A Prognostic Microenvironment-Related Immune Signature via ESTIMATE (PROMISE Model) Predicts Overall Survival of Patients With Glioma

ObjectiveIn the development of immunotherapies in gliomas, the tumor microenvironment (TME) needs to be investigated. We aimed to construct a prognostic microenvironment-related immune signature via ESTIMATE (PROMISE model) for glioma.MethodsStromal score (SS) and immune score (IS) were calculated via ESTIMATE for each glioma sample in the cancer genome atlas (TCGA), and differentially expressed genes (DEGs) were identified between high-score and low-score groups. Prognostic DEGs were selected via univariate Cox regression analysis. Using the lower-grcade glioma (LGG) data set in TCGA, we performed LASSO regression based on the prognostic DEGs and constructed a PROMISE model for glioma. The model was validated with survival analysis and the receiver operating characteristic (ROC) in TCGA glioma data sets (LGG, glioblastoma multiforme [GBM] and LGG+GBM) and Chinese glioma genome atlas (CGGA). A nomogram was developed to predict individual survival chances. Further, we explored the underlying mechanisms using gene set enrichment analysis (GSEA) and Cibersort analysis of tumor-infiltrating immune cells between risk groups as defined by the PROMISE model.ResultsWe obtained 220 upregulated DEGs and 42 downregulated DEGs in both high-IS and high-SS groups. The Cox regression highlighted 155 prognostic DEGs, out of which we selected 4 genes (CD86, ANXA1, C5AR1, and CD5) to construct a PROMISE model. The model stratifies glioma patients in TCGA as well as in CGGA with distinct survival outcome (P<0.05, Hazard ratio [HR]>1) and acceptable predictive accuracy (AUCs>0.6). With the nomogram, an individualized survival chance could be predicted intuitively with specific age, tumor grade, Isocitrate dehydrogenase (IDH) status, and the PROMISE risk score. ROC showed significant discrimination with the area under curves (AUCs) of 0.917 and 0.817 in TCGA and CGGA, respectively. GSEA between risk groups in both data sets were significantly enriched in multiple immune-related pathways. The Cibersort analysis highlighted four immune cells, i.e., CD 8 T cells, neutrophils, follicular helper T (Tfh) cells, and Natural killer (NK) cells.ConclusionsThe PROMISE model can further stratify both LGG and GBM patients with distinct survival outcomes.These findings may help further our understanding of TME in gliomas and shed light on immunotherapies.

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Development and Validation of an Mesenchymal-Related Long Non-Coding RNA Prognostic Model in Glioma

Frontiers in Oncology ◽

10.3389/fonc.2021.726745 ◽

2021 ◽

Vol 11 ◽

Author(s):

Kebing Huang ◽

Xiaoyu Yue ◽

Yinfei Zheng ◽

Zhengwei Zhang ◽

Meng Cheng ◽

...

Keyword(s):

High Risk ◽

Prognostic Model ◽

Cox Regression ◽

Risk Groups ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Migration And Invasion ◽

Aberrant Expression ◽

Cox Regression Analysis ◽

Genome Atlas

Glioma is well known as the most aggressive and prevalent primary malignant tumor in the central nervous system. Molecular subtypes and prognosis biomarkers remain a promising research area of gliomas. Notably, the aberrant expression of mesenchymal (MES) subtype related long non-coding RNAs (lncRNAs) is significantly associated with the prognosis of glioma patients. In this study, MES-related genes were obtained from The Cancer Genome Atlas (TCGA) and the Ivy Glioblastoma Atlas Project (Ivy GAP) data sets of glioma, and MES-related lncRNAs were acquired by performing co-expression analysis of these genes. Next, Cox regression analysis was used to establish a prognostic model, that integrated ten MES-related lncRNAs. Glioma patients in TCGA were divided into high-risk and low-risk groups based on the median risk score; compared with the low-risk groups, patients in the high-risk group had shorter survival times. Additionally, we measured the specificity and sensitivity of our model with the ROC curve. Univariate and multivariate Cox analyses showed that the prognostic model was an independent prognostic factor for glioma. To verify the predictive power of these candidate lncRNAs, the corresponding RNA-seq data were downloaded from the Chinese Glioma Genome Atlas (CGGA), and similar results were obtained. Next, we performed the immune cell infiltration profile of patients between two risk groups, and gene set enrichment analysis (GSEA) was performed to detect functional annotation. Finally, the protective factors DGCR10 and HAR1B, and risk factor SNHG18 were selected for functional verification. Knockdown of DGCR10 and HAR1B promoted, whereas knockdown of SNHG18 inhibited the migration and invasion of gliomas. Collectively, we successfully constructed a prognostic model based on a ten MES-related lncRNAs signature, which provides a novel target for predicting the prognosis for glioma patients.

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TEAD4 Serves as a Prognostic Biomarker and Correlates With Immune Phenotype in Lower-Grade Gliomas

10.21203/rs.3.rs-957415/v1 ◽

2021 ◽

Author(s):

Mu Chen ◽

Bingsong Huang ◽

Lei Zhu ◽

Kui Chen ◽

Hao Lian ◽

...

Keyword(s):

Tumor Microenvironment ◽

Clinical Outcome ◽

Immune Cells ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

Lower Grade ◽

Ppi Network ◽

Mesenchymal Transition ◽

Cox Regression Analysis ◽

Genome Atlas

Abstract Background: Tumor-infiltrating immune cells (TIICs), which play a pivotal role in the tumor microenvironment, are intimately related to tumor progression and clinical outcome. It remains unclear which factors influence tumor immune infiltration in lower-grade gliomas (LGGs). TEAD4 (TEA Domain Transcription Factor 4) is an essential member of the Hippo pathway that is involved in cancer progression, epithelial-mesenchymal transition, metastasis, and cancer stem cell function across multiple types of cancers. However, the prognostic value of TEAD4 and its association with TIICs in LGG have been hardly studied. Methods: LGG data were obtained from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA). TEAD4 expression between different groups was compared by R and survival analysis was implemented by Kaplan–Meier curves. In Virto experiments were conducted to investigate the role of TEAD4 in glioma cells. Gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) network were used to investigate the differential biological processes and signaling pathways. Multiple computational methods were employed to estimate the association between TEAD4 expression and tumor microenvironment in LGG. Correlations were analyzed by Spearman correlationResults: TEAD4 expression was up-regulated in higher-grade gliomas and correlated with a poorer clinical outcome. Glioma cell proliferation and migration were promoted by TEAD4 overexpression. GSEA and PPI network indicated that multiple immune-related pathways and hub genes were closely associated with TEAD4 expression in LGG specimens. TEAD4 expression was negatively associated with glioma purity. Multivariate Cox regression analysis indicated that TEAD4 expression and tumor purity were independent prognostic factors in LGG. TEAD4 expression was positively correlated with the infiltration of multiple immune cells, including plasma cells, CD8+ T cells, and macrophages M1 and M2. Correlation analysis showed that the TEAD4 level can predict the efficacy of immune checkpoint blockade therapy. Conclusions: TEAD4 is highly related to glioma malignancy grades and multiple immune cell infiltration, suggesting TEAD4 can serve as a new biomarker for anti-cancer therapies in LGG.

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Identification of Potential Biomarkers Associated With Immune Infiltration in Papillary Renal Cell Carcinoma

10.21203/rs.3.rs-769170/v1 ◽

2021 ◽

Author(s):

Ran Deng ◽

Jianpeng Li ◽

Hong Zhao ◽

Zhirui Zou ◽

Jiangwei Man ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Immune Cells ◽

Renal Cell ◽

Cox Regression ◽

Papillary Renal Cell Carcinoma ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

Hub Genes ◽

Cox Regression Analysis

Abstract Background: Immunotherapeutic approaches have recently emerged as effective treatment regimens against various types of cancer. However, the immune-mediated mechanisms surrounding papillary renal cell carcinoma (pRCC) remain unclear. This study aimed to investigate the tumor microenvironment (TME) and identify the potential immune-related biomarkers for pRCC.Methods: The CIBERSORT algorithm was used to calculate the abundance ratio of immune cells in each pRCC sample downloaded from the database UCSC Xena. Univariate Cox analysis was used to select the prognostic-related tumor-infiltrating immune cells (TIICs). Multivariate Cox regression analysis was performed to develop a signature based on the selected prognostic-related TIICs. Then, these pRCC samples were divided into low- and high-risk groups according to the obtained signature. Analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were performed to investigate the biological function of the DEGs (differentially expressed genes) between the high- and low-risk groups. The hub genes were identified using a Weighted Gene Co-Expression Network Analysis (WGCNA) and a Protein-Protein Interaction (PPI) analysis. The hub genes were subsequently validated using Kaplan-Meier survival analysis, Receiver Operating Characteristic (ROC) analysis, a nomogram prediction model, and via the Gene Expression Omnibus (GEO) database, and the Human Protein Atlas (HPA) database. Finally, we validated the correlation between the nine hub genes and immune cells using the XCELL algorithm.Results: According to our analyses, nine immune cells play a vital role in the TME of pRCC. Our analyses also obtained nine potential immune-related biomarkers for pRCC, including TOP2A, BUB1B, BUB1, TPX2, PBK, CEP55, ASPM, RRM2, and CENPF.Conclusion: In this study, our data revealed the crucial TIICs and potential immune-related biomarkers for pRCC and provided compelling insights into the pathogenesis and potential therapeutic targets for pRCC.

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Prognostic Value and Immune Infiltration of Novel Biomarkers in Glioma: Evidence From a Comprehensive Analysis of Tumor Microenvironment

10.21203/rs.3.rs-125889/v1 ◽

2020 ◽

Author(s):

Bolun Zhang ◽

Feng Guan ◽

Bin Dai ◽

Guangtong Zhu ◽

Beibei Mao ◽

...

Keyword(s):

Tumor Microenvironment ◽

Tumor Progression ◽

Immune Cells ◽

Stromal Cells ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

Ppi Network ◽

Cox Regression Analysis ◽

Intersection Analysis ◽

Genome Atlas

Abstract BackgroundIn the glioma microenvironment, infiltrating immune cells has been shown to possess beneficial effects for tumor progression. Immune cells and stromal cells dominate the tumor microenvironment in glioma. The complex interplay between the tumor progression with immune cells or stromal cells was still unknown. MethodsIn this study, we used Estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) calculations to calculate the proportion of tumour-infiltrating immune cells (TIC) and the number of immune and stromal components in glioma cases from the cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases. Differentially expressed genes (DEG) were analyzed by COX regression analysis and protein-protein interaction (PPI) network construction. Then, JAK3, IL2RB and CD3E were identified as predictors by the intersection analysis of univariate COX and PPI, and further analysis showed that the expression of them were positively correlated with survival and clinicopathological characteristics of glioma patients. Finally, the Cell type Identification By Estimating Relative Subsets Of known RNA Transcripts (CIBERSORT) deconvolution algorithm was applied to quantify the fraction and infiltration of 22 types of immune cells in glioma. ResultsOur results showed that ESTIMATEScores Were Correlated with the Survival of glioma Patients, DEGs Shared by ImmuneScore and StromalScore were predominantly presented as the enrichment of immune-related genes gene set enrichment analysis (GSEA). The intersection analysis of PPI network and univariate COX regression enabled us to identify three genes (JAK3, IL2RB and CD3E) that had never been reported before, whose expression was correlated with clinical characteristics such as survival and WHO grading of these patients. CITICSORT analysis of TIC ratio showed that B cell memory and CD8 + T cells were positively correlated with JAK3, IL2RB and CD3E expression, suggesting that these genes may be responsible for maintaining the immunodominant state of TME. CIBERSORT analysis for the proportion of TICs revealed that the levels of JAK3, IL2RB and CD3E affected the immune activity of TME.ConclusionOur results confirmed that the JAK3, IL2RB and CD3E can be used as diagnostic and prognostic biomarkers for glioma and may be used as therapeutic targets in the future.

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Molecular Classification Based on Prognostic and Cell Cycle-Associated Genes in Patients With Colon Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.636591 ◽

2021 ◽

Vol 11 ◽

Author(s):

Zhiyuan Zhang ◽

Meiling Ji ◽

Jie Li ◽

Qi Wu ◽

Yuanjian Huang ◽

...

Keyword(s):

Cell Cycle ◽

Colon Cancer ◽

Cox Regression ◽

Enrichment Analysis ◽

Molecular Classification ◽

Original Data ◽

Gene Set Enrichment Analysis ◽

Data Sets ◽

Data Set ◽

Cox Regression Analysis

The molecular classification of patients with colon cancer is inconclusive. The gene set enrichment analysis (GSEA) of dysregulated genes among normal and tumor tissues indicated that the cell cycle played a crucial role in colon cancer. We performed univariate Cox regression analysis to find out the prognostic-related genes, and these genes were then intersected with cell cycle-associated genes and were further recognized as prognostic and cell cycle-associated genes. Unsupervised non-negative matrix factorization (NMF) clustering was performed based on cell cycle-associated genes. Two subgroups were identified with different overall survival, clinical features, cell cycle enrichment profile, and mutation profile. Through nearest template prediction (NTP), the molecular classification could be effectively repeated in the original data set and validated in several independent data sets indicating that the classification is highly repeatable. Furthermore, we constructed two prognostic signatures in two subgroups, respectively. Our molecular classification based on cell cycle may provide novel insight into the treatment and the prognosis of colon cancer.

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Significance of m 6A Methyltransferase‑Related lncRNAs in the Prognosis and Immunotherapy of Hepatocellular Carcinoma

10.21203/rs.3.rs-165071/v1 ◽

2021 ◽

Author(s):

Lili Li ◽

Rongrong Xie ◽

Qichun Wei

Keyword(s):

Hepatocellular Carcinoma ◽

Correlation Analysis ◽

Immune Cells ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Immune Checkpoints ◽

Regression Analyses ◽

Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is one of the leading causes of mortality worldwide. N6-methyladenosine (m6A) methyltransferase, has been proved to act as an oncogene in several human cancers. However, little is known about its relationship with the long non-coding RNAs (lncRNAs) that remains elusive in HCC.Methods: We comprehensively integrated gene expression data acquired from 371 HCC and 50 normal tissues in The Cancer Genome Atlas (TCGA) database. Differentially expressed protein-coding genes (DE-PCGs)/lncRNAs (DE-lncRs) analysis and univariate regression & Kaplan-Meier (K-M) analysis was performed to identify m6A methyltransferase‑related lncRNAs that were related to overall survival (OS). m6A methyltransferase‑related lncRNA signature was constructed using the Least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Furthermore, Cox regression analysis was applied to identify independent prognostic factors in HCC. The signature was validated in an internal validation set. Finally, the correlation analysis between gene signature and immune cells infiltration was also investigated via single-sample Gene Set Enrichment Analysis (ssGSEA) and immunotherapy response was calculated through Tumor Immune Dysfunction and Exclusion (TIDE) algorithm.Results: A total of 21 m6A methyltransferase-related lncRNAs were screened out according to Spearman correlation analysis with the immune score (|R| > 0.3, P < 0.05). We selected 3 prognostic lncRNAs to construct m6A methyltransferase-related lncRNA signature through univariate and LASSO Cox regression analyses. The univariate and multivariate Cox regression analyses demonstrated that the lncRNAs signature was a robust independent prognostic factor in OS prediction with high accuracy. The GSEA also suggested that the m6A methyltransferase-related lncRNAs were involved in the immune-related biological processes and pathways which were very well-known in the context of HCC tumorigenesis. Besides, we found that the lncRNAs signature was strikingly correlated with the tumor microenvironment (TME) immune cells infiltration and expression of critical immune checkpoints. Finally, results from the TIDE analysis revealed that the m6A methyltransferase-related lncRNAs could efficiently predict the clinical response of immunotherapy in HCC.Conclusion: Together, our study screened potential prognostic m6A methyltransferase related lncRNAs and established a novel m6A methyltransferase-based prognostic model of HCC, which not only provides new potential prognostic biomarkers and therapeutic targets but also deepens our understanding of tumor immune microenvironment status and lays a theoretical foundation for immunotherapy.

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CLEC10A is a Potential Prognostic Biomarker and Immune Regulator of Tumor Microenvironment in Breast Cancer

10.21203/rs.3.rs-295872/v1 ◽

2021 ◽

Author(s):

Jincheng He ◽

Lei Jiang ◽

Jun Wang ◽

Guangtao Min ◽

Xiangwen Wang ◽

...

Keyword(s):

Breast Cancer ◽

T Cells ◽

B Cells ◽

Tumor Microenvironment ◽

Immune Cells ◽

Cox Regression ◽

Clinical Stage ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis

Abstract The communication between tumor cells and immune cells influences the ecology of the tumor microenvironment in breast cancer, as well as the disease progression and clinical outcome. The aim of this study was to investigate the prognostic value of the immunomodulatory factor CLEC10A in breast cancer. We applied the CIBERSORT and ESTIMATE calculation methods to calculate the proportion of tumor-infiltrating immune cells (TICs) and the amount of immune and stromal components in 1053 BRCA cases from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were analyzed by COX regression analysis and protein-protein interaction (PPI) network construction. Then, CLEC10A was identified as a prognostic factor by the intersection analysis of univariate COX and PPI. Further analysis revealed that CLEC10A expression was negatively correlated with the clinical pathologic characteristics (age, clinical stage) and positively correlated with survival of BRCA patients. Gene set enrichment analysis (GSEA) showed that genes in the high CLEC10A expression group were mainly enriched in immune-related activities. Genes in the low CLEC10A expression group were enriched in biochemical functions. CIBERSORT analysis of the proportion of TICs revealed that Macrophages M1, B cells memory, B cells naive, T cells CD4+ memory activated, T cells CD8+, and T cells gamma delta were positively correlated with CLEC10A expression, and Macrophages M0, Macrophages M2, Neutrophils, and NK cells resting were positively correlated with CLEC10A expression was negatively correlated, suggesting that CLEC10A may be an important factor in the immune regulation of the tumor microenvironment, especially in mediating the anti-tumor immune response of tumor-infiltrating immune cells at the tumor initiation stage. Therefore, CLEC10A expression may contribute to the prognosis of BRCA patients and provide a new idea for the immunotherapy of BRCA.

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Prognostic Signature for Lung Adenocarcinoma Patients Based on Cell-Cycle-Related Genes

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.655950 ◽

2021 ◽

Vol 9 ◽

Author(s):

Wei Jiang ◽

Jiameng Xu ◽

Zirui Liao ◽

Guangbin Li ◽

Chengpeng Zhang ◽

...

Keyword(s):

Cell Cycle ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Training Group ◽

Risk Groups ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

External Test

ObjectiveTo screen lung adenocarcinoma (LUAC)-specific cell-cycle-related genes (CCRGs) and develop a prognostic signature for patients with LUAC.MethodsThe GSE68465, GSE42127, and GSE30219 data sets were downloaded from the GEO database. Single-sample gene set enrichment analysis was used to calculate the cell cycle enrichment of each sample in GSE68465 to identify CCRGs in LUAC. The differential CCRGs compared with LUAC data from The Cancer Genome Atlas were determined. The genetic data from GSE68465 were divided into an internal training group and a test group at a ratio of 1:1, and GSE42127 and GSE30219 were defined as external test groups. In addition, we combined LASSO (least absolute shrinkage and selection operator) and Cox regression analysis with the clinical information of the internal training group to construct a CCRG risk scoring model. Samples were divided into high- and low-risk groups according to the resulting risk values, and internal and external test sets were used to prove the validity of the signature. A nomogram evaluation model was used to predict prognosis. The CPTAC and HPA databases were chosen to verify the protein expression of CCRGs.ResultsWe identified 10 LUAC-specific CCRGs (PKMYT1, ETF1, ECT2, BUB1B, RECQL4, TFRC, COCH, TUBB2B, PITX1, and CDC6) and constructed a model using the internal training group. Based on this model, LUAC patients were divided into high- and low-risk groups for further validation. Time-dependent receiver operating characteristic and Cox regression analyses suggested that the signature could precisely predict the prognosis of LUAC patients. Results obtained with CPTAC, HPA, and IHC supported significant dysregulation of these CCRGs in LUAC tissues.ConclusionThis prognostic prediction signature based on CCRGs could help to evaluate the prognosis of LUAC patients. The 10 LUAC-specific CCRGs could be used as prognostic markers of LUAC.

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Mining novel cell glycolysis related gene markers that can predict the survival of colon adenocarcinoma patients

Bioscience Reports ◽

10.1042/bsr20201427 ◽

2020 ◽

Vol 40 (8) ◽

Author(s):

Sihan Chen ◽

Guodong Cao ◽

Wei Wu ◽

Yida Lu ◽

Xiaobo He ◽

...

Keyword(s):

Cox Regression ◽

Mechanistic Model ◽

Single Gene ◽

Colon Adenocarcinoma ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Gene Markers ◽

Cox Regression Analysis

Abstract Colon adenocarcinoma (COAD) is a malignant gastrointestinal tumor, often occurring in the left colon, which is regulated by glycolysis-related processes. In past studies, multiple genes that influence the prognosis for survival have been discovered through bioinformatics analysis. However, the prediction of disease prognosis using a single gene is not an accurate method. In the present study, a mechanistic model was established to achieve better prediction for the prognosis of COAD. COAD-related data downloaded from The Cancer Genome Atlas (TCGA) were correlated with the glycolysis process using gene set enrichment analysis (GSEA) to determine the glycolysis-related genes that regulate COAD. Using COX regression analysis, glycolysis-related genes associated with the prognosis of COAD were identified, and the genes screened to establish a predictive model. The risk scores of this model were correlated with relevant clinical data to obtain a connection diagram between the model and survival rate, tumor characteristic data, etc. Finally, genes in the model were correlated with cells in the tumor microenvironment, finding that they affected specific immune cells in the model. Seven genes related to glycolysis were identified (PPARGC1A, DLAT, 6PC2, P4HA1, STC2, ANKZF1, and GPC1), which affect the prognosis of patients with COAD and constitute the model for prediction of survival of COAD patients.

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Clinical Value of lncRNA LUCAT1 Expression in Liver Cancer and its Potential Pathways

Journal of Gastrointestinal and Liver Diseases ◽

10.15403/jgld-356 ◽

2019 ◽

Vol 28 (4) ◽

pp. 439-447 ◽

Cited By ~ 7

Author(s):

Yan Jiao ◽

Yanqing Li ◽

Bai Ji ◽

Hongqiao Cai ◽

Yahui Liu

Keyword(s):

Liver Cancer ◽

Roc Curve ◽

Cox Regression ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cancer Prognosis ◽

Rank Test ◽

P Value ◽

Rna Seq ◽

Cox Regression Analysis

Background and Aims: Emerging studies indicate that long noncoding RNAs (lncRNAs) play a role as prognostic markers in many cancers, including liver cancer. Here, we focused on the lncRNA lung cancer-associated transcript 1 (LUCAT1) for liver cancer prognosis. Methods: RNA-seq and phenotype data were downloaded from the Cancer Genome Atlas (TCGA). Chisquare tests were used to evaluate the correlations between LUCAT1 expression and clinical features. Survival analysis and Cox regression analysis were used to compare different LUCAT1 expression groups (optimal cutoff value determined by ROC). The log-rank test was used to calculate the p-value of the Kaplan-Meier curves. A ROC curve was used to evaluate the diagnostic value. Gene Set Enrichment Analysis (GSEA) was performed, and competing endogenous RNA (ceRNA) networks were constructed to explore the potential mechanism. Results: Data mining of the TCGA -Liver Hepatocellular Carcinoma (LIHC) RNA-seq data of 371 patients showed the overexpression of LUCAT1 in cancerous tissue. High LUCAT1 expression was associated with age (p=0.007), histologic grade (p=0.009), T classification (p=0.022), and survival status (p=0.002). High LUCAT1 patients had a poorer overall survival and relapse-free survival than low LUCAT1 patients. Multivariate analysis identified LUCAT1 as an independent risk factor for poor survival. The ROC curve indicated modest diagnostic performance. GSEA revealed the related signaling pathways, and the ceRNA network uncovered the underlying mechanism. Conclusion: High LUCAT1 expression is an independent prognostic factor for liver cancer.

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