UltraSur: A Versatile Survival Analysis Tool for Multi-Omics Data in Cancer Research

UltraSur: A Versatile Survival Analysis Tool for Multi-Omics Data in Cancer Research | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article UltraSur: A Versatile Survival Analysis Tool for Multi-Omics Data in Cancer Research Binbin Zou, Xiaoya Huo, Liying Song, Yuanfang Zhai, Xiaomin Li, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7958498/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Survival analysis is fundamental to translational oncology for identifying prognostic biomarkers and therapeutic targets. Despite numerous web-based tools, current platforms face critical limitations: restricted data compatibility (often single-omics only) and lack of analytical flexibility. These constraints impede biomedical researchers and clinicians lacking advanced computational expertise from extracting robust, clinically actionable insights from complex datasets. Results: We developed UltraSur, a comprehensive platform accommodating heterogeneous molecular data. Its core innovation is a dual continuous variable analysis capability: 1. Manual definition of clinically relevant thresholds. 2. Automated determination of statistically optimized cutoffs using maximally selected rank statistics. UltraSur integrates multi-omics data from The Cancer Genome Atlas (TCGA) and supports user-customized dataset upload, enabling flexible analysis across experimental and clinical contexts. UltraSur overcomes the rigidity of existing tools by enabling analysis beyond predefined grouping methods and single-omics data. It uniquely provides both manual and statistically optimized threshold determination strategies. The platform successfully integrates TCGA multi-omics data with user-provided data, facilitating hypothesis-free exploration. Conclusions: UltraSur significantly enhances translational oncology research by providing broad analytical flexibility. It ensures wide applicability, particularly for biomarker discovery and therapeutic target prioritization. By democratizing sophisticated survival analysis, UltraSur accelerates the extraction of clinically actionable insights from complex datasets. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Cancer Survival analysis Prognosis Mutations Gene expression Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Over the past decade, advancements in genomics[ 1 ] and epigenomics technologies[ 2 ] have generated an enormous amount of data, providing critical insights into the complex mechanisms underlying cancer. In cancer research, survival analysis has proven to be an invaluable tool for identifying and evaluating reliable tumor prognostic markers[ 3 ]. By leveraging survival analysis[ 4 ], researchers and clinicians can assess the effects of different therapeutic strategies on patient survival and identify biomarkers associated with survival outcomes, which are essential for improving prognosis and guiding treatment decisions. However, conducting survival analysis typically requires the use of specialized programming tools, such as R and Python, which demand a certain level of computational expertise. Unfortunately, many researchers and clinicians lack proficiency in these programming languages, posing a significant challenge to performing such analyses. This highlights the urgent need for the development of user-friendly online platforms that can streamline survival analysis. Such platforms would not only lower the technical barriers for researchers but also enhance the efficiency of biomarker discovery and facilitate a deeper understanding of cancer biology. To date, numerous survival analysis tools have been developed, including LOGpc ( http://bioinfo.henu.edu.cn/DatabaseList.jsp ), GENT2[ 5 ] ( http://gent2.appex.kr/gent2/),K M Plotter[ 6 ] ( https://kmplot.com/analysis/),UALCA N[ 7 ] ( https://ualcan.path.uab.edu/index.html ), GEPIA[ 8 ] ( http://gepia.cancer-pku.cn/ ), cBioPortal[ 9 ] ( http://www.cbioportal.org/ ), and UCSC Xena[ 10 ] ( https://xena.ucsc.edu/ ). While these tools have significantly facilitated survival analysis in cancer research, they also have certain limitations. For instance, most of these tools do not allow users to select the optimal cutoff value when grouping continuous variables, such as gene expression levels. Additionally, the majority of these platforms are restricted to performing survival analysis on preloaded public datasets, such as TCGA and GEO, and lack the flexibility to analyze user-provided custom datasets. To address this need, the UltraSur platform is developed, and which integrates TCGA Pancancer datasets from six different cancer projects and also supports the upload of custom datasets. The platform allows users to evaluate the impact of gene expression levels (or protein expression levels, etc.) on survival outcomes by selecting various grouping methods, including mean, median, upper quartile, upper tertile, lower tertile, lower quartile, custom, optimal cutoff, and k-means clustering[ 11 ]. UltraSur provides comprehensive results, including four types of p-values and corresponding hazard ratios (HRs) derived from the cox proportional hazards model[ 12 ]: (1) Number-coxph, the p-value for survival analysis using continuous variables; (2) Number-categorize, the p-value for survival analysis based on user-defined categorical variables; (3) Number-optimal cutoff, the p-value for survival analysis using the optimal cutoff value for grouping; and (4) Number-kmeans, the p-value for survival analysis based on k-means clustering. Additionally, the platform generates three Kaplan-Meier plots corresponding to univariate cox proportional hazards models under three different grouping settings, providing users with a comprehensive and flexible tool for survival analysis. 2. Methods 2.1. Survival Analysis of TCGA Data TCGA data downloaded from UCSC Xena ( https://xenabrowser.net/datapages/?dataset=TCGA.PANCAN.sampleMap%2FHumanMethylation27&host=https%3A%2F%2Ftcga.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443 ) , this data set contains various omics data of TCGA Pan-Cancer. mRNA data (gene expression RNAseq) downloaded from https://tcga-pancan-atlas-hub.s3.us-east-1.amazonaws.com/download/EB%2B%2BAdjustPANCAN_IlluminaHiSeq_RNASeqV2.geneExp.xena.gz . miRNA data downloaded from https://tcga-pancan-atlas-hub.s3.us-east-1.amazonaws.com/download/pancanMiRs_EBadjOnProtocolPlatformWithoutRepsWithUnCorrectMiRs_08_04_16.xena.gz . Copy_Number data (copy number (gene-level) - gene-level copy number (gistic2)) downloaded from https://tcga-xena-hub.s3.us-east-1.amazonaws.com/download/TCGA.PANCAN.sampleMap%2FGistic2_CopyNumber_Gistic2_all_data_by_genes.gz . Protein data (protein expression - RPPA) downloaded from https://tcga-pancan-atlas-hub.s3.us-east-1.amazonaws.com/download/TCGA-RPPA-pancan-clean.xena.gz . Methylation data(DNA methylation - DNA methylation (Methylation27K)) downloaded from https://tcga-xena-hub.s3.us-east-1.amazonaws.com/download/TCGA.PANCAN.sampleMap%2FHumanMethylation27.gz . Methylation probe data downloaded from https://tcga-xena-hub.s3.us-east-1.amazonaws.com/download/probeMap%2FilluminaMethyl27K_hg18_gpl8490_TCGAlegacy . Mutation data (somatic mutation (SNP and INDEL) - Gene level non-silent mutation) downloaded from https://tcga-pancan-atlas-hub.s3.us-east-1.amazonaws.com/download/mc3.v0.2.8.PUBLIC.nonsilentGene.xena.gz . Clinical data (dataphenotype - Curated clinical data) downloaded from https://tcga-pancan-atlas-hub.s3.us-east-1.amazonaws.com/download/Survival_SupplementalTable_S1_20171025_xena_sp . 2.2. Survival Analysis of Custom Data Custom data can be uploaded to UltraSur. UltraSur performs statistical analyses for overall survival (OS), disease-free interval (DFI), progression-free interval (PFI), and disease-specific survival (DSS), as implemented in the "survival" R package. Optimal cutoff for gene expression levels is determined using the surv_cutpoint function, and gene expression levels are categorized into high and low groups using k-means clustering. Univariate analysis of data is conducted using the cox proportional hazards regression model via the coxph function in R/Bioconductor. Kaplan-Meier survival curves for OS, DFI, PFI, and DSS are estimated using the survfit function, and differences between the defined high-expression and low-expression groups are assessed using the Log-Rank Test. P value < 0.05 was considered statistically signifcant. Finally, Kaplan-Meier survival curves are visualized using the survminer package and the ggsurvplot function, providing a comprehensive visualization of survival data. The UltraSur source code was developed using the R software (version 4.4.3), with the interactive web server implemented through the shiny package. Clinical cancer datasets and multi-omics data for survival analysis were processed and analyzed using R-based computational frameworks. This integrated implementation leverages R's statistical ecosystem for comprehensive biomedical data exploration. 3. Results 3.1. UltraSur Interface and Features UltraSur comprises three core modules (Fig. 1 ): parameter input (Fig. 1 A), result output (Figs. 1 B and 1 C), and data download (Supplementary Fig. 1). The parameter input module enables users to upload datasets and configure gene-specific parameters. Specific genes and cancer types can be selected for survival analysis across 33 cancer types and six data modalities (e.g., mRNA, mutation, methylation) within the TCGA pan-cancer dataset[ 13 ]. For mutation data, grouping must be defined as categorical variables, while numerical variables are required for continuous data types (e.g., expression profiles). Grouping thresholds include mean, median, upper/lower quartile, tertile, custom values, optimal cutoff (determined via maximally selected rank statistics), and k-means clustering. If a custom threshold is selected, a numerical value must be specified in the designated field. Survival outcomes are evaluated across four clinical endpoints: progression-free interval (PFI), disease-free interval (DFI), disease-specific survival (DSS), and overall survival (OS).The result output module dynamically generates Kaplan-Meier survival curves and tabulated statistical results, including p-values, cutoff thresholds, and hazard ratios (HRs) with 95% confidence intervals. The data download module provides access to processed tables containing analyzed gene expression profiles, mutation status, and associated clinical metadata for further offline analysis. 3.2. Comparison of UltraSur with Other Tools A multitude of computational tools currently exist for survival analysis, each offering distinct analytical capabilities. LOGpc, a web-based platform, enables single-gene survival analysis using mRNA expression profiles across 27 cancer types derived from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Similarly, GENT2 supports single-gene survival analysis with TCGA mRNA expression data. KM Plotter facilitates survival analysis of individual genes through multiple grouping strategies (e.g., median, percentile, quartile thresholds), though its functionality remains confined to single-gene assessments even when multi-gene datasets are uploaded. UALCAN integrates cancer omics data from TCGA, MET500[ 14 ], and Clinical Proteomic Tumor Analysis Consortium (CPTAC)[ 15 ], focusing on associations between mRNA/long non-coding RNA ( lncRNA ) expression[ 16 ] and patient survival. GEPIA extends this capability to TCGA-derived mRNA, lncRNA , and microRNA ( miRNA )[ 17 ] expression analyses. cBioPortal specializes in single-gene survival analysis based on TCGA DNA mutation data, while UCSC-Xena permits single-gene multi-omics analysis with user-defined cutoff customization. In contrast, UltraSur (Supplementary Fig. 2) provides a comprehensive framework for TCGA multi-omics survival analysis, encompassing mRNA, miRNA, copy number variation[ 18 ], protein expression[ 19 ], methylation profiles[ 20 ], and mutation data[ 21 ]. This platform supports diverse grouping methodologies for single-gene analyses, including median, mean, upper/lower quartile, tertile, k-means clustering, optimal cutoff determination, and user-defined thresholds. Unique functionalities include the ability to upload custom datasets and export publication-quality visualizations in multiple formats (JPEG, PNG, PDF, TIFF). A systematic comparison of UltraSur’s features with existing tools is provided in Table 1 . Table 1 Compare the features of UltraSur with other software Tools LOGpc GENT2 KM Plotter UALCAN GEPIA cBioPortal UCSC-Xena UltraSur Cut-off upper 25% upper 30% upper 50% upper 25%vs lower25% upper 30%vs lower30% lower 25% lower 30% lower 50% trichotomy quartile median lower quartile lower tertile median upper tertile upper quartile - median quartile custom - quartiles custom median mean upper quartile upper tertile lower tertile lower quartile custom kmeans Data Type mRNA mRNA mRNA miRNA protein DNA mRNA miRNA lncRNA mRNA Mutation mRNA miRNA Copy_Number exon_expression protein Methylation Mutation mRNA miRNA Copy_Number protein Methylation Mutation Data Set TCGA GEO TCGA TCGA GEO TCGA TCGA TCGA TCGA TCGA Optimal cut-off NO NO YES - NO - NO YES Follow-up information OS DFI PFI DSS PFS OS DFS OS RFS - OS RFS DFI PFI OS DSS OS DFI PFI DSS OS DFI PFI DSS OUTPUT NO NO pdf pdf svg png pdf svg png pdf pdf jpeg png pdf tif PDF OUTPUT NO NO YES YES YES YES YES YES Multi-gene NO NO YES YES NO NO NO NO Clinincal information selcet YES YES YES NO NO NO YES NO Self data NO NO YES NO NO NO NO YES 3.3. Comparison of Survival Analysis Results Between UltraSur and Other Tools Survival analyses were conducted to evaluate the prognostic relevance of MKI67[ 22 ] expression (or mutation) data using median, mean, and optimal cutoff values in relation to overall survival (OS). Due to inherent differences in the computational algorithms and built-in databases across analytical platforms, significant variability was observed in the results. Notably, UltraSur and selected tools provided comprehensive statistical outputs, including hazard ratios (HR)[ 23 ], 95% confidence intervals (CI)[ 24 ], and p-values, whereas others limited their reporting to p-values alone. Comparative results from UltraSur and alternative platforms are systematically summarized in Table 2 , highlighting platform-specific discrepancies in risk stratification accuracy. Table 2 comparison of the survival analysis results from different tools survival analysis tools Data Survival timle p-value HR 95% CI sample LOGpc TCGA-BRCA–upper 50% OS 0.2976 1.189 0.8585–1.6467 590 − 589 GENT2 Breast-median OS 0.0006 250–252 KM Plotter TCGA-BRCA-median OS 0.41 1.14 0.83–1.57 777 − 761 KM Plotter TCGA-BRCA-optimal cutoff OS 0.27 1.2 0.87–1.66 521–1017 UALCAN TCGA-BRCA 0.16 267–814 Gepia TCGA-BRCA–median OS 0.51 1.1 534–535 cBioportal TCGA-BRCA–mutation OS 0.0310 21-1045 UCSC-Xena TCGA-BRCA–median OS 0.259 612 − 602 UCSC-Xena TCGA-BRCA–mutation OS 0.002205 16–774 UltraSurv TCGA-BRCA–coxph OS 0 0.9 0.83–0.97 UltraSurv TCGA-BRCA–median OS 0.31 1.16 0.87–1.53 782–792 UltraSurv TCGA-BRCA–mean OS 0.09 1.27 0.96–1.68 893 − 681 UltraSurv TCGA-BRCA–optimal cutoff OS 0 1.84 1.31–2.6 1402 − 172 UltraSurv TCGA-BRCA–kmeans OS 0.01 1.48 1.11–1.97 1103 − 471 UltraSurv TCGA-BRCA–mutation OS 0 0.27 0.11–0.66 20–970 3.4. Comparison of KM Curves Between UltraSur and Other Tools Compared to existing web-based tools for survival analysis, UltraSur demonstrates enhanced functionality in both data processing and visualization(Fig. 2 ). For continuous numerical variables (e.g., gene expression data), UltraSur simultaneously displays three distinct plots: a median-based survival curve, an optimal cutoff analysis, and a k-means clustering-derived survival plot. In contrast to platforms such as UALCAN and UCSC-Xena, UltraSur provides comprehensive statistical test results accompanied by p-values, enabling clinicians and researchers to efficiently derive clinically actionable insights for patient care.When analyzing categorical variables (e.g., mutation status), UltraSur outperforms cBioPortal and UCSC-Xena by integrating statistical validation methods with p-value calculations. Additionally, unlike UCSC-Xena, UltraSur includes clear subgroup legend annotations to improve interpretability.A notable technical advancement lies in UltraSur’s image export capabilities. Unlike LOGpcp and GENT2, which lack image export functions, UltraSur supports four publication-ready formats (JPEG, PNG, PDF, TIFF) with customizable resolution. The exported figures exhibit optimized typography, balanced color schemes, and high-resolution outputs that meet stringent journal requirements for scientific publications. These features collectively position UltraSur as a versatile and user-centric tool for translational research and clinical decision-making. 3.5. The critical importance of threshold determination in continuous variable analysis In survival analysis of continuous variables using UltraSurv (Fig. 3 ), the selection of grouping strategies significantly influenced the prognostic interpretation of MKI67 gene expression in breast cancer. When applying conventional grouping methods, MKI67 demonstrated no significant prognostic value with p-values of 0.31 (mean-based grouping, Fig. 3 A) and 0.094 (median-based grouping, Fig. 3 B). Similarly, non-significant associations were observed using upper quartile (p = 0.66, Fig. 3 E) and upper tertile groupings (p = 0.59, Fig. 3 F). However, statistically significant prognostic associations emerged when alternative grouping approaches were employed. Notably, optimal cutoff selection (p = 0.00044, Fig. 3 C), k-means clustering (p = 0.0068, Fig. 3 D), lower quartile (p = 0.034, Fig. 3 G), lower tertile (p = 0.012, Fig. 3 H), and a custom cutoff at 10 (p = 0.0079, Fig. 3 I) all demonstrated significant survival differences. Intriguingly, these significant results consistently indicated better prognosis in the high-expression MKI67 group. This analysis highlights the critical impact of grouping methodology on prognostic interpretation, where MKI67's apparent prognostic significance becomes evident only through specific data stratification approaches. The findings emphasize the necessity for methodological transparency in survival analysis of continuous biomarkers. To investigate the relationship between genes and prognosis and identify robust biomarkers, we employed five distinct grouping methods to analyze pan-cancer genes with prognostic significance (Supplementary Table 1), followed by intersection Venn diagram visualization (Fig. 4 ). In pan-cancer analysis, initial univariate Cox proportional hazards regression analysis of continuous gene expression values identified 19,237 genes significantly associated with overall survival (p < 0.05). Subsequent stratified analyses revealed substantial variability in prognostic gene detection across methods: median (19,108 survival-related genes), mean (19,119), optimal cutoff (19,507), and k-means clustering (19,101). Intersection analysis of prognostic genes via Venn diagrams identified 4,053 consensus biomarkers across all five methods, while method-specific discoveries were quantified as follows: coxph (4,555 unique genes), median (2,918), mean (2,031), optimal cutoff (8,963), and k-means clustering (3,543) (Fig. 4 A). This analysis highlights that optimal cutoff and k-means clustering yield the highest numbers of unique prognostic genes.To further elucidate the biological relevance of method-specific prognostic genes, we applied all five grouping approaches to 33 cancer types and generated corresponding Venn diagrams (Supplementary Fig. 3). Notably, testicular germ cell tumors (TGCT), mesenchymal neoplasms originating from tendon sheaths and synovial membranes, exhibited a distinct pattern[ 25 ]. Venn analysis demonstrated zero overlapping genes among the five methods for TGCT, with unique gene counts as follows: coxph (6), optimal cutoff (712), while mean, median, and k-means clustering yielded no unique genes (Fig. 4 B). Univariate Cox regression identified 42 prognosis-associated genes in TGCT. Mean and median analyses revealed 7 overlapping prognostic genes (C3orf79, C6orf191, FTLP10, OR10A2, OR52K2, PSG6, USP17), whereas k-means clustering detected 9 genes and optimal cutoff identified 755 genes. Compared to mean/median methods, optimal cutoff uncovered 748 additional prognostic genes. Subsequent GO and KEGG enrichment analyses(analysis by clusterProfiler: https://github.com/YuLab-SMU/clusterProfiler ) of these 748 genes revealed significant biological insights. GO analysis identified 31 enriched terms (p < 0.05), with 12 terms directly associated with TGCT pathogenesis, including: tryptophan catabolic processes, tryptophan metabolic process[ 26 ], regulation of canonical NF-κB signal transduction[ 27 ], innate immune response modulation[ 28 ], negative regulation of defense responses, negative regulation of inflammatory response, toll-like receptor signaling[ 29 ], apoptotic cell clearance[ 30 ], and positive regulation of NF-κB signaling (Fig. 4 C). KEGG analysis yielded 38 enriched pathways (p < 0.05), of which 13 were TGCT-relevant, such as Antigen processing and presentation[ 31 ], Toll-like receptor signaling pathway, Rap1 signaling pathway[ 32 ], Primary immunodeficiency[ 33 ], PD-L1 expression and PD-1 checkpoint pathway in cancer[ 34 ], Tryptophan metabolism[ 35 ], TNF signaling pathway[ 36 ], C-type lectin receptor signaling pathway[ 37 ], IL-17 signaling pathway[ 38 ], NOD-like receptor signaling pathway[ 39 ], Chemokine signaling pathway[ 40 ], MAPK signaling pathway[ 41 ], Th17 cell differentiation[ 42 ] (Fig. 4 D). 4. Discussion In the era of cancer genomics characterized by an abundance of multi-omics data, a critical bottleneck in identifying robust prognostic biomarkers lies in the lack of tools capable of systematically evaluating the prognostic utility of individual genes while harnessing the power of big data. Although numerous survival analysis tools have been developed[ 43 ], existing platforms frequently fail to address critical requirements for clinical and research applications, including multi-omics compatibility, user-friendly interfaces, and scalable data integration. To bridge this gap, we present UltraSur, a versatile web-based survival analysis platform designed to empower clinicians and researchers in exploring gene-specific prognostic effects across diverse cancer types. Compared with recently emerged survival analysis tools (Table 1 ), UltraSur demonstrates distinct advantages in the number of genes studied, cancer types, survival information comprehensiveness, and analytical method diversity.Over the past few years, a large number of studies have shown that miRNAs are involved in regulating tumorigenesis and progression[ 44 ]. Investigating miRNAs can lead to a deeper understanding of cancer mechanisms. One of UltraSur’s advantages is that it allows for miRNA-based survival analysis. In addition, UltraSur provides survival analysis data and univariate Cox analysis results in tabular form, making it easier for researchers and clinicians to intuitively interpret research outcomes. Moreover, UltraSur can generate three types of survival analysis figures in one go (median, optimal cutoff, and kmeans), which meet publication requirements and can be directly used for research publication. However, UltraSur also has certain limitations. Firstly, in terms of data inclusion and collection, we utilized the unified cancer datasets from UCSC Xena. While this unified data processing approach minimizes bias in survival analysis, to achieve a more comprehensive analysis, we plan to incorporate data from other cancer genomics programs, including the Cancer Genomics Characterization Initiative (CGCI) and GEO. Secondly, UltraSur currently only supports survival analysis for individual genes. In the future, we intend to add the capability for survival analysis of multiple genes. Furthermore, UltraSur does not currently support the selection of clinical information and journal - specific color schemes. In subsequent versions, we plan to add clinical information subgrouping and multiple journal color scheme options to enhance UltraSur's functionality and provide more comprehensive survival analysis support for clinicians and researchers. In summary, UltraSur is a versatile and universal survival analysis tool applicable to TCGA multi - omics data and user - uploaded datasets. It facilitates survival analysis for clinicians and researchers without programming skills, thereby enhancing clinical decision - making. 5. Conclusions UltraSur addresses a critical gap in cancer genomics by providing a scalable platform for systematic evaluation of gene-level prognostic utility across multi-omics datasets. Key advancements include: comprehensive analytical capacity: simultaneous survival analysis of > 60,000 genes and miRNAs across 33 cancer types, with integrated processing of TCGA multi-omics data and user-submitted datasets.; clinical translation enhancements: automated generation of publication-ready survival plots (median/optimal-cutoff/k-means clustering) and intuitive tabular presentation of univariate Cox regression results; accessibility innovation: zero-code interface enabling clinician-led biomarker exploration. Abbreviations HR hazard ratio OS overall survival DFI disease-free interval PFI progression-free interval DSS disease-specific survival Declarations Institutional Review Board Statement Not applicable. Informed Consent Statement Not applicable. Data Availability Statement TCGA multi-omics data can be obtained from https://xena.ucsc.edu, and the relevant codes are available in the article. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the following funds: The Science and Technology Innovation Team of Shanxi Province (No. 202204051001024 to L.Z.); The Science and Technology Achievements Transformation Project of Shanxi Province (No. 202204021301062 to L.Z.); Shanxi Province Higher Education “Billion Project” Science and Technology Guidance Project (No. BYJL005 to L.Z. ); Natural Science Foundation of Shanxi Province (No. 202303021222128 to YF.Z. ); Natural Science Foundation of Shanxi Province (No. 202303021222129 to BB.Z); Shanxi Provincial Health Commission (No. 2024048to J.L ); Shanxi Medical University Doctoral Start-up Fund Project (No. XD2245 to BB.Z); Shanxi Medical University Provincial Doctoral Fund Project (No. SD2302 to BB.Z ); Shanxi Provincial Health Commission Scientific Research Project Plan (No. 2021149 to XM.L ); Shanxi Bethune Hospital Scientific Research Project Plan Academy Level Scientific Research Fund (No. 2021YJ17 to XM.L); Shanxi Bethune Hospital “136” Hospital-level Open Fund (No. 2021YZ01 to XM.L). The Article Processing Fee (APF) was supported by the authors' institutional research funds. Authors' contributions JL and LZ were responsible for study design and critical revision of the manuscript.BBZ, XYH, LYS were responsible for analyzing the data, writing code,maintaining the website and writing the manuscript. YFZ and XML were responsible for consulting the literature and preparing all the figures. References Sherman, R. M. & Salzberg, S. L. Pan-genomics in the human genome era. Nat. Rev. Genet. 21 (4), 243–254. 10.1038/s41576-020-0210-7 (2020). PMID: 32034321. Arslan, E., Schulz, J. & Rai, K. Machine Learning in Epigenomics: Insights into Cancer Biology and Medicine. Biochim. Biophys. Acta Rev. Cancer . 1876 (2), 188588. 10.1016/j.bbcan.2021.188588 (2021). PMID: 34245839. Lee, J. et al. Prognostic accuracy of FIB-4, NAFLD fibrosis score and APRI for NAFLD-related events: A systematic review. Liver Int. 41 (2), 261–270. 10.1111/liv.14669 (2021). PMID: 32946642. 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Hum. Genomics . 8 (1), 21. 10.1186/s40246-014-0021-z (2014). PMID: 25421963. Vahabi, M., Dehni, B., Antomás, I., Giovannetti, E. & Peters, G. J. Targeting miRNA and using miRNA as potential therapeutic options to bypass resistance in pancreatic ductal adenocarcinoma. Cancer Metastasis Rev. 42 (3), 725–740. 10.1007/s10555-023-10127-w (2023). PMID: 37490255. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFile.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-7958498","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":544601880,"identity":"09059c0c-f2e3-4732-abf3-c57f449f087d","order_by":0,"name":"Binbin Zou","email":"","orcid":"","institution":"Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital","correspondingAuthor":false,"prefix":"","firstName":"Binbin","middleName":"","lastName":"Zou","suffix":""},{"id":544601881,"identity":"e4226cf3-9865-441b-ae18-c141a3c1b225","order_by":1,"name":"Xiaoya Huo","email":"","orcid":"","institution":"Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoya","middleName":"","lastName":"Huo","suffix":""},{"id":544601882,"identity":"151bdeac-8f9e-473e-b905-0473c4b9fb12","order_by":2,"name":"Liying Song","email":"","orcid":"","institution":"First Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liying","middleName":"","lastName":"Song","suffix":""},{"id":544601883,"identity":"d352e393-869b-43b4-9652-4a9905d0d9d9","order_by":3,"name":"Yuanfang Zhai","email":"","orcid":"","institution":"Basic Medical Sciences Center,Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuanfang","middleName":"","lastName":"Zhai","suffix":""},{"id":544601884,"identity":"4fd12a83-c823-4072-9b67-85abb6225221","order_by":4,"name":"Xiaomin Li","email":"","orcid":"","institution":"Shanxi Bethune Hospital, Third Hospital of Shanxi Medical 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19:20:31","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":141498,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7958498/v1/9868114d3e41c5c1b16b1726.html"},{"id":95950762,"identity":"e9ad955b-34cc-4a2e-80ff-adce149df5e1","added_by":"auto","created_at":"2025-11-14 19:20:31","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31972,"visible":true,"origin":"","legend":"\u003cp\u003eScreenshots of Ultrasur web server. (A) showed the parameter input. (B) and (C) showed the result output.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7958498/v1/50349e0b76a3437afb888c55.png"},{"id":96246330,"identity":"914f3503-b01a-4dfa-bc5d-5d7dd7b2d6a5","added_by":"auto","created_at":"2025-11-19 07:25:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74672,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan -Meier survival curve of softwares. (A) Kaplan-Meier survival curve of MKI67 Expression in BRCA of LOG. (B) Kaplan-Meier survival curve of MKI67 Expression in BRCA of GENT2. (C) Kaplan-Meier survival curve of MKI67 Expression in BRCA of KM Plotter. (D) Kaplan-Meier survival curve of MKI67 Expression in BRCA of UALCAN. (E) Kaplan-Meier survival curve of MKI67 Expression in BRCA of Gepia. (F) Kaplan-Meier survival curve of MKI67 Expression in BRCA of UCSC-Xena. (G) Kaplan-Meier survival curve of MKI67 Mutation in BRCA of UltraSurv. (threshold: median). (H) Kaplan-Meier survival curve of MKI67 expression in BRCA of UltraSurv (threshold: optimal cutoff). (I) Kaplan-Meier survival curve of MKI67 expression in BRCA of UltraSurv (threshold: kmeans). (J) Kaplan-Meier survival curve of MKI67 Mutation in BRCA of cBioportal. (K) Kaplan-Meier survival curve of MKI67 Mutation in BRCA of UCSC-Xena. (L) Kaplan-Meier survival curve of MKI67 Mutation in BRCA of UltraSurv.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7958498/v1/6730b16036f11708f3929f01.png"},{"id":95950771,"identity":"49505951-799b-438d-8838-179ebf831e01","added_by":"auto","created_at":"2025-11-14 19:20:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":103238,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curve of MKI67 RNA expression. (A) Kaplan-Meier survival curve of MKI67 expression in BRCA of UltraSurv (threshold: median). (B) Kaplan-Meier survival curve of MKI67 expression in BRCA of UltraSurv (threshold: mean). (C) Kaplan-Meier survival curve of MKI67 expression in BRCA of UltraSurv (threshold: optimal cutoff). (D) Kaplan-Meier survival curve of MKI67 expression in BRCA of UltraSurv (threshold: kmeans). (E) Kaplan-Meier survival curve of MKI67 expression in BRCA of UltraSurv (threshold: upper quartile). (F) Kaplan-Meier survival curve of MKI67 expression in BRCA of UltraSurv (threshold: upper tertile). (G) Kaplan-Meier survival curve of MKI67 expression in BRCA of UltraSurv (threshold: lower quartile). (H) Kaplan-Meier survival curve of MKI67 expression in BRCA of UltraSurv (threshold: lower tertile). (I) Kaplan-Meier survival curve of MKI67 expression in BRCA of UltraSurv (threshold: custom).\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7958498/v1/80aee20724857f910bb853e0.png"},{"id":96244976,"identity":"330570c1-7ab9-4607-ad7e-218525700da0","added_by":"auto","created_at":"2025-11-19 07:19:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":68406,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of different thresholds. (A) Venn diagram of intersection genes with prognostic significance in TCGA of 33 cancers identified by five thresholds. (B) Venn diagram of intersection genes with prognostic significance in TGCT by five thresholds. (C) Differential go pathway enrichment analysis of k-means-specific genes clusters vs mean-value grouped genes. (D) Differential kegg pathway enrichment analysis of k-means-specific genes clusters vs mean-value grouped genes.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7958498/v1/18b40c5e1a0b31d4c773f860.png"},{"id":105565807,"identity":"7a9e25ad-7126-402d-b64a-784c6cf15854","added_by":"auto","created_at":"2026-03-27 12:54:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1170362,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7958498/v1/efece666-92ec-410d-abbe-d0c92d6dd7ee.pdf"},{"id":95950777,"identity":"d3079534-3ed0-436c-967b-a68f665e6b26","added_by":"auto","created_at":"2025-11-14 19:20:32","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":27730941,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile.zip","url":"https://assets-eu.researchsquare.com/files/rs-7958498/v1/339bf0559fb0b5c39eeb78ff.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"UltraSur: A Versatile Survival Analysis Tool for Multi-Omics Data in Cancer Research","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOver the past decade, advancements in genomics[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and epigenomics technologies[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] have generated an enormous amount of data, providing critical insights into the complex mechanisms underlying cancer. In cancer research, survival analysis has proven to be an invaluable tool for identifying and evaluating reliable tumor prognostic markers[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. By leveraging survival analysis[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], researchers and clinicians can assess the effects of different therapeutic strategies on patient survival and identify biomarkers associated with survival outcomes, which are essential for improving prognosis and guiding treatment decisions. However, conducting survival analysis typically requires the use of specialized programming tools, such as R and Python, which demand a certain level of computational expertise. Unfortunately, many researchers and clinicians lack proficiency in these programming languages, posing a significant challenge to performing such analyses. This highlights the urgent need for the development of user-friendly online platforms that can streamline survival analysis. Such platforms would not only lower the technical barriers for researchers but also enhance the efficiency of biomarker discovery and facilitate a deeper understanding of cancer biology.\u003c/p\u003e\u003cp\u003eTo date, numerous survival analysis tools have been developed, including LOGpc (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfo.henu.edu.cn/DatabaseList.jsp\u003c/span\u003e\u003cspan address=\"http://bioinfo.henu.edu.cn/DatabaseList.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), GENT2[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gent2.appex.kr/gent2/),K\u003c/span\u003e\u003cspan address=\"http://gent2.appex.kr/gent2/),K\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eM Plotter[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://kmplot.com/analysis/),UALCA\u003c/span\u003e\u003cspan address=\"https://kmplot.com/analysis/),UALCA\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003eN[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ualcan.path.uab.edu/index.html\u003c/span\u003e\u003cspan address=\"https://ualcan.path.uab.edu/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), GEPIA[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), cBioPortal[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"http://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and UCSC Xena[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xena.ucsc.edu/\u003c/span\u003e\u003cspan address=\"https://xena.ucsc.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). While these tools have significantly facilitated survival analysis in cancer research, they also have certain limitations. For instance, most of these tools do not allow users to select the optimal cutoff value when grouping continuous variables, such as gene expression levels. Additionally, the majority of these platforms are restricted to performing survival analysis on preloaded public datasets, such as TCGA and GEO, and lack the flexibility to analyze user-provided custom datasets.\u003c/p\u003e\u003cp\u003eTo address this need, the UltraSur platform is developed, and which integrates TCGA Pancancer datasets from six different cancer projects and also supports the upload of custom datasets. The platform allows users to evaluate the impact of gene expression levels (or protein expression levels, etc.) on survival outcomes by selecting various grouping methods, including mean, median, upper quartile, upper tertile, lower tertile, lower quartile, custom, optimal cutoff, and k-means clustering[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. UltraSur provides comprehensive results, including four types of p-values and corresponding hazard ratios (HRs) derived from the cox proportional hazards model[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]: (1) Number-coxph, the p-value for survival analysis using continuous variables; (2) Number-categorize, the p-value for survival analysis based on user-defined categorical variables; (3) Number-optimal cutoff, the p-value for survival analysis using the optimal cutoff value for grouping; and (4) Number-kmeans, the p-value for survival analysis based on k-means clustering. Additionally, the platform generates three Kaplan-Meier plots corresponding to univariate cox proportional hazards models under three different grouping settings, providing users with a comprehensive and flexible tool for survival analysis.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Survival Analysis of TCGA Data\u003c/h2\u003e\u003cp\u003eTCGA data downloaded from UCSC Xena (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/datapages/?dataset=TCGA.PANCAN.sampleMap%2FHumanMethylation27\u0026amp;host=https%3A%2F%2Ftcga.xenahubs.net\u0026amp;removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/datapages/?dataset=TCGA.PANCAN.sampleMap%2FHumanMethylation27\u0026amp;host=https%3A%2F%2Ftcga.xenahubs.net\u0026amp;removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, this data set contains various omics data of TCGA Pan-Cancer. mRNA data (gene expression RNAseq) downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcga-pancan-atlas-hub.s3.us-east-1.amazonaws.com/download/EB%2B%2BAdjustPANCAN_IlluminaHiSeq_RNASeqV2.geneExp.xena.gz\u003c/span\u003e\u003cspan address=\"https://tcga-pancan-atlas-hub.s3.us-east-1.amazonaws.com/download/EB%2B%2BAdjustPANCAN_IlluminaHiSeq_RNASeqV2.geneExp.xena.gz\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. miRNA data downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcga-pancan-atlas-hub.s3.us-east-1.amazonaws.com/download/pancanMiRs_EBadjOnProtocolPlatformWithoutRepsWithUnCorrectMiRs_08_04_16.xena.gz\u003c/span\u003e\u003cspan address=\"https://tcga-pancan-atlas-hub.s3.us-east-1.amazonaws.com/download/pancanMiRs_EBadjOnProtocolPlatformWithoutRepsWithUnCorrectMiRs_08_04_16.xena.gz\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Copy_Number data (copy number (gene-level) - gene-level copy number (gistic2)) downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcga-xena-hub.s3.us-east-1.amazonaws.com/download/TCGA.PANCAN.sampleMap%2FGistic2_CopyNumber_Gistic2_all_data_by_genes.gz\u003c/span\u003e\u003cspan address=\"https://tcga-xena-hub.s3.us-east-1.amazonaws.com/download/TCGA.PANCAN.sampleMap%2FGistic2_CopyNumber_Gistic2_all_data_by_genes.gz\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eProtein data (protein expression - RPPA) downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcga-pancan-atlas-hub.s3.us-east-1.amazonaws.com/download/TCGA-RPPA-pancan-clean.xena.gz\u003c/span\u003e\u003cspan address=\"https://tcga-pancan-atlas-hub.s3.us-east-1.amazonaws.com/download/TCGA-RPPA-pancan-clean.xena.gz\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eMethylation data(DNA methylation - DNA methylation (Methylation27K)) downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcga-xena-hub.s3.us-east-1.amazonaws.com/download/TCGA.PANCAN.sampleMap%2FHumanMethylation27.gz\u003c/span\u003e\u003cspan address=\"https://tcga-xena-hub.s3.us-east-1.amazonaws.com/download/TCGA.PANCAN.sampleMap%2FHumanMethylation27.gz\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eMethylation probe data downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcga-xena-hub.s3.us-east-1.amazonaws.com/download/probeMap%2FilluminaMethyl27K_hg18_gpl8490_TCGAlegacy\u003c/span\u003e\u003cspan address=\"https://tcga-xena-hub.s3.us-east-1.amazonaws.com/download/probeMap%2FilluminaMethyl27K_hg18_gpl8490_TCGAlegacy\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eMutation data (somatic mutation (SNP and INDEL) - Gene level non-silent mutation) downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcga-pancan-atlas-hub.s3.us-east-1.amazonaws.com/download/mc3.v0.2.8.PUBLIC.nonsilentGene.xena.gz\u003c/span\u003e\u003cspan address=\"https://tcga-pancan-atlas-hub.s3.us-east-1.amazonaws.com/download/mc3.v0.2.8.PUBLIC.nonsilentGene.xena.gz\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eClinical data (dataphenotype - Curated clinical data) downloaded from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcga-pancan-atlas-hub.s3.us-east-1.amazonaws.com/download/Survival_SupplementalTable_S1_20171025_xena_sp\u003c/span\u003e\u003cspan address=\"https://tcga-pancan-atlas-hub.s3.us-east-1.amazonaws.com/download/Survival_SupplementalTable_S1_20171025_xena_sp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Survival Analysis of Custom Data\u003c/h2\u003e\u003cp\u003eCustom data can be uploaded to UltraSur.\u003c/p\u003e\u003cp\u003eUltraSur performs statistical analyses for overall survival (OS), disease-free interval (DFI), progression-free interval (PFI), and disease-specific survival (DSS), as implemented in the \"survival\" R package. Optimal cutoff for gene expression levels is determined using the surv_cutpoint function, and gene expression levels are categorized into high and low groups using k-means clustering. Univariate analysis of data is conducted using the cox proportional hazards regression model via the coxph function in R/Bioconductor. Kaplan-Meier survival curves for OS, DFI, PFI, and DSS are estimated using the survfit function, and differences between the defined high-expression and low-expression groups are assessed using the Log-Rank Test. P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically signifcant. Finally, Kaplan-Meier survival curves are visualized using the survminer package and the ggsurvplot function, providing a comprehensive visualization of survival data.\u003c/p\u003e\u003cp\u003eThe UltraSur source code was developed using the R software (version 4.4.3), with the interactive web server implemented through the shiny package. Clinical cancer datasets and multi-omics data for survival analysis were processed and analyzed using R-based computational frameworks. This integrated implementation leverages R's statistical ecosystem for comprehensive biomedical data exploration.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1. UltraSur Interface and Features\u003c/h2\u003e\u003cp\u003eUltraSur comprises three core modules (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e): parameter input (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), result output (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), and data download (Supplementary Fig.\u0026nbsp;1). The parameter input module enables users to upload datasets and configure gene-specific parameters. Specific genes and cancer types can be selected for survival analysis across 33 cancer types and six data modalities (e.g., mRNA, mutation, methylation) within the TCGA pan-cancer dataset[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For mutation data, grouping must be defined as categorical variables, while numerical variables are required for continuous data types (e.g., expression profiles). Grouping thresholds include mean, median, upper/lower quartile, tertile, custom values, optimal cutoff (determined via maximally selected rank statistics), and k-means clustering. If a custom threshold is selected, a numerical value must be specified in the designated field. Survival outcomes are evaluated across four clinical endpoints: progression-free interval (PFI), disease-free interval (DFI), disease-specific survival (DSS), and overall survival (OS).The result output module dynamically generates Kaplan-Meier survival curves and tabulated statistical results, including p-values, cutoff thresholds, and hazard ratios (HRs) with 95% confidence intervals. The data download module provides access to processed tables containing analyzed gene expression profiles, mutation status, and associated clinical metadata for further offline analysis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Comparison of UltraSur with Other Tools\u003c/h2\u003e\u003cp\u003eA multitude of computational tools currently exist for survival analysis, each offering distinct analytical capabilities. LOGpc, a web-based platform, enables single-gene survival analysis using mRNA expression profiles across 27 cancer types derived from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Similarly, GENT2 supports single-gene survival analysis with TCGA mRNA expression data. KM Plotter facilitates survival analysis of individual genes through multiple grouping strategies (e.g., median, percentile, quartile thresholds), though its functionality remains confined to single-gene assessments even when multi-gene datasets are uploaded. UALCAN integrates cancer omics data from TCGA, MET500[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], and Clinical Proteomic Tumor Analysis Consortium (CPTAC)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], focusing on associations between mRNA/long non-coding RNA (\u003cem\u003elncRNA\u003c/em\u003e) expression[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and patient survival. GEPIA extends this capability to TCGA-derived mRNA, \u003cem\u003elncRNA\u003c/em\u003e, and microRNA (\u003cem\u003emiRNA\u003c/em\u003e)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] expression analyses. cBioPortal specializes in single-gene survival analysis based on TCGA DNA mutation data, while UCSC-Xena permits single-gene multi-omics analysis with user-defined cutoff customization.\u003c/p\u003e\u003cp\u003eIn contrast, UltraSur (Supplementary Fig.\u0026nbsp;2) provides a comprehensive framework for TCGA multi-omics survival analysis, encompassing mRNA, miRNA, copy number variation[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], protein expression[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], methylation profiles[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and mutation data[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This platform supports diverse grouping methodologies for single-gene analyses, including median, mean, upper/lower quartile, tertile, k-means clustering, optimal cutoff determination, and user-defined thresholds. Unique functionalities include the ability to upload custom datasets and export publication-quality visualizations in multiple formats (JPEG, PNG, PDF, TIFF). A systematic comparison of UltraSur\u0026rsquo;s features with existing tools is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCompare the features of UltraSur with other software\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTools\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLOGpc\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGENT2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKM Plotter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUALCAN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGEPIA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ecBioPortal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eUCSC-Xena\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eUltraSur\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCut-off\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eupper 25%\u003c/p\u003e\u003cp\u003eupper 30%\u003c/p\u003e\u003cp\u003eupper 50%\u003c/p\u003e\u003cp\u003eupper 25%vs lower25%\u003c/p\u003e\u003cp\u003eupper 30%vs lower30%\u003c/p\u003e\u003cp\u003elower 25%\u003c/p\u003e\u003cp\u003elower 30%\u003c/p\u003e\u003cp\u003elower 50%\u003c/p\u003e\u003cp\u003etrichotomy\u003c/p\u003e\u003cp\u003equartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emedian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003elower quartile\u003c/p\u003e\u003cp\u003elower tertile\u003c/p\u003e\u003cp\u003emedian\u003c/p\u003e\u003cp\u003eupper tertile\u003c/p\u003e\u003cp\u003eupper quartile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emedian\u003c/p\u003e\u003cp\u003equartile\u003c/p\u003e\u003cp\u003ecustom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003equartiles\u003c/p\u003e\u003cp\u003ecustom\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003emedian\u003c/p\u003e\u003cp\u003emean\u003c/p\u003e\u003cp\u003eupper quartile\u003c/p\u003e\u003cp\u003eupper tertile\u003c/p\u003e\u003cp\u003elower tertile\u003c/p\u003e\u003cp\u003elower quartile\u003c/p\u003e\u003cp\u003ecustom\u003c/p\u003e\u003cp\u003ekmeans\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eData Type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003emRNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003emRNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003emRNA\u003c/p\u003e\u003cp\u003emiRNA\u003c/p\u003e\u003cp\u003eprotein\u003c/p\u003e\u003cp\u003eDNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003emRNA\u003c/p\u003e\u003cp\u003emiRNA\u003c/p\u003e\u003cp\u003elncRNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003emRNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eMutation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003emRNA\u003c/p\u003e\u003cp\u003emiRNA\u003c/p\u003e\u003cp\u003eCopy_Number\u003c/p\u003e\u003cp\u003eexon_expression\u003c/p\u003e\u003cp\u003eprotein\u003c/p\u003e\u003cp\u003eMethylation\u003c/p\u003e\u003cp\u003eMutation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003emRNA\u003c/p\u003e\u003cp\u003emiRNA\u003c/p\u003e\u003cp\u003eCopy_Number\u003c/p\u003e\u003cp\u003eprotein\u003c/p\u003e\u003cp\u003eMethylation\u003c/p\u003e\u003cp\u003eMutation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eData Set\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTCGA\u003c/p\u003e\u003cp\u003eGEO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTCGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTCGA\u003c/p\u003e\u003cp\u003eGEO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTCGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTCGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTCGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eTCGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eTCGA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOptimal cut-off\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFollow-up information\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003cp\u003eDFI\u003c/p\u003e\u003cp\u003ePFI\u003c/p\u003e\u003cp\u003eDSS\u003c/p\u003e\u003cp\u003ePFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003cp\u003eDFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003cp\u003eRFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003cp\u003eRFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eDFI\u003c/p\u003e\u003cp\u003ePFI\u003c/p\u003e\u003cp\u003eOS\u003c/p\u003e\u003cp\u003eDSS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003cp\u003eDFI\u003c/p\u003e\u003cp\u003ePFI\u003c/p\u003e\u003cp\u003eDSS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003cp\u003eDFI\u003c/p\u003e\u003cp\u003ePFI\u003c/p\u003e\u003cp\u003eDSS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOUTPUT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003epdf\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003epdf\u003c/p\u003e\u003cp\u003esvg\u003c/p\u003e\u003cp\u003epng\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003epdf\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003esvg\u003c/p\u003e\u003cp\u003epng\u003c/p\u003e\u003cp\u003epdf\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003epdf\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003ejpeg\u003c/p\u003e\u003cp\u003epng\u003c/p\u003e\u003cp\u003epdf\u003c/p\u003e\u003cp\u003etif\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePDF OUTPUT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMulti-gene\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinincal information selcet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelf data\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eYES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Comparison of Survival Analysis Results Between UltraSur and Other Tools\u003c/h2\u003e\u003cp\u003eSurvival analyses were conducted to evaluate the prognostic relevance of MKI67[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] expression (or mutation) data using median, mean, and optimal cutoff values in relation to overall survival (OS). Due to inherent differences in the computational algorithms and built-in databases across analytical platforms, significant variability was observed in the results. Notably, UltraSur and selected tools provided comprehensive statistical outputs, including hazard ratios (HR)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], 95% confidence intervals (CI)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and p-values, whereas others limited their reporting to p-values alone. Comparative results from UltraSur and alternative platforms are systematically summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, highlighting platform-specific discrepancies in risk stratification accuracy.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ecomparison of the survival analysis results from different tools\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003esurvival analysis tools\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eData\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSurvival timle\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003esample\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLOGpc\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTCGA-BRCA\u0026ndash;upper 50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2976\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.8585\u0026ndash;1.6467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e590\u0026thinsp;\u0026minus;\u0026thinsp;589\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGENT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBreast-median\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e250\u0026ndash;252\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKM Plotter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTCGA-BRCA-median\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.83\u0026ndash;1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e777\u0026thinsp;\u0026minus;\u0026thinsp;761\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKM Plotter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTCGA-BRCA-optimal cutoff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.87\u0026ndash;1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e521\u0026ndash;1017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUALCAN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTCGA-BRCA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e267\u0026ndash;814\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGepia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTCGA-BRCA\u0026ndash;median\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e534\u0026ndash;535\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecBioportal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTCGA-BRCA\u0026ndash;mutation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21-1045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUCSC-Xena\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTCGA-BRCA\u0026ndash;median\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e612\u0026thinsp;\u0026minus;\u0026thinsp;602\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUCSC-Xena\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTCGA-BRCA\u0026ndash;mutation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.002205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16\u0026ndash;774\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUltraSurv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTCGA-BRCA\u0026ndash;coxph\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.83\u0026ndash;0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUltraSurv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTCGA-BRCA\u0026ndash;median\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.87\u0026ndash;1.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e782\u0026ndash;792\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUltraSurv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTCGA-BRCA\u0026ndash;mean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.96\u0026ndash;1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e893\u0026thinsp;\u0026minus;\u0026thinsp;681\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUltraSurv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTCGA-BRCA\u0026ndash;optimal cutoff\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.31\u0026ndash;2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1402\u0026thinsp;\u0026minus;\u0026thinsp;172\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUltraSurv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTCGA-BRCA\u0026ndash;kmeans\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.11\u0026ndash;1.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1103\u0026thinsp;\u0026minus;\u0026thinsp;471\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUltraSurv\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTCGA-BRCA\u0026ndash;mutation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.11\u0026ndash;0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20\u0026ndash;970\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Comparison of KM Curves Between UltraSur and Other Tools\u003c/h2\u003e\u003cp\u003eCompared to existing web-based tools for survival analysis, UltraSur demonstrates enhanced functionality in both data processing and visualization(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For continuous numerical variables (e.g., gene expression data), UltraSur simultaneously displays three distinct plots: a median-based survival curve, an optimal cutoff analysis, and a k-means clustering-derived survival plot. In contrast to platforms such as UALCAN and UCSC-Xena, UltraSur provides comprehensive statistical test results accompanied by p-values, enabling clinicians and researchers to efficiently derive clinically actionable insights for patient care.When analyzing categorical variables (e.g., mutation status), UltraSur outperforms cBioPortal and UCSC-Xena by integrating statistical validation methods with p-value calculations. Additionally, unlike UCSC-Xena, UltraSur includes clear subgroup legend annotations to improve interpretability.A notable technical advancement lies in UltraSur\u0026rsquo;s image export capabilities. Unlike LOGpcp and GENT2, which lack image export functions, UltraSur supports four publication-ready formats (JPEG, PNG, PDF, TIFF) with customizable resolution. The exported figures exhibit optimized typography, balanced color schemes, and high-resolution outputs that meet stringent journal requirements for scientific publications. These features collectively position UltraSur as a versatile and user-centric tool for translational research and clinical decision-making.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.5. The critical importance of threshold determination in continuous variable analysis\u003c/h2\u003e\u003cp\u003eIn survival analysis of continuous variables using UltraSurv (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the selection of grouping strategies significantly influenced the prognostic interpretation of MKI67 gene expression in breast cancer. When applying conventional grouping methods, MKI67 demonstrated no significant prognostic value with p-values of 0.31 (mean-based grouping, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eA) and 0.094 (median-based grouping, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Similarly, non-significant associations were observed using upper quartile (p\u0026thinsp;=\u0026thinsp;0.66, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eE) and upper tertile groupings (p\u0026thinsp;=\u0026thinsp;0.59, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). However, statistically significant prognostic associations emerged when alternative grouping approaches were employed. Notably, optimal cutoff selection (p\u0026thinsp;=\u0026thinsp;0.00044, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), k-means clustering (p\u0026thinsp;=\u0026thinsp;0.0068, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), lower quartile (p\u0026thinsp;=\u0026thinsp;0.034, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eG), lower tertile (p\u0026thinsp;=\u0026thinsp;0.012, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eH), and a custom cutoff at 10 (p\u0026thinsp;=\u0026thinsp;0.0079, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003eI) all demonstrated significant survival differences. Intriguingly, these significant results consistently indicated better prognosis in the high-expression MKI67 group. This analysis highlights the critical impact of grouping methodology on prognostic interpretation, where MKI67's apparent prognostic significance becomes evident only through specific data stratification approaches. The findings emphasize the necessity for methodological transparency in survival analysis of continuous biomarkers.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo investigate the relationship between genes and prognosis and identify robust biomarkers, we employed five distinct grouping methods to analyze pan-cancer genes with prognostic significance (Supplementary Table\u0026nbsp;1), followed by intersection Venn diagram visualization (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In pan-cancer analysis, initial univariate Cox proportional hazards regression analysis of continuous gene expression values identified 19,237 genes significantly associated with overall survival (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Subsequent stratified analyses revealed substantial variability in prognostic gene detection across methods: median (19,108 survival-related genes), mean (19,119), optimal cutoff (19,507), and k-means clustering (19,101). Intersection analysis of prognostic genes via Venn diagrams identified 4,053 consensus biomarkers across all five methods, while method-specific discoveries were quantified as follows: coxph (4,555 unique genes), median (2,918), mean (2,031), optimal cutoff (8,963), and k-means clustering (3,543) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). This analysis highlights that optimal cutoff and k-means clustering yield the highest numbers of unique prognostic genes.To further elucidate the biological relevance of method-specific prognostic genes, we applied all five grouping approaches to 33 cancer types and generated corresponding Venn diagrams (Supplementary Fig.\u0026nbsp;3). Notably, testicular germ cell tumors (TGCT), mesenchymal neoplasms originating from tendon sheaths and synovial membranes, exhibited a distinct pattern[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Venn analysis demonstrated zero overlapping genes among the five methods for TGCT, with unique gene counts as follows: coxph (6), optimal cutoff (712), while mean, median, and k-means clustering yielded no unique genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Univariate Cox regression identified 42 prognosis-associated genes in TGCT. Mean and median analyses revealed 7 overlapping prognostic genes (C3orf79, C6orf191, FTLP10, OR10A2, OR52K2, PSG6, USP17), whereas k-means clustering detected 9 genes and optimal cutoff identified 755 genes. Compared to mean/median methods, optimal cutoff uncovered 748 additional prognostic genes. Subsequent GO and KEGG enrichment analyses(analysis by clusterProfiler: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/YuLab-SMU/clusterProfiler\u003c/span\u003e\u003cspan address=\"https://github.com/YuLab-SMU/clusterProfiler\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) of these 748 genes revealed significant biological insights. GO analysis identified 31 enriched terms (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with 12 terms directly associated with TGCT pathogenesis, including: tryptophan catabolic processes, tryptophan metabolic process[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], regulation of canonical NF-κB signal transduction[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], innate immune response modulation[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], negative regulation of defense responses, negative regulation of inflammatory response, toll-like receptor signaling[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], apoptotic cell clearance[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], and positive regulation of NF-κB signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). KEGG analysis yielded 38 enriched pathways (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), of which 13 were TGCT-relevant, such as Antigen processing and presentation[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], Toll-like receptor signaling pathway, Rap1 signaling pathway[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], Primary immunodeficiency[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], PD-L1 expression and PD-1 checkpoint pathway in cancer[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], Tryptophan metabolism[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], TNF signaling pathway[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], C-type lectin receptor signaling pathway[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], IL-17 signaling pathway[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], NOD-like receptor signaling pathway[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], Chemokine signaling pathway[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], MAPK signaling pathway[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], Th17 cell differentiation[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn the era of cancer genomics characterized by an abundance of multi-omics data, a critical bottleneck in identifying robust prognostic biomarkers lies in the lack of tools capable of systematically evaluating the prognostic utility of individual genes while harnessing the power of big data. Although numerous survival analysis tools have been developed[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], existing platforms frequently fail to address critical requirements for clinical and research applications, including multi-omics compatibility, user-friendly interfaces, and scalable data integration. To bridge this gap, we present UltraSur, a versatile web-based survival analysis platform designed to empower clinicians and researchers in exploring gene-specific prognostic effects across diverse cancer types.\u003c/p\u003e\u003cp\u003eCompared with recently emerged survival analysis tools (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), UltraSur demonstrates distinct advantages in the number of genes studied, cancer types, survival information comprehensiveness, and analytical method diversity.Over the past few years, a large number of studies have shown that miRNAs are involved in regulating tumorigenesis and progression[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Investigating miRNAs can lead to a deeper understanding of cancer mechanisms. One of UltraSur\u0026rsquo;s advantages is that it allows for miRNA-based survival analysis. In addition, UltraSur provides survival analysis data and univariate Cox analysis results in tabular form, making it easier for researchers and clinicians to intuitively interpret research outcomes. Moreover, UltraSur can generate three types of survival analysis figures in one go (median, optimal cutoff, and kmeans), which meet publication requirements and can be directly used for research publication.\u003c/p\u003e\u003cp\u003eHowever, UltraSur also has certain limitations. Firstly, in terms of data inclusion and collection, we utilized the unified cancer datasets from UCSC Xena. While this unified data processing approach minimizes bias in survival analysis, to achieve a more comprehensive analysis, we plan to incorporate data from other cancer genomics programs, including the Cancer Genomics Characterization Initiative (CGCI) and GEO. Secondly, UltraSur currently only supports survival analysis for individual genes. In the future, we intend to add the capability for survival analysis of multiple genes. Furthermore, UltraSur does not currently support the selection of clinical information and journal - specific color schemes. In subsequent versions, we plan to add clinical information subgrouping and multiple journal color scheme options to enhance UltraSur's functionality and provide more comprehensive survival analysis support for clinicians and researchers.\u003c/p\u003e\u003cp\u003eIn summary, UltraSur is a versatile and universal survival analysis tool applicable to TCGA multi - omics data and user - uploaded datasets. It facilitates survival analysis for clinicians and researchers without programming skills, thereby enhancing clinical decision - making.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eUltraSur addresses a critical gap in cancer genomics by providing a scalable platform for systematic evaluation of gene-level prognostic utility across multi-omics datasets. Key advancements include: comprehensive analytical capacity: simultaneous survival analysis of \u0026gt;\u0026thinsp;60,000 genes and miRNAs across 33 cancer types, with integrated processing of TCGA multi-omics data and user-submitted datasets.; clinical translation enhancements: automated generation of publication-ready survival plots (median/optimal-cutoff/k-means clustering) and intuitive tabular presentation of univariate Cox regression results; accessibility innovation: zero-code interface enabling clinician-led biomarker exploration.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eHR hazard ratio\u003c/p\u003e\n\u003cp\u003eOS overall survival\u003c/p\u003e\n\u003cp\u003eDFI disease-free interval\u003c/p\u003e\n\u003cp\u003ePFI progression-free interval\u003c/p\u003e\n\u003cp\u003eDSS disease-specific survival\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eInstitutional Review Board Statement\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eInformed Consent Statement\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003eTCGA multi-omics data can be obtained from https://xena.ucsc.edu, and the relevant codes are available in the article.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by the following funds:\u003c/p\u003e\n\u003cp\u003eThe Science and Technology Innovation Team of Shanxi Province (No. 202204051001024 to L.Z.);\u003c/p\u003e\n\u003cp\u003eThe Science and Technology Achievements Transformation Project of Shanxi Province (No. 202204021301062 to L.Z.);\u003c/p\u003e\n\u003cp\u003eShanxi Province Higher Education \u0026ldquo;Billion Project\u0026rdquo; Science and Technology Guidance Project (No. BYJL005 to L.Z. );\u003c/p\u003e\n\u003cp\u003eNatural Science Foundation of Shanxi Province (No. 202303021222128 to YF.Z. );\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNatural Science Foundation of Shanxi Province (No. 202303021222129 to BB.Z);\u003c/p\u003e\n\u003cp\u003eShanxi Provincial Health Commission (No. 2024048to J.L );\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eShanxi Medical University Doctoral Start-up Fund Project (No. XD2245 to BB.Z);\u003c/p\u003e\n\u003cp\u003eShanxi Medical University Provincial Doctoral Fund Project (No. SD2302 to BB.Z );\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eShanxi Provincial Health Commission Scientific Research Project Plan (No. 2021149 to XM.L );\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eShanxi Bethune Hospital Scientific Research Project Plan Academy Level Scientific Research Fund (No. 2021YJ17 to XM.L);\u003c/p\u003e\n\u003cp\u003eShanxi Bethune Hospital \u0026ldquo;136\u0026rdquo; Hospital-level Open Fund (No. 2021YZ01 to XM.L).\u003c/p\u003e\n\u003cp\u003eThe Article Processing Fee (APF) was supported by the authors\u0026apos; institutional research funds.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eJL and LZ were responsible for study design and critical revision of the manuscript.BBZ, XYH, LYS were responsible for analyzing the data, writing code,maintaining the website and writing the manuscript. 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PMID: 37490255.\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Cancer, Survival analysis, Prognosis, Mutations, Gene expression","lastPublishedDoi":"10.21203/rs.3.rs-7958498/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7958498/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Survival analysis is fundamental to translational oncology for identifying prognostic biomarkers and therapeutic targets. Despite numerous web-based tools, current platforms face critical limitations: restricted data compatibility (often single-omics only) and lack of analytical flexibility. These constraints impede biomedical researchers and clinicians lacking advanced computational expertise from extracting robust, clinically actionable insights from complex datasets.\u003c/p\u003e\n\u003cp\u003eResults: We developed UltraSur, a comprehensive platform accommodating heterogeneous molecular data. Its core innovation is a dual continuous variable analysis capability:\u003c/p\u003e\n\u003cp\u003e1. Manual definition of clinically relevant thresholds.\u003c/p\u003e\n\u003cp\u003e2. Automated determination of statistically optimized cutoffs using maximally selected rank statistics.\u003c/p\u003e\n\u003cp\u003eUltraSur integrates multi-omics data from The Cancer Genome Atlas (TCGA) and supports user-customized dataset upload, enabling flexible analysis across experimental and clinical contexts.\u003c/p\u003e\n\u003cp\u003eUltraSur overcomes the rigidity of existing tools by enabling analysis beyond predefined grouping methods and single-omics data. It uniquely provides both manual and statistically optimized threshold determination strategies. The platform successfully integrates TCGA multi-omics data with user-provided data, facilitating hypothesis-free exploration.\u003c/p\u003e\n\u003cp\u003eConclusions: UltraSur significantly enhances translational oncology research by providing broad analytical flexibility. It ensures wide applicability, particularly for biomarker discovery and therapeutic target prioritization. By democratizing sophisticated survival analysis, UltraSur accelerates the extraction of clinically actionable insights from complex datasets.\u003c/p\u003e","manuscriptTitle":"UltraSur: A Versatile Survival Analysis Tool for Multi-Omics Data in Cancer Research","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 19:20:26","doi":"10.21203/rs.3.rs-7958498/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":"686484ad-a2de-4929-a7b5-7dc549cdf582","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57943862,"name":"Health sciences/Biomarkers"},{"id":57943863,"name":"Biological sciences/Cancer"},{"id":57943864,"name":"Biological sciences/Computational biology and bioinformatics"}],"tags":[],"updatedAt":"2026-03-26T12:12:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-14 19:20:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7958498","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7958498","identity":"rs-7958498","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}