Identify Prognostic Biomarkers of Lung Cancer

NCT ID: NCT05010330

Last Updated: 2021-08-18

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

500 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-07-01

Study Completion Date

2021-09-30

Brief Summary

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

Detailed Description

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Lung cancer is the leading cause of cancer-related death in China. In order to improve prognosis of lung cancer as well as provide new therapeutic targets, the identification of effective biomarkers for the prognosis of lung cancer is of great significance. It has been reported that some small molecules such as lncRNA, circRNA and polypeptides in human plasm have good prospects in diagnosing or evaluating the stage of diseases. In this study, we planned to use multi-omics combined with clinical data to discovery some small molecules that are potential to predict the prognosis of lung cancer patients. In addition, we want to construct a new risk score model that provide a candidate model for prognostic evaluation of lung cancer. And we hope our study can 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.

Conditions

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

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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healthy control

healthy people

No interventions assigned to this group

lung cancer

patients diagnosed with lung cancer

No interventions assigned to this group

Eligibility Criteria

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Inclusion Criteria

* 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.

Exclusion Criteria

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

18 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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RenJi Hospital

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Huijing Huang

Role: PRINCIPAL_INVESTIGATOR

RenJi Hospital

Locations

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Renji Hospital, Shanghai Jiaotong University school of medicine

Shanghai, , China

Site Status RECRUITING

Countries

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China

Central Contacts

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Kaimin Mao, Doctor

Role: CONTACT

86-15071027291

Huang, Doctor

Role: CONTACT

86-18217720058

References

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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.

Reference Type RESULT
PMID: 32805489 (View on PubMed)

Other Identifiers

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MKM2021-720

Identifier Type: -

Identifier Source: org_study_id

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