpoint inhibitors: An immune-related signature for gastric cancer

Gastric cancer (GC) is common in East Asia and South and Central America. Most GC patients miss the opportunities for surgery. Despite their therapeutic potential, immune checkpoint inhibitors (ICIs) only work in part of patients with GC. Thus, this study was aimed at constructing a signature for diagnosis, prognosis, and prediction of response to ICIs. A multivariate analysis showed that the 8-immune-related-gene (IRG) signature was an independent prognostic factor of overall survival among GC patients. In the high-risk group of 8IRG signature risk score, the fractions of CD4 T cells, macrophage M2 and monocyte, which is associated with the progression of cancers, were higher. The low-risk group had a higher immunophenoscore, which meant a better response to ICIs.     

A dual immune signature of CD8+ T cells and MMP9 improves the survival of patients with hepatocellular carcinoma

The 5-year survival of hepatocellular carcinoma (HCC) is difficult due to the high recurrence rate and metastasis. Tumor infiltrating immune cells (TICs) and immune-related genes (IRGs) bring hope to improve survival and treatment of HCC patients. However, there are problems in predicting immune signatures and identifying novel therapeutic targets. In the study, the CIBERSORT algorithm was used to evaluate 22 immune cell infiltration patterns in gene expression omnibus (GEO) and the cancer genome atlas (TCGA) data. Eight immune cells were found to have significant infiltration differences between the tumor and normal groups. The CD8+ T cells immune signature was constructed by least absolute shrinkage and selection operator (LASSO) algorithm. The high infiltration level of CD8+ T cells could significantly improve survival of patients. The weighted gene co-expression network analysis (WGCNA) algorithm identified MMP9 was closely related to the overall survival of HCC patients. K-M survival and tROC analysis confirmed that MMP9 had an excellent prognostic prediction. Cox regression showed that a dual immune signature of CD8+ T cells and MMP9 was independent survival factor in HCC. Therefore, a dual prognostic immune signature could improve the survival of patient and may provide a new strategy for the immunotherapy of HCC.     

Analysis of DNA and morphometry in breast carcinoma

Feulgen-stained imprints and smears from 730 cases of invasive breast cancer were investigated using an image analysis system. From each tumor sample 100 cells were randomly scanned and several DNA and morphometrical parameters evaluated. Their prognostic value for a prediction of distant metastases within 5 years was investigated with the multivariate Cox regression analysis, which was performed for all consecutive cases, as well as for node-negative and node-positive patients separately. The multivariate analyses showed a strong prognostic value of the anisonucleosis (variation of nuclear radius) and the DNA histogram type in addition to the nodal status, the tumor size (pT), and the histological tumor grade. However, performing this analysis for both node-positive patients and for those without lymph node metastases demonstrated a different prognostic meaning of the variables. The combination of each of the group-specific variables led to a prognostic factor, which allowed an assignment of patients to several subgroups with significantly different risk for distant metastases. Thus, both a low-risk group of node-negative patients with a 5-year distant recurrence rate of only 5.8%, and a higher risk group of node-negative patients with a recurrence rate of 38.6% could be identified. Among the node-positive patients, a low-risk group with a distant recurrence rate of 8.6%, and also a high risk group with 69% distant recurrence, could be identified.     

Systematic Analysis of Immune Infiltrates in High-Grade Serous Ovarian Cancer Reveals CD20, FoxP3 and TIA-1 as Positive Prognostic Factors

Background

Tumor-infiltrating T cells are associated with survival in epithelial ovarian cancer (EOC), but their functional status is poorly understood, especially relative to the different risk categories and histological subtypes of EOC.

Methodology/Principal Findings

Tissue microarrays containing high-grade serous, endometrioid, mucinous and clear cell tumors were analyzed immunohistochemically for the presence of lymphocytes, dendritic cells, neutrophils, macrophages, MHC class I and II, and various markers of activation and inflammation. In high-grade serous tumors from optimally debulked patients, positive associations were seen between intraepithelial cells expressing CD3, CD4, CD8, CD45RO, CD25, TIA-1, Granzyme B, FoxP3, CD20, and CD68, as well as expression of MHC class I and II by tumor cells. Disease-specific survival was positively associated with the markers CD8, CD3, FoxP3, TIA-1, CD20, MHC class I and class II. In other histological subtypes, immune infiltrates were less prevalent, and the only markers associated with survival were MHC class II (positive association in endometrioid cases) and myeloperoxidase (negative association in clear cell cases).

Conclusions/Significance

Host immune responses to EOC vary widely according to histological subtype and the extent of residual disease. TIA-1, FoxP3 and CD20 emerge as new positive prognostic factors in high-grade serous EOC from optimally debulked patients.     

A robust immune-related lncRNA signature for the prognosis of human colorectal cancer

Background: Colorectal cancer (CRC) is one of the most prevalent malignant cancers worldwide. Immune-related long non-coding RNAs (IRlncRNAs) are proved to be essential in the development and progression of carcinoma. The purpose of the present study was to develop and validate a prognostic IRlncRNA signature for CRC patients.Methods: Gene expression profiles of CRC samples were downloaded from The Cancer Genome Atlas (TCGA) database. Immune-related genes were obtained from the ImmPort database and were used to identify IRlncRNA by correlation analysis. Through LASSO Cox regression analyses, a prognostic signature was constructed. Functional enrichment analysis was performed by gene set enrichment analysis (GSEA). TIMER2.0 web server and tumor immune dysfunction and exclusion (TIDE) algorithm were employed to analyze the association between our model and tumor-infiltrating immune cells and immunotherapy response. The expression levels of IRlncRNAs in cell lines were detected by quantitative real-time PCR (qPCR).Results: A 9-IRlncRNA signature was developed by a LASSO Cox proportional regression model. Based on the signature, CRC patients were divided into high- and low-risk groups with different prognoses. GSEA results indicated that patients in high-risk group were associated with cancer-related pathways. In addition, patients in low-risk group were found to have more infiltration of anti-tumor immune cells and might show a favorable response to immunotherapy. Finally, the result of qPCR revealed that most IRlncRNAs were differently expressed between normal and tumor cell lines.Conclusion: The constructed 9-IRlncRNA signature has potential to predict the prognosis of CRC patients and may be helpful to guide personalized immunotherapy.     

Comprehensive molecular analysis of a four-pyroptosis-gene signature with prognosis and immune landscape in lung adenocarcinoma

Pyroptosis plays an important role in tumor immunity. However, the biological behavior and prognostic significance of pyroptosis remain unclear. We identified 41 pyroptosis regulators differently expressed in lung adenocarcinoma (LUAD). All cases of LUAD can be classified into two molecular subtypes using unsupervised clustering algorithm. Using multiple analyses, a four-pyroptosis-gene signature was constructed, and all LUAD patients were categorized as low-risk or high-risk with a longer overall survival (OS) time in the low-risk group(P < 0.001). This signature had power prognosis and stratification which was validated by six independent datasets and clinical subtypes. Besides, this signature showed distinct clinical outcomes, immune landscapes in different risk groups. Moreover, the low-risk group had a higher response against immunotherapy with a lower TIDE score. Importantly, this signature surpassed other biomarkers (TIDE, TMB, PD-L1) in predicting prognosis. Overall, the current study might help with precise prognostic prediction and crucial treatment strategies, eventually promoting tailored therapy for LUAD patients.     

IGF1R Axis Inhibition Restores Dendritic Cell Antitumor Response in Ovarian Cancer

Epithelial ovarian cancer (EOC) is the most lethal gynecological malignancy. The insulin-like growth factor (IGF) system plays a key role in regulating growth and invasiveness in several malignancies, including ovarian cancer. IGF1R targeting showed antiproliferative activity of EOC cells. However, clinical studies failed to show significant benefit. EOC cells suppress antitumor immune responses by inducing dendritic cell (DC) dysfunction. The IGF1 axis can regulate DC maturation.The current study evaluated involvement of the IGF1 axis in DC differentiation in EOC. Studies were conducted on EOC and on a human monocyte cell line. Tissue microarray analysis (TMA) was performed on 36 paraffin blocks from EOC patients. Expression of IGF1R, p53, Ki67, BRCA1, and DC markers was evaluated using immunohistochemistry. Co-culture of EOC cells with DC pretreated with IGF1R inhibitor blocked cancer cell migration. TMA demonstrated higher rate of IGF1R protein expression in patients with advanced (76.9%) as compared to early (40%) EOC. A negative correlation between IGF1R protein expression and the CD1c marker was found. These findings provide evidence that IGF1R axis inhibition could be a therapeutic strategy for ovarian cancer by restoring DC-mediated antitumor immunity.     

Comprehensive analysis of tumor mutation burden and immune microenvironment in gastric cancer

Tumor mutation burden (TMB) was a promising marker for immunotherapy. We aimed to investigate the prognostic role of TMB and its relationship with immune cells infiltration in gastric cancer (GC). We analyzed the mutation landscape of all GC cases and TMB of each GC patient was calculated and patients were divided into TMB-high and TMB-low group. Differentially expressed genes (DEGs) between the two groups were identified and pathway analysis was performed. The immune cells infiltration in each GC patient was evaluated and Kaplan–Meier analysis was performed to investigate the prognostic role of immune cells infiltration. At last, hub immune genes were identified and a TMB prognostic risk score (TMBPRS) was constructed to predict the survival outcome of GC patients. The relationships between mutants of hub immune genes and immune infiltration level in GC was investigated. We found higher TMB was correlated with better survival outcome and female patients, patients with T1-2 and N0 had higher TMB score. Altogether 816 DEGs were harvested and pathway analysis demonstrated that patients in TMB-high group were associated with neuroactive ligand–receptor interaction, cAMP signaling pathway, calcium signaling pathway. The infiltration of activated CD4⁺ memory T cells, follicular helper T cells, resting NK cells, M0 and M1 macrophages and neutrophils in TMB-high group were higher compared than that in TMB-low group and high macrophage infiltration was correlated with inferior survival outcome of GC patients. Lastly, the TMBPRS was constructed and GC patients with high TMBPRS had poor prognosis.     

Construction of an N6-methyladenosine lncRNA- and immune cell infiltration-related prognostic model in colorectal cancer

The present paper aims to shed light on the influence of N6-methyladenosine (m6A) long non-coding RNAs (lncRNAs) and immune cell infiltration on colorectal cancer (CRC). We downloaded workflow-type data and xml-format clinical data on CRC from The Cancer Genome Atlas project. The relationship between lncRNA and m6A was identified by using Perl and R software. Kyoto Encyclopedia of Genes and Genomes enrichment analysis was performed. Lasso regression was utilized to construct a prognostic model. Survival analysis was used to explore the relationship between clusters of m6A lncRNAs and clinical survival data. Differential analysis of the tumor microenvironment and an immune correlation analysis were used to determine immune cell infiltration levels in different clusters and their correlation with clinical prognosis. The expression of lncRNA was tightly associated with m6A. The univariate Cox regression analysis showed that lncRNA was a risk factor for the prognosis. Differential expression analysis demonstrated that m6A lncRNAs were partially highly expressed in tumor tissue. m6A lncRNA-related prognostic model could predict the prognosis of CRC independently. “ECM_RECEPTOR_INTERACTION” was the most significantly enriched gene set. PARP8 was overexpressed in tumor tissue and high-risk cluster. CD4 memory T cells, activated resting NK cells, and memory B cells were highly clustered in the high-risk cluster. All of the scores were higher in the low-risk group. m6A lncRNA is closely related to the occurrence and progression of CRC. The corresponding prognostic model can be utilized to evaluate the prognosis of CRC. m6A lncRNA and related immune cell infiltration in the tumor microenvironment can provide novel therapeutic targets for further research.

     

Development and validation of stable ferroptosis- and pyroptosis-related signatures in predicting prognosis and immune status in breast cancer

To develop and validate the predictive effects of stable ferroptosis- and pyroptosis-related features on the prognosis and immune status of breast cancer (BC). We retrieved as well as downloaded ferroptosis- and pyroptosis-related genes from the FerrDb and GeneCards databases. The minimum absolute contraction and selection operator (LASSO) algorithm in The Cancer Genome Atlas (TCGA) was used to construct a prognostic classifier combining the above two types of prognostic genes with differential expression, and the Integrated Gene Expression (GEO) dataset was used for validation. Seventeen genes presented a close association with BC prognosis. Thirteen key prognostic genes with prognostic value were considered to construct a new expression signature for classifying patients with BC into high- and low-risk groups. Kaplan–Meier analysis revealed a worse prognosis in the high-risk group. The receiver operating characteristic (ROC) curve and multivariate Cox regression analysis identified its predictive and independent features. Immune profile analysis showed that immunosuppressive cells were upregulated in the high-risk group, and this risk model was related to immunosuppressive molecules. We successfully constructed combined features of ferroptosis and pyroptosis in BC that are closely related to prognosis, clinicopathological and immune features, chemotherapy efficacy and immunosuppressive molecules. However, further experimental studies are required to verify these findings.     

Pyroptosis-related lncRNA pairs to estimate the molecular features and prognostic outcomes of pancreatic ductal adenocarcinoma

Pyroptosis is a form of programmed cell death associated with inflammatory alterations. However, the intrinsic mechanisms and underlying correlation of pyroptosis-related lncRNAs (PRLs) in pancreatic ductal adenocarcinoma (PDAC) remain unclear. The objective of the current research was to identify pyroptosis-related lncRNAs and a prognostic model to predict the prognosis of patients. We extracted pyroptosis-related lncRNAs to construct a risk model and validated them at Fudan University Shanghai Cancer Center. Crosstalk between lncRNA SNHG10 and GSDMD was found to regulate pyroptosis levels. A new algorithm was used to establish a 0 or 1 PRL pair matrix and prognostic model. Six pyroptosis-related lncRNA pairs were identified and utilized to construct a risk model. The low-risk groups exhibited better prognoses than the high-risk groups. The area under the curve (AUC) indicated extremely high accuracy, reaching 0.810 at 1 year, 0.850 at 2 years, and 0.850 at 3 years in the training set. Patients with different risk scores exhibited distinct metabolic, inflammatory, and immune microenvironments as well as tumor mutation landscapes. Additionally, 9 commonly used chemotherapeutic drugs exhibited different sensitivities between the high- and low-risk groups. To conclude, we propose that pyroptosis exhibits a close correlation with PDAC. Our risk model based on PRL pairs may be beneficial for the accurate estimation of prognostic outcomes, the immune microenvironment, and drug sensitivity, bringing therapeutic hope for patients with PDAC.     

Identification and validation of autophagy-related prognostic signature for head and neck squamous cell carcinoma

BackgroundMany studies have demonstrated that autophagy plays a significant role in regulating tumor growth and progression. However, the effect of autophagy-related genes (ARGs) on the prognosis have rarely been analyzed in head and neck squamous cell carcinoma (HNSCC).MethodsWe obtained differentially expressed ARGs from HNSCC mRNA data in The Cancer Genome Atlas (TCGA) database. And then we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to explore the autophagy-related biological functions. The overall survival (OS)-related and disease specific survival (DSS)-related ARGs were identified by univariate Cox regression analyses. With these genes, we established OS-related and DSS-related risk signature by LASSO regression method, respectively. We validated the reliability of the risk signature with receiver operating characteristic (ROC) analysis, Kaplan-Meier survival curves, clinical correlation analysis, and nomogram. Then we analyzed relationships between risk signature and immune cell infiltration.ResultsWe established the prognostic signatures based on 14 ARGs for OS and 12 ARGs for DSS. The ROC curves, survival analysis, and nomogram validated the predictive accuracy of the models. Clinic correlation analysis showed that the risk group was closely related to Stage, pathological T stage, pathological N stage and human papilloma virus (HPV) subtype. Cox regression demonstrated that the risk score was an independent predictor for the prognosis of HNSCC patients. Furthermore, patients in low-risk score group exhibited higher immunescore and distinct immune cell infiltration than high-risk score group. And we further analysis revealed that the copy number alterations (CNAs) of ARGs-based signature affected the abundance of tumor-infiltrating immune cells.ConclusionIn this study, we identified novel autophagy-related signature for the prediction of OS and DSS in patients with HNSCC. Meanwhile, our study provides a novel sight to understand the role of autophagy and elucidate the important role of autophagy in tumor immune microenvironment (TIME) of HNSCC.     

Survival prediction and response to immune checkpoint inhibitors: A prognostic immune signature for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common cancers all over the world. Several studies have explored if immune-related genes and tumor immune microenvironment could play roles in HCC prognoses. This study is aimed at developing a prognostic signature of HCC based on immune-related genes or tumor immune microenvironment to predict survival and response to immune checkpoint inhibitors (ICIs). We constructed a prognostic signature using bioinformatics method and validated its predictive capability. The mechanisms of the signature prediction were explored with The Cancer Immunome Atlas (TCIA) and mutation analysis. We also explored the association between the signature and immunophenoscore (IPS), which is the marker of ICIs response. A 6 immune-related-gene (6-IRG) signature was developed. It was revealed in a multivariate analysis that the 6-IRG signature was an independent prognostic factor of overall survival and progression-free interval among HCC patients. In the high-risk group of 6-IRG signature score, macrophage M0 cells and regulatory T cells, which are observed associated with poor overall survival in our study, were higher. The low-risk group had a higher IPS, which meant a better response to ICIs. Taken together, we constructed a reliable 6-IRG signature for prediction of survival and response to ICIs. The signature needs further testing for clinical application.     

Developing a risk scoring system based on immune-related lncRNAs for patients with gastric cancer

The immune system and the tumor interact closely during tumor development. Aberrantly expressed long non-coding RNAs (lncRNAs) may be potentially applied as diagnostic and prognostic markers for gastric cancer (GC). At present, the diagnosis and treatment of GC patients remain a formidable clinical challenge. The present study aimed to build a risk scoring system to improve the prognosis of GC patients. In the present study, ssGSEA was used to evaluate the infiltration of immune cells in GC tumor tissue samples, and the samples were split into a high immune cell infiltration group and a low immune cell infiltration group. About 1262 differentially expressed lncRNAs between the high immune cell infiltration group and the low immune cell infiltration group. About 3204 differentially expressed lncRNAs between GC tumor tissues and paracancerous tissues were identified. Then, 621 immune-related lncRNAs were screened using a Venn analysis based on the above results, and 85 prognostic lncRNAs were identified using a univariate Cox analysis. We constructed a prognostic signature using LASSO analysis and evaluated the predictive performance of the signature using ROC analysis. GO and KEGG enrichment analyses were performed on the lncRNAs using the R package, ‘clusterProfiler’. The TIMER online database was used to analyze correlations between the risk score and the abundances of the six types of immune cells. In conclusion, our study found that specific immune-related lncRNAs were clinically significant. These lncRNAs were used to construct a reliable prognostic signature and analyzed immune infiltrates, which may assist clinicians in developing individualized treatment strategies for GC patients.