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Background: Lung cancer contributes significantly to the total of cancer-linked deaths globally, accounting for 1.3 million deaths each year. Preoperative albumin (Alb) concentration and neutrophil-to-lymphocyte ratio (NLR) may reflect chronic inflammation and be used to predict lung cancer outcomes. Methods: The clinical records of 293 patients with non-small cell lung cancer (NSCLC) in Fujian Medical University Cancer Hospital & Fujian Cancer Hospital were reviewed retrospectively in this current study. Clinicopathologic pretreatment, including NLR, Glasgow prognostic score (GPS), and post-treatment value, such as tumor-node-metastasis (TNM) were documented. The cut-off finder application was employed to calculate the optimal threshold values. The significance of Alb concentration combined with NLR (COA-NLR) on the prediction of overall survival (OS) was explored using Kaplan-Meier analysis along with Cox proportional hazards. Results: The results revealed that COA-NLR could independently assess the OS of patients with NSCLC [hazard ratio (HR) =1.952, 95% confidence interval (CI): 1.367 to 2.647, P<0.001]. Moreover, the 3-year OS rates were 87.2%, 68.5%, and 52.8% for the COA-NLR =0, COA-NLR =1, and COA-NLR =2, respectively (P<0.001).Conclusions: Preoperative COA-NLR value can effectively stratifies prognosis in NSCLC patients by classified patients into three independent groups. It can be adopted as an effective biomarker for prognosis in NSCLC patients treated with resection.

Background Recurrence after initial primary resection is still a major and ultimate cause of death for non-small cell lung cancer patients. We attempted to build an early recurrence associated gene signature to improve prognostic prediction of non-small cell lung cancer. Methods Propensity score matching was conducted between patients in early relapse group and long-term survival group from The Cancer Genome Atlas training series (N = 579) and patients were matched 1:1. Global transcriptome analysis was then performed between the paired groups to identify tumour-specific mRNAs. Finally, using LASSO Cox regression model, we built a multi-gene early relapse classifier incorporating 40 mRNAs. The prognostic and predictive accuracy of the signature was internally validated in The Cancer Genome Atlas patients. Results A total of 40 mRNAs were finally identified to build an early relapse classifier. With specific risk score formula, patients were classified into a high-risk group and a low-risk group. Relapse-free survival was significantly different between the two groups in both discovery (HR: 3.244, 95% CI: 2.338-4.500, P &lt; 0.001) and internal validation series (HR 1.970, 95% CI 1.181-3.289, P = 0.009). Further analysis revealed that the prognostic value of this signature was independent of tumour stage, histotype and epidermal growth factor receptor mutation (P &lt; 0.05). Time-dependent receiver operating characteristic analysis showed that the area under receiver operating characteristic curve of this signature was higher than TNM stage alone (0.771 vs 0.686, P &lt; 0.05). Further, decision curve analysis curves analysis at 1 year revealed the considerable clinical utility of this signature in predicting early relapse. Conclusions We successfully established a reliable signature for predicting early relapse in stage I–III non-small cell lung cancer.

BACKGROUND Mitophagy plays essential role in the development and progression of colorectal cancer (CRC). However, the effect of mitophagy-related genes in CRC remains largely unknown. AIM To develop a mitophagy-related gene signature to predict the survival, immune infiltration and chemotherapy response of CRC patients. METHODS Non-negative matrix factorization was used to cluster CRC patients from Gene Expression Omnibus database (GSE39582, GSE17536, and GSE37892) based on mitophagy-related gene expression. The CIBERSORT method was applied for the evaluation of the relative infiltration levels of immune cell types. The performance signature in predicting chemotherapeutic sensitivity was generated using data from the Genomics of Drug Sensitivity in Cancer database. RESULTS Three clusters with different clinicopathological features and prognosis were identified. Higher enrichment of activated B cells and CD4 + T cells were observed in cluster III patients with the most favorable prognosis. Next, a risk model based on mitophagy-related genes was developed. Patients in training and validation sets were categorized into low-risk and high-risk subgroups. Low risk patients showed significantly better prognosis, higher enrichment of immune activating cells and greater response to chemotherapy (oxaliplatin, irinotecan, and 5-fluorouracil) compared to high-risk patients. Further experiments identified CXCL3 as novel regulator of cell proliferation and mitophagy. CONCLUSION We revealed the biological roles of mitophagy-related genes in the immune infiltration, and its ability to predict patients’ prognosis and response to chemotherapy in CRC. These interesting findings would provide new insight into the therapeutic management of CRC patients.