Novel Nutrition-Based Nomograms to Assess the Outcomes of Lung Cancer Patients Treated With Anlotinib or Apatinib

Exosomal miR-181a-3p Regulates the Anlotinib Resistance of Lung Cancer Cells

This study investigates the mechanism of Anlotinib in the treatment of lung cancer drug resistance. A total of 30 lung cancer tissue specimens were retrospectively analyzed and 30 normal lung tissues were included as a control. Real-time PCR detected miR-181a-3p expression along with analysis of cell viability by MTT assay, cell invasion by transwell, and the exosomal miR-181a-3p/UPR/ERAD signaling pathway. The expression of miR-181a-3p in peripheral blood of lung cancer was increased and the overall survival rate of patients with high miR-181a-3p in exosomes was shorter than patients with low expression. In A549 and H292 cell lines, anlotinib is added to overexpress exosomal miR-181a-3p, cell viability and invasion were significantly increased. After knocking down exosomal miR-181a-3p, cell viability and invasion were significantly reduced. The expression of miR-181a-3p is directly regulated by exosomes UPR/ERAD. After overexpression of exosomes miR-181a-3p, the protein levels of UPR and ERAD were significantly reduced and increased after knockdown of exosomes miR-181a-3p. In conclusion, the secretory miR-181a-3p/UPR/ERAD pathway promotes the proliferation of A549 and H292 cells, regulates the resistance of Anlotinib, and can increase the resistance of lung cancer to Anlotinib by promoting the proliferation signaling pathway, and promote the growth of tumor cells.

A Linear Discriminant Analysis Model Based on the Changes of 7 Proteins in Plasma Predicts Response to Anlotinib Therapy in Advanced Non-Small Cell Lung Cancer Patients

Background

Anlotinib is a multi-targeted tyrosine kinase inhibitor mainly targeting angiogenesis signaling. The predictive marker of anlotinib’s efficacy remains elusive. This study was designed to explore the predictive marker of anlotinib in non-small cell lung cancer (NSCLC).

Methods

We prospectively enrolled 52 advanced NSCLC patients who underwent at least one line of targeted therapy or chemotherapy between August 2018 and March 2020. Patients were divided into durable responders (DR) and non-durable responders (NDR) based on the median progression-free survival (PFS, 176 days). The Olink Immuno-Oncology panel (92 proteins) was used to explore the predictive protein biomarkers in plasma samples before treatment (baseline) and on the first treatment evaluation (paired).

Results

At baseline, the response to anlotinib was not significantly associated with age, gender, smoke history, histology, oligo-metastases, EGFR mutations, and other clinical characteristics. The results of PFS-related protein biomarkers at baseline were all not satisfying. Then we assessed the changes of 92 proteins levels in plasma on the first treatment evaluation. We obtained a Linear discriminant analysis (LDA) model based on 7 proteins, with an accuracy of 100% in the original data and an accuracy of 89.2% in cross validation. The 7 proteins were CD70, MIC-A/B, LAG3, CAIX, PDCD1, MMP12, and PD-L2. Multivariate Cox analysis further showed that the changes of CD70 (HR 25.48; 95% CI, 4.90–132.41, P=0.000) and MIC-A/B (HR 15.04; 95% CI, 3.81–59.36, P=0.000) in plasma were the most significant prognostic factors for PFS.

Conclusion

We reported herein a LDA model based on the changes of 7 proteins levels in plasma before and after treatment, which could predict anlotinib responders among advanced NSCLC patients with an accuracy of 100%. Further studies are warranted to verify the prediction performance of the LDA model.

Prognostic Score System Using Preoperative Inflammatory, Nutritional and Tumor Markers to Predict Prognosis for Gastric Cancer: A Two-Center Cohort Study

INTRODUCTION

Gastric cancer (GC) is the fourth leading cause of cancer-related death worldwide. Our study aimed to investigate the prognostic value of preoperative inflammatory, nutritional and tumor markers and develop an effective prognostic score system to predict the prognosis of GC patients.

METHODS

We retrospectively analyzed 1587 consecutive GC patients who received curative gastrectomy from two medical centers. A novel prognostic score system was proposed based on independently preoperative markers associated with overall survival (OS) of GC patients. A nomogram based on prognostic score system was further established and validated internally and externally.

RESULTS

Based on multivariate analysis in the training set, a novel BLC (body mass index-lymphocyte-carbohydrate antigen 19-9) score system was proposed, which showed an effective predictability of OS in GC patients (log-rank P < 0.001). Moreover, receiver-operating characteristic (ROC) analysis showed that BLC had better performance in predicting OS than the traditional prognostic markers. The C-index of the BLC based-nomogram was 0.710 (95% CI 0.686-0.734), and the areas under ROC curves for predicting 3- and 5-year OS were 0.781 (95% CI 0.750-0.813) and 0.755 (95% CI 0.723-0.786), respectively, which were higher than those of tumor node metastasis (TNM) staging system alone. The calibration curve for probability of 3- and 5-year OS rate showed a good fitting effect between prediction by nomogram and actual observation. Verification in the internal and external validation sets showed results consistent with those in the training set.

CONCLUSIONS

The BLC combining inflammatory, nutritional and tumor markers was an independent prognostic predictor for GC patients, and the nomogram based on BLC could accurately predict the personalized survival of patients with GC.