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Prognostic Value of an Immune Long Non-Coding RNA Signature in Liver Hepatocellular Carcinoma
1Department of Gastroenterology, Suzhou Municipal Hospital, The Affiliated Suzhou Hospital of Nanjing Medical University, Gusu School, Nanjing Medical University, P.R. China
2Department of Gastroenterology, Pu Dong Area Gongli Hospital, School of Medicine, Shanghai University, Shanghai 200135, P.R. China
3Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, P.R. China
J. Microbiol. Biotechnol. 2024; 34(4): 958-968
Published April 28, 2024 https://doi.org/10.4014/jmb.2308.08022
Copyright © The Korean Society for Microbiology and Biotechnology.
Abstract
Keywords
Graphical Abstract

Introduction
Liver hepatocellular carcinoma, a prevalent malignancy globally, exhibits escalating rates of mortality and incidence [1, 2]. The primary approach utilized in LIHC administration is surgery, however, many patients are in the middle-advanced stage at first diagnosis and miss the chance of accepting surgery [3-5]. In a broad sense, liver is classified as a lymphoid organ [6]. It has been documented that during tumor progression, immunological tolerance is influenced by various factors including cytokines, hepatic nonparenchymal cells, dendritic cells, and lymphocytes, which actively modulate this process [7-10]. Meanwhile, immunology therapy comprising immune checkpoints, adoptive cellular immunotherapy (ACT) and vaccines has presented promising possibilities for the treatment of liver cancer (LIHC). These advancements have significantly broadened the horizons of LIHC treatment [11, 12]. Several studies have clarified that clinical application of immune checkpoint blockade programmed cell death-1(PD-1) and cytotoxic T-lymphocyte antigen-4 (CTLA4) has enhanced the survival rate of some advanced patients [13-15]. Consequently, there is an urgent requirement for the study of immune biomarkers that exhibit both high sensitivity and specificity in terms of diagnosing and predicting the prognosis of hepatocellular carcinoma (LIHC). Long noncoding RNA is a type of poorly conserved RNA in length from 200 base pairs to 100 kilobase pairs. This particular RNA is capable of modulating gene expression at four primary levels: epigenetic regulation, epigenetic transcriptional regulation, posttranscriptional regulation and translational regulation [16-21]. According to their location with respect to protein-coding mRNAs, lncRNAs can be categorized into four categories: antisense, pseudogene, long intergenic ncRNA and intronic lncRNA [22]. Recent research has brought to light the crucial significance of lncRNAs in the innate immune response and the development, differentiation, and activation of T cells [23, 24]. Additionally, certain investigations have examined the correlation between aberrant expression of lncRNAs and tumorigenesis, metastasis, diagnosis or prognosis [25-27]. For example, HULC, a lncRNA that is specifically situated on cell plasma, exhibits a significant expression in hepatoma cells and promotes cell proliferation [28, 29]. In HBV-associated HCC, H19 has been documented to exhibit upregulated levels and represses the metastasis of tumors [30]. Through bioinformatic analysis, we developed a reliable immunological lncRNA model which serves as a valuable tool for facilitating the diagnosis and prognosis of liver hepatocellular carcinoma (LIHC). The long non-coding RNAs AC009005.1, AC099850.3, AL031985.3, AL117336.3, AL365203.2 and MSC−AS1 were critical components of the whole model. The data suggested that a high expression level of these biomarkers was positively correlated with poor survival and malignant phenotypes in the TCGA dataset. Univariate Cox regression and multivariate Cox regression analyses further clarified that this signature had an independent influence on overall survival. The results derived from KM plot, ROC curve, and PCA further proved the sensitivity and reliability of this prognostic model.
Methods
Data Source and Processing
RNA-sequencing data of LIHC samples were retrieved from the TCGA repository [31]. The data was generated using the Illumina HiSeq RNA-Seq platform. Additionally, we collected the corresponding clinical data, including survival time, TNM classification information, and risk factors. The dataset consisted of a total of 424 samples, with 50 being normal and 374 being primary hepatocellular carcinoma. The utilization and acquisition of this data were conducted in accordance with TCGA data access policies and publication guidelines. In our study, we excluded clinical samples that did not have precise outcomes or had follow-up times of less than 30 days. To match the names of mRNAs and long non-coding RNAs in the ensemble, we utilized the human general transfer format provided by the ensemble website [32].
Selection of Immune-Related Long Non-Coding RNAs
Construction of the Prognostic LncRNA Signature and Statistical Analysis
The gene sets for 'immune responsé and 'immune system process' were obtained from the Molecular Signatures Database [33]. These gene sets were then used to identify immune-related genes in LIHC samples. Correlation analysis was performed on these genes using the 'limma' packages and the 'cor function' in R. The filter criteria were set as absolute cor (corresponding coefficients) > 0.4 and adjusted
lncRNA risk score = |coef|* expression value
risk score of samples = ∑ (lncRNA risk score)
Analysis of the Clinical Features of the LncRNA Signature
Following the preceding analysis, we obtained the expression matrix of lncRNA biomarkers in individuals diagnosed with LIHC. We subsequently merged the clinical information with the expression data. We then examined the relationship between the expression of lncRNA biomarkers (significance level:
Gene Set Enrichment Analysis
The utilization of Gene Set Enrichment Analysis (GSEA) allowed for the investigation of the underlying relationship between risk scores obtained from co-expression analysis. For reference purposes, two sets of immune genes, namely 'immune system process' (M13664 genes annotated by GO term GO:0002376) and 'immune responsé (M19817 genes annotated by GO term GO:0006955), were obtained from the Molecular Signatures Database (MSigDB). Enrichment outcomes were considered statistically significant if they met the criterion of FDR < 0.25.
Principal Component Analysis
Principal component analysis (PCA) was conducted to visualize the separation of samples with different risk scores based on the six-lncRNA signature, immune lncRNAs, immune-related genes, and all genes. Prior to PCA, the expression matrices were preprocessed by deduplicating the values through averaging and excluding any data with no change in expression level. The resulting graphs exhibited the three major components (PC1, PC2, PC3) in a three-dimensional space. The analysis was performed using the 'limma' and 'scatterplot3d' packages.
Statistics
Data expression was performed using the mean ± SD. Two-group comparisons were analyzed using the Student's
Results
Construction of the Six-LncRNA Signature
In the present research, the transcriptome data of both LIHC tissues (
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Table 1 . Correlation of immune genes and associated lncRNAs.
Immune Gene lncRNA Cor P valueRegulation KMT2A AP001318.2 0.460 5.45E-21 Positive NCOA6 AC012510.1 0.462 3.55E-21 Positive TRAF2 SREBF2-AS1 0.451 3.66E-20 Positive RPS19 AC132192.2 0.614 4.03E-40 Positive TRAF2 AL035446.1 0.463 3.12E-21 Positive HELLS AL360181.2 0.455 1.55E-20 Positive PRKRA AP002884.1 0.416 4.67E-17 Positive RPS19 AL109615.3 -0.401 7.64E-16 Negative APOA2 FLJ42351 -0.460 6.18E-21 Negative NCK2 MSC-AS1 0.408 2.10E-16 Positive KMT2A AC009005.1 0.483 2.82E-23 Positive ITGB2 AL031985.3 0.679 7.50E-52 Positive DPP4 AL117336.3 0.512 2.18E-26 Positive HDAC7 AL365203.2 0.462 3.49E-21 Positive CKLF AC099850.3 0.431 2.26E-18 Positive
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Table 2 . Detailed information of the six-lncRNA signature.
Gene symbol Gene_ID Location coef MSC-AS1 ENSG00000235531.8 chr8: 71828167-72002405 0.3293 AC009005.1 ENSG00000267751.4 chr19: 567212-571745 0.3111 AL117336.3 ENSG00000271335.4 chr10: 35314552-35320998 0.3428 AL031985.3 ENSG00000260920.2 chr1: 40464319-40466767 0.4886 AL365203.2 ENSG00000273038.2 chr10:32887255-32889311 0.2210 AC099850.3 ENSG00000265415.1 chr17:59202677-59203829 0.1741
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Fig. 1. Construction of an immune lncRNA signature for liver hepatocellular carcinoma. (A) The network of partial immune genes and associated lncRNAs. B. Candidate lncRNA for the prognostic model with information about hazard ratio. (C) Relative gene expression of MSC-AS1 among the low-risk group, high-risk group, and non-tumor samples. (D) Relative gene expression of AC009005.1 among the low-risk group, high-risk group, and non-tumor samples. (E) Relative gene expression of AL365203.2 among the low-risk group, high-risk group, and non-tumor samples. (F) Relative gene expression of AC099850.3 among the low-risk group, high-risk group, and non-tumor samples. (G) Relative gene expression of AL031985.3 among the low-risk group, high-risk group, and non-tumor samples. (H) Relative gene expression of AL117336.3 among the low-risk group, high-risk group, and non-tumor samples.
Prognostic Value of the LncRNA Signature for Assessing Clinical Outcome
In Fig. 2A, we present the final survival state and expression profiles of the six-lncRNA signature for each sample, aiming to assess its potential in predicting the prognosis of LIHC patients. The scatter graph demonstrates a clear correlation between increasing risk score and worsened survival estimate. Furthermore, our survival analysis reveals significantly lower death rates in the low-risk group compared to the high-risk group (
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Fig. 2. Validation of the prognostic lncRNA signature for hepatocellular carcinoma. (A) The upper graph shows the relationship between survival time and risk score, the medium graph shows the distribution of lncRNA risk score, the bottom heat map shows expression patterns of six-lncRNA signature for LIHC patients. (B) The Kaplan-Meier curve of different tumor groups based on the median risk score. (C) The ROC curve for the risk score, age, gender, grade, and TNM stage.
Correlation between LncRNA Signature and Clinical Characteristics
To assess the correlation between patients' clinical indicators and outcomes based on their risk score, we conducted a stratified analysis on a total of 373 samples obtained from the TCGA cohort (Table 3). Moreover, a univariate analysis highlighted a significant association between TNM staging, as well as the six-lncRNA signature, with overall survival (OS). Furthermore, through multivariate analysis, T staging along with the six-lncRNA signature emerged as potential independent prognostic factors (Fig. 3A and 3B). Additionally, we investigated the relationship between the expression of the six lncRNAs and tumor stage as well as tumor grade. Our findings indicated a strong connection between the expression of AC009005.1, AC099850.3, AL031985.3, and MSC−AS1 with clinical grade. Additionally, AC009005.1 and AC099850.3 displayed associations with TNM staging, while AC009005.1, AC099850.3, and AL031985.3 levels were found to be correlated with T staging (Fig. 3C-3E).
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Table 3 . Relationship between the risk score of the lncRNA signature for OS and clinical features.
Low risk/high risk Pearson χ2 P Age 0.072 0.788 > = 55 119/116 < 55 53/55 Gender 3.565 0.059 Female 47/63 Male 125/108 TNM stage 8.404 0.004 I/II 130/108 III/IV 30/53 G 12.032 0.001 G1/G2 123/91 G3/G4 47/77 AFP (ng/ml) 5.917 0.015 > = 20 58/94 < 20 64/57 BMI 0.213 0.645 > =25 80/73 < 25 81/82 Race 0.268 0.605 White 83/86 Asian 77/71
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Fig. 3. Evaluation of the six-lncRNA as an independent factor. (A) The tree diagram of multivariate analysis shows the statistical significance and hazard ratios of several indices as prognostic factors. (B) The tree diagram of univariate analysis shows the statistical significance and hazard ratios of several indices as prognostic factors. (C) Boxplot indicates the correlation of lncRNA biomarkers’ expression and tumor grade. (D) Boxplot indicates the correlation of lncRNA biomarkers expression and TNM stage. (E) Boxplot indicates the correlation of lncRNA biomarkers expression and T-staging.
Gene Set Enrichment Analysis
The utilization of GSEA was employed in order to investigate the potential connection between biomarkers and biological processes based on the risk score. As depicted in Fig. 4A-4B, GSEA data exhibited that protein-coding genes coexpressed within the high-risk group demonstrated significant enrichment in the reference gene sets "immune response" and "immune system process" (with FDR
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Fig. 4. Highly enriched biological pathways for corresponding immune genes of six-lncRNA signature in TCGA. (A) Regulation of cell cycle phase transition (FDR < 0.007), (B) RNA splicing (FDR < 0.004), (C) mRNA CIS splicing via spliceosome (FDR < 0.002) (D) Positive regulation of mRNA processing (FDR < 0.002) (E) Regulation of cell cycle G1 S phase transition (FDR < 0.002) (F) ATP dependent chromatin remodeling (FDR < 0.002) (G) Chromatin remodeling (FDR < 0.002) (H) Regulation of response to DNA damage stimulus (FDR < 0.002) (I) Regulation of chromosome organization (FDR < 0.002) (J) Recombinational repair (FDR < 0.001) (K) Immune response (FDR < 0.203) (L) Immune system process (FDR < 0.087).
Principal Component Analysis
Principal component analysis (PCA) was conducted to aggregate samples based on gene expression patterns in two groups at risk. By reducing multiple indices and extracting main parameters, tumor samples were examined at four levels: lncRNA signature, immune lncRNAs, immune genes, and all genes. Sample data from the high-risk group is depicted by red points on the graph, while green points represent the low-risk group. Our analysis revealed that the six-lncRNA signature exhibited the highest degree of separation among the four levels (Fig. 5A-5D). These findings partially support the prognostic accuracy of the immune lncRNA model. Additionally, we assessed the expression profiles of these six lncRNAs in pan-cancers using the Lnc2Catlas database, and the resulting boxplots are displayed in Fig. 6.
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Fig. 5. Principal component analysis of samples for TCGA. (A) PCA shows samples divisibility from the low risk- and high-risk group based on all gene expression. (B) PCA shows samples divisibility from the low risk- and high-risk group based on immune gene expression. (C) PCA shows samples divisibility from the low risk- and high-risk group based on immune lncRNA expression. (D) PCA shows samples divisibility from the low risk- and high-risk group based on lncRNA signature expression.
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Fig. 6. Expression profiles of the lncRNA signature in pan-cancer. (A) AL031985.3expression in pan-cancer. (B) AC099850.3 expression in pan-cancer. (C) AL365203.2 expression in pan-cancer. (D) AL117336.3 expression in pan-cancer. (E) MSC−AS1 expression in pan-cancer. (F) AC009005.1 expression in pan-cancer.
Discussion
Long noncoding RNA has drawn widespread attention as a potential molecular target for cancer diagnosis and treatment in recent years. Multiomics has developed rapidly following the application of high-throughput screening to cancer diagnosis and therapy [36-38]. Several studies have proposed that long noncoding RNAs are involved in tumor genesis and progression and that they contribute to the immune system [39-41]. For example, Jiang
Conclusion
We have successfully devised an immune model comprising of six lncRNAs to forecast the outcome of LIHC samples. Our study extensively examined the bioinformatics analysis to evaluate the functionality and efficacy of this lncRNA signature. The results derived from the observations suggest that this signature holds the potential to introduce a novel method for precise diagnosis and prognosis. Nevertheless, it is crucial to conduct clinical trials and functional tests in order to comprehend the underlying mechanism before considering its applicability in a clinical setting.
Abbreviations
ACT: adoptive cellular immunotherapy, AFP: alpha-fetoprotein, AIC: Akaike information criterion, AJCC: American Joint Committee on Cancer, AUC: area under the curve, BMI: body mass index, CTLA4: cytotoxic T-lymphocyte antigen-4, FPKM: fragments per kilobase of exon model per million mapped fragments, GO: gene ontology, GSEA: gene set enrichment analysis, LIHC: liver hepatocellular carcinoma, lncRNA: long non-coding RNA, OS: overall survival, PD-1: programmed cell death-1, ROC: receiver operating characteristic, PCA: principal component analysis, TCGA: The Cancer Genome Atlas, TME: tumor microenvironment, Treg cells: regulatory T cells.
Acknowledgments
This study was supported by : (1) “Special Project for Scientific Research and Cultivation of Young Physician” of Gusu School, Nanjing Medical University (GSKY20230503) to Rui Kong. (2) Shanghai “Rising Stars of Medical Talent” Youth Development Program-Outstanding Youth Medical Talents (No.SHWJRS2021-99) to Jie Lu. (3) Shanghai Pudong New Area Science and Technology Commission (No.PKJ2021-Y10) to Jie Lu. (4) Specialty Feature Construction Project of Pudong Health and Family Planning Commission of Shanghai?PWZzb2022-14) to Jie Lu.
Authors Contributions
Rui Kong and Nan Wang performed the data analyses. Rui Kong and Jie Lu wrote and revised the manuscript. Jie Lu and Chunli Zhou designed the study. All the authors have read and approved the final manuscript.
Conflict of Interest
The authors have no financial conflicts of interest to declare.
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Article
Research article
J. Microbiol. Biotechnol. 2024; 34(4): 958-968
Published online April 28, 2024 https://doi.org/10.4014/jmb.2308.08022
Copyright © The Korean Society for Microbiology and Biotechnology.
Prognostic Value of an Immune Long Non-Coding RNA Signature in Liver Hepatocellular Carcinoma
Rui Kong1#, Nan Wang3, Chun li Zhou1*, and Jie Lu2,3*
1Department of Gastroenterology, Suzhou Municipal Hospital, The Affiliated Suzhou Hospital of Nanjing Medical University, Gusu School, Nanjing Medical University, P.R. China
2Department of Gastroenterology, Pu Dong Area Gongli Hospital, School of Medicine, Shanghai University, Shanghai 200135, P.R. China
3Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, P.R. China
Correspondence to:Jie Lu, kennisren@hotmail.com
Chun li Zhou, zhouchunli079@163.com
#Rui Kong makes the main contribution to this work.
Abstract
In recent years, there has been a growing recognition of the important role that long non-coding RNAs (lncRNAs) play in the immunological process of hepatocellular carcinoma (LIHC). An increasing number of studies have shown that certain lncRNAs hold great potential as viable options for diagnosis and treatment in clinical practice. The primary objective of our investigation was to devise an immune lncRNA profile to explore the significance of immune-associated lncRNAs in the accurate diagnosis and prognosis of LIHC. Gene expression profiles of LIHC samples obtained from TCGA database were screened for immune-related genes. The optimal immune-related lncRNA signature was built via correlational analysis, univariate and multivariate Cox analysis. Then, the Kaplan-Meier plot, ROC curve, clinical analysis, gene set enrichment analysis, and principal component analysis were performed to evaluate the capability of the immune lncRNA signature as a prognostic indicator. Six long non-coding RNAs were identified via correlation analysis and Cox regression analysis considering their interactions with immune genes. Subsequently, tumor samples were categorized into two distinct risk groups based on different clinical outcomes. Stratification analysis indicated that the prognostic ability of this signature acted as an independent factor. The Kaplan-Meier method was employed to conduct survival analysis, results showed a significant difference between the two risk groups. The predictive performance of this signature was validated by principal component analysis (PCA). Additionally, data obtained from gene set enrichment analysis (GSEA) revealed several potential biological processes in which these biomarkers may be involved. To summarize, this study demonstrated that this six-lncRNA signature could be identified as a potential factor that can independently predict the prognosis of LIHC patients.
Keywords: Long non-coding RNA, immune prognostic signature, hepatocellular carcinoma, overall survival
Introduction
Liver hepatocellular carcinoma, a prevalent malignancy globally, exhibits escalating rates of mortality and incidence [1, 2]. The primary approach utilized in LIHC administration is surgery, however, many patients are in the middle-advanced stage at first diagnosis and miss the chance of accepting surgery [3-5]. In a broad sense, liver is classified as a lymphoid organ [6]. It has been documented that during tumor progression, immunological tolerance is influenced by various factors including cytokines, hepatic nonparenchymal cells, dendritic cells, and lymphocytes, which actively modulate this process [7-10]. Meanwhile, immunology therapy comprising immune checkpoints, adoptive cellular immunotherapy (ACT) and vaccines has presented promising possibilities for the treatment of liver cancer (LIHC). These advancements have significantly broadened the horizons of LIHC treatment [11, 12]. Several studies have clarified that clinical application of immune checkpoint blockade programmed cell death-1(PD-1) and cytotoxic T-lymphocyte antigen-4 (CTLA4) has enhanced the survival rate of some advanced patients [13-15]. Consequently, there is an urgent requirement for the study of immune biomarkers that exhibit both high sensitivity and specificity in terms of diagnosing and predicting the prognosis of hepatocellular carcinoma (LIHC). Long noncoding RNA is a type of poorly conserved RNA in length from 200 base pairs to 100 kilobase pairs. This particular RNA is capable of modulating gene expression at four primary levels: epigenetic regulation, epigenetic transcriptional regulation, posttranscriptional regulation and translational regulation [16-21]. According to their location with respect to protein-coding mRNAs, lncRNAs can be categorized into four categories: antisense, pseudogene, long intergenic ncRNA and intronic lncRNA [22]. Recent research has brought to light the crucial significance of lncRNAs in the innate immune response and the development, differentiation, and activation of T cells [23, 24]. Additionally, certain investigations have examined the correlation between aberrant expression of lncRNAs and tumorigenesis, metastasis, diagnosis or prognosis [25-27]. For example, HULC, a lncRNA that is specifically situated on cell plasma, exhibits a significant expression in hepatoma cells and promotes cell proliferation [28, 29]. In HBV-associated HCC, H19 has been documented to exhibit upregulated levels and represses the metastasis of tumors [30]. Through bioinformatic analysis, we developed a reliable immunological lncRNA model which serves as a valuable tool for facilitating the diagnosis and prognosis of liver hepatocellular carcinoma (LIHC). The long non-coding RNAs AC009005.1, AC099850.3, AL031985.3, AL117336.3, AL365203.2 and MSC−AS1 were critical components of the whole model. The data suggested that a high expression level of these biomarkers was positively correlated with poor survival and malignant phenotypes in the TCGA dataset. Univariate Cox regression and multivariate Cox regression analyses further clarified that this signature had an independent influence on overall survival. The results derived from KM plot, ROC curve, and PCA further proved the sensitivity and reliability of this prognostic model.
Methods
Data Source and Processing
RNA-sequencing data of LIHC samples were retrieved from the TCGA repository [31]. The data was generated using the Illumina HiSeq RNA-Seq platform. Additionally, we collected the corresponding clinical data, including survival time, TNM classification information, and risk factors. The dataset consisted of a total of 424 samples, with 50 being normal and 374 being primary hepatocellular carcinoma. The utilization and acquisition of this data were conducted in accordance with TCGA data access policies and publication guidelines. In our study, we excluded clinical samples that did not have precise outcomes or had follow-up times of less than 30 days. To match the names of mRNAs and long non-coding RNAs in the ensemble, we utilized the human general transfer format provided by the ensemble website [32].
Selection of Immune-Related Long Non-Coding RNAs
Construction of the Prognostic LncRNA Signature and Statistical Analysis
The gene sets for 'immune responsé and 'immune system process' were obtained from the Molecular Signatures Database [33]. These gene sets were then used to identify immune-related genes in LIHC samples. Correlation analysis was performed on these genes using the 'limma' packages and the 'cor function' in R. The filter criteria were set as absolute cor (corresponding coefficients) > 0.4 and adjusted
lncRNA risk score = |coef|* expression value
risk score of samples = ∑ (lncRNA risk score)
Analysis of the Clinical Features of the LncRNA Signature
Following the preceding analysis, we obtained the expression matrix of lncRNA biomarkers in individuals diagnosed with LIHC. We subsequently merged the clinical information with the expression data. We then examined the relationship between the expression of lncRNA biomarkers (significance level:
Gene Set Enrichment Analysis
The utilization of Gene Set Enrichment Analysis (GSEA) allowed for the investigation of the underlying relationship between risk scores obtained from co-expression analysis. For reference purposes, two sets of immune genes, namely 'immune system process' (M13664 genes annotated by GO term GO:0002376) and 'immune responsé (M19817 genes annotated by GO term GO:0006955), were obtained from the Molecular Signatures Database (MSigDB). Enrichment outcomes were considered statistically significant if they met the criterion of FDR < 0.25.
Principal Component Analysis
Principal component analysis (PCA) was conducted to visualize the separation of samples with different risk scores based on the six-lncRNA signature, immune lncRNAs, immune-related genes, and all genes. Prior to PCA, the expression matrices were preprocessed by deduplicating the values through averaging and excluding any data with no change in expression level. The resulting graphs exhibited the three major components (PC1, PC2, PC3) in a three-dimensional space. The analysis was performed using the 'limma' and 'scatterplot3d' packages.
Statistics
Data expression was performed using the mean ± SD. Two-group comparisons were analyzed using the Student's
Results
Construction of the Six-LncRNA Signature
In the present research, the transcriptome data of both LIHC tissues (
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Table 1 . Correlation of immune genes and associated lncRNAs..
Immune Gene lncRNA Cor P valueRegulation KMT2A AP001318.2 0.460 5.45E-21 Positive NCOA6 AC012510.1 0.462 3.55E-21 Positive TRAF2 SREBF2-AS1 0.451 3.66E-20 Positive RPS19 AC132192.2 0.614 4.03E-40 Positive TRAF2 AL035446.1 0.463 3.12E-21 Positive HELLS AL360181.2 0.455 1.55E-20 Positive PRKRA AP002884.1 0.416 4.67E-17 Positive RPS19 AL109615.3 -0.401 7.64E-16 Negative APOA2 FLJ42351 -0.460 6.18E-21 Negative NCK2 MSC-AS1 0.408 2.10E-16 Positive KMT2A AC009005.1 0.483 2.82E-23 Positive ITGB2 AL031985.3 0.679 7.50E-52 Positive DPP4 AL117336.3 0.512 2.18E-26 Positive HDAC7 AL365203.2 0.462 3.49E-21 Positive CKLF AC099850.3 0.431 2.26E-18 Positive
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Table 2 . Detailed information of the six-lncRNA signature..
Gene symbol Gene_ID Location coef MSC-AS1 ENSG00000235531.8 chr8: 71828167-72002405 0.3293 AC009005.1 ENSG00000267751.4 chr19: 567212-571745 0.3111 AL117336.3 ENSG00000271335.4 chr10: 35314552-35320998 0.3428 AL031985.3 ENSG00000260920.2 chr1: 40464319-40466767 0.4886 AL365203.2 ENSG00000273038.2 chr10:32887255-32889311 0.2210 AC099850.3 ENSG00000265415.1 chr17:59202677-59203829 0.1741
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Figure 1. Construction of an immune lncRNA signature for liver hepatocellular carcinoma. (A) The network of partial immune genes and associated lncRNAs. B. Candidate lncRNA for the prognostic model with information about hazard ratio. (C) Relative gene expression of MSC-AS1 among the low-risk group, high-risk group, and non-tumor samples. (D) Relative gene expression of AC009005.1 among the low-risk group, high-risk group, and non-tumor samples. (E) Relative gene expression of AL365203.2 among the low-risk group, high-risk group, and non-tumor samples. (F) Relative gene expression of AC099850.3 among the low-risk group, high-risk group, and non-tumor samples. (G) Relative gene expression of AL031985.3 among the low-risk group, high-risk group, and non-tumor samples. (H) Relative gene expression of AL117336.3 among the low-risk group, high-risk group, and non-tumor samples.
Prognostic Value of the LncRNA Signature for Assessing Clinical Outcome
In Fig. 2A, we present the final survival state and expression profiles of the six-lncRNA signature for each sample, aiming to assess its potential in predicting the prognosis of LIHC patients. The scatter graph demonstrates a clear correlation between increasing risk score and worsened survival estimate. Furthermore, our survival analysis reveals significantly lower death rates in the low-risk group compared to the high-risk group (
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Figure 2. Validation of the prognostic lncRNA signature for hepatocellular carcinoma. (A) The upper graph shows the relationship between survival time and risk score, the medium graph shows the distribution of lncRNA risk score, the bottom heat map shows expression patterns of six-lncRNA signature for LIHC patients. (B) The Kaplan-Meier curve of different tumor groups based on the median risk score. (C) The ROC curve for the risk score, age, gender, grade, and TNM stage.
Correlation between LncRNA Signature and Clinical Characteristics
To assess the correlation between patients' clinical indicators and outcomes based on their risk score, we conducted a stratified analysis on a total of 373 samples obtained from the TCGA cohort (Table 3). Moreover, a univariate analysis highlighted a significant association between TNM staging, as well as the six-lncRNA signature, with overall survival (OS). Furthermore, through multivariate analysis, T staging along with the six-lncRNA signature emerged as potential independent prognostic factors (Fig. 3A and 3B). Additionally, we investigated the relationship between the expression of the six lncRNAs and tumor stage as well as tumor grade. Our findings indicated a strong connection between the expression of AC009005.1, AC099850.3, AL031985.3, and MSC−AS1 with clinical grade. Additionally, AC009005.1 and AC099850.3 displayed associations with TNM staging, while AC009005.1, AC099850.3, and AL031985.3 levels were found to be correlated with T staging (Fig. 3C-3E).
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Table 3 . Relationship between the risk score of the lncRNA signature for OS and clinical features..
Low risk/high risk Pearson χ2 P Age 0.072 0.788 > = 55 119/116 < 55 53/55 Gender 3.565 0.059 Female 47/63 Male 125/108 TNM stage 8.404 0.004 I/II 130/108 III/IV 30/53 G 12.032 0.001 G1/G2 123/91 G3/G4 47/77 AFP (ng/ml) 5.917 0.015 > = 20 58/94 < 20 64/57 BMI 0.213 0.645 > =25 80/73 < 25 81/82 Race 0.268 0.605 White 83/86 Asian 77/71
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Figure 3. Evaluation of the six-lncRNA as an independent factor. (A) The tree diagram of multivariate analysis shows the statistical significance and hazard ratios of several indices as prognostic factors. (B) The tree diagram of univariate analysis shows the statistical significance and hazard ratios of several indices as prognostic factors. (C) Boxplot indicates the correlation of lncRNA biomarkers’ expression and tumor grade. (D) Boxplot indicates the correlation of lncRNA biomarkers expression and TNM stage. (E) Boxplot indicates the correlation of lncRNA biomarkers expression and T-staging.
Gene Set Enrichment Analysis
The utilization of GSEA was employed in order to investigate the potential connection between biomarkers and biological processes based on the risk score. As depicted in Fig. 4A-4B, GSEA data exhibited that protein-coding genes coexpressed within the high-risk group demonstrated significant enrichment in the reference gene sets "immune response" and "immune system process" (with FDR
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Figure 4. Highly enriched biological pathways for corresponding immune genes of six-lncRNA signature in TCGA. (A) Regulation of cell cycle phase transition (FDR < 0.007), (B) RNA splicing (FDR < 0.004), (C) mRNA CIS splicing via spliceosome (FDR < 0.002) (D) Positive regulation of mRNA processing (FDR < 0.002) (E) Regulation of cell cycle G1 S phase transition (FDR < 0.002) (F) ATP dependent chromatin remodeling (FDR < 0.002) (G) Chromatin remodeling (FDR < 0.002) (H) Regulation of response to DNA damage stimulus (FDR < 0.002) (I) Regulation of chromosome organization (FDR < 0.002) (J) Recombinational repair (FDR < 0.001) (K) Immune response (FDR < 0.203) (L) Immune system process (FDR < 0.087).
Principal Component Analysis
Principal component analysis (PCA) was conducted to aggregate samples based on gene expression patterns in two groups at risk. By reducing multiple indices and extracting main parameters, tumor samples were examined at four levels: lncRNA signature, immune lncRNAs, immune genes, and all genes. Sample data from the high-risk group is depicted by red points on the graph, while green points represent the low-risk group. Our analysis revealed that the six-lncRNA signature exhibited the highest degree of separation among the four levels (Fig. 5A-5D). These findings partially support the prognostic accuracy of the immune lncRNA model. Additionally, we assessed the expression profiles of these six lncRNAs in pan-cancers using the Lnc2Catlas database, and the resulting boxplots are displayed in Fig. 6.
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Figure 5. Principal component analysis of samples for TCGA. (A) PCA shows samples divisibility from the low risk- and high-risk group based on all gene expression. (B) PCA shows samples divisibility from the low risk- and high-risk group based on immune gene expression. (C) PCA shows samples divisibility from the low risk- and high-risk group based on immune lncRNA expression. (D) PCA shows samples divisibility from the low risk- and high-risk group based on lncRNA signature expression.
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Figure 6. Expression profiles of the lncRNA signature in pan-cancer. (A) AL031985.3expression in pan-cancer. (B) AC099850.3 expression in pan-cancer. (C) AL365203.2 expression in pan-cancer. (D) AL117336.3 expression in pan-cancer. (E) MSC−AS1 expression in pan-cancer. (F) AC009005.1 expression in pan-cancer.
Discussion
Long noncoding RNA has drawn widespread attention as a potential molecular target for cancer diagnosis and treatment in recent years. Multiomics has developed rapidly following the application of high-throughput screening to cancer diagnosis and therapy [36-38]. Several studies have proposed that long noncoding RNAs are involved in tumor genesis and progression and that they contribute to the immune system [39-41]. For example, Jiang
Conclusion
We have successfully devised an immune model comprising of six lncRNAs to forecast the outcome of LIHC samples. Our study extensively examined the bioinformatics analysis to evaluate the functionality and efficacy of this lncRNA signature. The results derived from the observations suggest that this signature holds the potential to introduce a novel method for precise diagnosis and prognosis. Nevertheless, it is crucial to conduct clinical trials and functional tests in order to comprehend the underlying mechanism before considering its applicability in a clinical setting.
Abbreviations
ACT: adoptive cellular immunotherapy, AFP: alpha-fetoprotein, AIC: Akaike information criterion, AJCC: American Joint Committee on Cancer, AUC: area under the curve, BMI: body mass index, CTLA4: cytotoxic T-lymphocyte antigen-4, FPKM: fragments per kilobase of exon model per million mapped fragments, GO: gene ontology, GSEA: gene set enrichment analysis, LIHC: liver hepatocellular carcinoma, lncRNA: long non-coding RNA, OS: overall survival, PD-1: programmed cell death-1, ROC: receiver operating characteristic, PCA: principal component analysis, TCGA: The Cancer Genome Atlas, TME: tumor microenvironment, Treg cells: regulatory T cells.
Acknowledgments
This study was supported by : (1) “Special Project for Scientific Research and Cultivation of Young Physician” of Gusu School, Nanjing Medical University (GSKY20230503) to Rui Kong. (2) Shanghai “Rising Stars of Medical Talent” Youth Development Program-Outstanding Youth Medical Talents (No.SHWJRS2021-99) to Jie Lu. (3) Shanghai Pudong New Area Science and Technology Commission (No.PKJ2021-Y10) to Jie Lu. (4) Specialty Feature Construction Project of Pudong Health and Family Planning Commission of Shanghai?PWZzb2022-14) to Jie Lu.
Authors Contributions
Rui Kong and Nan Wang performed the data analyses. Rui Kong and Jie Lu wrote and revised the manuscript. Jie Lu and Chunli Zhou designed the study. All the authors have read and approved the final manuscript.
Conflict of Interest
The authors have no financial conflicts of interest to declare.
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Table 1 . Correlation of immune genes and associated lncRNAs..
Immune Gene lncRNA Cor P valueRegulation KMT2A AP001318.2 0.460 5.45E-21 Positive NCOA6 AC012510.1 0.462 3.55E-21 Positive TRAF2 SREBF2-AS1 0.451 3.66E-20 Positive RPS19 AC132192.2 0.614 4.03E-40 Positive TRAF2 AL035446.1 0.463 3.12E-21 Positive HELLS AL360181.2 0.455 1.55E-20 Positive PRKRA AP002884.1 0.416 4.67E-17 Positive RPS19 AL109615.3 -0.401 7.64E-16 Negative APOA2 FLJ42351 -0.460 6.18E-21 Negative NCK2 MSC-AS1 0.408 2.10E-16 Positive KMT2A AC009005.1 0.483 2.82E-23 Positive ITGB2 AL031985.3 0.679 7.50E-52 Positive DPP4 AL117336.3 0.512 2.18E-26 Positive HDAC7 AL365203.2 0.462 3.49E-21 Positive CKLF AC099850.3 0.431 2.26E-18 Positive
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Table 2 . Detailed information of the six-lncRNA signature..
Gene symbol Gene_ID Location coef MSC-AS1 ENSG00000235531.8 chr8: 71828167-72002405 0.3293 AC009005.1 ENSG00000267751.4 chr19: 567212-571745 0.3111 AL117336.3 ENSG00000271335.4 chr10: 35314552-35320998 0.3428 AL031985.3 ENSG00000260920.2 chr1: 40464319-40466767 0.4886 AL365203.2 ENSG00000273038.2 chr10:32887255-32889311 0.2210 AC099850.3 ENSG00000265415.1 chr17:59202677-59203829 0.1741
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Table 3 . Relationship between the risk score of the lncRNA signature for OS and clinical features..
Low risk/high risk Pearson χ2 P Age 0.072 0.788 > = 55 119/116 < 55 53/55 Gender 3.565 0.059 Female 47/63 Male 125/108 TNM stage 8.404 0.004 I/II 130/108 III/IV 30/53 G 12.032 0.001 G1/G2 123/91 G3/G4 47/77 AFP (ng/ml) 5.917 0.015 > = 20 58/94 < 20 64/57 BMI 0.213 0.645 > =25 80/73 < 25 81/82 Race 0.268 0.605 White 83/86 Asian 77/71
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