The Tumor Marker Database is a comprehensive database focusing on tumor signaling pathways and genetic information. It aims to collect, integrate, and analyze tumor-related signaling pathways, genetic mutations, and other critical data to provide robust support for tumor research, clinical diagnosis, and treatment. With the Tumor Marker Database, researchers and clinicians can easily access the latest tumor research data, accelerating the knowledge update and application in the field of oncology.

Jinhao Cheng1, Zhitao He1, Juehua Jing1Corresponding address, Yuyao Liu2Corresponding address, Haoxue Zhang345Corresponding address
1.Department of Orthopedics, The Second Affiliated Hospital of Anhui Medical University, Hefei 230601, China.
2. Bioinformatics R&D Department, Hefei GuangRe Biotechnology Co., Ltd
3.Department of Dermatovenerology, First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China.
4. Key Laboratory of Dermatology, Ministry of Education, Hefei, Anhui Province, China.
5. Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Anhui Medical University, Hefei, Anhui Province, China

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Introduction

Graphic abstract
Background and Purpose of the Database

The establishment of the Tumor Marker Database aims to address the complexity and diversity in the field of tumor research. As gene sequencing technology rapidly advances and oncology research deepens, more and more tumor signaling pathways and genetic variations are being uncovered. However, these data are scattered across various literature, databases, and experimental data, lacking unified and systematic integration. Therefore, the Tumor Marker Database strives to collect and integrate these critical data, providing a comprehensive and efficient information platform for tumor research.

Database Content

The Tumor Marker Database covers multiple tumor-related signaling pathways, including common ones such as PI3K/Akt/mTOR, MAPK/ERK, and Wnt/β-catenin. The Tumor Marker Database offers powerful data retrieval functions, allowing users to quickly search by gene name, signaling pathway name, tumor type, and other keywords. Search results are displayed in a list format, including detailed information such as gene name, signaling pathway name, mutation information, and data source. Tumor Marker Database can help users uncover potential patterns and regularities in the data, providing new ideas and methods for tumor research.

Database Maintenance and Updates

The database supports data sharing functionality. Data sharing can promote scientific collaboration and academic exchanges, driving the development of tumor research.The Tumor Marker Database has established a strict data quality control system to ensure data accuracy and reliability.The database regularly audits and verifies data, promptly correcting erroneous data and updating the latest research findings. The database regularly updates its content, including new signaling pathways and other critical data. Simultaneously, the database regularly maintains and upgrades the system to ensure its stability and security.

Database Application Prospects

The establishment of the Tumor Marker Database will provide powerful support for tumor research, clinical diagnosis, and treatment. Through this database, researchers can more conveniently access the latest tumor research data, accelerating the knowledge update and application in the field of oncology. Additionally, the database can provide personalized tumor diagnosis and treatment plans for clinicians, enhancing the precision and effectiveness of tumor treatment. In the future, with continuous technological advancements and data accumulation, the Tumor Marker Database will play an increasingly important role in the field of tumor research.

Help

Introduction

TumorMarker will specialize in the compilation of oncogenic signaling pathways, integrating diverse public databases to facilitate seamless access for users to comprehensive tumor-related gene sets. By aggregating information from a wide array of reputable sources, our platform aims to streamline the process of acquiring insights into the intricate genetic landscapes of tumors, enabling researchers and clinicians to make more informed decisions in their diagnostic, prognostic, and therapeutic endeavors.

Data collection and processing

Our data originates from well-established and validated gene sets sourced from public databases such as Msigdb, KEGG, GO, Reactome, and others. By leveraging these reputable resources, we ensure that our data is of high quality and reliability. The process of data collection involves meticulously gathering information from these databases, while data processing entails refining and organizing the data in a manner that is both comprehensive and user-friendly. This allows for efficient utilization of the information in various research and clinical applications.

Function of TumorMarker

FAQ

Download

1. Is there a way to download all datasets in a batch?
Reply: Unfortunately, we cannot provide the raw data download since we don't own these data. Users could download the related data with the 'analysis' menu.

2. How to download the pictures of high resolution in TumorMarker?
TumorMarker offers vector graphics in PDF format, allowing users to download and display them with clarity. Additionally, users can freely adjust the resolution of the images according to their needs.

Acknowledgements

Thanks

We would like to express our sincere gratitude to all the researchers and contributors who have made their data openly available. The success of this study would not have been possible without the generous sharing of datasets, which has greatly facilitated our research and analysis. We are deeply appreciative of the commitment and dedication of the scientific community to open science, which fosters collaboration and accelerates scientific progress.

Author contributions

  • Jinhao Cheng: Responsible for the structural design and optimization of the database. Leads the team in data integration, ensuring data consistency and integrity.
  • Zhitao He: Ensures the database has robust security mechanisms to protect sensitive data. Formulates and enforces project security policies, ensuring strict adherence to security standards.
  • Juehua Jing: Ensures data accuracy and reliability through data management and quality control. Establishes and executes data cleaning processes, enhancing data quality.
  • Yuyao Liu: Ensures projects are completed on time and delivered promptly through project management and coordination. Optimizes resource allocation and tracks progress, ensuring smooth progression at each project stage.Supports the data validation process, ensuring data accuracy. Provides valuable feedback and suggestions during the development process, contributing to the continuous improvement of the database.
  • Haoxue Zhang: Ensures database stability and functionality through testing and debugging. Conducts performance optimization, improving the database's response speed and processing capacity.

Funding information

  1. This research was funded in part by the Sparkle Database (Internal Grant No. GRSWSCITOP202406). More details can be found at https://grswsci.top/.

Competing Interests

We have declared that no competing interest exists.

Public Databases integrated

  1. GSEA|MSigDB: https://www.gsea-msigdb.org/gsea/msigdb
  2. KEGG: https://www.genome.jp/kegg/
  3. GO: https://www.geneontology.org/
  4. Reactome: https://reactome.org/
  5. CancerSEA: http://biocc.hrbmu.edu.cn/CancerSEA/
  6. Biocarta: https://maayanlab.cloud/Harmonizome/dataset/Biocarta+Pathways/

References

  1. Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015 Dec 23;1(6):417-425. doi: 10.1016/j.cels.2015.12.004. PMID: 26771021; PMCID: PMC4707969.
  2. Arthur Liberzon, Aravind Subramanian, Reid Pinchback, Helga Thorvaldsdóttir, Pablo Tamayo, Jill P. Mesirov, Molecular signatures database (MSigDB) 3.0, Bioinformatics, Volume 27, Issue 12, June 2011, Pages 1739–1740
  3. A. Subramanian, P. Tamayo, V.K. Mootha, S. Mukherjee, B.L. Ebert, M.A. Gillette, A. Paulovich, S.L. Pomeroy, T.R. Golub, E.S. Lander, J.P. Mesirov, Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles, Proc. Natl. Acad. Sci. U.S.A. 102 (43) 15545-15550.
  4. Minoru Kanehisa, Miho Furumichi, Yoko Sato, Yuriko Matsuura, Mari Ishiguro-Watanabe, KEGG: biological systems database as a model of the real world, Nucleic Acids Research, 2024;, gkae909.
  5. Thomas PD, Ebert D, Muruganujan A, Mushayahama T, Albou LP, Mi H. PANTHER: Making genome-scale phylogenetics accessible to all. Protein Sci. 2022 Jan;31(1):8-22.
  6. Orlic-Milacic M, Rothfels K, Matthews L, Wright A, Jassal B, Shamovsky V, Trinh Q, Gillespie M, Sevilla C, Tiwari K, Ragueneau E, Gong C, Stephan R, May B, Haw R, Weiser J, Beavers D, Conley P, Hermjakob H, Stein LD, D'Eustachio P, Wu G. Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes. Database, 2024.
  7. Huating Yuan, Min Yan, Guanxiong Zhang, Wei Liu, Chunyu Deng, Gaoming Liao, Liwen Xu, Tao Luo, Haoteng Yan, Zhilin Long, Aiai Shi, Tingting Zhao, Yun Xiao, Xia Li, CancerSEA: a cancer single-cell state atlas, Nucleic Acids Research, Volume 47, Issue D1, 08 January 2019, Pages D900–D908.
  8. Diamant I, Clarke DJB, Evangelista JE, Lingam N, Ma'ayan A. Harmonizome 3.0: integrated knowledge about genes and proteins from diverse multi-omics resources. Nucleic Acids Res. 2024 Nov 20. pii: gkae1080.

Updates

Project Update

Preparation Phase (2024-10-01 to 2024-10-19)

Project initiation, team formation, and role assignment.

Weekly updates:

  • Structure design and optimization progress (by Jinhao Cheng)
  • Security mechanism updates (by Zhitao He)
  • Data management and quality control progress (by Juehua Jing)
  • Resource allocation and planning (by Yuyao Liu, during this phase)

Mid-preparation review (2024-10-15): Assess progress and address any issues or bottlenecks.

Pilot Phase (2024-10-20 to 2024-10-30)

Begin database testing with limited users or data.

Daily/weekly updates (as needed):

  • Testing and debugging progress (by Haoxue Zhang)
  • User feedback integration (by Yuyao Liu)

Pilot review (2024-11-27): Evaluate pilot results and prioritize required changes or improvements.

Full Operation Preparation (2024-10-30 to 2024-11-01)

Make final adjustments based on pilot feedback.

Ensure all documentation, user guides, and training materials are ready.

Full Operation Phase (Starting from 2024-11-01)

Officially launch the database to all users.

Monthly updates:

  • System performance review (by Haoxue Zhang)
  • User training and support summary (by Yuyao Liu)
  • Continuous improvement initiatives (collaborative effort by all team members)

Quarterly review: Comprehensive assessment of system performance, user feedback, and areas for improvement.

Terms of Use

TumorMarker is a database maintained by The Sparkle Research Network, providing comprehensive expression resources and functional analysis for transcriptional regulation. The Sparkle Research Network may update the content on https://www.grswsci.top/TumorMarker/ ("Content") from time to time. The Sparkle Research Network makes no warranties or representations, express or implied, regarding any of the Content, including but not limited to its current accuracy, completeness, timeliness, adequacy, or usefulness. By using this website, you agree that The Sparkle Research Network will not be liable for any losses or damages arising from your use of or reliance on the Content, or other websites or information to which this website may be linked.

TumorMarker is freely accessible for research use in an academic setting. You may view the Content solely for your own personal reference or for research in an academic setting. All academic research use of the Content must credit TumorMarker as the source and reference these Terms of Use. Outside of scientific publications, you may not redistribute or share the Content, in part or in whole, with any third party for any purpose without the express permission of The Sparkle Research Network.

Unless you have signed a license agreement with The Sparkle Research Network, you may not use any part of the Content for any of the following purposes:

  1. Use or incorporation into a commercial product or towards the performance of a commercial service;
  2. Research use in a commercial setting;
  3. Use for patient services; or
  4. Generation of reports in a hospital or other patient care setting. You may not copy, transfer, reproduce, modify, or create derivative works of TumorMarker for any commercial purpose without the express permission of Sparkle Network. If you seek to use TumorMarker for such purposes, please request the appropriate license below:
  1. Research use in a commercial setting
  2. Use in a commercial product
  3. Use for patient services or reports in a hospital setting. Please contact us at ahmulyy@163.com.

Start preparation time 2024-10-01
Pilot run time 2024-10-20
Formal running time 2024-11-01

This study is part of the Sparkle Platform and operates as a dedicated sub-project within its ecosystem
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©2024 TumorMarker. Terms of use