Comprehensive Analysis of a Nine-Gene Signature Related to Tumor Microenvironment in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common malignancy, leading to more than 1 million related deaths each year. Due to low long-term survival rates, the exploration of molecular mechanisms underlying LUAD progression and novel prognostic predictors is urgently needed to improve LUAD treatment. In our study, cancer-specific differentially expressed genes (DEGs) were identified using the robust rank aggregation (RRA) method between tumor and normal tissues from six Gene Expression Omnibus databases (GSE43458, GSE62949, GSE68465, GSE115002, GSE116959, and GSE118370), followed by a selection of prognostic modules using weighted gene co-expression network analysis. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were applied to identify nine hub genes (CBFA2T3, CR2, SEL1L3, TM6SF1, TSPAN32, ITGA6, MAPK11, RASA3, and TLR6) that constructed a prognostic risk model. The RNA expressions of nine hub genes were validated in tumor and normal tissues by RNA-sequencing and single-cell RNA-sequencing, while immunohistochemistry staining from the Human Protein Atlas database showed consistent results in the protein levels. The risk model revealed that high-risk patients were associated with poor prognoses, including advanced stages and low survival rates. Furthermore, a multivariate Cox regression analysis suggested that the prognostic risk model could be an independent prognostic factor for LUAD patients. A nomogram that incorporated the signature and clinical features was additionally built for prognostic prediction. Moreover, the levels of hub genes were related to immune cell infiltration in LUAD microenvironments. A CMap analysis identified 13 small molecule drugs as potential agents based on the risk model for LUAD treatment. Thus, we identified a prognostic risk model including CBFA2T3, CR2, SEL1L3, TM6SF1, TSPAN32, ITGA6, MAPK11, RASA3, and TLR6 as novel biomarkers and validated their prognostic and predicted values for LUAD.

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The Prognostic Value of Bone Morphogenetic Proteins and Their Receptors in Lung Adenocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.608239 ◽

2021 ◽

Vol 11 ◽

Author(s):

Wangyang Meng ◽

Han Xiao ◽

Rong Zhao ◽

Dong Li ◽

Kuo Li ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Differential Expression ◽

Bone Morphogenetic Proteins ◽

Prognostic Value ◽

Risk Model ◽

Cox Regression ◽

Differentially Expressed ◽

Hub Genes ◽

Significant Differential Expression ◽

Cox Regression Analysis

BackgroundBone morphogenetic proteins (BMPs) regulate tumor progression via binding to their receptors (BMPRs). However, the expression and clinical significance of BMPs/BMPRs in lung adenocarcinoma remain unclear due to a lack of systematic studies.MethodsThis study screened differentially expressed BMPs/BMPRs (deBMPs/BMPRs) in a training dataset combining TCGA-LUAD and GTEx-LUNG and verified them in four GEO datasets. Their prognostic value was evaluated via univariate and multivariate Cox regression analyses. LASSO was performed to construct an initial risk model. Subsequently, after weighted gene co-expression network analysis (WGCNA), differential expression analysis, and univariate Cox regression analysis, hub genes co-expressed with differentially expressed BMPs/BMPRs were filtered out to improve the risk model and explore potential mechanisms. The improved risk model was re-established via LASSO combining hub genes with differentially expressed BMPs/BMPRs as the core. In the testing cohort including 93 lung adenocarcinoma patients, immunohistochemistry (IHC) was performed to verify BMP5 protein expression and its association with prognosis.ResultsBMP2, BMP5, BMP6, GDF10, and ACVRL1 were verified as downregulated in lung adenocarcinoma. Survival analysis identified BMP5 as an independent protective prognostic factor. We also found that BMP5 was significantly correlated with EGFR expression and mutations, suggesting that BMP5 may play a role in targeted therapy. The initial risk model containing only BMP5 showed a significant correlation (HR: 1.71, 95% CI: 1.28−2.28, p: 3e-04) but low prognostic accuracy (AUC of 1-year survival: 0.6, 3-year survival: 0.6, 5-year survival: 0.63). Seventy-nine hub genes co-expressed with BMP5 were identified, and their functions were enriched in cell migration and tumor metastasis. The re-established risk model showed greater prognostic correlation (HR: 2.58, 95% CI: 1.92–3.46, p: 0) and value (AUC of 1-year survival: 0.72, 3-year survival: 0.69, and 5-year survival: 0.68). IHC results revealed that BMP5 protein was also downregulated in lung adenocarcinoma and higher expression was markedly associated with better prognosis (HR: 0.44, 95% CI: 0.23–0.85, p: 0.0145).ConclusionBMP5 is a potential crucial target for lung adenocarcinoma treatment based on significant differential expression and superior prognostic value.

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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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Identification and Analysis of Three Hub Prognostic Genes Related to Osteosarcoma Metastasis

Journal of Oncology ◽

10.1155/2021/6646459 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Jianye Tan ◽

Haofeng Liang ◽

Bingsheng Yang ◽

Shuang Zhu ◽

Guofeng Wu ◽

...

Keyword(s):

Regression Analysis ◽

Cox Regression ◽

Survival Rates ◽

Prognostic Biomarkers ◽

Messenger Rnas ◽

Hub Genes ◽

Cox Regression Analysis ◽

Expression Levels ◽

Kaplan Meier ◽

Before And After

Osteosarcoma (OS) often occurs in children and often undergoes metastasis, resulting in lower survival rates. Information on the complexity and pathogenic mechanism of OS is limited, and thus, the development of treatments involving alternative molecular and genetic targets is hampered. We categorized transcriptome data into metastasis and nonmetastasis groups, and 400 differential RNAs (230 messenger RNAs (mRNAs) and 170 long noncoding RNAs (lncRNAs)) were obtained by the edgeR package. Prognostic genes were identified by performing univariate Cox regression analysis and the Kaplan–Meier (KM) survival analysis. We then examined the correlation between the expression level of prognostic lncRNAs and mRNAs. Furthermore, microRNAs (miRNAs) corresponding to the coexpression of lncRNA-mRNA was predicted, which was used to construct a competitive endogenous RNA (ceRNA) regulatory network. Finally, multivariate Cox proportional risk regression analysis was used to identify hub prognostic genes. Three hub prognostic genes (ABCG8, LOXL4, and PDE1B) were identified as potential prognostic biomarkers and therapeutic targets for OS. Furthermore, transcriptions factors (TFs) (DBP, ESX1, FOS, FOXI1, MEF2C, NFE2, and OTX2) and lncRNAs (RP11-357H14.16, RP11-284N8.3, and RP11-629G13.1) that were able to affect the expression levels of genes before and after transcription were found to regulate the prognostic hub genes. In addition, we identified drugs related to the prognostic hub genes, which may have potential clinical applications. Immunohistochemistry (IHC) and quantitative real-time polymerase chain reaction (qRT-PCR) confirmed that the expression levels of ABCG8, LOXL4, and PDE1B coincided with the results of bioinformatics analysis. Moreover, the relationship between the hub prognostic gene expression and patient prognosis was also validated. Our study elucidated the roles of three novel prognostic biomarkers in the pathogenesis of OS as well as presenting a potential clinical treatment for OS.

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Prognostic Value and Regulatory Network of Immune-Related Genes in Hepatocellular Carcinoma

10.21203/rs.3.rs-123214/v1 ◽

2020 ◽

Author(s):

Xiang Zhou ◽

Keying Zhang ◽

Fa Yang ◽

Chao Xu ◽

Jianhua Jiao ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Risk Model ◽

Cox Regression ◽

Wilcoxon Test ◽

Differentially Expressed ◽

Cox Regression Analysis ◽

Immune Related Genes

Abstract Background: Hepatocellular carcinoma (HCC) is a disease with higher morbidity, mortality, and poor prognosis in the whole world. Understanding the crosslink between HCC and the immune system is essential for people to uncover a few potential and valuable therapeutic strategies. This study aimed to reveal the correlation between HCC and immune-related genes and establish a clinical evaluation model. Methods: We had analyzed the clinical information consisted of 373 HCC and 49 normal samples from the cancer genome atlas (TCGA). The differentially expressed genes (DEGs) were selected by the Wilcoxon test and the immune-related differentially expressed genes (IRDEGs) in DEGs were identified by matching DEGs with immune-related genes downloaded from the ImmPort database. Furthermore, the univariate Cox regression analysis and multivariate Cox regression analysis were performed to construct a prognostic risk model. Then, twenty-two types of tumor immune-infiltrating cells (TIICs) were downloaded from Tumor Immune Estimation Resource (TIMER) and were used to construct the correlational graphs between the TIICs and risk score by the CIBERSORT. Subsequently, the transcription factors (TFs) were gained in the Cistrome website and the differentially expressed TFs (DETFs) were achieved. Finally, the KEGG pathway analysis and GO analysis were performed to further understand the molecular mechanisms between DETFs and PDIRGs.Results: In our study, 5839 DEGs, 326 IRDEGs, and 31 prognosis-related IRDEGs (PIRDEGs) were identified. And 8 optimal PIRDEGs were employed to construct a prognostic risk model by multivariate Cox regression analysis. The correlation between risk genes and clinical characterizations and TIICs has verified that the prognostic model was effective in predicting the prognosis of HCC patients. Finally, several important immune-related pathways and molecular functions of the eight PIRDEGs were significantly enriched and there was a distinct association between the risk IRDEGs and TFs. Conclusion: The prognostic risk model showed a more valuable predicting role for HCC patients, and produced many novel therapeutic targets and strategies for HCC.

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A Tumor Progression Related 7-Gene Signature Indicates Prognosis and Tumor Immune Characteristics of Gastric Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.690129 ◽

2021 ◽

Vol 11 ◽

Author(s):

Fen Liu ◽

Zongcheng Yang ◽

Lixin Zheng ◽

Wei Shao ◽

Xiujie Cui ◽

...

Keyword(s):

Gastric Cancer ◽

High Risk ◽

Cancer Progression ◽

Risk Score ◽

Risk Model ◽

Cox Regression ◽

Hub Genes ◽

Cox Regression Analysis ◽

High Risk Patients ◽

Risk Patients

BackgroundGastric cancer is a common gastrointestinal malignancy. Since it is often diagnosed in the advanced stage, its mortality rate is high. Traditional therapies (such as continuous chemotherapy) are not satisfactory for advanced gastric cancer, but immunotherapy has shown great therapeutic potential. Gastric cancer has high molecular and phenotypic heterogeneity. New strategies for accurate prognostic evaluation and patient selection for immunotherapy are urgently needed.MethodsWeighted gene coexpression network analysis (WGCNA) was used to identify hub genes related to gastric cancer progression. Based on the hub genes, the samples were divided into two subtypes by consensus clustering analysis. After obtaining the differentially expressed genes between the subtypes, a gastric cancer risk model was constructed through univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis. The differences in prognosis, clinical features, tumor microenvironment (TME) components and immune characteristics were compared between subtypes and risk groups, and the connectivity map (CMap) database was applied to identify potential treatments for high-risk patients.ResultsWGCNA and screening revealed nine hub genes closely related to gastric cancer progression. Unsupervised clustering according to hub gene expression grouped gastric cancer patients into two subtypes related to disease progression, and these patients showed significant differences in prognoses, TME immune and stromal scores, and suppressive immune checkpoint expression. Based on the different expression patterns between the subtypes, we constructed a gastric cancer risk model and divided patients into a high-risk group and a low-risk group based on the risk score. High-risk patients had a poorer prognosis, higher TME immune/stromal scores, higher inhibitory immune checkpoint expression, and more immune characteristics suitable for immunotherapy. Multivariate Cox regression analysis including the age, stage and risk score indicated that the risk score can be used as an independent prognostic factor for gastric cancer. On the basis of the risk score, we constructed a nomogram that relatively accurately predicts gastric cancer patient prognoses and screened potential drugs for high-risk patients.ConclusionsOur results suggest that the 7-gene signature related to tumor progression could predict the clinical prognosis and tumor immune characteristics of gastric cancer.

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A Five Autophagy-Related Long Non-Coding RNA Prognostic Model for Patients with Lung Adenocarcinoma

10.21203/rs.3.rs-543033/v1 ◽

2021 ◽

Author(s):

Boxuan Liu ◽

Yun Zhao ◽

Shuanying Yang

Keyword(s):

Lung Adenocarcinoma ◽

Risk Score ◽

Prognostic Model ◽

Risk Model ◽

Cox Regression ◽

Predictive Ability ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Lasso Regression ◽

Cox Regression Analysis

Abstract Background: Lung adenocarcinoma is the most occurred pathological type among non-small cell lung cancer. Although huge progress has been made in terms of early diagnosis, precision treatment in recent years, the overall 5-year survival rate of a patient remains low. In our study, we try to construct an autophagy-related lncRNA prognostic signature that may guide clinical practice.Methods: The mRNA and lncRNA expression matrix of lung adenocarcinoma patients were retrieved from TCGA database. Next, we constructed a co-expression network of lncRNAs and autophagy-related genes. Lasso regression and multivariate Cox regression were then applied to establish a prognostic risk model. Subsequently, a risk score was generated to differentiate high and low risk group and a ROC curve and Nomogram to visualize the predictive ability of current signature. Finally, gene ontology and pathway enrichment analysis were executed via GSEA.Results: A total of 1,703 autophagy-related lncRNAs were screened and five autophagy-related lncRNAs (LINC01137, AL691432.2, LINC01116, AL606489.1 and HLA-DQB1-AS1) were finally included in our signature. Judging from univariate(HR=1.075, 95% CI: 1.046–1.104) and multivariate(HR =1.088, 95%CI = 1.057 − 1.120) Cox regression analysis, the risk score is an independent factor for LUAD patients. Further, the AUC value based on the risk score for 1-year, 3-year, 5-year, was 0.735, 0.672 and 0.662 respectively. Finally, the lncRNAs included in our signature were primarily enriched in autophagy process, metabolism, p53 pathway and JAK/STAT pathway. Conclusions: Overall, our study indicated that the prognostic model we generated had certain predictability for LUAD patients’ prognosis.

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Upregulation of GNPNAT1 Predicts Poor Prognosis and Correlates With Immune Infiltration in Lung Adenocarcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.605754 ◽

2021 ◽

Vol 8 ◽

Author(s):

Wenting Liu ◽

Kaiting Jiang ◽

Jingya Wang ◽

Ting Mei ◽

Min Zhao ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Signaling Pathways ◽

Poor Prognosis ◽

Cox Regression ◽

Clinical Stage ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Clinical Value ◽

Cox Regression Analysis ◽

Normal Tissues

BackgroundGlucosamine 6-phosphate N-acetyltransferase (GNPNAT1) is a key enzyme in the hexosamine biosynthetic pathway (HBP), which functions as promoting proliferation in some tumors, yet its potential biological function and mechanism in lung adenocarcinoma (LUAD) have not been explored.MethodsThe mRNA differential expression of GNPNAT1 in LUAD and normal tissues was analyzed using the Cancer Genome Atlas (TCGA) database and validated by real-time PCR. The clinical value of GNPNAT1 in LUAD was investigated based on the data from the TCGA database. Then, immunohistochemistry (IHC) of GNPNAT1 was applied to verify the expression and clinical significance in LUAD from the protein level. The relationship between GNPNAT1 and epigenetics was explored using the cBioPortal database, and the miRNAs regulating GNPNAT1 were found using the miRNA database. The association between GNPNAT1 expression and tumor-infiltrating immune cells in LUAD was observed through the Tumor IMmune Estimation Resource (TIMER). Finally, Gene set enrichment analysis (GSEA) was used to explore the biological signaling pathways involved in GNPNAT1 in LUAD.ResultsGNPNAT1 was upregulated in LUAD compared with normal tissues, which was verified through qRT-PCR in different cell lines (P < 0.05), and associated with patients’ clinical stage, tumor size, and lymphatic metastasis status (all P < 0.01). Kaplan–Meier (KM) analysis suggested that patients with upregulated GNPNAT1 had a relatively poor prognosis (P < 0.0001). Furthermore, multivariate Cox regression analysis indicated that GNPNAT1 was an independent prognostic factor for LUAD (OS, TCGA dataset: HR = 1.028, 95% CI: 1.013–1.044, P < 0.001; OS, validation set: HR = 1.313, 95% CI: 1.130–1.526, P < 0.001). GNPNAT1 overexpression was correlated with DNA copy amplification (P < 0.0001), low DNA methylation (R = −0.52, P < 0.0001), and downregulation of hsa-miR-30d-3p (R = −0.17, P < 0.001). GNPNAT1 expression was linked to B cells (R = −0.304, P < 0.0001), CD4+T cells (R = −0.218, P < 0.0001), and dendritic cells (R = −0.137, P = 0.002). Eventually, GSEA showed that the signaling pathways of the cell cycle, ubiquitin-mediated proteolysis, mismatch repair and p53 were enriched in the GNPNAT1 overexpression group.ConclusionGNPNAT1 may be a potential prognostic biomarker and novel target for intervention in LUAD.

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Construction and Analysis of Survival-Associated Competing Endogenous RNA Network in Lung Adenocarcinoma

BioMed Research International ◽

10.1155/2021/4093426 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Lixian Chen ◽

Zhonglu Ren ◽

Yongming Cai

Keyword(s):

Prognostic Factors ◽

Lung Adenocarcinoma ◽

Molecular Mechanisms ◽

Cox Regression ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Competing Endogenous Rna ◽

Cox Regression Analysis ◽

Cerna Network

Increasing evidence has shown that noncoding RNAs play significant roles in the initiation, progression, and metastasis of tumours via participating in competing endogenous RNA (ceRNA) networks. However, the survival-associated ceRNA in lung adenocarcinoma (LUAD) remains poorly understood. In this study, we aimed to investigate the regulatory mechanisms underlying ceRNA in LUAD to identify novel prognostic factors. mRNA, lncRNA, and miRNA sequencing data obtained from the GDC data portal were utilized to identify differentially expressed (DE) RNAs. Survival-related RNAs were recognized using univariate Kaplan-Meier survival analysis. We performed functional enrichment analysis of survival-related mRNAs using the clusterProfiler package of R and STRING. lncRNA-miRNA and miRNA-mRNA interactions were predicted based on miRcode, Starbase, and miRanda. Subsequently, the survival-associated ceRNA network was constructed for LUAD. Multivariate Cox regression analysis was used to identify prognostic factors. Finally, we acquired 15 DE miRNAs, 49 DE lncRNAs, and 843 DE mRNAs associated with significant overall survival. Functional enrichment analysis indicated that survival-related DE mRNAs were enriched in cell cycle. The survival-associated lncRNA-miRNA-mRNA ceRNA network was constructed using five miRNAs, 49 mRNAs, and 21 lncRNAs. Furthermore, seven hub RNAs (LINC01936, miR-20a-5p, miR-31-5p, TNS1, TGFBR2, SMAD7, and NEDD4L) were identified based on the ceRNA network. LINC01936 and miR-31-5p were found to be significant using the multifactorial Cox regression model. In conclusion, we successfully constructed a survival-related lncRNA-miRNA-mRNA ceRNA regulatory network in LUAD and identified seven hub RNAs, which provide novel insights into the regulatory molecular mechanisms associated with survival of LUAD, and identified two independent prognostic predictors for LUAD.

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Application of an Autophagy-Related Gene Prognostic Risk Model Based on TCGA Database in Cervical Cancer

Frontiers in Genetics ◽

10.3389/fgene.2020.616998 ◽

2021 ◽

Vol 11 ◽

Author(s):

Huadi Shi ◽

Fulan Zhong ◽

Xiaoqiong Yi ◽

Zhenyi Shi ◽

Feiyan Ou ◽

...

Keyword(s):

Cervical Cancer ◽

Risk Score ◽

Risk Model ◽

Cox Regression ◽

Expression Profiles ◽

Survival Prediction ◽

Aberrant Expression ◽

Cox Regression Analysis ◽

Normal Tissues ◽

Model Based

Background: Autophagy plays an important role in the development of cancer. However, the prognostic value of autophagy-related genes (ARGs) in cervical cancer (CC) is unclear. The purpose of this study is to construct a survival model for predicting the prognosis of CC patients based on ARG signature.Methods: ARGs were obtained from the Human Autophagy Database and Molecular Signatures Database. The expression profiles of ARGs and clinical data were downloaded from the TCGA database. Differential expression analysis of CC tissues and normal tissues was performed using R software to screen out ARGs with an aberrant expression. Univariate Cox, Lasso, and multivariate Cox regression analyses were used to construct a prognostic model which was validated by using the test set and the entire set. We also performed an independent prognostic analysis of risk score and some clinicopathological factors of CC. Finally, a clinical practical nomogram was established to predict individual survival probability.Results: Compared with normal tissues, there were 63 ARGs with an aberrant expression in CC tissues. A risk model based on 3 ARGs was finally obtained by Lasso and Cox regression analysis. Patients with high risk had significantly shorter overall survival (OS) than low-risk patients in both train set and validation set. The ROC curve validated its good performance in survival prediction, suggesting that this model has a certain extent sensitivity and specificity. Multivariate Cox analysis showed that the risk score was an independent prognostic factor. Finally, we mapped a nomogram to predict 1-, 3-, and 5-year survival for CC patients. The calibration curves indicated that the model was reliable.Conclusion: A risk prediction model based on CHMP4C, FOXO1, and RRAGB was successfully constructed, which could effectively predict the prognosis of CC patients. This model can provide a reference for CC patients to make precise treatment strategy.

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Six-long Non-coding RNA Signature to Predict the Prognosis of Lung Adenocarcinoma

10.21203/rs.3.rs-56767/v1 ◽

2020 ◽

Author(s):

Yang Wang ◽

Chengping Hu

Keyword(s):

Lung Adenocarcinoma ◽

Cox Regression ◽

Survival Rates ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Protein Coding ◽

Cox Regression Analysis ◽

Protein Coding Genes

Abstract Background: Long non-coding RNAs (lncRNAs) have been reported to play essential roles in tumorigenesis and cancers prognosis, and they can be a potential cancer prognostic markers. However, in lung adenocarcinoma(LUAD), how lncRNA signatures predict the survival of patients is poorly understood. Our study aims to explore lncRNA signatures and prognostic function in LUAD.Methods: The expression and prognosis data of lncRNAs in LUAD patients was collected from the Cancer Genome Atlas (TCGA) data. All analyses were performed using the R package (version 3.6.2). Metascape, STRING and Cytoscape were used for enrichment analysis and function prediction of the lncRNA co-expressed protein-coding genes.Results: We have collected lncRNA expression data in 466 LUAD tumors, and a six-lncRNA signature(RP11-79H23.3, RP11-309M7.1, CTD-2357A8.3, RP11-108P20.4, U47924.29, LHFPL3-AS2) has been shown to be significantly related to LUAD patients’ overall survival. According to the lncRNA signatures, the high-risk and low-risk groups were divided in LUAD patients with different survival rates. Further multivariable cox regression analysis showed that the prognostic value of this signature was independent of clinical factors. The potential functional roles and hub co-expressed protein-coding genes in the six prognostic lncRNAs are shown in the functional enrichment analysis.Conclusions: These results showed that these six lncRNAs could be independent predicted prognostic biomarkers in LUAD patients.

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