Identify Prognostic Biomarkers of Lung Cancer

NCT05010330 | Unknown

• 1 other identifier: MKM2021-720
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 1

Brief Summary

Multi-omics and Clinical Data Analysis is potential to predict the prognosis of lung cancer patients.

Conditions

Lung Cancer; Lung Adenocarcinoma; Lung Squamous Cell Carcinoma; Non Small Cell Lung Cancer

Outcome Measures

Primary Outcomes (1)

• Identify some prognostic biomarkers in lung cancer.

  1. Our study will identify some biomarkers that can predict the prognosis of lung cancer patients. 2. Our study will construct a new risk score model that provide a candidate model for prognostic evaluation of lung cancer. 3. Our research will provide insights for precision immunotherapy of lung cancer by exploring the differences in clinical characteristics, tumor mutation burden, and tumor immune cell infiltration between different risk score groups.

  1 week

Study Arms (2)

healthy control

healthy people

lung cancer

patients diagnosed with lung cancer

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Individuals aged between 18 and 80 years old. Patients with diagnosis of lung cancer or healthy controls without any history of tumor will be eligible for our enrollment.

You may qualify if:

• Patients diagnosed with lung cancer;
• Untreated lung cancer patients;
• No history of chronic or serious diseases, such as cardiovascular disease, liver disease, kidney disease, respiratory disease, blood disease, lymphatic disease, endocrine disease, immune disease, mental disease, neuromuscular disease, gastrointestinal system disease, etc.

You may not qualify if:

• Patients with other tumors;
• Lung cancer patients who had been treated;
• Abnormal liver and kidney function;
• Acute and chronic infectious diseases

Sponsors & Collaborators

• RenJi Hospital: lead

Study Sites (1)

Renji Hospital, Shanghai Jiaotong University school of medicine

Shanghai, 021, China

RECRUITING

Related Publications (1)

• Zhang Y, Yang M, Ng DM, Haleem M, Yi T, Hu S, Zhu H, Zhao G, Liao Q. Multi-omics Data Analyses Construct TME and Identify the Immune-Related Prognosis Signatures in Human LUAD. Mol Ther Nucleic Acids. 2020 Sep 4;21:860-873. doi: 10.1016/j.omtn.2020.07.024. Epub 2020 Jul 23.

  PMID: 32805489 RESULT

Biospecimen

Retention: SAMPLES WITHOUT DNA

In this study ,we planned to collect plasm from the two cohorts, and lung cancer tissues and para-carcinoma tissue from lung cancer patients who had a tumor excision

MeSH Terms

Conditions

Lung Neoplasms; Adenocarcinoma of Lung; Carcinoma, Non-Small-Cell Lung

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases; Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Carcinoma, Bronchogenic; Bronchial Neoplasms

Study Officials

• Huijing Huang

  RenJi Hospital

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Kaimin Mao, Doctor

CONTACT

86-15071027291; mkm444931158@126.com

Huang, Doctor

CONTACT

86-18217720058; fangfeijin90@163.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 27, 2021
• First Posted: August 18, 2021
• Study Start: July 1, 2020
• Primary Completion: September 20, 2021
• Study Completion: September 30, 2021
• Last Updated: August 18, 2021

Record last verified: 2021-08

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)