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. 2023 Mar 1;31(1):35-61.
doi: 10.32604/or.2022.028193. eCollection 2023.

Identification and verification of aging-related lncRNAs for prognosis prediction and immune microenvironment in patients with head and neck squamous carcinoma

Qing Gao  1 , Yujing Shi  2 , Yuanyuan Sun  1 , Shu Zhou  1 , Zeyuan Liu  3 , Xinchen Sun  1 , Xiaoke DI  1
Affiliations

Identification and verification of aging-related lncRNAs for prognosis prediction and immune microenvironment in patients with head and neck squamous carcinoma

Qing Gao et al. Oncol Res. .

Abstract

Aging is highly associated with tumor formation and progression. However, little research has explored the association of aging-related lncRNAs (ARLs) with the prognosis and tumor immune microenvironment (TIME) of head and neck squamous cell carcinoma (HNSCC). RNA sequences and clinicopathological data of HNSCC patients and normal subjects were downloaded from The Cancer Genome Atlas. In the training group, we used Pearson correlation, univariate Cox regression, least absolute shrinkage/selection operator regression analyses, and multivariate Cox regression to build a prognostic model. In the test group, we evaluated the model. Multivariate Cox regression was done to screen out independent prognostic factors, with which we constructed a nomogram. Afterward, we demonstrated the predictive value of the risk scores based on the model and the nomogram using time-dependent receiver operating characteristics. Gene set enrichment analysis, immune correlation analysis, and half-maximal inhibitory concentration were also performed to reveal the different landscapes of TIME between risk groups and to predict immuno- and chemo-therapeutic responses. The most important LINC00861 in the model was examined in HNE1, CNE1, and CNE2 nasopharyngeal carcinoma cell lines and transfected into the cell lines CNE1 and CNE2 using the LINC00861-pcDNA3.1 construct plasmid. In addition, CCK-8, Edu, and SA-β-gal staining assays were conducted to test the biofunction of LINC00861 in the CNE1 and CNE2 cells. The signature based on nine ARLs has a good predictive value in survival time, immune infiltration, immune checkpoint expression, and sensitivity to multiple drugs. LINC00861 expression in CNE2 was significantly lower than in the HNE1 and CNE1 cells, and LINC00861 overexpression significantly inhibited the proliferation and increased the senescence of nasopharyngeal carcinoma cell lines. This work built and verified a new prognostic model for HNSCC based on ARLs and mapped the immune landscape in HNSCC. LINC00861 is a protective factor for the development of HNSCC.

Keywords: Aging; Bioinformatics; HNSCC; Prognosis; Tumor immune microenvironment; lncRNA.

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Conflict of interest statement

The authors declare that they have no conflicts of interest to report regarding the present study.

Figures

Figure 1
Figure 1. The flow chart of the whole analysis process.
Figure 2
Figure 2. (A) The regulatory network showed 565 lncRNAs associated with 156 AGs. (B) The 276 significantly different ALRs in cancer versus normal tissue samples.
Figure 3
Figure 3. Selecting eligible ARLs in the training group for building the model. (A) ARLs affecting OS extracted by univariate Cox regression analysis. (B) The heatmap showed the expression profiles of 38 prognostic lncRNAs. (C) The 10-fold cross-validation for variable selection in the LASSO model. (D) The LASSO coefficient profile of 16 ARLs, the intersection of the middle dashed line with the upper X-axis is the number of lncRNAs obtained from the LASSO regression, and the intersection with the lower X-axis is the value of Log(λ). (E) The Sankey diagram of 8 aging-related genes and 9 associated lncRNAs.
Figure 4
Figure 4. Prognosis value of the 9 ARLs model in the train, test, and all sets. (A) Exhibition of ARLs model based on risk score of the train, test, and entire sets, respectively. (B) Survival time and survival status between low- and high-risk groups in the train, test, and all sets, respectively. (C) The heat map of 9 ARLs expressions in the train, test, and all sets, respectively. (D) Kaplan-Meier survival curve of OS for patients between risk groups in the train, test, and all sets, respectively. (E) The train, test, and all sets’ 1-, 3-, and 5-year ROC curves, respectively. (F) Kaplan-Meier survival analysis for HNSCC patients stratified by age, gender, T, N, or M staging, and grade in all set.
Figure 4
Figure 4. Prognosis value of the 9 ARLs model in the train, test, and all sets. (A) Exhibition of ARLs model based on risk score of the train, test, and entire sets, respectively. (B) Survival time and survival status between low- and high-risk groups in the train, test, and all sets, respectively. (C) The heat map of 9 ARLs expressions in the train, test, and all sets, respectively. (D) Kaplan-Meier survival curve of OS for patients between risk groups in the train, test, and all sets, respectively. (E) The train, test, and all sets’ 1-, 3-, and 5-year ROC curves, respectively. (F) Kaplan-Meier survival analysis for HNSCC patients stratified by age, gender, T, N, or M staging, and grade in all set.
Figure 5
Figure 5. Nomogram and assessment of the risk model. (A) Uni-Cox and (B) multi-Cox analyses of clinical factors and risk score with OS. (C) The nomogram that integrated the age, tumor stage, and risk score predicted the probability of the 1-, 3-, and 5-year OS. (D) The calibration curves for 1-, 3-, and 5-year OS. (E) The 3-year ROC curves of risk score, nomogram total score, and clinical characteristics.
Figure 6
Figure 6. The investigation of tumor immune factors and immunotherapy. (A) GSEA of the top 10 pathways significantly enriched in the low- and high-risk groups. (B) The immune cell bubble of risk groups. (C) The correlation between risk score and immune cells. (D) The comparison of immune-related scores between low- and high-risk groups. (E) The difference between immune cell infiltration and immune functions in risk groups. *p < 0.05; **p < 0.01; ***p < 0.001.
Figure 7
Figure 7. Immune checkpoints and drug sensitivity analyses. (A) The difference in immune checkpoint expression in risk groups. (B) Fifteen immunotherapeutic drugs showed lower IC50 values in the low-risk group. (C) Three immunotherapeutic drugs showed lower IC50 values in the high-risk group. *p < 0.05; **p < 0.01; ***p < 0.001.
Figure 8
Figure 8. Cellular experiments. (A) and (B) Expression level of LINC00861. (C) CCK-8, (D) Edu, and (E) SA-β-gal staining assay in CNE1 and CNE2 cell lines in different treatment groups. *p < 0.05; **p < 0.01; ***p < 0.001.
Figure 8
Figure 8. Cellular experiments. (A) and (B) Expression level of LINC00861. (C) CCK-8, (D) Edu, and (E) SA-β-gal staining assay in CNE1 and CNE2 cell lines in different treatment groups. *p < 0.05; **p < 0.01; ***p < 0.001.
Figure S1
Figure S1. All 39 chemicals with lower IC50 values in the low-risk group.
Figure S2
Figure S2. All 16 chemicals with lower IC50 values in the high-risk group.
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