Lung CancerNCT05010330
Identify Prognostic Biomarkers of Lung Cancer
An observational study of Lung Cancer and Lung Adenocarcinoma, run by RenJi Hospital at 1 site in China.
Trial summary
Multi-omics and Clinical Data Analysis is potential to predict the prognosis of lung cancer patients. Read more about this study
- Who is it for?
- Ages 18 to 80 yearsHealthy volunteers can take part
- Conditions the study is for
- Lung Cancer, Lung Adenocarcinoma, Lung Squamous Cell Carcinoma
- How many people
- 500 participants
- Where
- 1 siteChina
- Study dates
- 1 Jul 2020to 30 Sep 2021
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- Patients with other tumors;
- Lung cancer patients who had been treated;
- Abnormal liver and kidney function;
- Acute and chronic infectious diseases
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Renji Hospital, Shanghai Jiaotong University school of medicine
Shanghai, 021
About this study
Official title: Multi-omics Combined With Clinical Data Analysis to Identify Prognostic Biomarkers of Lung Cancer
Multi-omics and Clinical Data Analysis is potential to predict the prognosis of lung cancer patients.
Read the detailed description
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
- Lung Cancer, Lung Adenocarcinoma, Lung Squamous Cell Carcinoma, Non Small Cell Lung Cancer
- Medical subject headings
- Lung Neoplasms, Adenocarcinoma of Lung, Carcinoma, Non-Small-Cell Lung
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| healthy control | healthy people | |
| lung cancer | patients diagnosed with lung cancer |
What is being measured
The primary outcome is the main question the study is designed to answer.
Identify some prognostic biomarkers in lung cancer.
- Our study will identify some biomarkers that can predict the prognosis of lung cancer patients.
- Our study will construct a new risk score model that provide a candidate model for prognostic evaluation of lung cancer.
- 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.
Measured: 1 week
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Aug 2021
- Study start1 Jul 2020
- Primary completion20 Sep 2021 (estimated)
- Study completion30 Sep 2021 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Lung CancerStudies of the same condition, open and under way.
- More trials run by RenJi HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- RenJi Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Huijing Huang, Principal investigator, RenJi Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05010330
- Sponsor's protocol ID
- MKM2021-720
- Status last verified
- Aug 2021
- First submitted
- 27 Jul 2021
- Last updated
- 18 Aug 2021
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: There is no plan to make individual participant data (IPD) available to other researchers.
Publications
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.
PubMed 32805489
Study at a glance
From the registry record, last updated by the sponsor on 18 Aug 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Multi-omics Combined With Clinical Data Analysis to Identify Prognostic Biomarkers of Lung Cancer
- ClinicalTrials.gov ID
- NCT05010330
- Acronym / sponsor ID
- MKM2021-720
- Conditions
- Lung Cancer, Lung Adenocarcinoma, Lung Squamous Cell Carcinoma, Non Small Cell Lung Cancer
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- RenJi HospitalUniversity, hospital or other
- Study start
- 1 Jul 2020 (actual)
- Primary completion
- 20 Sep 2021 (estimated)
- Study completion
- 30 Sep 2021 (estimated)
- First posted
- 18 Aug 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Multi-omics and Clinical Data Analysis is potential to predict the prognosis of lung cancer patients.
Detailed description
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.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| healthy control | – | –healthy people |
| lung cancer | – | –patients diagnosed with lung cancer |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (4 listed)
- Patients with other tumors;
- Lung cancer patients who had been treated;
- Abnormal liver and kidney function;
- Acute and chronic infectious diseases
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Identify some prognostic biomarkers in lung cancer.
Time frame: 1 week
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.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Aug 2021.
- Results publication: PubMed 32805489Zhang 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.
- Individual participant dataSharing planned: No. There is no plan to make individual participant data (IPD) available to other researchers.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Huijing Huang principal investigator
RenJi Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lung Cancer and Lung Adenocarcinoma, run by RenJi Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 18 Aug 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Kaimin Mao, Doctor (86-15071027291, mkm444931158@126.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Identify some prognostic biomarkers in lung cancer. (time frame: 1 week). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05010330, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 Aug 2021; AzTrials last refreshed its copy on 7 Nov 2026.