OShnscc: a novel user-friendly online survival analysis tool for head and neck squamous cell carcinoma based on RNA expression profiles and long-term survival information

OShnscc: a novel user-friendly online survival analysis tool for head and neck squamous cell carcinoma based on RNA expression profiles and long-term survival information OShnscc：一个基于表达谱数据及其长期生存信息开发的头颈鳞癌在线生存分析工具 1 first-author 2 1 1 1 1 1 1 1 1 3 4 5 6 corresp Jiancheng GUO, gjc@zzu.edu.cn Jiancheng GUO, gjc@zzu.edu.cn gjc@zzu.edu.cn https://orcid.org/0000-0001-8553-4105 1 corresp Xiangqian GUO, xqguo@henu.edu.cn Xiangqian GUO, xqguo@henu.edu.cn xqguo@henu.edu.cn https://orcid.org/0000-0001-6645-5786 Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China Department of Predictive Medicine, Institute of Biomedical Informatics, Cell Signal Transduction Laboratory, Bioinformatics Center, Henan Provincial Engineering Center for Tumor Molecular Medicine, School of Basic Medical Sciences, Henan University, Kaifeng 475004, China School of Software, Henan University, Kaifeng 475004, China School of Software, Henan University, Kaifeng 475004, China Department of Anesthesia, Stanford University, Stanford, CA 94305, USA Department of Anesthesia, Stanford University, Stanford, CA 94305, USA Department of Molecular Pathology, the Second Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China Department of Molecular Pathology, the Second Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China Precision Medicine Center, Academy of Medical Sciences, Zhengzhou University, Zhengzhou 450052, China Precision Medicine Center, Academy of Medical Sciences, Zhengzhou University, Zhengzhou 450052, China Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450052, China Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou 450052, China 目的： 2 头颈鳞癌是常见的恶性肿瘤之一，预后较差。开发新的预后分子标志物对于改善头颈鳞癌患者的预后具有重要意义。因此，本研究通过整合公共癌症数据库中的头颈鳞癌表达谱数据及临床生存信息，开发了头颈鳞癌在线生存分析工具OShnscc（Online consensus Survival tool for Head and Neck Squamous Cell Carcinoma），用于评价基因mRNA表达量与头颈鳞癌患者生存时间的相关性。 创新点： 2 本研究开发的OShnscc是目前为止包含头颈鳞癌预后数据集最多、样本量最大的在线生存分析工具。用户不仅可以在多个独立数据集中分析和比较基因的预后价值，还可以利用多种临床因素如肿瘤TNM分期、肿瘤分级、年龄、性别等因素作为筛选条件进行亚组预后分析。 方法： 2 OShnscc的开发包括三部分：（1）以样本为人头颈鳞癌临床样本的基因芯片或二代测序数据且数据集样本数大于20个为筛选标准，从公共癌症数据库中收集和筛选有效的头颈鳞癌数据集；（2）使用SQL Server 2008对样本头颈鳞癌表达谱数据与样本生存时间进行回归分析，构建头颈鳞癌预后数据库；（3）采用Java语言开发在线分析系统，使用R语言进行生存分析，应用R sever服务进行Java语言与R语言的连接。 结论： 2 OShnscc是一个公开免费的在线生存分析工具（网址：http://bioinfo.henu.edu.cn/HNSC/HNSCList.jsp），为缺少生物信息学背景的医学及相关科研人员提供一个快速分析基因mRNA表达量与头颈鳞癌患者预后关系的平台，有助于推动头颈鳞癌的预后标志物开发。 头颈鳞癌 在线生存分析工具 预后标志物 Head and neck squamous cell carcinoma (HNSCC), as the most common type ( > 90%) of head and neck cancer, includes various epithelial malignancies that arise in the nasal cavity, oral cavity, pharynx, and larynx. In 2020, approximately 878 ‍ 000 new cases and 444 000 deaths linked to HNSCC occurred worldwide ( Sung et al., 2021 Sung et al., 2021 ). Due to the associated frequent recurrence and metastasis, HNSCC patients have poor prognosis with a five-year survival rate of 40%–50% ( Jou and Hess, 2017 Jou and Hess, 2017 ). Therefore, novel prognostic biomarkers need to be developed to identify high-risk HNSCC patients and improve their disease outcomes. The expression of certain genes, especially oncogenes, has been reported to exhibit significant relationships with cancer prognosis, such as genes epidermal growth factor receptor ( EGFR ) and cyclin-dependent kinase inhibitor 2A ( CDKN2A ) that have prognostic value in HNSCC ( Polanska et al., 2014 Polanska et al., 2014 ). However, thus far, few reported biomarkers have been applied in clinical management because these were usually identified in a small number of patients and lacked independent validation ( Kim et al., 2014 Kim et al., 2014 ; Fauzi et al., 2020 Fauzi et al., 2020 ). With the availability of gene expression profiles and related clinical follow-up information in public databases, such as The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), the expression profiles of large cohorts could be used for the identification and evaluation of prognostic biomarkers. However, special bioinformatics techniques are needed to analyze and integrate these complex multidimensional omics and clinicopathological data, which has been hindering most researchers without much bioinformatics skills. The application of such bioinformatics tools has shown success in basic and translational medicine studies, especially in the field of biomarker development ( Zhang et al., 2019 Zhang et al., 2019 ). Unfortunately, a suitable and easy-to-use bioinformatics tool to allow the fast screening and evaluation of prognostic biomarkers in different HNSCC cohorts is still lacking. Therefore, in the current study, we developed the Online consensus Survival tool for Head and Neck Squamous Cell Carcinoma (OShnscc), which could offer a new possibility for clinicians and researchers to assess the prognostic value of genes independently in multiple HNSCC cohorts. The development process of OShnscc is presented in Fig. 1 Fig. 1 . This web-based tool can be accessed at http://bioinfo.henu.edu.cn/HNSC/HNSCList.jsp. http://bioinfo.henu.edu.cn/HNSC/HNSCList.jsp. OShnscc contains a total of 1366 clinical cases from nine datasets, including one TCGA dataset and eight GEO datasets. In this tool, six types of survival terms are provided for survival analysis, including overall survival (OS), disease-specific survival (DSS), disease-free survival (DFS), progression-free survival (PFS), disease-free interval (DFI), and progression-free interval (PFI). The survival terms have been described in detail in original studies ( Wichmann et al., 2015 Wichmann et al., 2015 ; Liu et al., 2018 Liu et al., 2018 ). In brief, OS is estimated from the time of diagnosis to the last follow-up, and DSS is similar to OS but only includes patients who died of cancer. DFS is measured from the time of primary recovery to the first observation of relapse or death from any cause by the last follow-up, and DFI is similar to DFS but excludes patients in Stage IV or those who deceased without relapse. PFS is defined from the registration date until the date of detection of definitive disease progression, recurrence, metastasis, new primary tumors in all sites, or death from any cause, and PFI is similar to PFS but excludes patients who died from causes other than cancer. A total of 888 (65%) patients are male, and the median age is 59 years; 731 (54%) patients have OS information, and the median OS time is 24 months. Patients without either clinical follow-up time or gene expression data were removed. The patient clinical characteristics, including tumor node metastasis (TNM) stage, grade, gender, smoking history, lymph invasion, human papilloma virus (HPV) status, and race, were also included and set as confounding clinical factors for subgroup analysis in OShnscc. The clinical characteristics and the detailed items of all datasets in OShnscc are summarized in Tables 1 Tables 1 and S1, respectively. HNSCC: head and neck squamous cell carcinoma; OShnscc: Online consensus Survival tool for Head and Neck Squamous Cell Carcinoma; OS: overall survival; HPV: human papilloma virus; NA: not available; TCGA: The Cancer Genome Atlas; OC: oral cavity; OP: oropharynx; HP: hypopharynx; L: larynx; T: tonsil; DSS: disease-specific survival; PFS: progression-free survival; DFI: disease-free interval; PFI: progression-free interval; RFS: relapse-free survival; DFS: disease-free survival. PFS, DFI, and PFI were defined by Wichmann et al. (2015) Wichmann et al. (2015) and Liu et al. (2018) Liu et al. (2018) . Data source Platform Sample size No. of deaths Median age(years) Median OS (months) Male(%) HPV status (+/-/NA) StageI/II/III/IV/NA(%) Smoking (%) Tumor subsite Survival term TCGA Illumina HiSeqV2 523 220 61 21.50 73.04 35/239/249 5.16/13.96/15.68/51.05/14.15 75.14 OC, OP,HP, L, T OS, DSS, PFS, DFI, PFI GSE10300 GPL570 44 16 53 79.55 2.27/6.82/18.18/43.18/29.55 65.91 OC, OP,HP, L RFS GSE27020 GPL96 109 64 95.45 11.01/16.51/33.03/39.45/0 99.08 L DFS GSE25727 GPL8432 56 60 92.86 87.50 L DFS GSE31056 GPL10526 96 36 59 13.54 62.50 0/20.83/3.13/37.50/38.54 OC OS, DFS GSE3292 GPL570 33 8 56 24.00 81.82 6.06/6.06/24.24/57.58/6.06 OC, OP,HP, L OS, DFS GSE39366 GPL9053 138 68 57 68.84 14/82/42 5.80/10.14/20.29/60.87/2.90 78.26 OC, OP,HP, L RFS GSE41613 GPL570 97 51 26.30 68.04 0/97/0 OC OS, DSS GSE65858 GPL10588 270 94 59 27.90 82.59 73/196/1 6.67/13.70/13.70/65.93/0 88.52 OC, OP,HP, L OS, PFS Total 1366 493 Six clinical characteristics including TNM stage, grade, age, gender, race, and tumor subsite were measured for association with the OS of HNSCC patients in the OShnscc datasets. As shown in Fig. 2 Fig. 2 , the clinical factors including TNM stage ( Fig. 2 Fig. 2 a), grade ( Fig. 2 Fig. 2 b), age ( Fig. 2 Fig. 2 c), gender ( Fig. 2 Fig. 2 d), race ( Fig. 2 Fig. 2 e), and tumor subsite ( Fig. 2 Fig. 2 f) showed significant associations with the OS of HNSCC patients ( P < 0.0001, P =0.0365, P= 0.0007, P =0.0255, P =0.0273, and P =0.0091, respectively), which was consistent with previous reports ( Pai and Westra, 2009 Pai and Westra, 2009 ; Polanska et al., 2014 Polanska et al., 2014 ; Naghavi et al., 2016 Naghavi et al., 2016 ). The main function of OShnscc is to evaluate and verify the prognostic value of a given gene in HNSCC. On its home page, OShnscc provides a fast survival analysis model using the default parameter of "Data Source" as "All," the "Survival" term as "OS," and "Split patients by" cutoff as "Upper 25%." The user only needs to input the genes of interest in the term "Gene symbol" and click the "Survival analysis" button. Then, a forest plot and a table that summarize the prognostic values, including hazard ratio (HR) with 95% confidence interval (CI) and the log-rank P value of queried genes in each dataset of OShnscc, will be generated. Taking the transmembrane p24 trafficking protein 4 ( TMED4 ) gene as an example, if the user inputs the gene symbol " TMED4 " and then clicks the "Survival analysis" button, a forest plot and a table containing the prognostic value of TMED4 will be presented on the output webpage. The results shown in Fig. 3 Fig. 3 indicate that the high expression of TMED4 is associated with the poor OS of HNSCC patients in the dataset of TCGA ( n =499, P < 0.0001, HR=1.9695, 95% CI=1.4794‍–‍2.6220) and GSE3292 ( n =33, P =0.0150, HR=5.9593, 95% CI=1.4137–25.1212), while no significant relationship was found between TMED4 expression and the OS of HNSCC patients in the dataset of GSE31056 ( n =96, P =0.3351, HR=0.6661, 95% CI=0.2916–1.5218) or GSE65858 ( n =270, P =0.4196, HR=1.2043, 95% CI=0.7668–1.8913). When the user query of a gene with a reported prognostic role is submitted, a reminder stating "The query gene has been previously reported as a prognostic biomarker in [Reference]" will be presented underneath the analysis result on the output page. By clicking on the "Go" button on the right side of the table, the user can easily acquire the Kaplan-Meier (KM) plots of TMED4 for individual cohorts such as the TCGA dataset ( Fig. 4 Fig. 4 ). The OShnscc platform also provides diverse options of parameters for survival analysis. Through choosing the datasets in the term "Data source," OShnscc turns to the individual dataset page, and several parameters with 2–5 options are provided. For example, users may want to evaluate the association between the gene of interest and the disease prognosis of patients from the TCGA dataset, and subsequently choose the TCGA dataset in "Data source." Two main parameters including "Survival" and "Split patients by" and nine optional parameters including TNM stage, grade, gender, etc. are displayed on the TCGA webpage ( Fig. 5 Fig. 5 ). Users only need to type the gene symbol, select the prognostic endpoint in "Survival" and the cutoff value in "Split patients by," and then click the "Kaplan-Meier plot" button for the survival curve with HR, 95% CI, and log-rank P value to be graphically displayed on the webpage. In addition, optional parameters including TNM stage, HPV status, smoking history, gender, lymph, histological type, and race are also provided for subgroup analysis to limit the prognosis in a subgroup for the intended clinical factor of HNSCCs. In order to measure the performance of OShnscc and the repeatability of prognostic biomarkers reported in the literature, a total of 39 prognostic genes reported by 36 studies were collected for testing in OShnscc. As shown in Tables S2 and S3, 38 out of 39 previously reported genes (97%) have been confirmed for their prognostic values in OShnscc, and 16 out of 39 genes (41%) showed significant association with HNSCC outcomes for at least two independent datasets in OShnscc. However, one gene (SRY-box transcription factor 4 ( SOX4 )) did not show any significance for prognosis in any cohort from OShnscc. A total of 39 previously reported prognostic genes, except SOX4 , were confirmed in at least one of the HNSCC cohorts in OShnscc. The discrepancy of the prognostic performance of SOX4 between OShnscc and the literature is likely due to race ( Naghavi et al., 2016 Naghavi et al., 2016 ). The race of cohorts reported in the literature was Asian (Korea), while the cohort included in OShnscc is mostly the Caucasian population. The survival analysis of previously reported prognostic biomarkers in OShnscc demonstrated that OShnscc performs well in evaluating the prognosis of genes and can be a highly useful tool for screening and evaluating prognostic biomarkers for HNSCC patients. OShnscc can be used to facilitate discovering new prognostic biomarkers. Through OShnscc, we found that interleukin 11 ( IL11 ) has a significant association with the OS of HNSCC patients in five OShnscc datasets ( Figs. 6 Figs. 6 a‍–‍6e). Highly expressed IL11 is significantly associated with poor OS in the HNSCC datasets including TCGA ( n =499, P =0.040, HR=1.365, 95% CI=1.015‍–‍1.838), GSE31056 ( n =96, P =0.007, HR=2.475, 95% CI=1.280‍–‍4.785), GSE3292 ( n =33, P =0.005, HR=8.013, 95% CI=1.865‍–‍34.424), GSE65858 ( n =270, P =0.017, HR=1.703, 95% CI=1.098‍–‍2.640), and GSE41613 ( n =97, P =0.047, HR=1.838, 95% CI=1.010‍–‍3.347). Moreover, HNSCC patients with high IL11 expression levels had shorter OS ( n =995, HR=1.64, 95% CI=1.32–2.03) based on the results of meta-analysis ( Fig. 6 Fig. 6 f). To our knowledge, IL11 is a member of glycoprotein-130 (GP-130) cytokines, and overexpressed IL11 has been reported in tumorigenesis through the Janus kinase (JAK)‍-signal transducers and activators of transcription 3 (STAT3) pathway in cancers, including breast cancer, colorectal cancer, and gastric cancer ( Xu et al., 2016 Xu et al., 2016 ). In addition, the upregulation of IL11 has been shown to be associated with shortened survival in clear cell renal cell carcinoma ( Pan et al., 2015 Pan et al., 2015 ), non-small cell lung cancer ( Zhao et al., 2018 Zhao et al., 2018 ), and breast cancer ( Hanavadi et al., 2006 Hanavadi et al., 2006 ). This gene has not been previously reported as a prognostic biomarker in HNSCC, and thus it should be validated in a clinical study for novel prognostic biomarker development. Based on the above data, we hypothesize that IL11 could be a potential candidate for further experimental verification. Currently, several messenger RNA (mRNA)‍-focused survival analysis tools exist, such as Gene Expression Profiling Interactive Analysis (GEPIA) ( Tang et al., 2017 Tang et al., 2017 ), KM plotter ( Nagy et al., 2018 Nagy et al., 2018 ), and UALCAN ( Xu et al., 2021 Xu et al., 2021 ). Nevertheless, most of these tools are mainly or exclusively based on datasets collected from the TCGA database, have limited number of HNSCC cases, and lack individual cohorts for independent validation. In comparison to these tools, OShnscc has several unique features: (1) it is an online survival assessment tool specifically for HNSCC, and contains by far the largest number of HNSCC cases (1366 cases) for prognostic analysis; (2) it enables users to evaluate the prognostic value of the queried gene independently in individual cohorts, which could generate more robust results for prognostic analysis; (3) it allows users to perform survival analysis for subgroups by filtering HNSCC patients through the use of different terms of clinical confounding factors. However, OShnscc has limitations in survival analysis. For example, although it features the largest number of HNSCC patients, the corresponding case numbers for the prognostic analysis of recurrence and metastasis are still low. Nevertheless, we aim to expand the number of HNSCC cases for OShnscc once the novel relative HNSCC datasets become available. In summary, OShnscc is proposed as a publicly available, free online web tool for the rapid analysis of the prognostic values of genes in HNSCC. This tool is user-friendly, allowing researchers with limited bioinformatics background to screen and evaluate the prognostic values of genes for different cohorts of HNSCC, which may accelerate the clinical development of prognostic biomarkers in HNSCC. Upon the release of additional HNSCC datasets, we will keep updating the database of OShnscc to perform prognostic analysis for HNSCC patients at a large scale. Materials and methods 1 Detailed methods are provided in the electronic supplementary materials of this paper. References Fauzi FH , Hamzan NI , Rahman NA , et al. , 2020 . Detection of human papillomavirus in oropharyngeal squamous cell carcinoma . 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Upregulation of IL-11, an IL-6 family cytokine, promotes tumor progression and correlates with poor prognosis in non-small cell lung cancer . Cell Physiol Biochem , 45 ( 6 ): 2213 - 2224 . https://doi.org/10.1159/000488166 https://doi.org/10.1159/000488166 Upregulation of IL-11, an IL-6 family cytokine, promotes tumor progression and correlates with poor prognosis in non-small cell lung cancer Guosen ZHANG performed data collection, developed the web tool, and wrote and edited the manuscript. Qiang WANG and Xinlei QI performed data collection and developed the web tool. Huimin YANG, Xiaodong SU, Manman YANG, Chao JIANG, and Yang AN contributed to data collection and analysis. Hong ZHENG, Lu ZHANG, and Wan ZHU performed data analysis and paper writing. Jiancheng GUO and Xiangqian GUO contributed to the study design, data analysis, writing and editing of the manuscript. All authors have read and approved the final manuscript, and therefore, have full access to all the data in the study and take responsibility for the integrity and security of the data. 10.1631/jzus.B2100512 1673-1581（2022）03-0249-09 This work was supported by the National Natural Science Foundation of China (No. U2004136), the Supporting Program for Central Plain Young Top Talents (No. ZYQR201912176), the Program for Innovative Talents of Science and Technology in Henan Province (No. 18HASTIT048), the Program for Science and Technology Development in Henan Province (Nos. 212102310150, 202102310063, and SBGJ2018041), the Kaifeng Science and Technology Project (No. 1908001), and the Supporting Grant of Henan University (Nos. 2018YLJC01, 2019YLXKJC01, and 2020YLZDYJ14), China. 2021-06-18 2021-09-08 2022-03-15 2022-04-19 2022-02-25 Copyright © Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2022 2022 浙江大学出版社 Guosen ZHANG, Qiang WANG, Xinlei QI, Huimin YANG, Xiaodong SU, Manman YANG, Chao JIANG, Yang AN, Hong ZHENG, Lu ZHANG, Wan ZHU, Jiancheng GUO, and Xiangqian GUO declare that they have no conflict of interest. This article does not contain any studies with human or animal subjects performed by any of the authors. Compliance with ethics guidelines Supplementary information 1 Fig. S1; Tables S1‒S3; Materials and methods 浙江大学学报（英文版）（B辑：生物医学和生物技术） 3 23