Characterization of a prognostic model for head and neck squamous cell carcinoma based on Telomere maintenance genes

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Telomere dysfunction is involved in malignant transformation and tumor development processes. We performed a comprehensive array of analyzes to assess and authenticate the prognostic significance of telomeres in HNSCC, including the identification and examination of differential telomere maintenance genes (TMGs) with prognostic significance, Cox regression analysis, survival analysis, nomogram prediction, time receiver operating characteristic (ROC) analysis, Immune characteristics, enrichment analysis, drug sensitivity analysis, Mendelian randomization (MR) analysis, and real-time quantitative PCR (qRT-PCR). Employing bioinformatics, we derived a prognostic model comprising 80 significantly differentially expressed genes (DEGs) of prognostic relevance. Subsequent analysis using the HPA database revealed 24 genes, and they were identified to exhibit elevated expression levels in tumor patients. The model predicted an area under the ROC curve (AUC) of 0.973 for the 1-year survival rates of patients with HNSCC. The high- and low-risk groups exhibited different immune statuses and drug sensitivities. More precisely, HNSCC individuals in high-risk groups were more prone to show a favorable response to 17 chemotherapeutic drugs. Additionally, our result of qRT-PCR was also consistent with the analysis. The prognostic model centered on differential TMGs shows great potential as a valuable tool for risk stratification, predicting survival outcomes, assessing immune status, screening potential drugs, and exploring genetic associations with HNSCC. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Telomere Head and neck squamous cell carcinoma Drug sensitivity Prognosis Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction HNSCC is a prevalent malignant tumor that comprises 95% of head and neck cancers. Globally, it ranks as the eighth most commonly diagnosed form of cancer 1, 2 . The clinical treatment options for HNSCC include surgical interventions, radiation therapy, chemotherapy, and the most recent advancement in immunotherapy 3 . Currently, natural killer immune cells demonstrate enhanced targeting precision, allowing effective clearance of cancer cells and mitigating the risks of metastasis and relapse. However, due to the invasiveness and heterogeneity of HNSCC, survival outcomes for individuals remain suboptimal 4 . The 5-year survival rate is still stands at approximately 60% 5 . Therefore, there is a pressing need to discover a novel, stable, and reliable molecular characteristic to predict the prognosis of the disease, assess the risk of recurrence, and evaluate the potential responsiveness of identified targets to therapy 6 . Telomeres, located at the termini of human chromosomes, are nucleoprotein complexes composed of a repetitive sequence, namely 5’-TTAGGG-3’, along with protective proteins 7 . These structures play a crucial role in protecting chromosome ends from atypical degradation, loss of nucleotides, breakage, recombination, and end-to-end fusion 8-10 . However, the length of telomeres progressively decreases with cell division and under specific disease conditions. Telomerase extends chromosomes by adding DNA sequences with the TTAGGG pattern to the end, ensuring the preservation of the telomere length. Studies suggest that a predominant method by which tumor cells attain immortality is by counteracting telomere shortening, primarily through the telomere maintenance mechanism (TMM) 11 . In cancer, abbreviated telomeres may manifest antitumor effects by hindering the proliferation of tumor cells or, conversely, favoring cancer advancement by inducing genomic instability 12 . Longer telomere length is associated with a favorable prognosis in specific cancer types, such as breast cancer 13 . On the contrary, shorter telomere lengths in tumor cells are observed in other types of cancer, such as renal cell carcinoma, although its impact on prognosis remains a subject of controversy 14 . Currently, there has been no examination to determine whether the expressions of telomere-associated genes can serve as diagnostic or prognostic biomarkers in patients with HNSCC. We used bioinformatic techniques to compute a prognostic model for differential TMG in HNSCC and our assessment focused on elucidating the roles of differential TMG in immune infiltration, prognosis, and response to treatment. Furthermore, we intend to assess the potential utility of this risk model in guiding the selection of treatment agents. Our results indicate that genes associated with telomeres hold significant promise in the prognosis, diagnosis, and therapeutic strategies tailored for the treatment of HNSCC. 2. Materials And Methods 2.1 Data acquisition We obtained raw data related to HNSCC from the Cancer Genome Atlas (TCGA) database and organized the information using R (version 4.3.1) and Perl (strawberry version). Additionally, we acquired TMGs data from the TelNet website. 2.2 Identification and analysis of DEGs with prognostic significance. A total of 2093 TMGs were obtained from the TelNet database. The limma packages were used to identify differential gene expression between tumor and normal tissues. Principal component analyses (PCA) were applied to assess unique distribution patterns between two groups in various datasets. The DEGs identified by screening underwent a single-factor independent prognostic analysis. The sva package was used to standardize TCGA data, minimizing batch effects. Subsequently, the TCGA expression data was integrated with survival information. 2.3 Construction of a prognostic risk model The TCGA cohort served for the construction of a lasso regression model. The model was cross-validated, and the optimal results were chosen to generate the model formula and identify certain genes associated with the model. Then a differential analysis was conducted on these model-related genes to observe variations between normal samples and tumor samples. The model formula was then applied to score all samples, resulting in the calculation of risk scores. Afterward, based on the median risk score, all samples can be divided into two groups: the high-risk sample group and the low-risk sample group. 2.4 External validation of the prognostic risk model. Survival analysis was performed for both groups to assess the robustness and reliability of the model. The accuracy of the model was evaluated by performing a progression-free survival analysis and examining the ROC curve. The potential of risk scoring to serve as an independent prognostic indicator can be assessed through independent univariate and multivariate prognostic analyzes. Univariate and multivariate Cox regression analyses were carried out, incorporating variables such as grade, age, stage, gender, and risk score, to independently assess the prognostic significance of the model. A significance level of logarithmic rank P < 0.05 was used as the threshold to identify meaningful associations. Clinical factors linked to independent prognoses were explored using tumor stage nomograms, integrating age and risk scoring for individuals with HNSCC. The effectiveness of the nomogram was validated by examination of the calibration curves. 2.5 Functional enrichment analysis Subsequent to identifying DEGs, we performed functional enrichment analyzes of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to explore the potential molecular mechanisms associated with the prognostic signature. 2.6 Immune characteristics and immune subtype analysis The presence of various types of immune cells within the tumor microenvironment of HNSCC was evaluated by analyzing the expression values of specific genes using CIBERSORT, with 1000 iterations performed (https://cibersort.stanford.edu/) 15 . In a previous study, immune subtypes were identified in various cancers using data from the TCGA database, and we obtained specific immune subtypes specific to HNSCC patients 16 . Additionally, the TIDE (Tumor Immune Dysfunction and Exclusion) score was computed in accordance with provided instructions (https://tide.dfci.harvard.edu/) 17 . 2.7 Analysis of drug sensitivity The analysis of drug sensitivity utilized information sourced from the Genome of Drug Sensitivity in Cancer 2 (GDSC2) database. (https://www.cancerrxgene.org/) 18 . We further acquire half-maximum inhibitory concentration (IC50) values for targeted or chemotherapeutic drugs in high and low risk populations using the R "oncoPredict" package, with the aim of assessing the correlation of prognostic characteristics with clinical treatment response in HNSCC 19 . 2.8 Mendelian randomization (MR) analysis Mendelian randomization (MR) represents a type of instrumental variables (IV) analysis. Exposure and outcome data sets were derived from the IEU Open GWAS project (https://gwas.mrcieu.ac.uk/). A two-sample MR investigation was conducted using the TwoSampleMR and MRPRESSO packages in R. The primary analysis method employed was the random-effects inverse variance weighted (IVW), with additional methods including weighted median, MR Egger, weighted mode, and simple mode. Heterogeneity and horizontal pleiotropy were assessed and an examination was carried out to determine if the analysis results were influenced by a single SNP. 2.9 Quantitative real-time PCR (qRT-PCR) We verified the expression of the TMGs that made up the prognostic risk score model in HNSCC tissues by qRT-PCR. We collected HNSCC tissues and adjacent normal tissues from eight patients with HNSCC who underwent surgical resection at Wuhan Union Hospital. All patients had signed informed consent forms prior to surgery. Total RNA was extracted from tissues using Trizol (Takara, China) and reversed to cDNA using Prime Script RTase (Takara, China). Subsequently, qRT-PCR was performed using SYBR green (Takara, China). The mean + standard error of three independent experiments were calculated, and each experiment was repeated three times. Relative TMG mRNA expression levels were calculated with GAPDH serving as an internal reference. The primer sequences are shown in Table 1. 2.10 Statistical analysis Statistical analyzes are performed using both R and Perl software. (version 4.3.1, packages: sva, limma, pheatmap, survival, survminer, glmnet, timeROC, ggpubr, regplot, rms, ggplot2, ggExtra, reshape2, tidyverse, preprocessCore, RColorBrewer, car, ridge, genefilter, biomaRt, GenomicFeatures, maftools, stringr, org.Hs.eg.db, oncoPredict and remotes). Unless otherwise specified, all analyzes in this study considered an estimated p-value of < 0.05 as statistically significant. For all comparisons: “*” p < 0.05, “**” p < 0.01, “**” p < 0.001. 3. Results 3.1 Identification of 80 prognostically relevant differential genes We illustrate the complete study procedure (Figure 1). A total of 1799 differentially expressed genes were identified in both tumor and normal tissues. We acquired the expression profiles of 519 DEG related to telomeres (|logFC| > 1, Fdr < 0.05), including 471 genes that exhibited upregulation and 48 genes that showed downregulation. Heatmaps were plotted to visualize these results, including only 100 DEGs (Figure 2A). Through the integration of these profiles with survival information through differential analysis, we identified 80 significantly DEGs with prognostic relevance. Among them, 56 genes were identified as high-risk genes, with a hazard ratio (HR) greater than 1, while 24 genes were identified as low-risk genes with an HR less than 1. A volcano plot was used to visualize these results (Figure 2B). 3.2 Construction of a risk model Through lasso regression analysis of 80 significantly DEGs with prognostic relevance, we identified 24 genes that exhibited minimal error during the construction of the optimal risk model (Figure 3A, B), namely ADA, ANKLE1, ASPG, BRIP1, CCT2, CCT6A, CTTN, DSG2, EIF5A2, ETV4, FOXL2, GEN1, GFPT2, GLDC, HMMR, IRAK1, KPNA2, LCK, LPIN1, MYF6, PASK, PLK1, PLOD2, and ZFP42. We visualized the comparative expression of 24 TMGs between HNSCC and corresponding normal tissues (Figure 3C). We observed that 3 genes (ASPG, LPIN1, and MYF6) observed that three genes exhibited low expression levels in tumor patients, while 21 genes (ADA, ANKLE1, BRIP1, CCT2, CCT6A, CTTN, DSG2, EIF5A2, ETV4, FOXL2, GEN1, GFPT2, GLDC, HMMR, IRAK1, KPNA2, LCK, PASK, PLK1, PLOD2 and ZFP42) showed high expression levels (Figure 3B). Therefore, Risk score = EXP [(ADA * 0.0163761818012764) + (ANKLE1 * -0.0780063108672532) + (ASPG * -0.0381922859730038) + (BRIP1 * -0.356091535914648) + (CCT2 * 0.000674837813465877) + (CCT6A * 0.062199924653352) + (CTTN * 0.0552505134570484) + (DSG2 * 0.0129990195647178) + (EIF5A2 * 0.134661775875434) + (ETV4 * 0.0406146194564598) + (FOXL2 * 0.0747336745392169) + (GEN1 * -0.00687790482196727) + (GFPT2 * 0.0642676182222645) + (GLDC * 0.0772969174824084) + (HMMR * 0.144601105580529) + (IRAK1 * 0.01182847922651 3.3 Survival analysis and independent analysis for prognostic model The patients were categorized into high- and low-risk groups according to their median risk scores, followed by a subsequent survival analysis. Our study indicates that patients in the high-risk group experience a more unfavorable prognosis compared to those in the low-risk group (Figure 4A). The AUC of the risk score in the multivariate ROC curve (AUC = 0.705) was significantly higher than the AUC of other indicators (Figure 4B). All the ROC values obtained in the timieROC curve were above 0.7 (0.705, 0.790, and 0.753, as shown in Figure 4C). Hence, a more aggressive treatment plan should be devised for patients in the low-risk group. Following the methodology outlined, Cox regression analyzes were performed individually for gender, age, stage, grade, and risk score. In the forest graph of the independent single factor prognostic analysis, we observed that the predictive power of the risk score was independent of other factors and better than other factors ( p < 0.001, HR = 4.842, Figure 4D). We reached the same conclusion in a multivariate independent prognostic analysis ( p < 0.001, HR = 5.075, Figure 4E). 3.4 Construction and verification of the nomogram From the aforementioned investigation, we developed a reliable scoring model. The nomogram illustrates the model scores in relation to readily available clinical factors such as age, sex, and tumor stage (Figure 4F). The initial patient in the study cohort is used to demonstrate the application of the nomogram in the research queue. The patient's survival rates were 0.973 at the end of the first year, 0.915 at the third year, and 0.857 at the fifth year. The calibration curve indicates that the nomogram calculations closely align with the actual survival rate for the first year, while there is a slight deviation in results for the third and fifth years (Figure 4G). 3.5 Functional enrichment analysis We used GO and KEGG analyzes to explore the potential biological functions associated with the 519 genes exhibiting differential expression between the low-risk and high-risk groups, given the significant differences in results among the two groups of patients with HNSCC. The GO annotation revealed significant associations of these genes with biological behaviors such as DNA replication，cellular components such as the chromosomal region and molecular functions such as catalytic activity, acting on DNA (Figure 5A, B, C). According to the KEGG analysis, these genes were found to be associated with a pathway such as the cell cycle. The observed biological behaviors and pathways probably contribute to the tendency of the high-risk group to experience less favorable clinical outcomes (Figure 5D, E, F). 3.6 Patients in different risk categories exhibit varying immune statuses. Different types of immune cells exhibited diverse infiltration rates within the tumor microenvironment between high- and low-risk groups. T cells CD4 memory resting, NK cells resting, Macrophages M0 and activated Mast cells exhibited a higher infiltration rate in the high-risk group compared to the low-risk group. Naive B cells, Plasma cells, CD8 T cells activated T cells CD4 memory, follicular helper T cells, regulatory T cells (Tregs), and resting Mast cells exhibited lower infiltration in the high-risk group compared to the low-risk group (Figure 6A, B). Based on the immune status of the tumors, early studies classified the tumors in the TCGA database into six subtypes: C1 (wund healing), C2 (IFN-g dominant), C3 (inflammatory), C4 (lymphocyte depleted), C5 (immunologically quiet), and C6 (TGF-b dominant) 16 . Examining immune subtypes revealed that people in the low-risk group showed a nearly equal prevalence of the C1 subtype (26%) and the C2 subtype (74%) compared to those in the high-risk group, where the distribution was the C1 subtype (25%) and the C2 subtype (75%) (Figure 6C). The TIDE score exhibited a significantly higher value in the high-risk group compared to the low-risk group (P<0.001) (Figure 6D, E). Figure 6. Immunocorrelated analysis between high-and low-risk groups. (A) Heatmap of immune cell infiltration rates in high- and low-risk groups. (B) Box plot of the distribution portion of immune cells in cancer tissues with different patients in the risk group of TMGs. (C) Results of the immune subtypes of 419 patients in the total cohort of TCGA. (D, E) The violin plot and the scatter diagram of the difference in TIDE scores between the high- and low-risk groups. 3.7 Potential drug screening Drug sensitivity scores for compounds in the GDSC database were calculated using oncoPredict. Statistically significant differences in responses were observed between the high and low risk groups for 74 of these drugs, with P < 0.001. More precisely, an elevated risk score demonstrated a correlation with decreased IC50 values for the 17 drugs including AZD7762, Staurosporine, BI-2536, Dasatinib, PD0325901, BMS-536924, SB505124, AZD1332, SCH772984, ERK_2440, ERK_6604, IGF1R_3801, OTX015, PAK_5339, AZD6738, MK-8776, and VX-11e (Figure 7A-Q). Furthermore, we examine the correlation between MRLncRI and IC50 through scatterplots. A lower IC50 value indicates greater drug sensitivity and a more favorable therapeutic effect. This implies that individuals with HNSCC in the high-risk groups were more inclined to demonstrate a positive response to these chemotherapeutic drugs. Figure 7. Drug sensitivity analysis. (A-Q) The box plot presents the half-maximum inhibitory concentration (IC50) values for patients in the high- and low-risk groups for the 17 corresponding drugs. 3.8 MR analysis result The random effect IVW results showed that the CTTN gene ( p = 0.364, OR 95% confidence interval [CI] = 1.000 [0.999-1.001]) and the PASK gene ( p = 0.734, OR 95% CI = 1.000 [0.999-1.001]) do not have a causal relationship with HNSCC at the genetic level (Figure 8A-F). 3.9 Validation of TMGs in HNSCC The qRT-PCR analysis revealed that the expression of ANKLE1, CCT6A, CTTN, GFPT2 and PASK were upregulated, while ASPG, LPIN1 and MYF6 was downregulated in HNSCC tissues compared to paired normal tissues (Figure 9). The results are consistent with the previously mentioned findings from our bioinformatic analysis. 4. Discussion The treatment of HNSCC presents a notable clinical challenge, primarily due to the increased rates of locoregional recurrence and resistance to chemotherapy. Variability in the tumor microenvironment and the genetic makeup of cancer cells has an impact on response to treatment and clinical results. However, existing research on biomarkers associated with HNSCC is inadequate to meet the clinical demands for the diagnosis and prognosis of this condition. It is crucial to discover new biomarkers for HNSCC. Telomeres in humans have been proposed to delineate the cellular lifespan and determine the number of cell divisions 20 . When telomeres reach a critical threshold, it can trigger double-strand breaks, leading to cellular senescence or apoptosis 21 . Currently, scientists have recognized an association between genetic variants linked to telomeres or telomere dysfunction and the increased risk of various cancers 22-24 . Carcinogenesis is promoted by both excessively short and excessively long telomeres in peripheral blood lymphocytes 25, 26 . Significantly, increasing amounts of epidemiological research have revealed associations between the length of the telomere in peripheral blood lymphocytes and the susceptibility to diverse cancer types such as lung 27, 28 , ovarian 29 , bladder 30 , breast 31 , and colon or rectum cancer 28, 32 . Therefore, the length of telomeres in peripheral blood lymphocytes could potentially function as a universal risk marker for human cancers. Research has indicated a notable correlation between the telomere length ratio in HNSCC compared to normal tissue and the recurrence rate of the disease. A reduced ratio suggests an elevated likelihood of recurrence 33 . The shorter telomere length, coupled with decreased 5-hydroxymethylcytosine levels and reduced translocation expression of 10 to 11, could contribute to the development of HNSCC 33 . Associations were identified between variants in TERT-CLPTM1L and both the mean relative telomere length and the susceptibility to HNSCC in Icelandic and European populations 34 . HNSCC exhibits widespread occurrences of telomere maintenance mechanisms. This study represents the first examination of the impact of differential TMGs on the prognosis of HNSCC. We established a scoring model for differential TMGs using a public database and the results obtained have been validated through external data verification and immunohistochemical queries of model-related genes, ensuring stability and reliability. We developed a nomogram that integrates the risk model derived from differential TMG with clinical pathological characteristics, and evaluated its effectiveness in predicting the prognosis of HNSCC. Scoring this model and taking into account standard clinical information allows us to forecast multi-year survival rates for each patient. Combining these predictions with results from immune feature analysis and chemotherapy drug sensitivity analysis, we can tailor appropriate treatment strategies for patients at different stages of tumor development. The 24 model genes identified in our differential TMG risk model serve various roles in the disease. The human protein ASPG functions as an enzyme with potential antitumor activity, exhibiting growth inhibition in leukemic cells 35 . Transaminase 2 (GFPT2) encodes Glutamine-Fructose-6-Phosphate Aminotransferase 2 (GFAT2), a crucial enzyme in the hexosamine biosynthesis pathway (HBP). GFAT2 serves as a rate limiting factor in HBP, a glycosylation pathway. This process has been recognized as a pivotal regulator in metabolic reprogramming of adenocarcinoma tumors 36 . LPIN1 plays a role in the progression of ovarian cancer 37 . ANKLE1 has been identified as a key component in the construction of a risk score model for lung squamous cell carcinoma 38 . Moreover, it has been acknowledged as a risk factor associated with epithelial ovarian tumors 39 . MYF6, located on chromosome 12q21, contributes to muscle differentiation and has been observed to undergo hypermethylation in the early stages of non-small cell lung cancer 40 . Furthermore, MYF6 and CCT6A play a crucial role in tumor immunity or prognosis of HNSCC 41, 42 . The results of the magnetic resonance analysis showed that two specific genes CTTN and PASK have no causal relationship with HNSCC at the genetic level. Recent investigations have indicated that cortactin might participate in the regulation of various emerging functions within cancer cells. These functions include angiogenesis, exosome secretion, and cell proliferation, suggesting a role for cortactin in shaping the tumor microenvironment. Its association with adverse prognostic factors in HNSCC, such as advanced clinical stage and poor tumor differentiation, may be attributed to this involvement 43 . Increasing evidence indicates that CTTN / cortactin alterations are central to head and neck oncogenic processes, with a particular emphasis on the development of lymph node metastasis 44, 45 . Several studies have suggested that the assessment of CTTN (cortactin) is important for predicting the prognosis of HNSCC 46, 47 . CTTN has a strong association with the prognosis of various cancers, including esophageal cancer, head and neck cancer, thyroid cancer, and glioma 46, 48-50 . Despite this, the utility of CTTN in routine clinical practice has not been firmly established. The debate continues on whether it can alter the prognostic significance in HNSCC. However, we believe that our research findings can serve as a supplementary prognostic tool in standard clinical practice. This tool can help in the selection of the most suitable therapeutic approach, ultimately striving to improve the survival outcomes for patients with HNSCC. PASK governs mitochondrial respiration, lipid and glucose metabolism, gene expression, and phosphorylation. It is additionally implicated in glucose metabolism 51 , myoblast fusion 52 and contributes to the reduction of ischemic stroke injury 53 . According to a recent study, PASK, identified as a downstream phosphorylation target of mTORC1, promotes muscle stem cell differentiation. This occurs through the activation of the myogenin promoter, leading to the initiation of stem cell self-renewal processes 52 . The role of PASK in HNSCC has not yet been explored in sufficient detail in existing research. Our findings may contribute to elucidating their promising significance in forecasting prognosis and predicting treatment response for HNSCC. In the calibration curve of the nomogram, the computed outcomes for the initial year align closely with the observed survival rate, with a slight deviation noted for the third and fifth years. The gradual decline in patient survival rates each year is consistent with clinical realities. The variances in drug sensitivity observed between the high- and low-risk groups serve as a foundation for risk stratification and personalized treatment. We observed the effectiveness of 17 chemotherapy drugs in patients with high-risk HNSCC. A lower IC50 value corresponds to higher drug sensitivity, suggesting more favorable treatment outcomes. Patients in varying tumor stages should integrate the results of the immune feature analysis and the sensitivity analysis of the chemotherapy drug once the expected survival period is determined. Subsequently, suitable treatment approaches can be devised for them. GO enrichment analysis indicated that the divergence in prognosis between the high-risk poor prognosis group and the low-risk good prognosis group could be ascribed to variations in DNA replication and function. The results of the GO enrichment analysis suggest that the difference in prognosis between the high-risk poor prognosis group and the low-risk good prognosis group could be related to variations in DNA replication and function. KEGG analysis further reveals a notable enrichment of DEGs in specific signaling pathways, with a particular emphasis on the cell cycle. This implies a potential correlation between differential TMGs and processes such as cancer cell growth, proliferation, migration, and invasion. In addition, different types of immune cells demonstrate different infiltration rates in the tumor microenvironment between the high-risk and low-risk groups. To our knowledge, this study represents the first exploration of the relationship between telomeres and prognosis in HNSCC, which includes both differential and survival analyzes. Our results indicate considerable potential for telomeres in the realm of diagnosis, prognosis, and targeted therapy for HNSCC. We formulate a prognostic model utilizing differential TMGs from publicly available databases. This model demonstrates potential to guide the selection of therapeutic drugs for HNSCC. Despite the promising outcomes of our study, it is essential to acknowledge certain limitations. Initially, HNSCC has been characterized as a highly heterogeneous tumor 54 . Despite the significant differences observed in the prognosis and treatment of HNSCC associated with differential TMG in the risk assessment model we constructed, further clinical validation is necessary to assess its effectiveness. Declarations Data availability statement The data sets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material. Ethics Statement Ethical review and approval were not required for the study of human participants according to local legislation and institutional requirements. Written informed consent was not required for participation in this study according to national legislation and institutional requirements. Authors' contributions Conception and design of the experiment: T.Z., L.Z., Y.W., and H.C. Performed the experiments and analyzed the data: L.Z., T.Z., Y.T., M.Z. and H.C. Interpretation of the findings: All authors contributed to the article and approved the submitted version. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was financially supported by the National Natural Science Foundation of China (82201301, CH), the Hubei Province Natural Science Foundation (2022CFB087, ZT), the Research Grant of the Union Hospital, Tongji Medical College, HUST (F016.02004.21003.126, ZT), and the Open Project of the Key Laboratory of Molecular Imaging (2022fzyx015, TZ) Conflict of interest The authors declare that the research was conducted in the absence of commercial or financial relationships that could be construed as a potential conflict of interest. References Saleh K, Eid R, Haddad FG, Khalife-Saleh N, Kourie HR. New developments in the management of head and neck cancer - impact of pembrolizumab. Ther Clin Risk Manag. 2018;14:295-303. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. May 2021;71(3):209-249. Alsahafi E, Begg K, Amelio I, et al. Clinical update on head and neck cancer: molecular biology and ongoing challenges. Cell Death Dis. Jul 15 2019;10(8):540. Liang F, Wang R, Du Q, Zhu S. An Epithelial-Mesenchymal Transition Hallmark Gene-Based Risk Score System in Head and Neck Squamous-Cell Carcinoma. Int J Gen Med. 2021;14:4219-4227. Pulte D, Brenner H. Changes in survival in head and neck cancers in the late 20th and early 21st century: a period analysis. Oncologist. 2010;15(9):994-1001. Economopoulou P, de Bree R, Kotsantis I, Psyrri A. Diagnostic Tumor Markers in Head and Neck Squamous Cell Carcinoma (HNSCC) in the Clinical Setting. Front Oncol. 2019;9:827. Gao J, Pickett HA. Targeting telomeres: advances in telomere maintenance mechanism-specific cancer therapies. Nat Rev Cancer. Sep 2022;22(9):515-532. Li Z, Tang J, Li H, et al. Shorter telomere length in peripheral blood leukocytes is associated with childhood autism. Sci Rep. Nov 17 2014;4:7073. Bodelon C, Savage SA, Gadalla SM. Telomeres in molecular epidemiology studies. Prog Mol Biol Transl Sci. 2014;125:113-131. Di Domenico EG, Romano E, Del Porto P, Ascenzioni F. Multifunctional role of ATM/Tel1 kinase in genome stability: from the DNA damage response to telomere maintenance. Biomed Res Int. 2014;2014:787404. Shay JW, Wright WE. Telomeres and telomerase: implications for cancer and aging. Radiat Res. Jan 2001;155(1 Pt 2):188-193. Rossiello F, Jurk D, Passos JF, d'Adda di Fagagna F. Telomere dysfunction in ageing and age-related diseases. Nat Cell Biol. Feb 2022;24(2):135-147. Ennour-Idrissi K, Maunsell E, Diorio C. Telomere Length and Breast Cancer Prognosis: A Systematic Review. Cancer Epidemiol Biomarkers Prev. Jan 2017;26(1):3-10. Morais M, Dias F, Teixeira AL, Medeiros R. Telomere Length in Renal Cell Carcinoma: The Jekyll and Hyde Biomarker of Ageing of the Kidney. Cancer Manag Res. 2020;12:1669-1679. Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. May 2015;12(5):453-457. Thorsson V, Gibbs DL, Brown SD, et al. The Immune Landscape of Cancer. Immunity. Apr 17 2018;48(4):812-830 e814. Jiang P, Gu S, Pan D, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. Oct 2018;24(10):1550-1558. Geeleher P, Cox NJ, Huang RS. Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. Genome Biol. Mar 3 2014;15(3):R47. Maeser D, Gruener RF, Huang RS. oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. Brief Bioinform. Nov 5 2021;22(6). Blackburn EH. Switching and signaling at the telomere. Cell. Sep 21 2001;106(6):661-673. Mathon NF, Lloyd AC. Cell senescence and cancer. Nat Rev Cancer. Dec 2001;1(3):203-213. Kim ES, Ye Y, Vaporciyan AA, et al. Telomere length and recurrence risk after curative resection in patients with early-stage non-small-cell lung cancer: a prospective cohort study. J Thorac Oncol. Feb 2015;10(2):302-308. Jones AM, Beggs AD, Carvajal-Carmona L, et al. TERC polymorphisms are associated both with susceptibility to colorectal cancer and with longer telomeres. Gut. Feb 2012;61(2):248-254. Russo A, Modica F, Guarrera S, et al. Shorter leukocyte telomere length is independently associated with poor survival in patients with bladder cancer. Cancer Epidemiol Biomarkers Prev. Nov 2014;23(11):2439-2446. Wu X, Amos CI, Zhu Y, et al. Telomere dysfunction: a potential cancer predisposition factor. J Natl Cancer Inst. Aug 20 2003;95(16):1211-1218. Gramatges MM, Telli ML, Balise R, Ford JM. Longer relative telomere length in blood from women with sporadic and familial breast cancer compared with healthy controls. Cancer Epidemiol Biomarkers Prev. Feb 2010;19(2):605-613. Walcott F, Rajaraman P, Gadalla SM, et al. Telomere length and risk of glioma. Cancer Epidemiol. Dec 2013;37(6):935-938. Hosgood HD, 3rd, Cawthon R, He X, Chanock S, Lan Q. Genetic variation in telomere maintenance genes, telomere length, and lung cancer susceptibility. Lung Cancer. Nov 2009;66(2):157-161. Mirabello L, Garcia-Closas M, Cawthon R, et al. Leukocyte telomere length in a population-based case-control study of ovarian cancer: a pilot study. Cancer Causes Control. Jan 2010;21(1):77-82. Broberg K, Bjork J, Paulsson K, Hoglund M, Albin M. Constitutional short telomeres are strong genetic susceptibility markers for bladder cancer. Carcinogenesis. Jul 2005;26(7):1263-1271. Shen J, Terry MB, Gurvich I, Liao Y, Senie RT, Santella RM. Short telomere length and breast cancer risk: a study in sister sets. Cancer Res. Jun 1 2007;67(11):5538-5544. Pooley KA, Sandhu MS, Tyrer J, et al. Telomere length in prospective and retrospective cancer case-control studies. Cancer Res. Apr 15 2010;70(8):3170-3176. Yamada S, Misawa K, Mima M, et al. Telomere shortening in head and neck cancer: association between DNA demethylation and survival. J Cancer. 2021;12(8):2165-2172. Rafnar T, Sulem P, Stacey SN, et al. Sequence variants at the TERT-CLPTM1L locus associate with many cancer types. Nat Genet. Feb 2009;41(2):221-227. Belviso S, Iuliano R, Amato R, Perrotti N, Menniti M. The human asparaginase enzyme (ASPG) inhibits growth in leukemic cells. PLoS One. 2017;12(5):e0178174. Zhang W, Bouchard G, Yu A, et al. GFPT2-Expressing Cancer-Associated Fibroblasts Mediate Metabolic Reprogramming in Human Lung Adenocarcinoma. Cancer Res. Jul 1 2018;78(13):3445-3457. Yang L, Ma HL. MiRNA-584 suppresses the progression of ovarian cancer by negatively regulating LPIN1. Eur Rev Med Pharmacol Sci. Feb 2020;24(3):1062-1071. Jiang W, Xie N, Xu C. Characterization of a prognostic model for lung squamous cell carcinoma based on eight stemness index-related genes. BMC Pulm Med. Jun 8 2022;22(1):224. Mostowska A, Sajdak S, Pawlik P, et al. Replication study for the association of seven genome- GWAS-identified Loci with susceptibility to ovarian cancer in the Polish population. Pathol Oncol Res. Apr 2015;21(2):307-313. Zhao Y, Zhou H, Ma K, et al. Abnormal methylation of seven genes and their associations with clinical characteristics in early stage non-small cell lung cancer. Oncol Lett. Apr 2013;5(4):1211-1218. Hu S, Duan H, Lu Y, Huang S. The genes regulating sensitivity of tumor cells to T cell-mediated killing: could they be potential personalized immunotherapeutic targets in head and neck squamous cell carcinoma? Discov Oncol. Nov 5 2023;14(1):199. Shi Y, Liang Y, Wang W, Zhang G. SEC61G identified as a prognostic biomarker of head and neck squamous cell carcinoma. Eur Arch Otorhinolaryngol. Apr 2022;279(4):2039-2048. Ramos-Garcia P, Gonzalez-Moles MA, Gonzalez-Ruiz L, et al. An update of knowledge on cortactin as a metastatic driver and potential therapeutic target in oral squamous cell carcinoma. Oral Dis. May 2019;25(4):949-971. Rivera C, Oliveira AK, Costa RAP, De Rossi T, Paes Leme AF. Prognostic biomarkers in oral squamous cell carcinoma: A systematic review. Oral Oncol. Sep 2017;72:38-47. Rodrigo JP, Suarez C, Ferlito A, Devaney KO, Petruzzelli GJ, Rinaldo A. Potential molecular prognostic markers for lymph node metastasis in head and neck squamous cell carcinoma. Acta Otolaryngol. Jan 2003;123(1):100-105. Ramos-Garcia P, Gonzalez-Moles MA, Ayen A, Gonzalez-Ruiz L, Ruiz-Avila I, Gil-Montoya JA. Prognostic and clinicopathological significance of CTTN/cortactin alterations in head and neck squamous cell carcinoma: Systematic review and meta-analysis. Head Neck. Jun 2019;41(6):1963-1978. Wang J, Chen X, Tian Y, et al. Six-gene signature for predicting survival in patients with head and neck squamous cell carcinoma. Aging (Albany NY). Jan 12 2020;12(1):767-783. Zhang S, Qi Q. MTSS1 suppresses cell migration and invasion by targeting CTTN in glioblastoma. J Neurooncol. Feb 2015;121(3):425-431. Hu X, Moon JW, Li S, et al. Amplification and overexpression of CTTN and CCND1 at chromosome 11q13 in Esophagus squamous cell carcinoma (ESCC) of North Eastern Chinese Population. Int J Med Sci. 2016;13(11):868-874. Long HC, Gao X, Lei CJ, et al. miR-542-3p inhibits the growth and invasion of colorectal cancer cells through targeted regulation of cortactin. Int J Mol Med. Apr 2016;37(4):1112-1118. Zhang DD, Zhang JG, Wang YZ, Liu Y, Liu GL, Li XY. Per-Arnt-Sim Kinase (PASK): An Emerging Regulator of Mammalian Glucose and Lipid Metabolism. Nutrients. Sep 7 2015;7(9):7437-7450. Kikani CK, Wu X, Fogarty S, et al. Activation of PASK by mTORC1 is required for the onset of the terminal differentiation program. Proc Natl Acad Sci U S A. May 21 2019;116(21):10382-10391. Wu J, Mao S, Wu X, Zhao Y, Zhang W, Zhu F. Jasminoidin reduces ischemic stroke injury by regulating microglia polarization via PASK-EEF1A1 axis. Chem Biol Drug Des. Sep 24 2023:e14354. Senovilla L, Vacchelli E, Galon J, et al. Trial watch: Prognostic and predictive value of the immune infiltrate in cancer. Oncoimmunology. Nov 1 2012;1(8):1323-1343. Tables Table 1. The list of primers used for qRT-PCR. ID primer The primer sequence (5'-3') NM_002046 H-GAPDH-S GGAAGCTTGTCATCAATGGAAATC H-GAPDH-A TGATGACCCTTTTGGCTCCC NM_001261427.3 H-LPIN1-S AAGGACAGGGCAGAAGAACC H-LPIN1-A CCGACCAGAGTTGGCGATT NM_005110.4 H-GFPT2-S GCTCAGACAAAGGCAACGAAT H-GFPT2-A GGTCTCTGTATCTGTTTCTGACTCAA NM_001080464.3 H-ASPG-S GCCTGGTCATCGTCAACTGTAC H-ASPG-A GATGTCATGTCGAAGCCTGAGAT NM_002469.3 H-MYF6-S GGGCTCGTGATAACGGCTAA H-MYF6-A AAGGCATCGAAGGCTACTCG NM_001278443.2 H-ANKLE1-S CTTCAGACTTTCATCCGTGCCA H-ANKLE1-A TAGGGCTTCCACAATACACGC NM_001009186.2 H-CCT6A-S ATCAGAGGGCTTGTTTTGGAC H-CCT6A-A TCACGAGTTTTTCTCTCTCTTCTGC NM_001184740.2 H-CTTN-S TCAGCTGTCGGCCACGAATAT H-CTTN-A AAAGCCTACAGCAGACTGATCAACT NM_001252119.2 H-PASK-S TACTCCCAAAAGTACAGTACCATGA H-PASK-A CTCAATCCAACAATCCTCCAAGA Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3991266","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":277499170,"identity":"7c447828-91e9-441b-855a-a99a3ac2d500","order_by":0,"name":"Hua Cai","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hua","middleName":"","lastName":"Cai","suffix":""},{"id":277499171,"identity":"76b20a04-7682-4d57-9640-4fa079598dfb","order_by":1,"name":"Yuan Tian","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Tian","suffix":""},{"id":277499172,"identity":"def1c0c9-ed90-409f-a414-fead3c0af651","order_by":2,"name":"Ying-Jie Wu","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ying-Jie","middleName":"","lastName":"Wu","suffix":""},{"id":277499173,"identity":"f974a7b9-2aa8-4559-b6e9-0dd5439b5b71","order_by":3,"name":"Ming-Zhu Zhou","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ming-Zhu","middleName":"","lastName":"Zhou","suffix":""},{"id":277499174,"identity":"4884b22c-e654-4b46-a52f-47c9ed1dc24d","order_by":4,"name":"Liu-Qing Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACNvbGhgMSFWxy/OwNRGrh4zl88IHFGT5jyZ4DRGqRk0hLNqhsk0vccCOBWIdJ5JhJ3GAzY2y4+XjjDYYam2jCWnjemEnO4EljZpydVmzBcCwtt4GgFvYcM2kJiWNszNJA6xgbDhOhhQGo5Y/Bfx42yTPEauEAel8igU2CR4KHWC2gQJY4wGYgwQP0SwIxfpFvB0al5D+2+v3HD2+88aHGhrAWZAB0ICnKIVpI1TEKRsEoGAUjAwAAzo47xvC10DEAAAAASUVORK5CYII=","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Liu-Qing","middleName":"","lastName":"Zhou","suffix":""},{"id":277499176,"identity":"fc87236c-182a-4d26-99c7-686ca3e83515","order_by":5,"name":"Tao Zhou","email":"","orcid":"","institution":"Huazhong University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-02-26 15:12:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3991266/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3991266/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52618975,"identity":"74ba7508-1b1e-4943-aa15-b6f439c0364f","added_by":"auto","created_at":"2024-03-13 16:33:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62165,"visible":true,"origin":"","legend":"\u003cp\u003eThe flow chart of our study.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3991266/v1/f927d63610995276f01b7d47.png"},{"id":52619965,"identity":"a9eb91b9-8daa-4d05-80d3-e45cc72a1e4d","added_by":"auto","created_at":"2024-03-13 16:42:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":461136,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of prognostic-relevant differential genes.\u003cstrong\u003e (A) \u003c/strong\u003eThe heatmap of 100 DEGs in normal and tumor samples.\u003cstrong\u003e (B)\u003c/strong\u003e The volcano plot of 80 significantly DEG with prognostic relevance.\u003c/p\u003e","description":"","filename":"Figure2AB.png","url":"https://assets-eu.researchsquare.com/files/rs-3991266/v1/057e77caf1d2b3f7050c2acb.png"},{"id":52618974,"identity":"8e7e09e6-3328-434e-be80-b3b5bd582827","added_by":"auto","created_at":"2024-03-13 16:33:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":357689,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of the model-related gene.\u003cstrong\u003e (A)\u003c/strong\u003e The tuning parameters of OS-related proteins were selected to cross-validate the error curve. The number of variables in the model is the point where the vertical line of the dashed line intersects with the horizontal coordinate above.\u003cstrong\u003e (B)\u003c/strong\u003e The LASSO coefficients of 24 OS-related TMGs selected by the model. \u003cstrong\u003e(C)\u003c/strong\u003e The scatter diagram indicates the comparative expression of 24 TMGs related to OS selected by the model between HNSCC and the corresponding normal tissues.\u003c/p\u003e","description":"","filename":"Figure3ABC.png","url":"https://assets-eu.researchsquare.com/files/rs-3991266/v1/702f24da20f1a3ecbbf2f372.png"},{"id":52618976,"identity":"efece39c-1050-4dd1-a8eb-4002b3774507","added_by":"auto","created_at":"2024-03-13 16:33:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":297023,"visible":true,"origin":"","legend":"\u003cp\u003eExternal validation of the predictive power of the model in the prognosis.\u003cstrong\u003e (A) The \u003c/strong\u003eKaplan-Meier survival curve demonstrated differences in OS between the high- and low-risk groups of patients with HNSCC.\u003cstrong\u003e (B) \u003c/strong\u003eThe ROC curves of the risk model and clinicopathological characteristics. \u003cstrong\u003e(C) \u003c/strong\u003eThe ROC curves to assess the precision of the risk model to predict survival at 1, 3, and 5 years. \u0026nbsp;\u003cstrong\u003e(D, E) \u003c/strong\u003eUnivariate and multivariate Cox regression analyzes were used to identify age, sex, grade, stage, and risk score as independent factors in the prognosis of patients with HNSCC.\u003cstrong\u003e (F)\u003c/strong\u003e A nomogram was constructed to predict OS of patients at 1, 3, and 5 years based on age, sex, grade, stage, and risk score, with the result indicating the predicted outcome of a patient with ID TCGA-T2-A6X0. \u003cstrong\u003e(G)\u003c/strong\u003e Calibration curve of the nomogram.\u003c/p\u003e","description":"","filename":"Figure4AG.png","url":"https://assets-eu.researchsquare.com/files/rs-3991266/v1/0a60525d8ef15740005a7470.png"},{"id":52618981,"identity":"f3180dae-fd57-4a1d-bb40-98aa212d6b4a","added_by":"auto","created_at":"2024-03-13 16:34:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":710408,"visible":true,"origin":"","legend":"\u003cp\u003ePathway enrichment of GO, KEGG.\u003cstrong\u003e (A-C)\u003c/strong\u003e Significance histogram, significance bubble chart, circle chart for GO analysis;\u003cstrong\u003e (D-F)\u003c/strong\u003e Significance histogram, significance bubble chart, circle chart for KEGG analysis\u003c/p\u003e","description":"","filename":"Figure5AF.png","url":"https://assets-eu.researchsquare.com/files/rs-3991266/v1/7d06922168d3fe484f35cc6b.png"},{"id":52618977,"identity":"541acbfd-9b8c-44c8-af34-e7558454dd7a","added_by":"auto","created_at":"2024-03-13 16:34:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":382775,"visible":true,"origin":"","legend":"\u003cp\u003eImmunocorrelated analysis between high-and low-risk groups. \u003cstrong\u003e(A) \u003c/strong\u003eHeatmap of immune cell infiltration rates in high- and low-risk groups.\u003cstrong\u003e (B) \u003c/strong\u003eBox plot of the distribution portion of immune cells in cancer tissues with different patients in the risk group of TMGs. \u003cstrong\u003e(C)\u003c/strong\u003e Results of the immune subtypes of 419 patients in the total cohort of TCGA. \u003cstrong\u003e(D, E) \u003c/strong\u003eThe violin plot and the scatter diagram of the difference in TIDE scores between the high- and low-risk groups.\u003c/p\u003e","description":"","filename":"Figure6AE.png","url":"https://assets-eu.researchsquare.com/files/rs-3991266/v1/5fa8640f0a028e09c074b463.png"},{"id":52619964,"identity":"7a35e3f6-2b6a-4004-9b41-15bab469a8aa","added_by":"auto","created_at":"2024-03-13 16:42:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":180334,"visible":true,"origin":"","legend":"\u003cp\u003eDrug sensitivity analysis.\u003cstrong\u003e (A-Q)\u003c/strong\u003e The box plot presents the half-maximum inhibitory concentration (IC50) values for patients in the high- and low-risk groups for the 17 corresponding drugs.\u003c/p\u003e","description":"","filename":"Figure7AQ.png","url":"https://assets-eu.researchsquare.com/files/rs-3991266/v1/3e5161b38a5465bcb4ee97d6.png"},{"id":52618980,"identity":"cf87637e-4990-4e3e-a60f-66d2a0a5e60a","added_by":"auto","created_at":"2024-03-13 16:34:00","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":172142,"visible":true,"origin":"","legend":"\u003cp\u003eMR analysis.\u003cstrong\u003e(A) \u003c/strong\u003eForest plots of the causal effects of CTTN-associated SNPs on HNSCC. \u003cstrong\u003e(B)\u003c/strong\u003e Scatter plots for MR analysis of the causal effect of CTTN on HNSCC. \u003cstrong\u003e(C)\u003c/strong\u003e 'Leave one out' analysis of CTTN. The red lines are the results of the analysis of IVW random effects IVW. \u003cstrong\u003e(D) \u003c/strong\u003eForest plots of the causal effects of PASK-associated SNPs on HNSCC. \u003cstrong\u003e(E)\u003c/strong\u003e Scatter plots for MR analysis of the causal effect of PASK on HNSCC. \u003cstrong\u003e(F)\u003c/strong\u003e 'Leave one out' analysis of PASK. The red lines are the results of the analysis of IVW random effects IVW.\u003c/p\u003e","description":"","filename":"Figure8AH.png","url":"https://assets-eu.researchsquare.com/files/rs-3991266/v1/b95badd499df6800b6376d91.png"},{"id":52618982,"identity":"208183c3-9055-4ed4-9294-cb172ef2522c","added_by":"auto","created_at":"2024-03-13 16:34:00","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":479722,"visible":true,"origin":"","legend":"\u003cp\u003eThe mRNA expression level of the TMG in individuals with HNSCC and normal tissues was assessed by polymerase chain reaction (qRT-PCR) analysis. (A–H) The expression of ANKLE1, ASPG, CCT6A, CTTN, GFPT2, LPIN1, MYF6 and PASK in HNSCC tissues and normal tissues by PCR. *P \u0026lt; 0.05, **P \u0026lt; 0.01, and ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure9AH.png","url":"https://assets-eu.researchsquare.com/files/rs-3991266/v1/42a4bd5da4935467093c03db.png"},{"id":65239112,"identity":"4013c898-5ce1-44b1-ab0a-4476a4eb148d","added_by":"auto","created_at":"2024-09-25 06:23:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3342506,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3991266/v1/3af44f35-0aef-415f-8549-a86df3f7bd89.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterization of a prognostic model for head and neck squamous cell carcinoma based on Telomere maintenance genes","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHNSCC is a prevalent malignant tumor that comprises 95% of head and neck cancers. Globally, it ranks as the eighth most commonly diagnosed form of cancer\u003csup\u003e1, 2\u003c/sup\u003e. The clinical treatment options for HNSCC include surgical interventions, radiation therapy, chemotherapy, and the most recent advancement in immunotherapy\u003csup\u003e3\u003c/sup\u003e. Currently, natural killer immune cells demonstrate enhanced targeting precision, allowing effective clearance of cancer cells and mitigating the risks of metastasis and relapse. However, due to the invasiveness and heterogeneity of HNSCC, survival outcomes for individuals remain suboptimal\u003csup\u003e4\u003c/sup\u003e. The 5-year survival rate is still stands at approximately 60%\u003csup\u003e5\u003c/sup\u003e. Therefore, there is a pressing need to discover a novel, stable, and reliable molecular characteristic to predict the prognosis of the disease, assess the risk of recurrence, and evaluate the potential responsiveness of identified targets to therapy\u003csup\u003e6\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTelomeres, located at the termini of human chromosomes, are nucleoprotein complexes composed of a repetitive sequence, namely 5\u0026rsquo;-TTAGGG-3\u0026rsquo;, along with protective proteins\u003csup\u003e7\u003c/sup\u003e. These structures play a crucial role in protecting chromosome ends from atypical degradation, loss of nucleotides, breakage, recombination, and end-to-end fusion\u003csup\u003e8-10\u003c/sup\u003e. However, the length of telomeres progressively decreases with cell division and under specific disease conditions. Telomerase extends chromosomes by adding DNA sequences with the TTAGGG pattern to the end, ensuring the preservation of the telomere length. Studies suggest that a predominant method by which tumor cells attain immortality is by counteracting telomere shortening, primarily through the telomere maintenance mechanism (TMM)\u003csup\u003e11\u003c/sup\u003e. In cancer, abbreviated telomeres may manifest antitumor effects by hindering the proliferation of tumor cells or, conversely, favoring cancer advancement by inducing genomic instability\u003csup\u003e12\u003c/sup\u003e.\u0026nbsp;Longer telomere length is associated with a favorable prognosis in specific cancer types, such as breast cancer\u003csup\u003e13\u003c/sup\u003e.\u0026nbsp;On the contrary, shorter telomere lengths in tumor cells are observed in other types of cancer, such as renal cell carcinoma, although its impact on prognosis remains a subject of controversy\u003csup\u003e14\u003c/sup\u003e.\u0026nbsp;Currently, there has been no examination to determine whether the expressions of telomere-associated genes can serve as diagnostic or prognostic biomarkers in patients with HNSCC.\u003c/p\u003e\n\u003cp\u003eWe used bioinformatic techniques to compute a prognostic model for differential TMG in HNSCC and our assessment focused on elucidating the roles of differential TMG in immune infiltration, prognosis, and response to treatment. Furthermore, we intend to assess the potential utility of this risk model in guiding the selection of treatment agents. Our results indicate that genes associated with telomeres hold significant promise in the prognosis, diagnosis, and therapeutic strategies tailored for the treatment of HNSCC.\u003c/p\u003e"},{"header":"2. Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Data acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe obtained raw data related to HNSCC from the Cancer Genome Atlas (TCGA) database and organized the information using R (version 4.3.1) and Perl (strawberry version). Additionally, we acquired TMGs data from the TelNet website.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Identification and analysis of DEGs with prognostic significance.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 2093 TMGs were obtained from the TelNet database. The limma packages were used to identify differential gene expression between tumor and normal tissues. Principal component analyses (PCA) were applied to assess unique distribution patterns between two groups in various datasets. The DEGs identified by screening underwent a single-factor independent prognostic analysis. The sva package was used to standardize TCGA data, minimizing batch effects. Subsequently, the TCGA expression data was integrated with survival information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Construction of a prognostic risk model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe TCGA cohort served for the construction of a lasso regression model. The model was cross-validated, and the optimal results were chosen to generate the model formula and identify certain genes associated with the model. Then a differential analysis was conducted on these model-related genes to observe variations between normal samples and tumor samples. The model formula was then applied to score all samples, resulting in the calculation of risk scores. Afterward, based on the median risk score, all samples can be divided into two groups: the high-risk sample group and the low-risk sample group.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 External validation of the prognostic risk model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurvival analysis was performed for both groups to assess the robustness and reliability of the model. The accuracy of the model was evaluated by performing a progression-free survival analysis and examining the ROC curve. The potential of risk scoring to serve as an independent prognostic indicator can be assessed through independent univariate and multivariate prognostic analyzes. Univariate and multivariate Cox regression analyses were carried out, incorporating variables such as grade, age, stage, gender, and risk score, to independently assess the prognostic significance of the model. A significance level of logarithmic rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used as the threshold to identify meaningful associations. Clinical factors linked to independent prognoses were explored using tumor stage nomograms, integrating age and risk scoring for individuals with HNSCC.\u0026nbsp;The effectiveness of the nomogram was validated by examination of the calibration curves.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Functional enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubsequent to identifying\u0026nbsp;DEGs, we performed functional enrichment analyzes of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to explore the potential molecular mechanisms associated with the prognostic signature.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Immune characteristics and immune subtype analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe presence of various types of immune cells within the tumor microenvironment of HNSCC was evaluated by analyzing the expression values of specific genes using CIBERSORT, with 1000 iterations performed (https://cibersort.stanford.edu/)\u003csup\u003e15\u003c/sup\u003e. In a previous study, immune subtypes were identified in various cancers using data from the TCGA database, and we obtained specific immune subtypes specific to HNSCC patients\u003csup\u003e16\u003c/sup\u003e. Additionally, the TIDE (Tumor Immune Dysfunction and Exclusion) score was computed in accordance with provided instructions (https://tide.dfci.harvard.edu/)\u003csup\u003e17\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Analysis of drug sensitivity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of drug sensitivity utilized information sourced from the Genome of Drug Sensitivity in Cancer 2 (GDSC2) database. (https://www.cancerrxgene.org/)\u003csup\u003e18\u003c/sup\u003e. We further acquire half-maximum inhibitory\u0026nbsp;concentration (IC50) values for targeted or chemotherapeutic drugs in high and low risk populations using the R \u0026quot;oncoPredict\u0026quot; package, with the aim of assessing the correlation of prognostic characteristics with clinical treatment response in HNSCC\u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Mendelian randomization (MR) analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMendelian randomization (MR) represents a type of instrumental variables (IV) analysis. Exposure and outcome data sets were derived from the IEU Open GWAS project (https://gwas.mrcieu.ac.uk/). A two-sample MR investigation was conducted using the TwoSampleMR and MRPRESSO packages in R. The primary analysis method employed was the random-effects inverse variance weighted (IVW), with additional methods including weighted median, MR Egger, weighted mode, and simple mode. Heterogeneity and horizontal pleiotropy were assessed and an examination was carried out to determine if the analysis results were influenced by a single SNP.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9 Quantitative real-time PCR (qRT-PCR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe verified the expression of the TMGs that made up the prognostic risk score model in HNSCC tissues by qRT-PCR. We collected HNSCC tissues and adjacent normal tissues from eight patients with HNSCC who underwent surgical resection at Wuhan Union Hospital. All patients had signed informed consent forms prior to surgery. Total RNA was extracted from tissues using Trizol (Takara, China) and reversed to cDNA using Prime Script RTase (Takara, China). Subsequently, qRT-PCR was performed using SYBR green (Takara, China). The mean + standard error of three independent experiments were calculated, and each experiment was repeated three times. Relative TMG mRNA expression levels were calculated with GAPDH serving as an internal reference. The primer sequences are shown in Table 1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.10 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyzes are performed using both R and Perl software. (version 4.3.1, packages: sva, limma, pheatmap, survival, survminer, glmnet, timeROC, ggpubr, regplot, rms, ggplot2, ggExtra, reshape2, tidyverse, preprocessCore, RColorBrewer, car, ridge, genefilter, biomaRt, GenomicFeatures, maftools, stringr, org.Hs.eg.db, oncoPredict and remotes). Unless otherwise specified, all analyzes in this study considered an estimated p-value of \u0026lt; 0.05 as statistically significant. For all comparisons: \u0026ldquo;*\u0026rdquo; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u0026ldquo;**\u0026rdquo; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, \u0026ldquo;**\u0026rdquo; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Identification of 80 prognostically relevant differential genes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe illustrate the complete study procedure (Figure 1). A total of 1799 differentially expressed genes were identified in both tumor and normal tissues. We acquired the expression profiles of 519 DEG related to telomeres (|logFC| \u0026gt; 1, Fdr \u0026lt; 0.05), including 471 genes that exhibited upregulation and 48 genes that showed downregulation. Heatmaps were plotted to visualize these results, including only 100 DEGs (Figure 2A). Through the integration of these profiles with survival information through differential analysis, we identified 80 significantly DEGs with prognostic relevance. Among them, 56 genes were identified as high-risk genes, with a hazard ratio (HR) greater than 1, while 24 genes were identified as low-risk genes with an HR less than 1. A volcano plot was used to visualize these results (Figure 2B).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Construction of a risk model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough lasso regression analysis of 80 significantly\u0026nbsp;DEGs\u0026nbsp;with prognostic relevance, we identified 24 genes that exhibited minimal error during the construction of the optimal risk model (Figure 3A, B), namely ADA, ANKLE1, ASPG, BRIP1, CCT2, CCT6A, CTTN, DSG2, EIF5A2, ETV4, FOXL2, GEN1, GFPT2, GLDC, HMMR, IRAK1, KPNA2, LCK, LPIN1, MYF6, PASK, PLK1, PLOD2, and ZFP42. We visualized the comparative expression of 24 TMGs\u0026nbsp;between HNSCC and corresponding normal tissues (Figure 3C). We observed that 3 genes (ASPG,\u0026nbsp;LPIN1, and MYF6) observed that three genes exhibited low expression levels in tumor patients, while 21 genes (ADA, ANKLE1,\u0026nbsp;BRIP1, CCT2, CCT6A, CTTN, DSG2, EIF5A2, ETV4, FOXL2, GEN1, GFPT2, GLDC, HMMR, IRAK1, KPNA2, LCK,\u0026nbsp;PASK, PLK1, PLOD2 and ZFP42) showed high expression levels (Figure 3B).\u0026nbsp;Therefore,\u003c/p\u003e\n\u003cp\u003eRisk score = EXP [(ADA * 0.0163761818012764) + (ANKLE1 * -0.0780063108672532) + (ASPG * -0.0381922859730038) + (BRIP1 * -0.356091535914648) + (CCT2 * 0.000674837813465877) + (CCT6A * 0.062199924653352) + (CTTN * 0.0552505134570484) + (DSG2 * 0.0129990195647178) + (EIF5A2 * 0.134661775875434) + (ETV4 * 0.0406146194564598) + (FOXL2 * 0.0747336745392169) + (GEN1 * -0.00687790482196727) + (GFPT2 * 0.0642676182222645) + (GLDC * 0.0772969174824084) + (HMMR * 0.144601105580529) + (IRAK1 * 0.01182847922651 \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Survival analysis\u003c/strong\u003e \u003cstrong\u003eand independent analysis for prognostic model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe patients were categorized into high- and low-risk groups according to their median risk scores, followed by a subsequent survival analysis. Our study indicates that patients in the high-risk group experience a more unfavorable prognosis compared to those in the low-risk group (Figure 4A). The AUC of the risk score in the multivariate ROC curve (AUC = 0.705) was significantly higher than the AUC of other indicators (Figure 4B). All the ROC values obtained in the timieROC curve were above 0.7 (0.705, 0.790, and 0.753, as shown in\u0026nbsp;Figure 4C). Hence, a more aggressive treatment plan should be devised for patients in the low-risk group.\u003c/p\u003e\n\u003cp\u003eFollowing the methodology outlined, Cox regression analyzes were performed individually for gender, age, stage, grade, and risk score. In the forest graph of the independent single factor prognostic analysis, we observed that the predictive power of the risk score was independent of other factors and better than other factors (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, HR = 4.842, Figure 4D). We reached the same conclusion in a multivariate independent prognostic analysis (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, HR = 5.075, Figure 4E).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Construction and verification of the nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom the aforementioned investigation, we developed a reliable scoring model. The nomogram illustrates the model scores in relation to readily available clinical factors such as age, sex, and tumor stage (Figure 4F). The initial patient in the study cohort is used to demonstrate the application of the nomogram in the research queue. The patient\u0026apos;s survival rates were 0.973 at the end of the first year, 0.915 at the third year, and 0.857 at the fifth year. The calibration curve indicates that the nomogram calculations closely align with the actual survival rate for the first year, while there is a slight deviation in results for the third and fifth years (Figure 4G).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Functional enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used GO and KEGG analyzes to explore the potential biological functions associated with the 519 genes exhibiting differential expression between the low-risk and high-risk groups, given the significant differences in results among the two groups of patients with HNSCC. The GO annotation revealed significant associations of these genes with biological behaviors such as DNA replication，cellular components such as the chromosomal region and molecular functions such as catalytic activity, acting on DNA (Figure 5A, B, C). According to the KEGG analysis, these genes were found to be associated with a pathway such as the cell cycle. The observed biological behaviors and pathways probably contribute to the tendency of the high-risk group to experience less favorable clinical outcomes (Figure 5D, E, F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Patients in different risk categories exhibit varying immune statuses.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferent types of immune cells exhibited diverse infiltration rates within the tumor microenvironment between high- and low-risk groups. T cells CD4 memory resting, NK cells resting, Macrophages M0 and activated Mast cells exhibited a higher infiltration rate in the high-risk group compared to the low-risk group. Naive B cells, Plasma cells, CD8 T cells activated T cells CD4 memory, follicular helper T cells, regulatory T cells (Tregs), and resting Mast cells exhibited lower infiltration in the high-risk group compared to the low-risk group (Figure 6A, B).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the immune status of the tumors, early studies classified the tumors in the TCGA database into six subtypes: C1 (wund healing), C2 (IFN-g dominant), C3 (inflammatory), C4 (lymphocyte depleted), C5 (immunologically quiet), and C6 (TGF-b dominant)\u003csup\u003e16\u003c/sup\u003e. Examining immune subtypes revealed that people in the low-risk group showed a nearly equal prevalence of the C1 subtype (26%) and the C2 subtype (74%) compared to those in the high-risk group, where the distribution was the C1 subtype (25%) and the C2 subtype (75%) (Figure 6C). The TIDE score exhibited a significantly higher value in the high-risk group compared to the low-risk group (P\u0026lt;0.001) (Figure 6D, E). \u003cstrong\u003e\u0026nbsp;Figure 6.\u003c/strong\u003e Immunocorrelated analysis between high-and low-risk groups. \u003cstrong\u003e(A)\u0026nbsp;\u003c/strong\u003eHeatmap of immune cell infiltration rates in high- and low-risk groups.\u003cstrong\u003e\u0026nbsp;(B)\u0026nbsp;\u003c/strong\u003eBox plot of the distribution portion of immune cells in cancer tissues with different patients in the risk group of TMGs. \u003cstrong\u003e(C)\u003c/strong\u003e Results of the immune subtypes of 419 patients in the total cohort of TCGA. \u003cstrong\u003e(D, E)\u0026nbsp;\u003c/strong\u003eThe violin plot and the scatter diagram of the difference in TIDE scores between the high- and low-risk groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 Potential drug screening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDrug sensitivity scores for compounds in the GDSC database were calculated using oncoPredict. Statistically significant differences in responses were observed between the high and low risk groups for 74 of these drugs, with P \u0026lt; 0.001. More precisely, an elevated risk score demonstrated a correlation with decreased IC50 values for the 17 drugs including AZD7762, Staurosporine, BI-2536, Dasatinib, PD0325901, BMS-536924, SB505124, AZD1332, SCH772984, ERK_2440, ERK_6604, IGF1R_3801, OTX015, PAK_5339, AZD6738, MK-8776, and VX-11e (Figure 7A-Q). Furthermore, we examine the correlation between MRLncRI and IC50 through scatterplots. A lower IC50 value indicates greater drug sensitivity and a more favorable therapeutic effect. This implies that individuals with HNSCC in the high-risk groups were more inclined to demonstrate a positive response to these chemotherapeutic drugs. \u003cstrong\u003e\u0026nbsp;Figure 7.\u0026nbsp;\u003c/strong\u003eDrug sensitivity analysis.\u003cstrong\u003e\u0026nbsp;(A-Q)\u003c/strong\u003e The box plot presents the half-maximum inhibitory concentration (IC50) values for patients in the high- and low-risk groups for the 17 corresponding drugs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.8 MR analysis result\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe random effect IVW results showed that the CTTN gene (\u003cem\u003ep\u003c/em\u003e = 0.364, OR 95% confidence interval [CI] = 1.000 [0.999-1.001]) and the PASK gene (\u003cem\u003ep\u003c/em\u003e = 0.734, OR 95% CI = 1.000 [0.999-1.001]) do not have a causal relationship with HNSCC at the genetic level (Figure 8A-F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.9 Validation of TMGs in HNSCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe qRT-PCR analysis revealed that the expression of ANKLE1, CCT6A, CTTN, GFPT2 and PASK were upregulated, while ASPG, LPIN1 and MYF6 was downregulated in HNSCC tissues compared to paired normal tissues (Figure 9). The results are consistent with the previously mentioned findings from our bioinformatic analysis.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe treatment of HNSCC presents a notable clinical challenge, primarily due to the increased rates of locoregional recurrence and resistance to chemotherapy. Variability in the tumor microenvironment and the genetic makeup of cancer cells has an impact on response to treatment and clinical results. \u0026nbsp; However, existing research on biomarkers associated with HNSCC is inadequate to meet the clinical demands for the diagnosis and prognosis of this condition. It is crucial to discover new biomarkers for HNSCC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTelomeres in humans have been proposed to delineate the cellular lifespan and determine the number of cell divisions\u003csup\u003e20\u003c/sup\u003e. When telomeres reach a critical threshold, it can trigger double-strand breaks, leading to cellular senescence or apoptosis\u003csup\u003e21\u003c/sup\u003e. Currently, scientists have recognized an association between genetic variants linked to telomeres or telomere dysfunction and the increased risk of various cancers\u003csup\u003e22-24\u003c/sup\u003e. Carcinogenesis is promoted by both excessively short and excessively long telomeres in peripheral blood lymphocytes\u003csup\u003e25, 26\u003c/sup\u003e. Significantly, increasing amounts of epidemiological research have revealed associations between the length of the telomere in peripheral blood lymphocytes and the susceptibility to diverse cancer types such as lung\u003csup\u003e27, 28\u003c/sup\u003e, ovarian\u003csup\u003e29\u003c/sup\u003e, bladder\u003csup\u003e30\u003c/sup\u003e, breast\u003csup\u003e31\u003c/sup\u003e, and colon or rectum cancer\u003csup\u003e28, 32\u003c/sup\u003e. Therefore, the length of telomeres in peripheral blood lymphocytes could potentially function as a universal risk marker for human cancers.\u0026nbsp;Research has indicated a notable correlation between the telomere length ratio in HNSCC compared to normal tissue and the recurrence rate of the disease. A reduced ratio suggests an elevated likelihood of recurrence\u003csup\u003e33\u003c/sup\u003e. The shorter telomere length, coupled with decreased 5-hydroxymethylcytosine levels and\u0026nbsp;reduced translocation expression of 10 to 11, could contribute to the development of HNSCC\u003csup\u003e33\u003c/sup\u003e. Associations were identified between variants in TERT-CLPTM1L and both the mean relative telomere length and the susceptibility to HNSCC in Icelandic and European populations\u003csup\u003e34\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eHNSCC exhibits widespread occurrences of telomere maintenance mechanisms. This study represents the first examination of the impact of\u0026nbsp;differential\u0026nbsp;TMGs on the prognosis of HNSCC.\u0026nbsp;We established a scoring model for\u0026nbsp;differential\u0026nbsp;TMGs using a public database and the results obtained have been validated through external data verification and immunohistochemical queries of model-related genes, ensuring stability and reliability.\u0026nbsp;We developed a nomogram that integrates the risk model derived from\u0026nbsp;differential\u0026nbsp;TMG with clinical pathological characteristics, and evaluated its effectiveness in predicting the prognosis of HNSCC. Scoring this model and taking into account standard clinical information allows us to forecast multi-year survival rates for each patient. Combining these predictions with results from immune feature analysis and chemotherapy drug sensitivity analysis, we can tailor appropriate treatment strategies for patients at different stages of tumor development.\u003c/p\u003e\n\u003cp\u003eThe 24 model genes identified in our\u0026nbsp;differential\u0026nbsp;TMG risk model serve various roles in the disease. The human protein ASPG functions as an enzyme with potential antitumor activity, exhibiting growth inhibition in leukemic cells\u003csup\u003e35\u003c/sup\u003e. Transaminase 2 (GFPT2) encodes Glutamine-Fructose-6-Phosphate Aminotransferase 2 (GFAT2), a crucial enzyme in the hexosamine biosynthesis pathway (HBP). GFAT2 serves as a rate limiting factor in HBP, a glycosylation pathway. This process has been recognized as a pivotal regulator in metabolic reprogramming of adenocarcinoma tumors\u003csup\u003e36\u003c/sup\u003e. LPIN1 plays a role in the progression of ovarian cancer\u003csup\u003e37\u003c/sup\u003e. ANKLE1 has been identified as a key component in the construction of a risk score model for lung squamous cell carcinoma\u003csup\u003e38\u003c/sup\u003e. Moreover, it has been acknowledged as a risk factor associated with epithelial ovarian tumors\u003csup\u003e39\u003c/sup\u003e. MYF6, located on chromosome 12q21, contributes to muscle differentiation and has been observed to undergo hypermethylation in the early stages of non-small cell lung cancer\u003csup\u003e40\u003c/sup\u003e. Furthermore, MYF6 and CCT6A play a crucial role in tumor immunity or prognosis of HNSCC\u003csup\u003e41, 42\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results of the magnetic resonance analysis showed that two specific genes CTTN and PASK have no causal relationship with HNSCC at the genetic level.\u0026nbsp;Recent investigations have indicated that cortactin might participate in the regulation of various emerging functions within cancer cells. These functions include angiogenesis, exosome secretion, and cell proliferation, suggesting a role for cortactin in shaping the tumor microenvironment. Its association with adverse prognostic factors in HNSCC, such as advanced clinical stage and poor tumor differentiation, may be attributed to this involvement\u003csup\u003e43\u003c/sup\u003e. Increasing evidence indicates that CTTN / cortactin alterations are central to head and neck oncogenic processes, with a particular emphasis on the development of lymph node metastasis\u003csup\u003e44, 45\u003c/sup\u003e. Several studies have suggested that the assessment of CTTN (cortactin) is important for predicting the prognosis of HNSCC\u003csup\u003e46, 47\u003c/sup\u003e. CTTN has a strong association with the prognosis of various cancers, including esophageal cancer, head and neck cancer, thyroid cancer, and glioma\u003csup\u003e46, 48-50\u003c/sup\u003e. Despite this, the utility of CTTN in routine clinical practice has not been firmly established. The debate continues on whether it can alter the prognostic significance in HNSCC. However, we believe that our research findings can serve as a supplementary prognostic tool in standard clinical practice. This tool can help in the selection of the most suitable therapeutic approach, ultimately striving to improve the survival outcomes for patients with HNSCC.\u003c/p\u003e\n\u003cp\u003ePASK governs mitochondrial respiration, lipid and glucose metabolism, gene expression, and phosphorylation. It is additionally implicated in glucose metabolism\u003csup\u003e51\u003c/sup\u003e, myoblast fusion\u003csup\u003e52\u003c/sup\u003e and contributes to the reduction of ischemic stroke injury\u003csup\u003e53\u003c/sup\u003e. According to a recent study, PASK, identified as a downstream phosphorylation target of mTORC1, promotes muscle stem cell differentiation. This occurs through the activation of the myogenin promoter, leading to the initiation of stem cell self-renewal processes\u003csup\u003e52\u003c/sup\u003e. The role of PASK in HNSCC has not yet been explored in sufficient detail in existing research. Our findings may contribute to elucidating their promising significance in forecasting prognosis and predicting treatment response for HNSCC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the calibration curve of the\u0026nbsp;nomogram, the computed outcomes for the initial year align closely with the observed survival rate, with a slight deviation noted for the third and fifth years. The gradual decline in patient survival rates each year is consistent with clinical realities. The variances in drug sensitivity observed between the high- and low-risk groups serve as a foundation for risk stratification and personalized treatment. We observed the effectiveness of 17 chemotherapy drugs in patients with high-risk HNSCC. A lower IC50 value corresponds to higher drug sensitivity, suggesting more favorable treatment outcomes. Patients in varying tumor stages should integrate the results of the immune feature analysis and the sensitivity analysis of the chemotherapy drug once the expected survival period is determined. Subsequently, suitable treatment approaches can be devised for them.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGO enrichment analysis indicated that the divergence in prognosis between the high-risk poor prognosis group and the low-risk good prognosis group could be ascribed to variations in DNA replication and function.\u0026nbsp;The results of the GO enrichment analysis suggest that the difference in prognosis between the high-risk poor prognosis group and the low-risk good prognosis group could be related to variations in DNA replication and function. KEGG analysis further reveals a notable enrichment of\u0026nbsp;DEGs\u0026nbsp;in specific signaling pathways, with a particular emphasis on the cell cycle. This implies a potential correlation between\u0026nbsp;differential\u0026nbsp;TMGs and processes such as cancer cell growth, proliferation, migration, and invasion. In addition, different types of immune cells demonstrate different infiltration rates in the tumor microenvironment between the high-risk and low-risk groups.\u003c/p\u003e\n\u003cp\u003eTo our knowledge, this study represents the first exploration of the relationship between telomeres and prognosis in HNSCC, which includes both differential and survival analyzes. Our results indicate considerable potential for telomeres in the realm of diagnosis, prognosis, and targeted therapy for HNSCC. We formulate a prognostic model utilizing\u0026nbsp;differential\u0026nbsp;TMGs from publicly available databases. This model demonstrates potential to guide the selection of therapeutic drugs for HNSCC. Despite the promising outcomes of our study, it is essential to acknowledge certain limitations. Initially, HNSCC has been characterized as a highly heterogeneous tumor\u003csup\u003e54\u003c/sup\u003e. Despite the significant differences observed in the prognosis and treatment of HNSCC associated with differential TMG in the risk assessment model we constructed, further clinical validation is necessary to assess its effectiveness.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data sets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical review and approval were not required for the study of human participants according to local legislation and institutional requirements. Written informed consent was not required for participation in this study according to national legislation and institutional requirements.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConception and design of the experiment: T.Z., L.Z., Y.W., and H.C. Performed the experiments and analyzed the data: L.Z., T.Z., Y.T., M.Z. and H.C. Interpretation of the findings: All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was financially supported by the National Natural Science Foundation of China (82201301, CH), the Hubei Province Natural Science Foundation (2022CFB087, ZT), the Research Grant of the Union Hospital, Tongji Medical College, HUST (F016.02004.21003.126, ZT), and the Open\u0026nbsp;Project of the\u0026nbsp;Key Laboratory of Molecular Imaging (2022fzyx015,\u0026nbsp;TZ)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of commercial or financial relationships that could be construed as a potential conflict of interest.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSaleh K, Eid R, Haddad FG, Khalife-Saleh N, Kourie HR. New developments in the management of head and neck cancer - impact of pembrolizumab. \u003cem\u003eTher Clin Risk Manag. \u003c/em\u003e2018;14:295-303.\u003c/li\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. \u003cem\u003eCA Cancer J Clin. \u003c/em\u003eMay 2021;71(3):209-249.\u003c/li\u003e\n\u003cli\u003eAlsahafi E, Begg K, Amelio I, et al. Clinical update on head and neck cancer: molecular biology and ongoing challenges. \u003cem\u003eCell Death Dis. \u003c/em\u003eJul 15 2019;10(8):540.\u003c/li\u003e\n\u003cli\u003eLiang F, Wang R, Du Q, Zhu S. An Epithelial-Mesenchymal Transition Hallmark Gene-Based Risk Score System in Head and Neck Squamous-Cell Carcinoma. \u003cem\u003eInt J Gen Med. \u003c/em\u003e2021;14:4219-4227.\u003c/li\u003e\n\u003cli\u003ePulte D, Brenner H. Changes in survival in head and neck cancers in the late 20th and early 21st century: a period analysis. \u003cem\u003eOncologist. \u003c/em\u003e2010;15(9):994-1001.\u003c/li\u003e\n\u003cli\u003eEconomopoulou P, de Bree R, Kotsantis I, Psyrri A. Diagnostic Tumor Markers in Head and Neck Squamous Cell Carcinoma (HNSCC) in the Clinical Setting. \u003cem\u003eFront Oncol. \u003c/em\u003e2019;9:827.\u003c/li\u003e\n\u003cli\u003eGao J, Pickett HA. Targeting telomeres: advances in telomere maintenance mechanism-specific cancer therapies. \u003cem\u003eNat Rev Cancer. \u003c/em\u003eSep 2022;22(9):515-532.\u003c/li\u003e\n\u003cli\u003eLi Z, Tang J, Li H, et al. Shorter telomere length in peripheral blood leukocytes is associated with childhood autism. \u003cem\u003eSci Rep. \u003c/em\u003eNov 17 2014;4:7073.\u003c/li\u003e\n\u003cli\u003eBodelon C, Savage SA, Gadalla SM. Telomeres in molecular epidemiology studies. \u003cem\u003eProg Mol Biol Transl Sci. \u003c/em\u003e2014;125:113-131.\u003c/li\u003e\n\u003cli\u003eDi Domenico EG, Romano E, Del Porto P, Ascenzioni F. Multifunctional role of ATM/Tel1 kinase in genome stability: from the DNA damage response to telomere maintenance. \u003cem\u003eBiomed Res Int. \u003c/em\u003e2014;2014:787404.\u003c/li\u003e\n\u003cli\u003eShay JW, Wright WE. Telomeres and telomerase: implications for cancer and aging. \u003cem\u003eRadiat Res. \u003c/em\u003eJan 2001;155(1 Pt 2):188-193.\u003c/li\u003e\n\u003cli\u003eRossiello F, Jurk D, Passos JF, d\u0026apos;Adda di Fagagna F. Telomere dysfunction in ageing and age-related diseases. \u003cem\u003eNat Cell Biol. \u003c/em\u003eFeb 2022;24(2):135-147.\u003c/li\u003e\n\u003cli\u003eEnnour-Idrissi K, Maunsell E, Diorio C. Telomere Length and Breast Cancer Prognosis: A Systematic Review. \u003cem\u003eCancer Epidemiol Biomarkers Prev. \u003c/em\u003eJan 2017;26(1):3-10.\u003c/li\u003e\n\u003cli\u003eMorais M, Dias F, Teixeira AL, Medeiros R. Telomere Length in Renal Cell Carcinoma: The Jekyll and Hyde Biomarker of Ageing of the Kidney. \u003cem\u003eCancer Manag Res. \u003c/em\u003e2020;12:1669-1679.\u003c/li\u003e\n\u003cli\u003eNewman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. \u003cem\u003eNat Methods. \u003c/em\u003eMay 2015;12(5):453-457.\u003c/li\u003e\n\u003cli\u003eThorsson V, Gibbs DL, Brown SD, et al. The Immune Landscape of Cancer. \u003cem\u003eImmunity. \u003c/em\u003eApr 17 2018;48(4):812-830 e814.\u003c/li\u003e\n\u003cli\u003eJiang P, Gu S, Pan D, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. \u003cem\u003eNat Med. \u003c/em\u003eOct 2018;24(10):1550-1558.\u003c/li\u003e\n\u003cli\u003eGeeleher P, Cox NJ, Huang RS. Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines. \u003cem\u003eGenome Biol. \u003c/em\u003eMar 3 2014;15(3):R47.\u003c/li\u003e\n\u003cli\u003eMaeser D, Gruener RF, Huang RS. oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. \u003cem\u003eBrief Bioinform. \u003c/em\u003eNov 5 2021;22(6).\u003c/li\u003e\n\u003cli\u003eBlackburn EH. Switching and signaling at the telomere. \u003cem\u003eCell. \u003c/em\u003eSep 21 2001;106(6):661-673.\u003c/li\u003e\n\u003cli\u003eMathon NF, Lloyd AC. Cell senescence and cancer. \u003cem\u003eNat Rev Cancer. \u003c/em\u003eDec 2001;1(3):203-213.\u003c/li\u003e\n\u003cli\u003eKim ES, Ye Y, Vaporciyan AA, et al. Telomere length and recurrence risk after curative resection in patients with early-stage non-small-cell lung cancer: a prospective cohort study. \u003cem\u003eJ Thorac Oncol. \u003c/em\u003eFeb 2015;10(2):302-308.\u003c/li\u003e\n\u003cli\u003eJones AM, Beggs AD, Carvajal-Carmona L, et al. TERC polymorphisms are associated both with susceptibility to colorectal cancer and with longer telomeres. \u003cem\u003eGut. \u003c/em\u003eFeb 2012;61(2):248-254.\u003c/li\u003e\n\u003cli\u003eRusso A, Modica F, Guarrera S, et al. Shorter leukocyte telomere length is independently associated with poor survival in patients with bladder cancer. \u003cem\u003eCancer Epidemiol Biomarkers Prev. \u003c/em\u003eNov 2014;23(11):2439-2446.\u003c/li\u003e\n\u003cli\u003eWu X, Amos CI, Zhu Y, et al. Telomere dysfunction: a potential cancer predisposition factor. \u003cem\u003eJ Natl Cancer Inst. \u003c/em\u003eAug 20 2003;95(16):1211-1218.\u003c/li\u003e\n\u003cli\u003eGramatges MM, Telli ML, Balise R, Ford JM. Longer relative telomere length in blood from women with sporadic and familial breast cancer compared with healthy controls. \u003cem\u003eCancer Epidemiol Biomarkers Prev. \u003c/em\u003eFeb 2010;19(2):605-613.\u003c/li\u003e\n\u003cli\u003eWalcott F, Rajaraman P, Gadalla SM, et al. Telomere length and risk of glioma. \u003cem\u003eCancer Epidemiol. \u003c/em\u003eDec 2013;37(6):935-938.\u003c/li\u003e\n\u003cli\u003eHosgood HD, 3rd, Cawthon R, He X, Chanock S, Lan Q. Genetic variation in telomere maintenance genes, telomere length, and lung cancer susceptibility. \u003cem\u003eLung Cancer. \u003c/em\u003eNov 2009;66(2):157-161.\u003c/li\u003e\n\u003cli\u003eMirabello L, Garcia-Closas M, Cawthon R, et al. Leukocyte telomere length in a population-based case-control study of ovarian cancer: a pilot study. \u003cem\u003eCancer Causes Control. \u003c/em\u003eJan 2010;21(1):77-82.\u003c/li\u003e\n\u003cli\u003eBroberg K, Bjork J, Paulsson K, Hoglund M, Albin M. Constitutional short telomeres are strong genetic susceptibility markers for bladder cancer. \u003cem\u003eCarcinogenesis. \u003c/em\u003eJul 2005;26(7):1263-1271.\u003c/li\u003e\n\u003cli\u003eShen J, Terry MB, Gurvich I, Liao Y, Senie RT, Santella RM. Short telomere length and breast cancer risk: a study in sister sets. \u003cem\u003eCancer Res. \u003c/em\u003eJun 1 2007;67(11):5538-5544.\u003c/li\u003e\n\u003cli\u003ePooley KA, Sandhu MS, Tyrer J, et al. Telomere length in prospective and retrospective cancer case-control studies. \u003cem\u003eCancer Res. \u003c/em\u003eApr 15 2010;70(8):3170-3176.\u003c/li\u003e\n\u003cli\u003eYamada S, Misawa K, Mima M, et al. Telomere shortening in head and neck cancer: association between DNA demethylation and survival. \u003cem\u003eJ Cancer. \u003c/em\u003e2021;12(8):2165-2172.\u003c/li\u003e\n\u003cli\u003eRafnar T, Sulem P, Stacey SN, et al. Sequence variants at the TERT-CLPTM1L locus associate with many cancer types. \u003cem\u003eNat Genet. \u003c/em\u003eFeb 2009;41(2):221-227.\u003c/li\u003e\n\u003cli\u003eBelviso S, Iuliano R, Amato R, Perrotti N, Menniti M. The human asparaginase enzyme (ASPG) inhibits growth in leukemic cells. \u003cem\u003ePLoS One. \u003c/em\u003e2017;12(5):e0178174.\u003c/li\u003e\n\u003cli\u003eZhang W, Bouchard G, Yu A, et al. GFPT2-Expressing Cancer-Associated Fibroblasts Mediate Metabolic Reprogramming in Human Lung Adenocarcinoma. \u003cem\u003eCancer Res. \u003c/em\u003eJul 1 2018;78(13):3445-3457.\u003c/li\u003e\n\u003cli\u003eYang L, Ma HL. MiRNA-584 suppresses the progression of ovarian cancer by negatively regulating LPIN1. \u003cem\u003eEur Rev Med Pharmacol Sci. \u003c/em\u003eFeb 2020;24(3):1062-1071.\u003c/li\u003e\n\u003cli\u003eJiang W, Xie N, Xu C. Characterization of a prognostic model for lung squamous cell carcinoma based on eight stemness index-related genes. \u003cem\u003eBMC Pulm Med. \u003c/em\u003eJun 8 2022;22(1):224.\u003c/li\u003e\n\u003cli\u003eMostowska A, Sajdak S, Pawlik P, et al. Replication study for the association of seven genome- GWAS-identified Loci with susceptibility to ovarian cancer in the Polish population. \u003cem\u003ePathol Oncol Res. \u003c/em\u003eApr 2015;21(2):307-313.\u003c/li\u003e\n\u003cli\u003eZhao Y, Zhou H, Ma K, et al. Abnormal methylation of seven genes and their associations with clinical characteristics in early stage non-small cell lung cancer. \u003cem\u003eOncol Lett. \u003c/em\u003eApr 2013;5(4):1211-1218.\u003c/li\u003e\n\u003cli\u003eHu S, Duan H, Lu Y, Huang S. The genes regulating sensitivity of tumor cells to T cell-mediated killing: could they be potential personalized immunotherapeutic targets in head and neck squamous cell carcinoma? \u003cem\u003eDiscov Oncol. \u003c/em\u003eNov 5 2023;14(1):199.\u003c/li\u003e\n\u003cli\u003eShi Y, Liang Y, Wang W, Zhang G. SEC61G identified as a prognostic biomarker of head and neck squamous cell carcinoma. \u003cem\u003eEur Arch Otorhinolaryngol. \u003c/em\u003eApr 2022;279(4):2039-2048.\u003c/li\u003e\n\u003cli\u003eRamos-Garcia P, Gonzalez-Moles MA, Gonzalez-Ruiz L, et al. An update of knowledge on cortactin as a metastatic driver and potential therapeutic target in oral squamous cell carcinoma. \u003cem\u003eOral Dis. \u003c/em\u003eMay 2019;25(4):949-971.\u003c/li\u003e\n\u003cli\u003eRivera C, Oliveira AK, Costa RAP, De Rossi T, Paes Leme AF. Prognostic biomarkers in oral squamous cell carcinoma: A systematic review. \u003cem\u003eOral Oncol. \u003c/em\u003eSep 2017;72:38-47.\u003c/li\u003e\n\u003cli\u003eRodrigo JP, Suarez C, Ferlito A, Devaney KO, Petruzzelli GJ, Rinaldo A. Potential molecular prognostic markers for lymph node metastasis in head and neck squamous cell carcinoma. \u003cem\u003eActa Otolaryngol. \u003c/em\u003eJan 2003;123(1):100-105.\u003c/li\u003e\n\u003cli\u003eRamos-Garcia P, Gonzalez-Moles MA, Ayen A, Gonzalez-Ruiz L, Ruiz-Avila I, Gil-Montoya JA. Prognostic and clinicopathological significance of CTTN/cortactin alterations in head and neck squamous cell carcinoma: Systematic review and meta-analysis. \u003cem\u003eHead Neck. \u003c/em\u003eJun 2019;41(6):1963-1978.\u003c/li\u003e\n\u003cli\u003eWang J, Chen X, Tian Y, et al. Six-gene signature for predicting survival in patients with head and neck squamous cell carcinoma. \u003cem\u003eAging (Albany NY). \u003c/em\u003eJan 12 2020;12(1):767-783.\u003c/li\u003e\n\u003cli\u003eZhang S, Qi Q. MTSS1 suppresses cell migration and invasion by targeting CTTN in glioblastoma. \u003cem\u003eJ Neurooncol. \u003c/em\u003eFeb 2015;121(3):425-431.\u003c/li\u003e\n\u003cli\u003eHu X, Moon JW, Li S, et al. Amplification and overexpression of CTTN and CCND1 at chromosome 11q13 in Esophagus squamous cell carcinoma (ESCC) of North Eastern Chinese Population. \u003cem\u003eInt J Med Sci. \u003c/em\u003e2016;13(11):868-874.\u003c/li\u003e\n\u003cli\u003eLong HC, Gao X, Lei CJ, et al. miR-542-3p inhibits the growth and invasion of colorectal cancer cells through targeted regulation of cortactin. \u003cem\u003eInt J Mol Med. \u003c/em\u003eApr 2016;37(4):1112-1118.\u003c/li\u003e\n\u003cli\u003eZhang DD, Zhang JG, Wang YZ, Liu Y, Liu GL, Li XY. Per-Arnt-Sim Kinase (PASK): An Emerging Regulator of Mammalian Glucose and Lipid Metabolism. \u003cem\u003eNutrients. \u003c/em\u003eSep 7 2015;7(9):7437-7450.\u003c/li\u003e\n\u003cli\u003eKikani CK, Wu X, Fogarty S, et al. Activation of PASK by mTORC1 is required for the onset of the terminal differentiation program. \u003cem\u003eProc Natl Acad Sci U S A. \u003c/em\u003eMay 21 2019;116(21):10382-10391.\u003c/li\u003e\n\u003cli\u003eWu J, Mao S, Wu X, Zhao Y, Zhang W, Zhu F. Jasminoidin reduces ischemic stroke injury by regulating microglia polarization via PASK-EEF1A1 axis. \u003cem\u003eChem Biol Drug Des. \u003c/em\u003eSep 24 2023:e14354.\u003c/li\u003e\n\u003cli\u003eSenovilla L, Vacchelli E, Galon J, et al. Trial watch: Prognostic and predictive value of the immune infiltrate in cancer. \u003cem\u003eOncoimmunology. \u003c/em\u003eNov 1 2012;1(8):1323-1343.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. The list of primers used for qRT-PCR.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.08398133748056%\" valign=\"top\"\u003e\n \u003cp\u003eID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.283048211508554%\" valign=\"top\"\u003e\n \u003cp\u003eprimer\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.632970451010884%\" valign=\"top\"\u003e\n \u003cp\u003eThe primer sequence (5\u0026apos;-3\u0026apos;)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.08398133748056%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNM_002046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.283048211508554%\" valign=\"top\"\u003e\n \u003cp\u003eH-GAPDH-S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.632970451010884%\" valign=\"top\"\u003e\n \u003cp\u003eGGAAGCTTGTCATCAATGGAAATC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73253493013972%\" valign=\"top\"\u003e\n \u003cp\u003eH-GAPDH-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.26746506986028%\" valign=\"top\"\u003e\n \u003cp\u003eTGATGACCCTTTTGGCTCCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.08398133748056%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNM_001261427.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.283048211508554%\" valign=\"top\"\u003e\n \u003cp\u003eH-LPIN1-S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.632970451010884%\" valign=\"top\"\u003e\n \u003cp\u003eAAGGACAGGGCAGAAGAACC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73253493013972%\" valign=\"top\"\u003e\n \u003cp\u003eH-LPIN1-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.26746506986028%\" valign=\"top\"\u003e\n \u003cp\u003eCCGACCAGAGTTGGCGATT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.08398133748056%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNM_005110.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.283048211508554%\" valign=\"top\"\u003e\n \u003cp\u003eH-GFPT2-S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.632970451010884%\" valign=\"top\"\u003e\n \u003cp\u003eGCTCAGACAAAGGCAACGAAT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73253493013972%\" valign=\"top\"\u003e\n \u003cp\u003eH-GFPT2-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.26746506986028%\" valign=\"top\"\u003e\n \u003cp\u003eGGTCTCTGTATCTGTTTCTGACTCAA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.08398133748056%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNM_001080464.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.283048211508554%\" valign=\"top\"\u003e\n \u003cp\u003eH-ASPG-S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.632970451010884%\" valign=\"top\"\u003e\n \u003cp\u003eGCCTGGTCATCGTCAACTGTAC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73253493013972%\" valign=\"top\"\u003e\n \u003cp\u003eH-ASPG-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.26746506986028%\" valign=\"top\"\u003e\n \u003cp\u003eGATGTCATGTCGAAGCCTGAGAT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.08398133748056%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNM_002469.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.283048211508554%\" valign=\"top\"\u003e\n \u003cp\u003eH-MYF6-S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.632970451010884%\" valign=\"top\"\u003e\n \u003cp\u003eGGGCTCGTGATAACGGCTAA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73253493013972%\" valign=\"top\"\u003e\n \u003cp\u003eH-MYF6-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.26746506986028%\" valign=\"top\"\u003e\n \u003cp\u003eAAGGCATCGAAGGCTACTCG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.08398133748056%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNM_001278443.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.283048211508554%\" valign=\"top\"\u003e\n \u003cp\u003eH-ANKLE1-S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.632970451010884%\" valign=\"top\"\u003e\n \u003cp\u003eCTTCAGACTTTCATCCGTGCCA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73253493013972%\" valign=\"top\"\u003e\n \u003cp\u003eH-ANKLE1-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.26746506986028%\" valign=\"top\"\u003e\n \u003cp\u003eTAGGGCTTCCACAATACACGC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.08398133748056%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNM_001009186.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.283048211508554%\" valign=\"top\"\u003e\n \u003cp\u003eH-CCT6A-S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.632970451010884%\" valign=\"top\"\u003e\n \u003cp\u003eATCAGAGGGCTTGTTTTGGAC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73253493013972%\" valign=\"top\"\u003e\n \u003cp\u003eH-CCT6A-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.26746506986028%\" valign=\"top\"\u003e\n \u003cp\u003eTCACGAGTTTTTCTCTCTCTTCTGC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.08398133748056%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNM_001184740.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.283048211508554%\" valign=\"top\"\u003e\n \u003cp\u003eH-CTTN-S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.632970451010884%\" valign=\"top\"\u003e\n \u003cp\u003eTCAGCTGTCGGCCACGAATAT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73253493013972%\" valign=\"top\"\u003e\n \u003cp\u003eH-CTTN-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.26746506986028%\" valign=\"top\"\u003e\n \u003cp\u003eAAAGCCTACAGCAGACTGATCAACT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.08398133748056%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eNM_001252119.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.283048211508554%\" valign=\"top\"\u003e\n \u003cp\u003eH-PASK-S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"51.632970451010884%\" valign=\"top\"\u003e\n \u003cp\u003eTACTCCCAAAAGTACAGTACCATGA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.73253493013972%\" valign=\"top\"\u003e\n \u003cp\u003eH-PASK-A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.26746506986028%\" valign=\"top\"\u003e\n \u003cp\u003eCTCAATCCAACAATCCTCCAAGA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Telomere, Head and neck squamous cell carcinoma, Drug sensitivity, Prognosis, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-3991266/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3991266/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Head and neck squamous cell carcinoma (HNSCC) typically presents unfavorable prognostic outcomes. Telomere dysfunction is involved in malignant transformation and tumor development processes. We performed a comprehensive array of analyzes to assess and authenticate the prognostic significance of telomeres in HNSCC, including the identification and examination of differential telomere maintenance genes (TMGs) with prognostic significance, Cox regression analysis, survival analysis, nomogram prediction, time receiver operating characteristic (ROC) analysis, Immune characteristics, enrichment analysis, drug sensitivity analysis, Mendelian randomization (MR) analysis, and real-time quantitative PCR (qRT-PCR). Employing bioinformatics, we derived a prognostic model comprising 80 significantly differentially expressed genes (DEGs) of prognostic relevance. Subsequent analysis using the HPA database revealed 24 genes, and they were identified to exhibit elevated expression levels in tumor patients. The model predicted an area under the ROC curve (AUC) of 0.973 for the 1-year survival rates of patients with HNSCC. The high- and low-risk groups exhibited different immune statuses and drug sensitivities. More precisely, HNSCC individuals in high-risk groups were more prone to show a favorable response to 17 chemotherapeutic drugs. Additionally, our result of qRT-PCR was also consistent with the analysis. The prognostic model centered on differential TMGs shows great potential as a valuable tool for risk stratification, predicting survival outcomes, assessing immune status, screening potential drugs, and exploring genetic associations with HNSCC.","manuscriptTitle":"Characterization of a prognostic model for head and neck squamous cell carcinoma based on Telomere maintenance genes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-13 16:33:55","doi":"10.21203/rs.3.rs-3991266/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f1243c62-899d-40ee-9160-f35571917d29","owner":[],"postedDate":"March 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":29245050,"name":"Biological sciences/Cancer"},{"id":29245051,"name":"Biological sciences/Computational biology and bioinformatics"}],"tags":[],"updatedAt":"2024-09-25T06:23:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-13 16:33:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3991266","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3991266","identity":"rs-3991266","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}