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BackgroundLung adenocarcinoma (LUAD) is a tumor with high incidence rate and high mortality rate. Previous studies have found that autophagy plays a vital role in tumorigenesis and biological progression. The aim of our research is to screen and analyze autophagy-related genes (ATGs) using bioinformatics technology, then establish a gene expression model for predicting the prognosis of LUAD patients. MethodsDifferentially expressed ATGs in LUAD and normal tissues were screened by The Cancer Genome Atlas (TCGA) dataset. We applied Go and KEGG enrichment analysis of ATGs for identifying the relevant signaling pathways. Finally, the ATGs related with survival were assessed using univariate and multivariate COX regression analyses. This project was analyzed by R software. ResultsA total of 232 ATGs were obtained in LUAD. Subsequently, 30 ATGs were screened out as genes associated with prognosis. GO analysis revealed that the 30 differently expressed ATGs (DE-ATGs) were enriched in intrinsic apoptotic signaling pathway, macroautophagy, and neuron death. In addition, KEGG analysis revealed that the DE-ATGs were associated with autophagy (animal), ErbB signaling pathway and IL-17 signaling pathway. Then, the risk score model was established by the 8 ATGs (ATG4A, CCR2, MBTPS2, APOL1, ERO1A, SPHK1, ST13 and ITGA6), and LUAD patients were divided into high-risk and low-risk groups with the risk score. The risk score was significantly related to overall survival and prognosis by univariate and multivariate analysis (P < 0.001). The survival time of low-risk score patients was showed by the cumulative curve that was obviously longer than high-risk score patients (P< 0.001). Finally, the correlation of the autophagy-related risk characteristics and multiple clinical parameters was analyzed. ConclusionA prognostic signature and nomogram based on 8 ATGs was constructed. This research provides a new direction for the prognosis evaluation and guides the potential treatment strategies for LUAD.

Studies have shown that cancer-associated fibroblasts (CAFs) play an irreplaceable role in the occurrence and development of tumors. Therefore, exploring the action and mechanism of CAFs on tumor cells is particularly important for designing new and effective treatments and improving prognosis of tumors. For exosomes have been shown to play vital roles in intercellular communication, in this study, we compared the effects of CAFs-derived exosomes and NFs-derived exosomes on breast cancer cell proliferation, migration, and metastasis. The results showed that exosomes from both CAFs and NFs could enter into breast cancer cells and CAFs-derived exosomes had a more enhancing effect on breast cancer cell proliferation and invasion than NFs-derived exosomes. Furthermore, it was found that the expression levels of miR-106a in exosomes derived from CAFs were significantly up-regulated than that of NFs-derived exosomes and what’s more, in vitro and in vivo studies have shown that miR-106a can promote breast cancer cell proliferation, migration and metastasis by specifically binding to the 3'UTR of TCEAL7. It is inspiring to find that the miR-106a-TCEAL7 pathway promotes Snail nuclear ectopic activation by activating NF-κB, thereby inducing epithelial-mesenchymal transition and promoting cell proliferation and metastasis. Moreover, a mouse xenograft model confirmed that CAFs-derived exosomes miR-106a could promote tumor metastasis. The above data shows that CAFs-derived exosomes miR-106a promote Snail nuclear ectopic by targeting TCEAL7 to activate the NF-κB pathway, thereby inducing EMT, invasion and metastasis of breast cancer. Targeting CAFs-derived exosome miR-106a may be a potential treatment option to overcome breast cancer progression.