Lung cancer

Comprehensive Explorations of CCL28 in Lung Adenocarcinoma Immunotherapy and Experimental Validation

Background

Chemokines have been reported to play an important role in cancer immunotherapy. This study aimed to explore the chemokines involved in lung cancer immunotherapy.

Methods

All the public data were downloaded from The Cancer Genome Atlas Program database. Quantitative real time-PCR was used to detect the mRNA level of specific molecules and Western blot was used for the protein level. Other experiments used include luciferase reporter experiments, flow cytometric analysis, Chromatin immunoprecipitation assay, ELISA and co-cultured system.

Results

We found that the CCL7, CCL11, CCL14, CCL24, CCL25, CCL26, CCL28 had a higher level, while the CCL17, CCL23 had a lower level in immunotherapy non-responders. Also, we found that immunotherapy non-responders had a higher level of CD56dim NK cells, NK cells, Th1 cells, Th2 cells and Treg, yet a lower level of iDC and Th17 cells. Biological enrichment analysis indicated that in the patients with high Treg infiltration, the pathways of pancreas beta cells, KRAS signaling, coagulation, WNT BETA catenin signaling, bile acid metabolism, interferon alpha response, hedgehog signaling, PI3K/AKT/mTOR signaling, apical surface, myogenesis were significantly enriched in. CCL7, CCL11, CCL26 and CCL28 were selected for further analysis. Compared with the patients with high CCL7, CCL11, CCL26 and CCL28 expression, the patients with low CCL7, CCL11, CCL26 and CCL28 expression had a better performance of immunotherapy response and this effect might partly be due to Treg cells. Furthermore, biological exploration and clinical correlation of CCL7, CCL11, CCL26 and CCL28 were conducted, Finally, CCL28 was selected for validation. Experiments showed that under the hypoxia condition, HIF-1α was upregulated, which can directly bind to the promoter region of CCL28 and lead to its higher level. Also, CCL28 secreted by lung cancer cells could induce Tregs infiltration.

Conclusion

Our study provides a novel insight focused on the chemokines in lung cancer immunotherapy. Also, CCL28 was identified as an underlying biomarker for lung cancer immunotherapy.

Exploration of the Long Noncoding RNAs Involved in the Crosstalk between M2 Macrophages and Tumor Metabolism in Lung Cancer

Background

Complex regulation exists between tumor metabolism and M2 macrophages. Long noncoding RNAs (lncRNAs) are famous for their wide regulatory role. This study aimed to identify the lncRNAs involved in the crosstalk between tumor metabolism and M2 macrophages.

Methods

The Cancer Genome Atlas was responsible for the public data. R software was responsible for the analysis of public data.

Results

Based on the input expression profile, we quantified the M2 macrophage infiltration using the CIBERSORT algorithm and found that M2 macrophages were a risk factor for lung cancer. Also, we found that M2 macrophages were correlated with multiple metabolism pathways. Then, 67 lncRNAs involved in both M2 macrophages and related metabolism pathways were identified. A prognosis signature based on AC027288.3, AP001189.3, FAM30A, GAPLINC, LINC00578, and LINC01936 was established, which had good prognosis prediction ability. The clinical parameters and risk score were combined into a nomogram plot for better prediction of the patient's prognosis. A high fit of actual survival and nomogram-predicted survival was found using the calibration plot. Moreover, in low-risk patients, immunotherapy was more effective, while cisplatin and docetaxel were more effective in high-risk patients. Biological enrichment analysis indicated pathways of notch signaling, TGF-β signaling, interferon alpha response, and interferon-gamma response were activated in the high-risk group. Meanwhile, the risk score was associated with tumor metabolism and M2 macrophages. Also, we found that the promoting effect of CAPLINC on M2 macrophage polarization might act through multiple metabolism pathways.

Conclusions

Our result can provide new insights into the interaction between M2 macrophages and tumor metabolism, as well as the involved lncRNAs, which can provide the direction for future studies.

The Underlying Mechanism Involved in Gefitinib Resistance and Corresponding Experiment Validation in Lung Cancer

Background

Gefitinib resistance remains a major problem in the treatment of lung cancer. However, the underlying mechanisms involved in gefitinib resistance are largely unclear.

Methods

Open-accessed data of lung cancer patients were downloaded from The Cancer Genome Atlas Program and Gene Expression Omnibus databases. CCK8, colony formation, and 5-ethynyl-2'-deoxyuridine assays were utilized to evaluate the cell proliferation ability. Transwell and wound-healing assays were utilized to evaluate the cell invasion and migration ability. Quantitative real-time PCR was utilized to detect the RNA level of specific genes.

Results

Here, we obtained the expression profile data of wild and gefitinib-resistant cells. Combined with the data from the TCGA and GDSC databases, we identified six genes, RNF150, FAT3, ANKRD33, AFF3, CDH2, and BEX1, that were involved in gefitinib resistance in both cell and tissue levels. We found that most of these genes were expressed in the fibroblast of the NSCLC microenvironment. Hence, we also comprehensively investigated the role of fibroblast in the NSCLC microenvironment, including its biological effect and cell interaction. Ultimately, CDH2 was selected for further analysis for its prognosis correlation. In vitro experiments presented the cancer-promoting role of CDH2 in NSCLC. Moreover, cell viability detection showed that the inhibition of CDH2 could significantly decrease the IC50 of gefitinib in NSCLC cells. GSEA showed that CDH2 could significantly affect the pathway activity of PI3K/AKT/mTOR signaling.

Conclusions

This study is aimed at investigating the underlying mechanism involved in gefitinib resistance to lung cancer. Our research has improved researchers' understanding of gefitinib resistance. Meanwhile, we found that CDH2 could lead to gefitinib resistance through PI3K/AKT/mTOR signaling.