Prognostic values, ceRNA network, and immune regulation function of SDPR in KRAS-mutant lung cancer

Abstract Background: Serum Deprivation Protein Response (SDPR) plays an important role in formation of pulmonary alveoli. However, the function and values of SDPR in lung cancer remain unknown. We explored prognostic value, expression pattern, and biological function of SDPR in non-small cell lung cancer (NSCLC) and KRAS-mutant lung cancers. Methods: SDPR expression was evaluated by quantitative real-time PCR (RT-qPCR), immunohistochemistry (IHC), and Western blot on human NSCLC cells, lung adenocarcinoma tissue array, KRAS-mutant transgenic mice, TCGA, and GEO datasets. Prognostic values of SDPR were evaluated by Kaplan–Meier and Cox regression analysis. Bioinformatics implications of SDPR including SDPR-combined transcription factors (TFs) and microRNAs were predicted. In addition, correlations between SDPR, immune checkpoint molecules, and tumor infiltration models were illustrated. Results: SDPR expression was downregulated in tumor cells and tissues. Low SDPR expression was an independent factor that correlated with shorter overall survival of patients both in lung cancer and KRAS-mutant subgroups. Meanwhile, ceRNA network was constructed to clarify the regulatory and biological functions of SDPR. Negative correlations were found between SDPR and immune checkpoint molecules (PD-L1, TNFRSF18, TNFRSF9, and TDO2). Moreover, diversity immune infiltration models were observed in NSCLC with different SDPR expression and copy number variation (CNV) patterns. Conclusions: This study elucidated regulation network of SDPR in KRAS-mutant NSCLC, and illustrated correlations between low SDPR expression and suppressed immune system, unfolding a prognostic factor and potential target for the treatment of lung cancer, especially for KRAS-mutant NSCLC.

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Prognostic values, ceRNA network, and immune regulation function of SDPR in KRAS-mutant lung cancer

Cancer Cell International ◽

10.1186/s12935-021-01756-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Xiaoqing Luo ◽

Shunli Peng ◽

Sijie Ding ◽

Qin Zeng ◽

Rong Wang ◽

...

Keyword(s):

Lung Cancer ◽

Immune Checkpoint ◽

Cox Regression ◽

Tissue Array ◽

Lung Cancers ◽

Cox Regression Analysis ◽

Immune Checkpoint Molecules ◽

Cerna Network ◽

Kaplan Meier ◽

Infiltration Models

Abstract Background Serum Deprivation Protein Response (SDPR) plays an important role in formation of pulmonary alveoli. However, the functions and values of SDPR in lung cancer remain unknown. We explored prognostic value, expression pattern, and biological function of SDPR in non-small cell lung cancer (NSCLC) and KRAS-mutant lung cancers. Methods SDPR expression was evaluated by quantitative real-time PCR (RT-qPCR), immunohistochemistry (IHC), and Western blot on human NSCLC cells, lung adenocarcinoma tissue array, KRAS-mutant transgenic mice, TCGA and GEO datasets. Prognostic values of SDPR were evaluated by Kaplan–Meier and Cox regression analysis. Bioinformatics implications of SDPR including SDPR-combined transcription factors (TFs) and microRNAs were predicted. In addition, correlations between SDPR, immune checkpoint molecules, and tumor infiltration models were illustrated. Results SDPR expression was downregulated in tumor cells and tissues. Low SDPR expression was an independent factor that correlated with shorter overall survival of patients both in lung cancer and KRAS-mutant subgroups. Meanwhile, ceRNA network was constructed to clarify the regulatory and biological functions of SDPR. Negative correlations were found between SDPR and immune checkpoint molecules (PD-L1, TNFRSF18, TNFRSF9, and TDO2). Moreover, diversity immune infiltration models were observed in NSCLC with different SDPR expression and copy number variation (CNV) patterns. Conclusions This study elucidated regulation network of SDPR in KRAS-mutant NSCLC, and it illustrated correlations between low SDPR expression and suppressed immune system, unfolding a prognostic factor and potential target for the treatment of lung cancer, especially for KRAS-mutant NSCLC.

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Prognostic values, ceRNA network, and immune regulation function of SDPR in KRAS-mutant lung cancer

10.21203/rs.3.rs-66108/v3 ◽

2021 ◽

Author(s):

Xiaoqing Luo ◽

Shunli Peng ◽

Sijie Ding ◽

Qin Zeng ◽

Rong Wang ◽

...

Keyword(s):

Lung Cancer ◽

Immune Checkpoint ◽

Cox Regression ◽

Tissue Array ◽

Lung Cancers ◽

Cox Regression Analysis ◽

Immune Checkpoint Molecules ◽

Cerna Network ◽

Kaplan Meier ◽

Infiltration Models

Abstract Background: Serum Deprivation Protein Response (SDPR) plays an important role in formation of pulmonary alveoli. However, the functions and values of SDPR in lung cancer remain unknown. We explored prognostic value, expression pattern, and biological function of SDPR in non-small cell lung cancer (NSCLC) and KRAS-mutant lung cancers.Methods: SDPR expression was evaluated by quantitative real-time PCR (RT-qPCR), immunohistochemistry (IHC), and Western blot on human NSCLC cells, lung adenocarcinoma tissue array, KRAS-mutant transgenic mice, TCGA and GEO datasets. Prognostic values of SDPR were evaluated by Kaplan–Meier and Cox regression analysis. Bioinformatics implications of SDPR including SDPR-combined transcription factors (TFs) and microRNAs were predicted. In addition, correlations between SDPR, immune checkpoint molecules, and tumor infiltration models were illustrated.Results: SDPR expression was downregulated in tumor cells and tissues. Low SDPR expression was an independent factor that correlated with shorter overall survival of patients both in lung cancer and KRAS-mutant subgroups. Meanwhile, ceRNA network was constructed to clarify the regulatory and biological functions of SDPR. Negative correlations were found between SDPR and immune checkpoint molecules (PD-L1, TNFRSF18, TNFRSF9, and TDO2). Moreover, diversity immune infiltration models were observed in NSCLC with different SDPR expression and copy number variation (CNV) patterns.Conclusions: This study elucidated regulation network of SDPR in KRAS-mutant NSCLC, and it illustrated correlations between low SDPR expression and suppressed immune system, unfolding a prognostic factor and potential target for the treatment of lung cancer, especially for KRAS-mutant NSCLC.

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Prognostic Values, ceRNA Network, and Immune Regulation Function of SDPR in KRAS-Mutant Lung Cancer

10.21203/rs.3.rs-66108/v1 ◽

2020 ◽

Author(s):

Xiaoqing Luo ◽

Shunli Peng ◽

Sijie Ding ◽

Qin Zeng ◽

Rong Wang ◽

...

Keyword(s):

Lung Cancer ◽

Cox Regression ◽

Lung Cancers ◽

Cox Regression Analysis ◽

Cerna Network ◽

Kaplan Meier ◽

Infiltration Models ◽

Number Variation ◽

Regulation Function ◽

Protein Response

Abstract Background: SDPR plays an important role in formation of pulmonary alveoli. However, the function and values of Serum Deprivation Protein Response (SDPR) remain unknown in lung cancer. We explored prognostic values, expression pattern and biological function of SDPR in NSCLC and KRAS-mutant lung cancers.Methods: SDPR expression were evaluated by RT-qPCR, IHC, Western blotting based on human NSCLC cells, lung adenocarcinoma tissues array, KRAS-mutant transgenetic mice, TCGA and GEO datasets. The prognosis values of SDPR were evaluated by Kaplan–Meier and Cox regression analysis. The bioinformatics implication of SDPR including SDPR-combinated Transcription factors (TFs) and microRNAs were predicted. In addition, correlations between SDPR, immune checkpoint molecules and tumor infiltration models were illustrated.Results: SDPR expression was downregulated among tumor cells and tissues. Low SDPR expression was an independent factor that correlated with shorter overall survival of patients in both lung cancer and KRAS-mutant subgroup. Meanwhile, ceRNA network to clarify the regulatory and biological functions of SDPR was constructed. Negative correlations were found between SDPR and immune checkpiont moluculars (PD-L1, TNFRSF18, TNFRSF9, and TDO2). Moreover, diversity immune infiltration models were observed in NSCLC with different SDPR expression and copy number variation (CNV) patterns.Conclusions: This study elucidates regulation network of SDPR in KRAS-mutant NSCLC, and illustrates correlations between low SDPR expression and suppressed immune systems, unfolding a prognostic factor and potential target for the treatment of lung cancer, especially for KRAS-mutant NSCLC.

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Prognostic Values, ceRNA Network, and Immune Regulation Function of SDPR in KRAS-mutant Lung Cancer

10.21203/rs.3.rs-50230/v1 ◽

2020 ◽

Author(s):

Xiaoqing Luo ◽

Shunli Peng ◽

Sijie Ding ◽

Qin Zeng ◽

Rong Wang ◽

...

Keyword(s):

Lung Cancer ◽

Cox Regression ◽

Lung Cancers ◽

Cox Regression Analysis ◽

Cerna Network ◽

Kaplan Meier ◽

Regulation Network ◽

Infiltration Models ◽

Number Variation ◽

Regulation Function

Abstract Background: SDPR plays an important role in formation of pulmonary alveoli. However, the function and values of SDPR remain unknown in lung cancer. We explored prognostic values, expression pattern and biological function of SDPR in NSCLC and KRAS-mutant lung cancers.Methods: SDPR expression were evaluated by RT-qPCR, IHC, Western blotting based on human NSCLC cells, lung adenocarcinoma tissues array, KRAS-mutant transgenetic mice, TCGA and GEO datasets. The prognosis values of SDPR were evaluated by Kaplan–Meier and Cox regression analysis. The bioinformatics implication of SDPR including SDPR-combinated TFs and microRNAs were predicted. In addition, correlations between SDPR, immune checkpoint molecules and tumor infiltration models were illustrated.Results: SDPR expression was downregulated among tumor cells and tissues. Low SDPR expression was an independent factor that correlated with shorter overall survival of patients in both lung cancer and KRAS-mutant subgroup. Meanwhile, ceRNA network to clarify the regulatory and biological functions of SDPR was constructed. Negative correlations were found between SDPR and immune checkpiont moluculars (PD-L1, TNFRSF18, TNFRSF9, and TDO2). Moreover, diversity immune infiltration models were observed in NSCLC with different SDPR expression and copy number variation (CNV) patterns.Conclusions: This study elucidates regulation network of SDPR in KRAS-mutant NSCLC, and illustrates correlations between low SDPR expression and suppressed immune systems, unfolding a prognostic factor and potential target for the treatment of lung cancer, especially for KRAS-mutant NSCLC.

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Matrix metalloproteinase 12 is an independent prognostic factor predicting postoperative relapse of conventional renal cell carcinoma - a short report

Cellular Oncology ◽

10.1007/s13402-021-00650-9 ◽

2021 ◽

Author(s):

Bence Beres ◽

Maria Yusenko ◽

Lehel Peterfi ◽

Gyula Kovacs ◽

Daniel Banyai

Keyword(s):

Renal Cell ◽

Cox Regression ◽

Tissue Array ◽

Short Report ◽

Free Survival ◽

Cox Regression Analysis ◽

Conventional Renal Cell Carcinoma ◽

Kaplan Meier ◽

Matrix Metalloproteinase 12

Abstract Purpose Approximately 15% of clinically localised conventional renal cell carcinomas (cRCC) develop metastases within 5 years of follow-up. Sarcomatous cRCC is a highly malignant cancer of the kidney. The aim of our study was to identify biomarkers for estimating the postoperative progression of cRCCs. Methods Global microarray-based gene expression analysis of RCCs with and without sarcomatous changes revealed that a high MMP12 expression was associated with a sarcomatous histology. Additionally, we analysed MMP12 expression using a multi-tissue array comprising 736 cRCC patients without metastasis at the time of surgery. The median follow-up time was 66 ± 29 months. Results Immunohistochemistry revealed MMP12 expression in 187 of 736 cRCCs with good follow-up data. Subsequent Kaplan–Meier analysis revealed that patients with MMP12 positive tumours exhibited a significantly shorter tumour-free survival (p < 0.001). In multivariate Cox regression analysis a weak to strong MMP12 expression indicated a 2.4–2.8 times higher risk of postoperative tumour relapse (p < 0.001; p < 0.003, respectively). Conclusions MMP12 may serve as a biomarker to estimate postoperative cRCC relapse and as a possible target for penfluridol therapy.

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Clinical predictors of efficacy for immune checkpoint inhibition in lung cancer patients.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e20600 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e20600-e20600 ◽

Cited By ~ 1

Author(s):

Shijia Zhang ◽

Daniel Fellows Pease ◽

Shilvi Joshi ◽

Yucai Wang ◽

Manish Patel

Keyword(s):

Lung Cancer ◽

Autoimmune Disease ◽

Brain Metastasis ◽

Immune Checkpoint ◽

Clinical Benefit ◽

Cox Regression ◽

Checkpoint Inhibitors ◽

Single Agent ◽

Cox Regression Analysis ◽

Heavily Pretreated

e20600 Background: Immune checkpoint inhibitors (ICIs) have changed the treatment paradigm of advanced NSCLC and SCLC. The populations in clinical trials generally were composed of patients (pts) with excellent performance status (PS), a minimal number of prior lines of therapy, and no history of (h/o) autoimmune disease (AD). The aim of this retrospective study was to investigate the predictors of survival in less fit and more heavily pretreated lung cancer pts at our institution. Methods: Medical records of lung cancer pts who started single-agent ICI from 2015 to 2018 were reviewed for data collection. Progression-free survival (PFS) and overall survival (OS) were compared by age, PS, smoking, PD-L1 expression, brain metastasis, prior lines of systemic therapy, h/o AD, and immune-related adverse events (irAE). Results: We included 150 pts who received at least 1 dose of nivolumab, pembrolizumab, or atezolizumab (median age 66, range 36-92, 56% female, 89% NSCLC, 97% stage 4, 13% never smoker, 34% ECOG ≥ 2, 37% ≥ 2 prior lines of therapy, 29% developed irAE). The overall response rate was 30% and clinical benefit rate 51% (3% CR, 27% PR, 21% SD). The median PFS was 3.7 months (m) overall and 12.3 m for those with ≥ 4 cycles of therapy. The median OS was 12.4 m overall and 26.0 m for those with ≥ 4 cycles. The median PFS and OS were not significantly different by age, PS, smoking, PD-L1, brain metastasis, prior lines of therapy, or h/o AD. The median OS was longer for pts with ECOG 0-1 than those ≥ 2 (14.6 vs 7.5 m, p < .001). The median PFS and OS for pts with irAE were longer compared to those without irAE (12.3 vs 2.6 m, p < .001 for PFS and 28.9 vs 9.1 m, p = .001 for OS). Multiple Cox regression analysis identified ECOG 0-1 (HR 0.54, p = .007) and irAE (HR 0.48, p = .003) to be independently associated with improved OS. 27 pts (18%) required steroids for irAE. There were no treatment-related deaths. Of 11 pts with h/o AD, only 1 experienced disease flare. Conclusions: The clinical benefit of ICIs persists in older or heavily pretreated pts. ECOG 0-1 and incident irAE predicted for improved survival. Survival for pts with ECOG ≥ 2 is very limited, suggesting ICIs should be used judiciously in this group. Therapy was well tolerated, with a low risk for flare of previous autoimmune disease.

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Proportions of Beneficial Factors in MLR, NLR, PLR and D-dimer in Preoperative Peripheral Blood of Patients With Early Stage Lung Cancer as Predictors of Patient Survival After Surgery

10.21203/rs.3.rs-1194999/v1 ◽

2021 ◽

Author(s):

jun wang ◽

huawei li ◽

ran xu ◽

tong lu ◽

jiaying zhao ◽

...

Keyword(s):

Lung Cancer ◽

Regression Analysis ◽

Cox Regression ◽

Early Stage ◽

Progression Free Survival ◽

Early Stage Lung Cancer ◽

Cox Regression Analysis ◽

Lymphocyte Ratio ◽

Kaplan Meier ◽

D Dimer

Abstract ObjectiveThe purpose of this paper is to predict the following items. preoperative baseline monocyte-to-lymphocyte ratio (MLR)、neutrophil-to-lymphocyte ratio (NLR) Platura-to-lymphocyte ratio (PLR) and dimeric fibrin fragment D (D-dimer) associated with clinical outcome in patients with Early Lung Cancer (LC).MethodsWe performed a retrospective analysis of 376 patients with LC. Progression-free survival (PFS) and overall survival (OS) were assessed by Kaplan-Meier, and univariate and multivariate Cox regression analyses were performed to identify prognostic factors. Finally, multivariate Cox regression analysis was used to evaluate the influence of favorable factors on patients’ OS and PFS combined with the basic clinical characteristics of the patient ResultsAmong the variables screened by univariate Cox regression, MLR < 0.22, NLR < 1.99, PLR < 130.55 and D-Dimer < 70.5 (ng/ml) were significantly associated with both better OS and PFS. In multivariate Cox regression analysis, it was determined that MLR and D-Dimer had a better independent correlation with OS (p = 0.009, p = 0.05, respectively), while MLR was only better independently associated with PFS (P = 0.005). Furthermore, according to the number of favorable factors, patients with none of these factors had a significantly worse prognosis than patients with at least one of these factors.ConclusionBaseline characteristics of low MLR, low NLR, low PLR and low D-dimer were associated with better outcomes.

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Identification of a Pyroptosis-Related lncRNA Risk Model for Predicting Prognosis and Immune Response in Colon Adenocarcinoma

10.21203/rs.3.rs-1234682/v1 ◽

2022 ◽

Author(s):

Yuying Tan ◽

Liqing Lu ◽

Xujun Liang ◽

Yongheng Chen

Keyword(s):

Regression Analysis ◽

Risk Model ◽

Cox Regression ◽

Malignant Tumors ◽

Colon Adenocarcinoma ◽

Sequencing Data ◽

Cox Regression Analysis ◽

Immune Infiltration ◽

Cerna Network ◽

Kaplan Meier

Abstract Background: Colon adenocarcinoma (COAD) is one of the most common malignant tumors and diagnosed at an advanced stage with poor prognosis in the world. Pyroptosis is involved in the initiation and progression of tumors. This research focused on constructing a pyroptosis-related ceRNA network to generate a reliable risk model for risk prediction and immune infiltration analysis of COAD.Methods: Transcriptome data, miRNA-sequencing data and clinical information were downloaded from the TCGA database. Firstly, differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs), and lncRNAs (DElncRNAs) were identified to construct a pyroptosis-related ceRNA network. Secondly, a pyroptosis-related lncRNA risk model was developed applying univariate Cox regression analysis and least absolute shrinkage and selection operator method (LASSO) regression analysis. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were utilized to functionally annotate RNAs contained in the ceRNA network. In addition, Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, univariate and multivariate Cox regression, and nomogram were applied to validate this risk model. Finally, the relationship of this risk model with immune cells and immune checkpoint blockade (ICB) related genes were analyzed.Results: Totally 5373 DEmRNAs, 1159 DElncRNAs and 355 DEmiRNAs were identified. A pyroptosis-related ceRNA regulatory network containing 132 lncRNAs, 7miRNAs and 5 mRNAs was constructed and a ceRNA-based pyroptosis-related risk model including 11 lncRNAs was built. Tumor tissues were classified into high- and low- risk groups according to the median risk score. Kaplan-Meier analysis showed that the high-risk group had a shorter survival time; ROC analysis, independent prognostic analysis and nomogram further indicated the risk model was a significant independent prognostic factor which had excellent ability to predict patients’ risk. Moreover, immune infiltration analysis indicated that the risk model was related to immune infiltration cells (i.e., B cells naïve, T cells follicular helper, Macrophages M1, etc.) and ICB-related genes (i.e., PD-1, CTLA4, HAVCR2, etc).Conclusions: This pyroptosis-related lncRNA risk model possessed good prognostic value and the ability to predict the outcome of ICB immunotherapy in COAD.

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Association of Diabetes Status With Survival After Immune Checkpoint Inhibitor Treatment for Advanced Lung Cancer: a Retrospective Study in Japan

10.21203/rs.3.rs-49035/v1 ◽

2020 ◽

Author(s):

Kaori Hisanaga ◽

hiroshi uchino ◽

Naoko Kakisu ◽

Masahiko Miyagi ◽

Fukumi Yoshikawa ◽

...

Keyword(s):

Lung Cancer ◽

Propensity Score ◽

Immune Checkpoint ◽

Cox Regression ◽

Tnm Staging ◽

Wilcoxon Test ◽

Advanced Lung Cancer ◽

Kaplan Meier ◽

Diabetes Status ◽

Organ Systems

Abstract Introduction: Although immune checkpoint inhibitors (ICIs) are promising in the treatment of advanced cancer, they may lead to immune-related adverse events (irAEs), which can affect endocrine organ systems. However, development of the irAEs was associated with improved cancer-specific survival, the risk for years have not been elucidated. We investigated the association of pre-ICI comorbidities—including diabetes—with years and overall survival (OS) and progression-free survival (PFS) in advanced lung cancer. Research design and methods: Patients with lung cancer who were treated with ICIs at the period from September 1, 2015 through July 31, 2018 were retrospectively enrolled. All data were collected from the university patient NEPTUNE database. Hazard ratios were estimated by using Cox regression weighted for propensity scores. Odds ratios were calculated by logistic regression and adjusted for unbalanced variables. The Kaplan–Meier method was used to compare OS, and the generalized Wilcoxon test was used to compare median survival. The results: Among the 88 patients identified, 22 (25.0%) had diabetes (DM) before ICI treatment and 57 (75.0%) did not (non-DM). Iris developed in 12.2% of patients with DM and in 9.1% of patients in non-DM (p=0.87). Diabetes status was not associated with auras risk in relation to baseline characteristics (age, sex, TNM staging, thyroid and renal function) or after propensity score matching analysis (age, TNM staging). During a mean follow-up of 30 months, OS and cancer-specific PFS were significantly higher in patients who developed iris (Kaplan–Meier estimates, p=0·04 and 0·03, respectively). In propensity score–matched analysis, diabetes was significantly associated with lower OS (multivariate hazard ratio, 0·36; 95% CI, 0·13–0·98). Irrespective of eras, PFS was lower among patients with DM than among non-DM (Kaplan–Meier estimate, p=0·04). Conclusions: Pre-existing diabetes was associated with higher mortality in advanced lung cancer, regardless of irAEs development after treatment with ICIs.

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Screening Prognosis-Related lncRNAs Based on WGCNA to Establish a New Risk Score for Predicting Prognosis in Patients with Hepatocellular Carcinoma

Journal of Immunology Research ◽

10.1155/2021/5518908 ◽

2021 ◽

Vol 2021 ◽

pp. 1-20

Author(s):

Xueliang Zhou ◽

Mengmeng Dou ◽

Zaoqu Liu ◽

Dechao Jiao ◽

Zhaonan Li ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Scoring System ◽

Normal Tissue ◽

Cox Regression ◽

Cox Regression Analysis ◽

Cerna Network ◽

Kaplan Meier ◽

Risk Scoring ◽

Risk Scoring System

Background. Hepatocellular carcinoma (HCC) remains an important cause of cancer death. The molecular mechanism of hepatocarcinogenesis and prognostic factors of HCC have not been completely uncovered. Methods. In this study, we screened out differentially expressed lncRNAs (DE lncRNAs), miRNAs (DE miRNAs), and mRNAs (DE mRNAs) by comparing the gene expression of HCC and normal tissue in The Cancer Genome Atlas (TCGA) database. DE mRNAs were used to perform Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Then, the miRNA and lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a competitive endogenous RNA (ceRNA) network by weighted gene coexpression network analysis (WGCNA). Moreover, univariable Cox regression and Kaplan-Meier curve analyses of DE lncRNAs and DE mRNAs were conducted. Finally, the lasso-penalized Cox regression analysis and nomogram model were used to establish a new risk scoring system and predict the prognosis of patients with liver cancer. The expression of survival-related DE lncRNAs was verified by qRT-PCR. Results. A total of 1896 DEmRNAs, 330 DElncRNAs, and 76 DEmiRNAs were identified in HCC and normal tissue samples. Then, the turquoise miRNA and turquoise lncRNA/mRNA modules that were most closely related to the survival time of patients with HCC were screened to construct a ceRNA network by WGCNA. In this ceRNA network, there were 566 lncRNA-miRNA-mRNA regulatory pairs, including 30 upregulated lncRNAs, 16 downregulated miRNAs, and 75 upregulated mRNAs. Moreover, we screened out 19 lncRNAs and 14 hub mRNAs related to prognosis from this ceRNA network by univariable Cox regression and Kaplan-Meier curve analyses. Finally, a new risk scoring system was established by selecting the optimal risk lncRNAs from the 19 prognosis-related lncRNAs through lasso-penalized Cox regression analysis. In addition, we established a nomogram model consisting of independent prognostic factors to predict the survival rate of HCC patients. Finally, the correlation between the risk score and immune cell infiltration and gene set enrichment analysis were determined. Conclusions. In conclusion, the results may provide potential biomarkers or therapeutic targets for HCC and the establishment of the new risk scoring system and nomogram model provides the new perspective for predicting the prognosis of HCC.

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