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.

Download Full-text

A Signature of Autophagy-Related Long Non-coding RNA to Predict the Prognosis of Breast Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.569318 ◽

2021 ◽

Vol 12 ◽

Author(s):

Xiaoping Li ◽

Jishang Chen ◽

Qihe Yu ◽

Hui Huang ◽

Zhuangsheng Liu ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

High Risk ◽

Cox Regression ◽

Enrichment Analysis ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Lasso Regression ◽

Cox Regression Analysis ◽

Risk Scoring

Background: A surge in newly diagnosed breast cancer has overwhelmed the public health system worldwide. Joint effort had beed made to discover the genetic mechanism of these disease globally. Accumulated research has revealed autophagy may act as a vital part in the pathogenesis of breast cancer.Objective: Aim to construct a prognostic model based on autophagy-related lncRNAs and investigate their potential mechanisms in breast cancer.Methods: The transcriptome data and clinical information of patients with breast cancer were obtained from The Cancer Genome Atlas (TCGA) database. Autophagy-related genes were obtained from the Human Autophagy Database (HADb). Long non-coding RNAs (lncRNAs) related to autophagy were acquired through the Pearson correlation analysis. Univariate Cox regression analysis as well as the least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify autophagy-related lncRNAs with prognostic value. We constructed a risk scoring model to assess the prognostic significance of the autophagy-related lncRNAs signatures. The nomogram was then established based on the risk score and clinical indicators. Through the calibration curve, the concordance index (C-index) and receiver operating characteristic (ROC) curve analysis were evaluated to obtain the model's predictive performance. Subgroup analysis was performed to evaluate the differential ability of the model. Subsequently, gene set enrichment analysis was conducted to investigate the potential functions of these lncRNAs.Results: We attained 1,164 breast cancer samples from the TCGA database and 231 autophagy-related genes from the HAD database. Through correlation analysis, 179 autophagy-related lncRNAs were finally identified. Univariate Cox regression analysis and LASSO regression analysis further screened 18 prognosis-associated lncRNAs. The risk scoring model was constructed to divide patients into high-risk and low-risk groups. It was found that the low-risk group had better overall survival (OS) than those of the high-risk group. Then, the nomogram model including age, tumor stage, TNM stage and risk score was established. The evaluation index (C-index: 0.78, 3-year OS AUC: 0.813 and 5-year OS AUC: 0.785) showed that the nomogram had excellent predictive power. Subgroup analysis showed there were difference in OS between high-risk and low-risk patients in different subgroups (stage I-II, ER positive, Her-2 negative and non-TNBC subgroups; all P < 0.05). According to the results of gene set enrichment analysis, these lncRNAs were involved in the regulation of multicellular organismal macromolecule metabolic process in multicellular organisms, nucleotide excision repair, oxidative phosphorylation, and TGF-β signaling pathway.Conclusions: We identified 18 autophagy-related lncRNAs with prognostic value in breast cancer, which may regulate tumor growth and progression in multiple ways.

Download Full-text

Identification of a Novel Immune Landscape Signature for Predicting Prognosis and Response of Endometrial Carcinoma to Immunotherapy and Chemotherapy

10.21203/rs.3.rs-117855/v1 ◽

2020 ◽

Author(s):

Jinhui Liu ◽

Yichun Wang ◽

Jie Mei ◽

Sipei Nie ◽

Yan Zhang

Keyword(s):

Endometrial Carcinoma ◽

Immune Cell ◽

Cox Regression ◽

Gynecological Cancer ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Lasso Regression ◽

Interactive Analysis ◽

Mrna And Protein Expression ◽

Immune Related Genes

Abstract Background: Uterine Corpus Endometrial Carcinoma (UCEC) is the most common gynecological cancer. Here, we have investigated the significance of immune-related genes in predicting the prognosis and response of UCEC patients to immunotherapy and chemotherapy.Methods: Based on TCGA database, the single-sample gene-set enrichment analysis (ssGSEA scores) was utilized to obtain enrichment of 29 immune signatures. Univariate, multivariate Cox regression and LASSO regression analyses were performed to generate an immune-related prognostic signature (IRPS). The biological functions of IRPS-associated genes were evaluated using GSEA, TIMER Database analysis, Mutation analysis, IPS analysis, Gene Expression Profiling Interactive Analysis (GEPIA), Genomics of Drug Sensitivity in Cancer (GDSC) and Immune Cell Abundance Identifier (ImmuCellAI). Potential small molecule drugs for UCEC were predicted using the connectivity map (Cmap). The mRNA and protein expression levels of IRPS-associated genes were tested via quantitative real-time PCR (qPCR) and immunohistology.Results: Two immune-related genes (CCL13 and KLRC1) were identified to construct the IRPS. Both genes were related to the prognosis of UCEC patients (P<0.05). The IRPS could distinguish patients with different prognosis and was closely associated with the infiltration of several types of immune cells. Our findings showed that patients with low IRPS benefited more from immunotherapy and developed stronger response to several chemotherapies, which was also confirmed by the results of ImmuCellAI. Finally, we identified three small molecular drugs that might improve the prognosis of patients with high IRPS. Conclusion: IRPS can be utilized to predict the prognosis of UCEC patients and provide valuable information about their therapeutic response to immunotherapy and chemotherapy.

Download Full-text

Identification of a Novel Lipid Metabolism-Related Gene Signature in Prognosis of Bladder Cancer

10.21203/rs.3.rs-645714/v1 ◽

2021 ◽

Author(s):

Ke Zhu ◽

Liu Xiaoqiang ◽

Wen Deng ◽

Gongxian Wang ◽

Bin Fu

Keyword(s):

Bladder Cancer ◽

Regression Analysis ◽

Lipid Metabolism ◽

Signaling Pathway ◽

Small Molecule ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Cox Regression Analysis ◽

Small Molecule Drugs

Abstract BackgroundBladder cancer (BLCA) is a destructive cancer with unfavorable prognosis. Mounting studies have demonstrated that lipid metabolism affected the progression and treatment of tumor. Therefore, we aimed to explore the function and prognostic value of lipid metabolism-related genes in patients with bladder cancer.MethodsLipid metabolism-related genes (LRGs) were acquired from Molecular Signature Database (MSigDB). LRG mRNA expression and clinical data were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify the prognostic gene for predicting overall survival in BLCA. The Kaplan-Meier analysis was performed to assess the prognosis. The function of LRGs was explored via enrichment analysis and single sample Gene Set Enrichment Analysis (ssGSEA). CMAP database was used to find the small molecule drugs for treatment.ResultsWe successfully constructed and validated a 11-LRG risk model for predicting the prognosis of BLCA patients. Furthermore, we also found that the 11-gene signature was an independent hazardous factor. Functional analysis suggested that LRGs were closely related with the PPAR signaling pathway, fatty acid metabolism and AMPK signaling pathway. LRGs were also involved in immune cells infiltration. Five small molecule drugs could be the candidate treatment for BLCA patients based on CMAP dataset.ConclusionIn conclusion, we identified a reliable prognostic biomarker based on11-LRG signature and found five small molecule drugs for BLCA patient treatments.

Download Full-text

Identification of a Novel Immune Landscape Signature for Predicting Prognosis and Response of Endometrial Carcinoma to Immunotherapy and Chemotherapy

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.671736 ◽

2021 ◽

Vol 9 ◽

Author(s):

Jinhui Liu ◽

Yichun Wang ◽

Jie Mei ◽

Sipei Nie ◽

Yan Zhang

Keyword(s):

Endometrial Carcinoma ◽

Immune Cell ◽

Cox Regression ◽

Gynecological Cancer ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Lasso Regression ◽

Selection Operator ◽

Immune Related Genes

Uterine Corpus Endometrial Carcinoma (UCEC) is the most common gynecological cancer. Here, we have investigated the significance of immune-related genes in predicting the prognosis and response of UCEC patients to immunotherapy and chemotherapy. Based on the Cancer Genome Atlas (TCGA) database, the single-sample gene-set enrichment analysis (ssGSEA) scores was utilized to obtain enrichment of 29 immune signatures. Univariate, multivariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to generate an immune-related prognostic signature (IRPS). The biological functions of IRPS-associated genes were evaluated using GSEA, Tumor Immune Estimation Resource (TIMER) Database analysis, Mutation analysis, Immunophenoscore (IPS) analysis, Gene Expression Profiling Interactive Analysis (GEPIA), Genomics of Drug Sensitivity in Cancer (GDSC) and Immune Cell Abundance Identifier (ImmuCellAI). Potential small molecule drugs for UCEC were predicted using the connectivity map (Cmap). The mRNA and protein expression levels of IRPS-associated genes were tested via quantitative real-time PCR (qPCR) and immunohistology. Two immune-related genes (CCL13 and KLRC1) were identified to construct the IRPS. Both genes were related to the prognosis of UCEC patients (P < 0.05). The IRPS could distinguish patients with different prognosis and was closely associated with the infiltration of several types of immune cells. Our findings showed that patients with low IRPS benefited more from immunotherapy and developed stronger response to several chemotherapies, which was also confirmed by the results of ImmuCellAI. Finally, we identified three small molecular drugs that might improve the prognosis of patients with high IRPS. IRPS can be utilized to predict the prognosis of UCEC patients and provide valuable information about their therapeutic response to immunotherapy and chemotherapy.

Download Full-text

A Five-Immune-Related lncRNA Signature Predicts Survival of Patients With Osteosarcoma

10.21203/rs.3.rs-800834/v1 ◽

2021 ◽

Author(s):

Jie Huang ◽

Hongyi Lai ◽

Wentao Qin ◽

Zhandong Bo ◽

Zhen Tan ◽

...

Keyword(s):

Regression Analysis ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Data Matrix ◽

Lasso Regression ◽

Training Set ◽

Cox Regression Analysis ◽

Validation Set

Abstract Background: Osteosarcoma (OS) is the most common primary solid malignant bone tumor, and its metastasis is a prominent cause of high mortality in patients.Methods: A risk signature was constructed based on re-annotating the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) data matrix, of the lncRNAs related to OS prognosis and immunity. From the OS transcription data, which is downloaded from the TARGET, the 1126 lncRNAs those harbour co-expressions with immunity genes were selected by Pearson correlation test and later divided into the training set (n=44) and validation set (n=41) with the caret package of R. With the training set we build the model related to Osteosarcoma prognosis by the univariate and multivariate Cox, and the Lasso regression analysis, and in combination with the clinical factors we conducted the multivariate Cox regression analysis to build the 1-year, 3-year and 5-year survival rate nomograms. Afterwards, we validated the ROC and the calibration curve of the subjects with the validation set and the whole dataset. Lastly, we performed functional enrichment analysis with the GSEA, GO and KEGG to figure out the biological functions of the prognosis genes.Results: The training set was performed in univariate and multivariate Cox regression analysis, identifying 25 lncRNAs correlated with prognosis. Eleven lncRNAs were selected by the least absolute shrinkage and selection operator (LASSO) regression for multivariate cox analysis and Kaplan-Meier (KM) survival analysis. Finally, lncRNAs (RP11-69E11.4, SNHG6, MIR210HG, RP11-750H9.5 and CTD-2341M24.1) risk signature was constructed, and the validation set and the whole dataset were used to evaluate the prediction stability and accuracy of the signature. The survival times of high- and low-risk groups were significantly different in the training set, validation set and the whole dataset. Further, function enrichment and gene set enrichment analysis revealed that the lncRNAs in the signature may affect the proliferation, migration, chemotaxis and combination of Osteosarcoma-related immune cells, and involve in every pathways of OS metabolism. Conclusion: The five lncRNAs survival risk signature could potentially predict the prognosis of OS patients, additionally, may provide novel insights for future clinical diagnosis and treatment of OS.

Download Full-text

Construction and Analysis of a Prognostic Model of Osteosarcoma Based on m6A-Related lncRNA

10.21203/rs.3.rs-966782/v1 ◽

2021 ◽

Author(s):

Zhian Ling ◽

Yuting Liang ◽

Suping Wei ◽

Yuanming Chen ◽

Jinmin Zhao

Keyword(s):

Regression Analysis ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

Lasso Regression ◽

Gene Set Enrichment ◽

Cox Regression Analysis

Abstract Background N6-methylandenosine (m6A) methylation is one of the most common methylation modifications in RNA. At present, a large number of studies have found that m6A methylation can regulate the occurrence and development of tumors by modifying mRNA. However, it is still unclear how m6A modifies Long non-coding RNA (lncRNA) that regulates mRNA expression by interacting with miRNA to affect the occurrence and development of osteosarcoma(OS). Therefore, exploring the lncRNAs related to m6A methylation and identifying lncRNAs that have both prognostic effects and immune functions are things that need to be solved urgently. Methods The published gene expression data of OS and complete clinical annotation files were obtained from the TARGET database. LncRNAs with P <0.001 from the results of Pearson correlation coefficient analysis as m6A-related lncRNAs were screened. Single-factor Cox regression analysis was used to screening prognostic- related lncRNA combined with the clinical information of patients and constructed a prognostic model based on lasso regression analysis. Then we explored the differences in survival and immune function of different subtypes that be obtained using the Consensus Cluster. The enrichment of differential genes between high and low risk groups in the KEGG pathway is achieved through Gene set enrichment analysis(GSEA). Results We obtained 706 lncRNAs in the TARGET database. Consensus clustering method were used to divide patients with OS into subgroups based on the expression of 26 prognostic-related lncRNAs. Through Kaplan-Meier survival analysis, there are significant differences between the two subgroups. The average immune score (P = 0.02), stromal score(P =0.027), and estimate score༈P = 0.015༉were higher in cluster 1 than in cluster 2. We found that compared with cluster 2, SIGLEC15, HAVCR2, LAG3, and PDCD1 were highly expressed in cluster 1.We obtain a prognostic model by lasso regression analysis. In the training group and the text group, the OS curve showed that patients in the high-risk group had a poorer prognosis than those in the low-risk group. In the training set, univariate Cox regression analysis and multivariate Cox regression analysis showed that the risk score was correlated with the prognosis of OS patients. In the high-risk group, the Linoleic acid metabolism and the Glycine, serine and threonine metabolism pathway were mainly involved by Gene Set Enrichment analysis. The abundance of Mast cells activated (P ≦0.024) and T cells CD4 (P ≦0.0044) naive were positively association the risk score. Conclusions This study clarified the important role of m6A-related lncRNAs in the prognosis and immune microenvironment of patients with OS, and indicate that m6A-related prognostic lncRNA signals may provide new targets for the diagnosis and treatment of OS.

Download Full-text

A bioinformatics analysis of the contribution of m6A methylation to the occurrence of diabetes mellitus

Endocrine Connections ◽

10.1530/ec-21-0328 ◽

2021 ◽

Author(s):

Lei Lei ◽

Yi-Hua Bai ◽

Hong-Ying Jiang ◽

Ting He ◽

Meng Li ◽

...

Keyword(s):

Regression Analysis ◽

Differentially Expressed Genes ◽

Disease Process ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Lasso Regression ◽

Hub Genes

N6-methyladenosine (m6A) methylation has been reported to play a role in type 2 diabetes (T2D). However, the key component of m6A methylation has not been well explored in T2D. This study investigates the biological role and underlying mechanism of m6A methylation genes in T2D. The Gene Expression Omnibus (GEO) database combined with the m6A methylation and transcriptome data of T2D patients were used to identify m6A methylation differentially expressed genes (mMDEGs). Ingenuity Pathway Analysis was used to predict T2D-related differentially expressed genes (DEGs). Gene Ontology (GO) term enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to determine the biological functions of mMDEGs. Gene Set Enrichment Analysis (GSEA) was performed to further confirm the functional enrichment of mMDEGs and determine candidate hub genes. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was carried out to screen for the best predictors of T2D, and real-time polymerase chain reaction and western blot were used to verify the expression of the predictors. A total of 194 overlapping mMDEGs were detected. GO, KEGG, and GSEA analysis showed that mMDEGs were enriched in T2D and insulin signaling pathways, where the insulin gene (INS), the type 2 membranal glycoprotein gene MAFA, and hexokinase 2 (HK2) gene were found. The LASSO regression analysis of candidate hub genes showed the INS gene could be invoked as a predictive hub gene for T2D. INS, MAFA, and HK2 genes participate in the T2D disease process, but INS can better predict the occurrence of T2D.

Download Full-text

Identification and development of an independent immune-related genes prognostic model for breast cancer

BMC Cancer ◽

10.1186/s12885-021-08041-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Lin Chen ◽

Yuxiang Dong ◽

Yitong Pan ◽

Yuhan Zhang ◽

Ping Liu ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

Risk Scores ◽

Validation Dataset ◽

Immune Gene ◽

Cox Regression Analysis ◽

Immune Genes ◽

Immune Related Genes

Abstract Background Breast cancer is one of the main malignant tumors that threaten the lives of women, which has received more and more clinical attention worldwide. There are increasing evidences showing that the immune micro-environment of breast cancer (BC) seriously affects the clinical outcome. This study aims to explore the role of tumor immune genes in the prognosis of BC patients and construct an immune-related genes prognostic index. Methods The list of 2498 immune genes was obtained from ImmPort database. In addition, gene expression data and clinical characteristics data of BC patients were also obtained from the TCGA database. The prognostic correlation of the differential genes was analyzed through Survival package. Cox regression analysis was performed to analyze the prognostic effect of immune genes. According to the regression coefficients of prognostic immune genes in regression analysis, an immune risk scores model was established. Gene set enrichment analysis (GSEA) was performed to probe the biological correlation of immune gene scores. P < 0.05 was considered to be statistically significant. Results In total, 556 immune genes were differentially expressed between normal tissues and BC tissues (p < 0. 05). According to the univariate cox regression analysis, a total of 66 immune genes were statistically significant for survival risk, of which 30 were associated with overall survival (P < 0.05). Finally, a 15 immune genes risk scores model was established. All patients were divided into high- and low-groups. KM survival analysis revealed that high immune risk scores represented worse survival (p < 0.001). ROC curve indicated that the immune genes risk scores model had a good reliability in predicting prognosis (5-year OS, AUC = 0.752). The established risk model showed splendid AUC value in the validation dataset (3-year over survival (OS) AUC = 0.685, 5-year OS AUC = 0.717, P = 0.00048). Moreover, the immune risk signature was proved to be an independent prognostic factor for BC patients. Finally, it was found that 15 immune genes and risk scores had significant clinical correlations, and were involved in a variety of carcinogenic pathways. Conclusion In conclusion, our study provides a new perspective for the expression of immune genes in BC. The constructed model has potential value for the prognostic prediction of BC patients and may provide some references for the clinical precision immunotherapy of patients.

Download Full-text

Gene set enrichment analysis for genome-wide DNA methylation data

Genome Biology ◽

10.1186/s13059-021-02388-x ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Jovana Maksimovic ◽

Alicia Oshlack ◽

Belinda Phipson

Keyword(s):

Dna Methylation ◽

Enrichment Analysis ◽

R Package ◽

Gene Set Enrichment Analysis ◽

Methylation Array ◽

Gene Set ◽

Genome Wide ◽

Genome Methylation ◽

Unbiased Gene ◽

Gene Set Testing

AbstractDNA methylation is one of the most commonly studied epigenetic marks, due to its role in disease and development. Illumina methylation arrays have been extensively used to measure methylation across the human genome. Methylation array analysis has primarily focused on preprocessing, normalization, and identification of differentially methylated CpGs and regions. GOmeth and GOregion are new methods for performing unbiased gene set testing following differential methylation analysis. Benchmarking analyses demonstrate GOmeth outperforms other approaches, and GOregion is the first method for gene set testing of differentially methylated regions. Both methods are publicly available in the missMethyl Bioconductor R package.

Download Full-text

Identification and Validation of Immune-Related LncRNA Prognostic Signature for Lung Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.681277 ◽

2021 ◽

Vol 12 ◽

Author(s):

Guomin Wu ◽

Qihao Wang ◽

Ting Zhu ◽

Linhai Fu ◽

Zhupeng Li ◽

...

Keyword(s):

Regression Analysis ◽

Lung Adenocarcinoma ◽

Clinical Features ◽

Cox Regression ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Immune Infiltration

This study aimed to establish a prognostic risk model for lung adenocarcinoma (LUAD). We firstly divided 535 LUAD samples in TCGA-LUAD into high-, medium-, and low-immune infiltration groups by consensus clustering analysis according to immunological competence assessment by single-sample gene set enrichment analysis (ssGSEA). Profile of long non-coding RNAs (lncRNAs) in normal samples and LUAD samples in TCGA was used for a differential expression analysis in the high- and low-immune infiltration groups. A total of 1,570 immune-related differential lncRNAs in LUAD were obtained by intersecting the above results. Afterward, univariate COX regression analysis and multivariate stepwise COX regression analysis were conducted to screen prognosis-related lncRNAs, and an eight-immune-related-lncRNA prognostic signature was finally acquired (AL365181.2, AC012213.4, DRAIC, MRGPRG-AS1, AP002478.1, AC092168.2, FAM30A, and LINC02412). Kaplan–Meier analysis and ROC analysis indicated that the eight-lncRNA-based model was accurate to predict the prognosis of LUAD patients. Simultaneously, univariate COX regression analysis and multivariate COX regression analysis were undertaken on clinical features and risk scores. It was illustrated that the risk score was a prognostic factor independent from clinical features. Moreover, immune data of LUAD in the TIMER database were analyzed. The eight-immune-related-lncRNA prognostic signature was related to the infiltration of B cells, CD4+ T cells, and dendritic cells. GSEA enrichment analysis revealed significant differences in high- and low-risk groups in pathways like pentose phosphate pathway, ubiquitin mediated proteolysis, and P53 signaling pathway. This study helps to treat LUAD patients and explore molecules related to LUAD immune infiltration to deeply understand the specific mechanism.

Download Full-text