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

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.

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

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.

There is no plan to make individual participant data (IPD) available to other researchers.