A Stratification System of Ferroptosis and Iron-Metabolism Related Lncrnas Guides the Prediction of The Survival of Patients with Esophageal Squamous Cell Carcinoma

Abstract Background: As the major type of esophageal cancer (ESCA), esophageal squamous cell carcinoma (ESCC) is also related to the highest malignant level and low survival rates across the world. Increasing people recognize long non-coding RNAs (lncRNAs) as significant mediators in regulating ferroptosis and iron-metabolism. Determining the prognostic value of ferroptosis and iron-metabolism related lncRNAs (FIRLs) in ESCC is thus critical. Methods: Pearson’s correlation analysis was carried out between ferroptosis and iron-metabolism-related genes (FIRGs) and all lncRNAs to derive the FIRLs. Based on weighted gene co-expression network exploration (WCGNA), least absolute shrinkage and selection operator (LASSO) regression and Cox regression analysis, a risk stratification system was established. According to Kaplan-Meier analysis, receiver operating characteristic (ROC) curve analysis, and univariate and multivariate Cox regression analyses, the predictive ability and clinical relevance of the risk stratification system were evaluated. The validity of the established prognostic signature was further examined in TCGA (training set) and GEO (validation set) cohorts. A nomogram with enhanced precision for forecasting OS was set up on basis of the independent prognostic elements. Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied for the identification of pathways in which FIRLs significantly enriched. we used cell culture, transfection, CCK-8, and qRT-PCR as in vitro assays. Results: An 3-FIRLs risk stratification system was developed by multivariate Cox regression analysis to divide patients into two risk groups. Patients in the high-risk group had worse prognosis than patients in the low-risk group. Multivariate Cox regression analysis showed the risk stratification system was an independent prognostic indicator. Receiver operating characteristic curve (ROC) analysis proved the predictive accuracy of the signature. The area under time-dependent ROC curve (AUC) reached 0.853 at 1 year, 0.802 at 2 years, 0.740 at 5 years in the training cohort and 0.712 at 1 year, 0.822 at 2 years, 0.883 at 5 years in the validation cohort. Functional enrichment analysis predicted potential associations of 49 possible upstream regulated FIRGs with ferroptosis and iron-metabolism processes and oncological signatures. Analysis of the immune cell infiltration landscape showed that ESCC in the high-risk group tended be immunologically “cold”. In vitro experiments suggested that LINC01068 promoted ESCC cell proliferation. Conclusion: The risk stratification system based on FIRLs could serve as a reliable tool for forecasting the survival of patients with ESCC.

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A Risk Signature of Nine Autophagy-Related Genes Associated With Immune Microenvironment and Clinical Prognosis in Wilms Tumor

10.21203/rs.3.rs-892534/v1 ◽

2021 ◽

Author(s):

Shaopei Ye ◽

Wenbin Tang ◽

Ke Huang

Keyword(s):

Regression Analysis ◽

High Risk ◽

Risk Score ◽

Cox Regression ◽

Wilms Tumor ◽

Risk Group ◽

High Risk Group ◽

Differentially Expressed ◽

Clinical Prognosis ◽

Cox Regression Analysis

Abstract Background: Autophagy is a biological process to eliminate dysfunctional organelles, aggregates or even long-lived proteins. . Nevertheless, the potential function and prognostic values of autophagy in Wilms Tumor (WT) are complex and remain to be clarifed. Therefore, we proposed to systematically examine the roles of autophagy-associated genes (ARGs) in WT.Methods: Here, we obtained differentially expressed autophagy-related genes (ARGs) between healthy and Wilms tumor from Therapeutically Applicable Research To Generate Effective Treatments(TARGET) and The Cancer Genome Atlas (TCGA) database. The functionalities of the differentially expressed ARGs were analyzed using Gene Ontology. Then univariate COX regression analysis and multivariate COX regression analysis were performed to acquire nine autophagy genes related to WT patients’ survival. According to the risk score, the patients were divided into high-risk and low-risk groups. The Kaplan-Meier curve demonstrated that patients with a high-risk score tend to have a poor prognosis.Results: Eighteen DEARGs were identifed, and nine ARGs were fnally utilized to establish the FAGs based signature in the TCGA cohort. we found that patients in the high-risk group were associated with mutations in TP53. We further conducted CIBERSORT analysis, and found that the infiltration of Macrophage M1 was increased in the high-risk group. Finally, the expression levels of crucial ARGs were verifed by the experiment, which were consistent with our bioinformatics analysis.Conclusions: we emphasized the clinical significance of autophagy in WT, established a prediction system based on autophagy, and identified a promising therapeutic target of autophagy for WT.

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Prognostic model of head and neck squamous cell carcinoma based on 12 ferroptosis-related genes

10.21203/rs.3.rs-349666/v1 ◽

2021 ◽

Author(s):

Sijia Li ◽

Hongyang Zhang ◽

Wei Li

Keyword(s):

Regression Analysis ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Risk Scores ◽

Cox Regression Analysis ◽

Normal Tissues ◽

Model Based ◽

Cox Analysis

Abstract Background: The purpose of our study is establishing a model based on ferroptosis-related genes predicting the prognosis of patients with head and neck squamous cell carcinoma (HNSCC).Methods: In our study, transcriptome and clinical data of HNSCC patients were from The Cancer Genome Atlas, ferroptosis-related genes and pathways were from Ferroptosis Signatures Database. Differentially expressed genes (DEGs) were screened by comparing tumor and adjacent normal tissues. Functional enrichment analysis of DEGs, protein-protein interaction network and gene mutation examination were applied. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression were used to identified DEGs. The model was constructed by multivariate Cox regression analysis and verified by Kaplan-Meier analysis. The relationship between risk scores and other clinical features was also analyzed. Univariate and multivariate Cox analysis was used to verified the independence of our model. The model was evaluated by receiver operating characteristic analysis and calculation of the area under the curve (AUC). A nomogram model based on risk score, age, gender and TNM stages was constructed.Results: We analyzed data including 500 tumor tissues and 44 adjacent normal tissues and 259 ferroptosis-related genes, then obtained 73 DEGs. Univariate Cox regression analysis screened out 16 genes related to overall survival, and LASSO analysis fingered out 12 of them with prognostic value. A risk score model based on these 12 genes was constructed by multivariate Cox regression analysis. According to the median risk score, patients were divided into high-risk group and low-risk group. The survival rate of high-risk group was significantly lower than that of low-risk group in Kaplan-Meier curve. Risk scores were related to T and grade. Univariate and multivariate Cox analysis showed our model was an independent prognostic factor. The AUC was 0.669. The nomogram showed high accuracy predicting the prognosis of HNSCC patients.Conclusion: Our model based on 12 ferroptosis-related genes performed excellently in predicting the prognosis of HNSCC patients. Ferroptosis-related genes may be promising biomarkers for HNSCC treatment and prognosis.

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Risk Stratification for Diffuse Large B-Cell Lymphoma by Integrating Interim Evaluation and International Prognostic Index: A Multicenter Retrospective Study

Frontiers in Oncology ◽

10.3389/fonc.2021.754964 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xue Shi ◽

Xiaoqian Liu ◽

Xiaomei Li ◽

Yahan Li ◽

Dongyue Lu ◽

...

Keyword(s):

High Risk ◽

Risk Stratification ◽

Risk Group ◽

International Prognostic Index ◽

Prognostic Index ◽

B Cell Lymphoma ◽

Risk Groups ◽

High Risk Group ◽

Stratification System ◽

Risk Stratification System

The baseline International Prognostic Index (IPI) is not sufficient for the initial risk stratification of patients with diffuse large B-cell lymphoma (DLBCL) treated with R‐CHOP (rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone). The aims of this study were to evaluate the prognostic relevance of early risk stratification in DLBCL and develop a new stratification system that combines an interim evaluation and IPI. This multicenter retrospective study enrolled 314 newly diagnosed DLBCL patients with baseline and interim evaluations. All patients were treated with R-CHOP or R-CHOP-like regimens as the first-line therapy. Survival differences were evaluated for different risk stratification systems including the IPI, interim evaluation, and the combined system. When stratified by IPI, the high-intermediate and high-risk groups presented overlapping survival curves with no significant differences, and the high-risk group still had >50% of 3-year overall survival (OS). The interim evaluation can also stratify patients into three groups, as 3-year OS and progression-free survival (PFS) rates in patients with stable disease (SD) and progressive disease (PD) were not significantly different. The SD and PD patients had significantly lower 3-year OS and PFS rates than complete remission and partial response patients, but the percentage of these patients was only ~10%. The IPI and interim evaluation combined risk stratification system separated the patients into low-, intermediate-, high-, and very high-risk groups. The 3-year OS rates were 96.4%, 86.7%, 46.4%, and 40%, while the 3-year PFS rates were 87.1%, 71.5%, 42.5%, and 7.2%. The OS comparison between the high-risk group and very high-risk group was marginally significant, and OS and PFS comparisons between any other two groups were significantly different. This combined risk stratification system could be a useful tool for the prognostic prediction of DLBCL patients.

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A Novel Signature to Predict Thyroid Cancer Prognosis and Immune Landscape Using Immune-Related LncRNA Pairs

10.21203/rs.3.rs-1026831/v1 ◽

2021 ◽

Author(s):

BO SONG ◽

Lijun Tian ◽

Fan Zhang ◽

Zheyu Lin ◽

Boshen Gong ◽

...

Keyword(s):

Regression Analysis ◽

Thyroid Cancer ◽

High Risk ◽

Small Molecule ◽

Immune Cell ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Immune Cell Infiltration ◽

Cox Regression Analysis

Abstract Background: Thyroid cancer (TC) is the most common endocrine malignancy worldwide. The incidence of TC is high and increasing worldwide due to continuous improvements in diagnostic technology. TC is still often overtreated due to a lack of reliable diagnostic biomarkers. Therefore, determining accurate prognostic predictions to stratify TC patients is important.Methods: Raw data were downloaded from the TCGA database, and pairwise comparisons were applied to identify differentially expressed immune-related lncRNA (DEirlncRNA) pairs. Then, we used univariate Cox regression analysis and a modified Lasso algorithm on these pairs to construct a risk assessment model for TC. Next, TC patients were assigned to high- and low-risk groups based on the optimal cutoff score of the model for the 1-year ROC curve. We evaluated the signature in terms of prognostic independence, predictive value, immune cell infiltration, ICI-related molecules and small-molecule inhibitor efficacy. Results: We identified 30 DEirlncRNA pairs through Lasso regression, and 14 pairs served as the novel predictive signature. The high-risk group had a significantly poorer prognosis than the low-risk group. Cox regression analysis revealed that this immune-related signature can predict prognosis independently and reliably for TC. With the CIBERSORT algorithm, we found an association between the signature and immune cell infiltration. Additionally, several immune checkpoint inhibitor (ICI)-related molecules, such as PD-1 and PD-L1, showed a negative correlation with the high-risk group. We further found that some commonly used small-molecule inhibitors, such as sunitinib, were related to this new signature. Conclusions: We constructed a prognostic immune-related lncRNA signature that can predict TC patient survival without considering the technical bias of different platforms, and this signature also sheds light on TC overall prognosis and novel clinical treatments, such as ICB therapy and small molecular inhibitors.

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Systemic characterization of alternative splicing related to prognosis and immune infiltration in malignant mesothelioma

BMC Cancer ◽

10.1186/s12885-021-08548-3 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Jinzhi Lai ◽

Hainan Yang ◽

Tianwen Xu

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Risk Score ◽

Regulatory Network ◽

Immune Cell ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Immune Cell Infiltration ◽

Cox Regression Analysis

Abstract Background Malignant mesothelioma (MM) is a relatively rare and highly lethal tumor with few treatment options. Thus, it is important to identify prognostic markers that can help clinicians diagnose mesothelioma earlier and assess disease activity more accurately. Alternative splicing (AS) events have been recognized as critical signatures for tumor diagnosis and treatment in multiple cancers, including MM. Methods We systematically examined the AS events and clinical information of 83 MM samples from TCGA database. Univariate Cox regression analysis was used to identify AS events associated with overall survival. LASSO analyses followed by multivariate Cox regression analyses were conducted to construct the prognostic signatures and assess the accuracy of these prognostic signatures by receiver operating characteristic (ROC) curve and Kaplan–Meier survival analyses. The ImmuCellAI and ssGSEA algorithms were used to assess the degrees of immune cell infiltration in MM samples. The survival-related splicing regulatory network was established based on the correlation between survival-related AS events and splicing factors (SFs). Results A total of 3976 AS events associated with overall survival were identified by univariate Cox regression analysis, and ES events accounted for the greatest proportion. We constructed prognostic signatures based on survival-related AS events. The prognostic signatures proved to be an efficient predictor with an area under the curve (AUC) greater than 0.9. Additionally, the risk score based on 6 key AS events proved to be an independent prognostic factor, and a nomogram composed of 6 key AS events was established. We found that the risk score was significantly decreased in patients with the epithelioid subtype. In addition, unsupervised clustering clearly showed that the risk score was associated with immune cell infiltration. The abundances of cytotoxic T (Tc) cells, natural killer (NK) cells and T-helper 17 (Th17) cells were higher in the high-risk group, whereas the abundances of induced regulatory T (iTreg) cells were lower in the high-risk group. Finally, we identified 3 SFs (HSPB1, INTS1 and LUC7L2) that were significantly associated with MM patient survival and then constructed a regulatory network between the 3 SFs and survival-related AS to reveal potential regulatory mechanisms in MM. Conclusion Our study provided a prognostic signature based on 6 key events, representing a better effective tumor-specific diagnostic and prognostic marker than the TNM staging system. AS events that are correlated with the immune system may be potential therapeutic targets for MM.

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Comprehensive Analysis of Inflammatory Response–Related Genes, and Prognosis and Immune Infiltration in Patients With Low-Grade Glioma

Frontiers in Pharmacology ◽

10.3389/fphar.2021.748993 ◽

2021 ◽

Vol 12 ◽

Author(s):

Tao Han ◽

Zhifan Zuo ◽

Meilin Qu ◽

Yinghui Zhou ◽

Qing Li ◽

...

Keyword(s):

Regression Analysis ◽

High Risk ◽

Immune Cell ◽

Risk Model ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Grade ◽

Immune Microenvironment ◽

Cox Regression Analysis

Background: Although low-grade glioma (LGG) has a good prognosis, it is prone to malignant transformation into high-grade glioma. It has been confirmed that the characteristics of inflammatory factors and immune microenvironment are closely related to the occurrence and development of tumors. It is necessary to clarify the role of inflammatory genes and immune infiltration in LGG.Methods: We downloaded the transcriptome gene expression data and corresponding clinical data of LGG patients from the TCGA and GTEX databases to screen prognosis-related differentially expressed inflammatory genes with the difference analysis and single-factor Cox regression analysis. The prognostic risk model was constructed by LASSO Cox regression analysis, which enables us to compare the overall survival rate of high- and low-risk groups in the model by Kaplan–Meier analysis and subsequently draw the risk curve and survival status diagram. We analyzed the accuracy of the prediction model via ROC curves and performed GSEA enrichment analysis. The ssGSEA algorithm was used to calculate the score of immune cell infiltration and the activity of immune-related pathways. The CellMiner database was used to study drug sensitivity.Results: In this study, 3 genes (CALCRL, MMP14, and SELL) were selected from 9 prognosis-related differential inflammation genes through LASSO Cox regression analysis to construct a prognostic risk model. Further analysis showed that the risk score was negatively correlated with the prognosis, and the ROC curve showed that the accuracy of the model was better. The age, grade, and risk score can be used as independent prognostic factors (p < 0.001). GSEA analysis confirmed that 6 immune-related pathways were enriched in the high-risk group. We found that the degree of infiltration of 12 immune cell subpopulations and the scores of 13 immune functions and pathways in the high-risk group were significantly increased by applying the ssGSEA method (p < 0.05). Finally, we explored the relationship between the genes in the model and the susceptibility of drugs.Conclusion: This study analyzed the correlation between the inflammation-related risk model and the immune microenvironment. It is expected to provide a reference for the screening of LGG prognostic markers and the evaluation of immune response.

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Identification and Prognostic Value Exploration of Radiotherapy Sensitivity-Associated Genes in Non-Small-Cell Lung Cancer

BioMed Research International ◽

10.1155/2021/5963868 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Qing Ma ◽

Kai Geng ◽

Ping Xiao ◽

Lili Zeng

Keyword(s):

Lung Cancer ◽

Regression Analysis ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Small Cell ◽

Small Cell Lung ◽

Cox Regression Analysis ◽

Nsclc Patients

Background. Non-small-cell lung cancer (NSCLC) is a prevalent malignancy with high mortality and poor prognosis. The radiotherapy is one of the most common treatments of NSCLC, and the radiotherapy sensitivity of patients could affect the individual prognosis of NSCLC. However, the prognostic signatures related to radiotherapy response still remain limited. Here, we explored the radiosensitivity-associated genes and constructed the prognostically predictive model of NSCLC cases. Methods. The NSCLC samples with radiotherapy records were obtained from The Cancer Genome Atlas database, and the mRNA expression profiles of NSCLC patients from the GSE30219 and GSE31210 datasets were obtained from the Gene Expression Omnibus database. The Weighted Gene Coexpression Network Analysis (WGCNA), univariate, least absolute shrinkage and selection operator (LASSO), multivariate Cox regression analysis, and nomogram were conducted to identify and validate the radiotherapy sensitivity-related signature. Results. WGCNA revealed that 365 genes were significantly correlated with radiotherapy response. LASSO Cox regression analysis identified 8 genes, including FOLR3, SLC6A11, ALPP, IGFN1, KCNJ12, RPS4XP22, HIST1H2BH, and BLACAT1. The overall survival (OS) of the low-risk group was better than that of the high-risk group separated by the Risk Score based on these 8 genes for the NSCLC patients. Furthermore, the immune infiltration analysis showed that monocytes and activated memory CD4 T cells had different relative proportions in the low-risk group compared with the high-risk group. The Risk Score was correlated with immune checkpoints, including CTLA4, PDL1, LAG3, and TIGIT. Conclusion. We identified 365 genes potentially correlated with the radiotherapy response of NSCLC patients. The Risk Score model based on the identified 8 genes can predict the prognosis of NSCLC patients.

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Identification of Metabolism-Related Genes Influencing Prognosis of Multiple Myeloma Patients

Journal of Healthcare Engineering ◽

10.1155/2021/6574491 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Rui Wang ◽

Wenxuan Bu ◽

Yang Yang

Keyword(s):

Regression Analysis ◽

Candidate Genes ◽

Immune Cell ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

High Risk Group ◽

Immune Cell Infiltration ◽

Cox Regression Analysis ◽

Key Genes

Multiple myeloma (MM) is the second most commonly diagnosed hematological malignancy. Understanding the basic mechanisms of the metabolism in MM may lead to new therapies that benefit patients. We collected the gene expression profile data of GSE39754 and performed differential analysis. Furthermore, identify the candidate genes that affect the prognosis of the differentially expressed genes (DEGs) related to the metabolism. Enrichment analysis is used to identify the biological effects of candidate genes. Perform coexpression analysis on the verified DEGs. In addition, the candidate genes are used to cluster MM into different subtypes through consistent clustering. Use LASSO regression analysis to identify key genes, and use Cox regression analysis to evaluate the prognostic effects of key genes. Evaluation of immune cell infiltration in MM is by CIBERSORT. We identified 2821 DEGs, of which 348 genes were metabolic-related prognostic genes and were considered candidate genes. Enrichment analysis revealed that the candidate genes are mainly related to the proteasome, purine metabolism, and cysteine and methionine metabolism signaling pathways. According to the consensus clustering method, we identified the two subtypes of group 1 and group 2 that affect the prognosis of MM patients. Using the LASSO model, we have identified 10 key genes. The prognosis of the high-risk group identified by Cox regression analysis is worse than that of the low-risk group. Among them, PKLR has a greater impact on the prognosis of MM, and the prognosis of MM patients is poor when the expression is high. In addition, the level of immune cell infiltration in the high-risk group is higher than that in the low-risk group. In the summary, metabolism-related genes significantly affect the prognosis of MM patients through the metabolic process of MM patients. PKLR may be a prognostic risk factor for MM patients.

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Prognostic Model of Head and Neck Squamous Cell Carcinoma Based on 12 Ferroptosis-related Genes

10.21203/rs.3.rs-349666/v2 ◽

2021 ◽

Author(s):

Sijia Li ◽

Hongyang Zhang ◽

Wei Li

Keyword(s):

Regression Analysis ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Risk Scores ◽

Cox Regression Analysis ◽

Normal Tissues ◽

Model Based ◽

Cox Analysis

Abstract Background: The purpose of our study is establishing a model based on ferroptosis-related genes predicting the prognosis of patients with head and neck squamous cell carcinoma (HNSCC).Methods: In our study, transcriptome and clinical data of HNSCC patients were from The Cancer Genome Atlas, ferroptosis-related genes and pathways were from Ferroptosis Signatures Database. Differentially expressed genes (DEGs) were screened by comparing tumor and adjacent normal tissues. Functional enrichment analysis of DEGs, protein-protein interaction network and gene mutation examination were applied. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression were used to identified DEGs. The model was constructed by multivariate Cox regression analysis and verified by Kaplan-Meier analysis. The relationship between risk scores and other clinical features was also analyzed. Univariate and multivariate Cox analysis was used to verified the independence of our model. The model was evaluated by receiver operating characteristic analysis and calculation of the area under the curve (AUC). A nomogram model based on risk score, age, gender and TNM stages was constructed. Our model was also validated in the Gene Expression Omnibus (GEO) verification set. Results: We analyzed data including 500 tumor tissues and 44 adjacent normal tissues and 259 ferroptosis-related genes, then obtained 73 DEGs. Univariate Cox regression analysis screened out 16 genes related to overall survival, and LASSO analysis fingered out 12 of them with prognostic value. A risk score model based on these 12 genes was constructed by multivariate Cox regression analysis. According to the median risk score, patients were divided into high-risk group and low-risk group. The survival rate of high-risk group was significantly lower than that of low-risk group in Kaplan-Meier curve. Risk scores were related to T and grade. Univariate and multivariate Cox analysis showed our model was an independent prognostic factor. The AUC was 0.669. Those all could prove our model had great predictive ability of HNSCC prognosis and it could be validated in GEO dataset. The nomogram showed high accuracy predicting the prognosis of HNSCC patients.Conclusion: Our model based on 12 ferroptosis-related genes performed excellently in predicting the prognosis of HNSCC patients. Ferroptosis-related genes may be promising biomarkers for HNSCC treatment and prognosis.

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Immune-Related lncRNA Signature for Predicting the Immune Landscape of Head and Neck Squamous Cell Carcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.689224 ◽

2021 ◽

Vol 8 ◽

Author(s):

Ji Yin ◽

Xiaohui Li ◽

Caifeng Lv ◽

Xian He ◽

Xiaoqin Luo ◽

...

Keyword(s):

Squamous Cell Carcinoma ◽

Regression Analysis ◽

High Risk ◽

Cell Carcinoma ◽

Cox Regression ◽

Risk Group ◽

Risk Groups ◽

High Risk Group ◽

Survival Prediction ◽

Cox Regression Analysis

Background: Long non-coding RNA (lncRNA) plays a significant role in the development, establishment, and progression of head and neck squamous cell carcinoma (HNSCC). This article aims to develop an immune-related lncRNA (irlncRNA) model, regardless of expression levels, for risk assessment and prognosis prediction in HNSCC patients.Methods: We obtained clinical data and corresponding full transcriptome expression of HNSCC patients from TCGA, downloaded GTF files to distinguish lncRNAs from Ensembl, discerned irlncRNAs based on co-expression analysis, distinguished differentially expressed irlncRNAs (DEirlncRNAs), and paired these DEirlncRNAs. Univariate Cox regression analysis, LASSO regression analysis, and stepwise multivariate Cox regression analysis were then performed to screen lncRNA pairs, calculate the risk coefficient, and establish a prognosis model. Finally, the predictive power of this model was validated through the AUC and the ROC curves, and the AIC values of each point on the five-year ROC curve were calculated to select the maximum inflection point, which was applied as a cut-off point to divide patients into low- or high-risk groups. Based on this methodology, we were able to more effectively differentiate between these groups in terms of survival, clinico-pathological characteristics, tumor immune infiltrating status, chemotherapeutics sensitivity, and immunosuppressive molecules.Results: A 13-irlncRNA-pair signature was built, and the ROC analysis demonstrated high sensitivity and specificity of this signature for survival prediction. The Kaplan–Meier analysis indicated that the high-risk group had a significantly shorter survival rate than the low-risk group, and the chi-squared test certified that the signature was highly related to survival status, clinical stage, T stage, and N stage. Additionally, the signature was further proven to be an independent prognostic risk factor via the Cox regression analyses, and immune infiltrating analyses showed that the high-risk group had significant negative relationships with various immune infiltrations. Finally, the chemotherapeutics sensitivity and the expression level of molecular markers were also significantly different between high- and low-risk groups.Conclusion: The signature established by paring irlncRNAs, with regard to specific expression levels, can be utilized for survival prediction and to guide clinical therapy in HNSCC.

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