Immunoautophagy-Related Long Noncoding RNA (IAR-lncRNA) Signature Predicts Survival in Hepatocellular Carcinoma

Background: The dysregulation of autophagy and immunological processes has been linked to various pathophysiological conditions, including cancer. Most notably, their particular involvement in hepatocellular carcinoma (HCC) is becoming increasingly evident. This has led to the possibility of developing a prognostic signature based on immuno-autophagy-related (IAR) genes. Given that long non-coding RNAs (lncRNAs) also play a special role in HCC, a combined signature utilizing IAR genes and HCC-associated long noncoding RNAs (as IARlncRNA) may potentially help in the clinical scenario. Method: We used Pearson correlation analysis, Kaplan–Meier survival curves, univariate and multivariate Cox regression, and ROC curves to generate and validate a prognostic immuno-autophagy-related long non-coding RNA (IARlncRNA) signature. The Chi-squared test was utilized to investigate the correlation between the obtained signature and the clinical characteristics. CIBERSORT algorithms and the Wilcoxon rank sum test were applied to investigate the correlation between signature and infiltrating immune cells. GO and KEGG analyses were performed to derived signature-dependent pathways. Results: Herein, we build an IAR-lncRNA signature (as first in the literature) and demonstrate its prognostic ability in hepatocellular carcinoma. Primarily, we identified three IARlncRNAs (MIR210HG, AC099850.3 and CYTOR) as unfavorable prognostic determinants. The obtained signature predicted the high-risk HCC group with shorter overall survival, and was further associated with clinical characteristics such as tumor grade (t = 10.918, p = 0.001). Additionally, several infiltrating immune cells showed varied fractions between the low-risk group and the high-risk HCC groups in association with the obtained signature. In addition, pathways analysis described by the signature clearly distinguishes both risk groups in HCC. Conclusions: The immuno-autophagy-related long non-coding RNA (IARlncRNA) signature we established exhibits a prognostic ability in hepatocellular carcinoma. To our knowledge, this is the first attempt in the literature to combine three determinants (immune, autophagy and LnRNAs), thus requiring molecular validation of this obtained signature in clinical samples.

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A long non-coding RNA-based signature predicts early recurrence in hepatocellular carcinoma

Cancer Biomarkers ◽

10.3233/cbm-210193 ◽

2021 ◽

pp. 1-10

Author(s):

Shuai He ◽

Jin-Feng Li ◽

Hao Tian ◽

Ye Sang ◽

Xiao-Jing Yang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Curative Resection ◽

Risk Model ◽

Cox Regression ◽

Early Recurrence ◽

Support Vector ◽

Term Survival ◽

Cox Regression Analysis ◽

Non Coding Rna ◽

Long Non Coding Rna

BACKGROUND: Early recurrence is the main obstacle for long-term survival of hepatocellular carcinoma (HCC) patients after curative resection. OBJECTIVE: We aimed to develop a long non-coding RNA (lncRNA) based signature to predict early recurrence. METHODS: Using bioinformatics analysis and quantitative reverse transcription PCR (RT-qPCR), we screened for lncRNA candidates that were abnormally expressed in HCC. The expression levels of candidate lncRNAs were analyzed in HCC tissues from 160 patients who underwent curative resection, and a risk model for the prediction of recurrence within 1 year (early recurrence) of HCCs was constructed with linear support vector machine (SVM). RESULTS: A lncRNA-based classifier (Clnc), which contained nine differentially expressed lncRNAs including AF339810, AK026286, BC020899, HEIH, HULC, MALAT1, PVT1, uc003fpg, and ZFAS1 was constructed. In the test set, this classifier reliably predicted early recurrence (AUC, 0.675; sensitivity, 72.0%; specificity, 63.1%) with an odds ratio of 4.390 (95% CI, 2.120–9.090). Clnc showed higher accuracy than traditional clinical features, including tumor size, portal vein tumor thrombus (PVTT) in predicting early recurrence (AUC, 0.675 vs 0.523 vs 0.541), and had much higher sensitivity than Barcelona Clinical Liver Cancer (BCLC; 72.0% vs 50.0%), albeit their AUCs were comparable (0.675 vs 0.678). Moreover, combining Clnc with BCLC significantly increased the AUC, compared with Clnc or BCLC alone in predicting early recurrence (all P< 0.05). Finally, logistic and Cox regression analysis suggested that Clnc was an independent prognostic factor and associated with the early recurrence and recurrence-free survival of HCC patients after resection, respectively (all P= 0.001). CONCLUSIONS: Our lncRNA-based classifier Clnc can predict early recurrence of patients undergoing surgical resection of HCC.

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Construction of a Ferroptosis-Related Long Non-coding RNA Prognostic Signature and Competing Endogenous RNA Network in Lung Adenocarcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.751490 ◽

2021 ◽

Vol 9 ◽

Author(s):

Xiang Fei ◽

Congli Hu ◽

Xinyu Wang ◽

Chaojing Lu ◽

Hezhong Chen ◽

...

Keyword(s):

High Risk ◽

Lung Adenocarcinoma ◽

Risk Score ◽

Cox Regression ◽

Malignant Tumors ◽

Pearson Correlation ◽

Low Risk ◽

Clinical Samples ◽

Prognostic Signature ◽

Competing Endogenous Rna

Ferroptosis-related genes play an important role in the progression of lung adenocarcinoma (LUAD). However, the potential function of ferroptosis-related lncRNAs in LUAD has not been fully elucidated. Thus, to explore the potential role of ferroptosis-related lncRNAs in LUAD, the transcriptome RNA-seq data and corresponding clinical data of LUAD were downloaded from the TCGA dataset. Pearson correlation was used to mine ferroptosis-related lncRNAs. Differential expression and univariate Cox analysis were performed to screen prognosis related lncRNAs. A ferroptosis-related lncRNA prognostic signature (FLPS), which included six ferroptosis-related lncRNAs, was constructed by the least absolute shrinkage and selection operator (LASSO) Cox regression. Patients were divided into a high risk-score group and low risk-score group by the median risk score. Receiver operating characteristic (ROC) curves, principal component analysis (PCA), and univariate and multivariate Cox regression were performed to confirm the validity of FLPS. Enrichment analysis showed that the biological processes, pathways and markers associated with malignant tumors were more common in high-risk subgroups. There were significant differences in immune microenvironment and immune cells between high- and low-risk groups. Then, a nomogram was constructed. We further investigated the relationship between six ferroptosis-related lncRNAs and tumor microenvironment and tumor stemness. A competing endogenous RNA (ceRNA) network was established based on the six ferroptosis-related lncRNAs. Finally, we detected the expression levels of ferroptosis-related lncRNAs in clinical samples through quantitative real-time polymerase chain reaction assay (qRT-PCR). In conclusion, we identified the prognostic ferroptosis-related lncRNAs in LUAD and constructed a prognostic signature which provided a new strategy for the evaluation and prediction of prognosis in LUAD.

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Prognostic evaluation of serum long non-coding RNA H19 for endoscopic keyhole surgery or craniotomy in glioma

Annals of Clinical Biochemistry International Journal of Laboratory Medicine ◽

10.1177/0004563220941888 ◽

2020 ◽

Vol 57 (5) ◽

pp. 365-372

Author(s):

Likui Shen ◽

Min Xu ◽

Zhimin Wang ◽

Zhengquan Yu

Keyword(s):

Survival Rate ◽

Cox Regression ◽

Pearson Correlation ◽

Who Grade ◽

Keyhole Surgery ◽

Relative Expression Level ◽

Non Coding Rna ◽

Endoscopic Keyhole Surgery ◽

Cancer Tissues ◽

Long Non Coding Rna

Background Our study aims to explore the effect of serum long non-coding RNA (lncRNA) H19 level on the long-term prognosis of endoscopic keyhole surgery or craniotomy for glioma. Methods A total of 264 glioma patients were selected. Patients were randomly divided into the Craniotomy-high H19 group, the Craniotomy-low H19 group, the Endoscopic keyhole surgery-high H19 group and the Endoscopic keyhole surgery-low H19 group. Results Compared with adjacent tissues (5.19 ± 1.42), H19 level in cancer tissues (7.45 ± 1.60) and serum (6.44 ± 1.57) was significantly increased ( P < 0.05). Compared with serum, H19 level in cancer tissues was significantly increased ( P < 0.05). Pearson correlation analysis found that the relative expression level of serum H19 in glioma patients was positively correlated with cancer tissues ( rPearson = 0.547, P < 0.001), but had no significant correlation with adjacent tissues ( rPearson = 0.126, P = 0.207). The expression of H19 in serum was significantly related to WHO grade ( rPearson = 0.514, P < 0.001). Compared with the Endoscopic keyhole surgery-high H19 group and the Endoscopic keyhole surgery-low H19 group, the survival rate of patients in the Craniotomy-high H19 group (χ2 = 17.115 and log-rank P < 0.001; χ2 = 18.406 and log-rank P < 0.001) and the Craniotomy-low H19 group was significantly reduced (χ2 = 15.007 and log-rank P < 0.001; χ2 = 16.121 and log-rank P < 0.001). Cox regression results showed that serum H19 level, craniotomy and WHO grade were risk factors for glioma. When H19 level was lower than 6.28, the 30-month survival rate of patients with the endoscopic keyhole surgery was 100%. Conclusion For patients with low H19 level (<5.36), both endoscopic keyhole surgery and craniotomy are available, otherwise, endoscopic keyhole surgery is more recommended.

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An Autophagy-related Long Non-coding RNA Signature for Breast Cancer

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207324666210603122718 ◽

2021 ◽

Vol 24 ◽

Author(s):

Feng Jiang ◽

Chuyan Wu ◽

Ming Wang ◽

Ke Wei ◽

Jimei Wang

Keyword(s):

Breast Cancer ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

Risk Groups ◽

Low Risk ◽

Optimal Cutoff ◽

Non Coding Rna ◽

Long Non Coding Rna

Background: The most prevalent malignant tumor in women is breast cancer (BC). Autophagic therapies have been identified for their contribution in BC cell death. Therefore, the potential prognostic role of long non-coding RNA (lncRNA) related to autophagy in patients with BC was examined. Methods: The lncRNAs expression profiles were derived from The Cancer Genome Atlas (TCGA) database. Throughout univariate Cox regression and multivariate Cox regression test, lncRNA with BC prognosis have been differentially presented. We then defined the optimal cutoff point between high and low-risk groups. The receiver operating characteristic (ROC) curves were drawn to test this signature. In order to examine possible signaling mechanisms linked to these lncRNAs, the Gene Set Enrichment Analysis (GSEA) has been carried out. Results: Based on the lncRNA expression profiles for BC, a 9 lncRNA signature associated with autophagy was developed. The optimal cutoff value for high-risk and low-risk groups was used. The high-risk group had less survival time than the low-risk group. The result of this lncRNA signature was highly sensitive and precise. GSEA study found that the gene sets have been greatly enriched in many cancer pathways. Conclusions: Our signature of 9 lncRNAs related to autophagy has prognostic value for BC, and these lncRNAs related to autophagy may play an important role in BC biology.

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Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.781307 ◽

2021 ◽

Vol 8 ◽

Author(s):

Shenglan Huang ◽

Jian Zhang ◽

Xiaolan Lai ◽

Lingling Zhuang ◽

Jianbing Wu

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Tumor Microenvironment ◽

Immune Cells ◽

Risk Model ◽

Cox Regression ◽

Risk Group ◽

Low Risk ◽

Kaplan Meier ◽

Treatment Responses

Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. The tumor microenvironment (TME) plays a vital role in HCC progression. Thus, this research was designed to analyze the correlation between the TME and the prognosis of HCC patients and to construct a TME-related long noncoding RNA (lncRNA) signature to determine HCC patients’ prognosis and response to immunotherapy.Methods: We assessed the stromal–immune–estimate scores within the HCC microenvironment using the ESTIMATE (Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data) algorithm based on The Cancer Genome Atlas database, and their associations with survival and clinicopathological parameters were also analyzed. Thereafter, differentially expressed lncRNAs were filtered out according to the immune and stromal scores. Cox regression analysis was performed to build a TME-related lncRNA risk signature. Kaplan–Meier analysis was used to explore the prognostic value of the risk signature. Furthermore, we explored the biological functions and immune microenvironment features in the high- and low-risk groups. Lastly, we probed the association of the risk model with treatment responses to immune checkpoint inhibitors (ICIs) in HCC.Results: The stromal, immune, and estimate scores were obtained utilizing the ESTIMATE algorithm for patients with HCC. Kaplan–Meier analysis showed that high scores were significantly correlated with better prognosis in HCC patients. Six TME-related lncRNAs were screened to construct the prognostic model. The Kaplan–Meier curves suggested that HCC patients with low risk had better prognosis than those with high risk. Receiver operating characteristic (ROC) curve and Cox regression analyses indicated that the risk model could predict HCC survival exactly and independently. Functional enrichment analysis revealed that some tumor- and immune-related pathways were activated in the high-risk group. We also revealed that some immune cells, which were important in enhancing immune responses toward cancer, were significantly increased in the low-risk group. In addition, there was a close correlation between ICIs and the risk signature, which can be used to predict the treatment responses of HCC patients.Conclusion: We analyzed the influence of the stromal, immune, and estimate scores on the prognosis of HCC patients. A novel TME-related lncRNA risk model was established, which could be effectively applied as an independent prognostic biomarker and predictor of ICIs for HCC patients.

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Development and Validation of a Pyroptosis-Related Long Non-coding RNA Signature for Hepatocellular Carcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.713925 ◽

2021 ◽

Vol 9 ◽

Author(s):

Zeng-Hong Wu ◽

Zi-Wei Li ◽

Dong-Liang Yang ◽

Jia Liu

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Tumor Microenvironment ◽

Risk Score ◽

Low Risk ◽

Lasso Regression ◽

Non Coding Rna ◽

Novel Biomarkers ◽

Development And Validation ◽

Long Non Coding Rna

Background: Hepatocellular carcinoma (HCC) is a highly aggressive malignant disease, and numerous studies have demonstrated that an inflammatory environment can induce normal cells to transform into cancerous.Methods: We integrated genomic data to comprehensively assess the association between pyroptosis and tumor microenvironment (TME) cell-infiltrating characteristics in HCC, as well as the potential molecular function and clinical significance of lncRNA.Results: The analysis of CNV alteration frequency displayed that CNV changes were common in 33 PRGs, and most were focused on copy number amplification. As a result of lasso regression analysis, nine differentially expressed lncRNAs (AL031985.3, NRAV, OSMR-AS1, AC073611.1, MKLN1-AS, AL137186.2, AL049840.4, MIR4435-2HG, and AL118511.1) were selected as independent prognosis factors of HCC patients. Patients at high risk have poorer survival than those in the low-risk group in training and testing cohorts. A low-risk score was significantly associated with an IC50 of chemotherapeutics such as bortezomib (p < 0.001), but a high-risk score was significantly linked to docetaxel (p < 0.001), implying that signature served as a prospective predictor for chemosensitivity.Conclusion: This work suggests pyroptosis-related lncRNAs features and their potential mechanisms on tumor microenvironment. The exploration may assist in identifying novel biomarkers and assist patients in predicting their prognosis, clinical diagnosis, and management.

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Construction of a Six Immune-Related lncRNAs Signature to Predict Outcome, Immune Cell Infiltration and Immunotherapy Response in Patients with Hepatocellular Carcinoma

10.21203/rs.3.rs-77070/v1 ◽

2020 ◽

Author(s):

Pengcheng Zhou ◽

Jiang Wei ◽

Lu Yuhua ◽

Wang Lei ◽

Zhang Yewei

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Tumor Microenvironment ◽

Immune Cells ◽

Immune Cell ◽

Cox Regression ◽

Risk Group ◽

Immune Cell Infiltration ◽

Biological Processes ◽

Qrt Pcr

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most lethal malignant tumors worldwide with poor prognosis. Growing evidence has demonstrated that immune-related long non-coding RNAs (lncRNAs) are relevant to tumor microenvironment (TME) and can help to assess the effects of immunotherapy and evaluate prognosis. This study aimed to identify an immune-related lncRNA signature for the prospective assessment of immunotherapy and prognosis in HCC. Methods: HCC RNA-seq data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) project database. Firstly, we used ESTIMATE to evaluate the tumor microenvironment (TME). Then, cox regression analysis was used to construct a prognostic signature and the risk score. Univariate Cox regression, multivariate Cox regression, principal components analysis (PCA), the receiver operating characteristic (ROC) curve and stratification analyses were applied to conﬁrm. Gene set enrichment analysis (GSEA) analysis was employed to explore the biological processes and pathways. Besides, CIBERSORT was used to estimate the abundance of tumor-inﬁltrating immune cells (TIICs). Furthermore, the relationship between the immune-related lncRNA signature and immune checkpoint genes was investigated. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) assays were used to demonstrated the expression of the six lncRNAs. Results:.We identified a six immune-related lncRNAs (MSC-AS1, AC145207.5, SNHG3, AL365203.2, AL031985.3, NRAV) with the ability to stratify patients into high-risk and low-risk groups with significantly different survival. Univariate Cox regression, multivariate Cox regression, ROC and stratification analyses conﬁrmed that the six immune-related lncRNA signature was a novel independent prognostic factor in HCC patients. The high-risk group and low-risk group illustrated different distributions in PCA. GSEA suggested that the six immune-related lncRNA signature is involved in the immune-related biological processes and pathways. Besides, the six immune-related lncRNA signature was associated with the infiltration of immune cells. Furthermore, the six immune-related lncRNA signature was associated with the expression of critical immune genes and could predict the clinical response of immunotherapy. Finally, qRT-PCR demonstrated that the six lncRNAs were signiﬁcantly differentially expressed in HCC cell lines and normal hepatic cell line. Conclusions: In summary, we identified a six immune-related lncRNA signature with the ability to predict outcome, immune cell infiltration and immunotherapy response in patients with hepatocellular carcinoma.

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