Dataset Information

An immune-related seven-lncRNA signature for head and neck squamous cell carcinoma.

ABSTRACT: In this study, we developed a long noncoding RNA (lncRNA)-based prognostic signature for stratification of patients with head a nd neck squamous cell carcinoma (HNSCC). In total, 493 HNSCC samples obtained from the Cancer Genome Atlas database were divided into training and testing cohorts (3:2 ratio). We identified 3913 immune-related lncRNAs in the HNSCC training cohort by Pearson correlation analysis; only seven were independently associated with overall survival and were used to develop an immune-related lncRNA prognostic signature (IRLPS) grouping of HNSCC patients into high- and low-IRLPS subgroups. Univariate and multivariate Cox analyses revealed that low-IRLPS patients had a better prognosis in all the cohorts, which was retained after stratification by sex, grade, and HPV status. Although the TNM stage was also an independent prognostic factor, the IRLPS had a better discriminability with higher AUC at the 3- and 5-year follow-ups in all cohorts. Low-IRLPS samples had more immune cell infiltration and were enriched in immune-related pathways, while high- IRLPS samples were enriched in metabolic pathways. A nomogram constructed including age, TNM stage, and IRLPS showed good calibration. Thus, IRLPS improves the prognostic prediction and also distinguishes different tumor microenvironment (TME) in HNSCC patients.

SUBMITTER: Chen Y

PROVIDER: S-EPMC7982618 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

An immune-related seven-lncRNA signature for head and neck squamous cell carcinoma.

Chen Yue Y Luo Tian-Qi TQ Xu Si-Si SS Chen Chun-Yan CY Sun Ying Y Lin Li L Mao Yan-Ping YP

Cancer medicine 20210303 7

In this study, we developed a long noncoding RNA (lncRNA)-based prognostic signature for stratification of patients with head a nd neck squamous cell carcinoma (HNSCC). In total, 493 HNSCC samples obtained from the Cancer Genome Atlas database were divided into training and testing cohorts (3:2 ratio). We identified 3913 immune-related lncRNAs in the HNSCC training cohort by Pearson correlation analysis; only seven were independently associated with overall survival and were used to develop an i ...[more]

PMID: 33660378

Similar Datasets

Project description: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.

Project description:Background: Cuproptosis has been recognized as a novel regulatory cell death, which has been confirmed to promote the occurrence and development of tumors. However, whether cuproptosis-related lncRNA has an impact on the prognosis of squamous cell carcinoma of the head and neck (HNSCC) is still unclear. Methods: In total, 501 HNSCC tumor samples and 44 normal were downloaded from the TCGA database. Cuproptosis-related lncRNAs were obtained by co-expressed analysis. We got prognostic lncRNA that was associated with cuproptosis by using univariate Cox regression analysis and LASSO Cox regression. Then we constructed and validated the prognostic signature of HNSCC and analyzed the immune landscape of the signature. Results: The Prognostic Signature is based on 10 cuproptosis-related lncRNAs including AC090587.1, AC004943.2, TTN-AS1, AL162458.1, AC106820.5, AC012313.5, AL132800.1, WDFY3-AS2, CDKN2A-DT, and AL136419.3. The results of overall survival, risk score distribution, and survival status in the low-risk group were better than those in the high-risk group. In addition, all immune checkpoint genes involved were significantly different between the two risk groups (p < 0.05). The risk score was positively correlated with Eosinophils. M0 and M2 phenotype macrophages, mast cells activated, NK cells activated, and negatively related with B cells naive, mast cells resting, plasma cells, CD8T cells, T cells follicular helper, T cells regulatory (Tregs). Consensus clustering was identified in molecular subtypes of HNSC. More high-risk samples concentrated in Cluster1, which had a higher Tumor Immune Dysfunction and Exclusion (TIDE) score and Single Nucleotide Polymorphisms (SNP) alternation than Cluster2. Conclusion: Our study elucidated the correlation between cuproptosis-related lncRNA with prognosis and immune landscape of HNSCC, which may provide references for further research on the exploration of the mechanism and functions of the prognosis for HNSCC.

Project description:BackgroundThe mechanistic aspects of the involvement of long noncoding RNAs (lncRNAs) in NETosis, the process of neutrophil extracellular trap (NET) formation in head and neck squamous cell carcinoma (HNSCC), lack comprehensive elucidation. The involvement of these molecules in the immune microenvironment and plausible HNSCC prognosis remain to see the light of the day. The plausible functioning of NETosis-related lncRNAs with their plausible prognostic impact in HNSCC was probed in this work.MethodsThe scrutiny of lncRNAs linked to NETosis entailed the probing of twenty-four genes associated with the process employing Pearson's correlation analysis on HNSCC patients' RNA sequencing data from The Cancer Genome Atlas (TCGA) database. The application of univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses yielded a NETosis-related lncRNA signature that was subjected to probing for its suitability in prognosis employing survival and nomogram analyses.ResultsThe NETosis-related lncRNA signature inclusive of five lncRNAs facilitated patients to be segregated as high-risk and low-risk groups with the former documenting a poor prognosis. Regression unearthed that the risk score was an independent factor for prognosis. The receiver operating characteristic (ROC) or receiver operating characteristic curve analysis documented a one-year area under time-dependent ROC curve (AUC) value of 0.711 that is corroborative of the accuracy of this signature. Additional probing documented an evident enriching of immune-linked pathways in the low-risk patients, while the high-risk patients documented an immunologically "cold" profile as per the infiltration of immune cells. We verified lncRNA expression from our NETosis-related lncRNA signature in vitro, which reflects the reliability of our model to a certain extent. Moreover, we also verified the function of the lncRNA. We found that LINC00426 contributes to the innate immune cGAS-STING signaling pathway, which explain to some extent the role of our prognostic model in predicting "hot" and "cold" tumors.ConclusionsThe plausible prognostic relevance of the NETosis-related lncRNA signature (with five lncRNAs) emerges that is suggestive of its promise in targeting HNSCC.

Dataset Information

An immune-related seven-lncRNA signature for head and neck squamous cell carcinoma.

Publications

An immune-related seven-lncRNA signature for head and neck squamous cell carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets