Autophagy is a self-degradative process that plays an essential role in equilibrating energy, eliminating misfolded or aggregated proteins, and reacting to stimuli [30]. To date, three types of autophagy have been found: macroautophagy, microautophagy and chaperone-mediated autophagy [11]. Macroautophagy is considered as the main form of autophagy, which is extensively studied compared to the other two types [31–33]. The process of autophagy is also classified into four critical steps: initiation, nucleation, maturation, and degradation [34]. In the past decades, researchers have discovered 32 autophagy-related genes (Atgs) in yeast, most of which are also highly conserved in mammals significantly [35].
In recent years, some researches have reported that autophagy has a strong relationship between the prognosis and survival in GC. Qu et al. [36] found that Beclin1 (protein homologue of the yeast ATG6) was much overexpressed in malignant tissues than the nonmalignant tissues in GC. What’s more, they also found that overexpression of Beclin1 was related with a poor prognosis for GC. Liao et al. [37] discovered that “stone-like” structure pattern of LC3A (an autophagosomal biomarker) was correlated with increased recurrence and worse survival possibility in gastric carcinoma.
As being recognized previously, lncRNAs such as H19 and HOTAIR played a key role as primary regulators in carcinogenesis of GC [38–40]. Until now, numerous studies have proved that lncRNAs were highly active in plenty of pathological processes of GC, such as proliferation and metastasis. Among them, some lncRNAs were defined as protective factors while others were risk factors [25, 41–44]. Furthermore, several studies have proved that lncRNAs participated in the progression, especially malignant progression of GC through regulating autophagy-related mRNAs [24–29, 45].
Although numerous researches have been performed and much is known about lncRNAs and autophagy in GC, previous studies mainly focused on the single lncRNA. The prognostic system relied on the multiple autophagy-related lncRNAs is still not clear. More importantly, as one of the deadliest cancers all over the world, prognosis evaluation of patients with GC still depend too much on the pathological analysis currently, which also facing many challenges and inconvenience in the clinic.
In this study, we obtained the autophagy-related lncRNAs through correlation test of the autophagy-related genes. The expression of the lncRNAs was profiled from the TCGA-STAD dataset. 24 lncRNAs were found strongly linked with the survival of TCGA-STAD through Kaplan-Meier and univariate Cox regression analysis. We used the LASSO regression analysis to build the model in the training set and found 11 prognostic signatures of lncRNAs. The RS was calculated by integrating lncRNAs expression levels and corresponding LASSO coefficients for each patient. AC005586.1, AL353804.1, IPO5P1, AP003392.1, AL355574.1 and AC092574.1 were considered as protective lncRNA while LINC01705, AP001528.2, AC009948.1, HAGLR and AP001033.2 were risk lncRNA. The accuracy of the model was tested in the testing set, and TCGA-STAD dataset and the RS was found significantly corresponded with patient outcomes in both testing set and TCGA-STAD dataset.
Go analysis revealed that the mRNAs in the prognostic network were mainly involved in the autophagy, which is consistent with the expected results. The MF of GO analysis uncovered that these mRNAs were also have a link with ubiquitin or ubiquitin-like protein ligase binding. Ubiquitin (Ub) is a protein highly conserved in all eukaryotes and bears many potential sites for additional post-translational modifications [46]. Ub was one of the most prominent factors in modifying protein substrates and degradation [47]. The proteolytic system based on ubiquitin and autophagy are two prime systems in eukaryotic cells [48, 49]. Studies have shown that ubiquitin, as a capital regulator, has participated in all processes in the autophagy flux [50]. Atg8 was a ubiquitin-like protein, which was also found crucial for the autophagosome formation and consisted the lipid conjugation system in autophagy [51].
KEGG analysis uncovered that the mRNAs were primarily involved in the apoptosis, mTOR pathway, p53 pathway and PI3K-Akt pathway. There are two types of autophagy-related signaling pathways. One is mTOR-dependent pathways, such as the AMPK/mTOR and PI3K/Akt/mTOR pathways, and the other is non-mTOR dependent pathways, such as the p53 pathway [52]. The PI3K/AKT/mTOR pathway regulates many biological processes, including autophagy and is frequently activated in various human cancers [53]. The molecular changes in the PI3K/Akt/mTOR signaling pathway were also found could increase the clinical stage and promote the recurrence in carcinoma [53, 54]. In GC, PI3K/Akt/mTOR activation was found significantly upregulated in GC cells, which results in the inhibition of autophagy of GC cells [55]. Additionally, inhibition of the PI3K/Akt/mTOR pathway increased the autophagic flux and promoted the apoptosis of cancer cells [56].
The results of the GSEA analysis showed that the high-risk group was much active in the ECM receptor interaction. Some studies indicated that autophagy affected the extracellular matrix (ECM), thus participating the invasiveness and metastasis of cancer cells. In a rapidly growing and aggressive tumor, biosynthesis is highly demanded obviously. And in this process, detachment-induced autophagy will help the cancer cells get rid of ECM contact and promoting the metastasis subsequently during advanced cancer stage [57, 58]. Autophagy will also induce metastatic cancer cells to hibernation if steady connection wasn’t established between the new ECM microenvironment and the cancer cells [59, 60].
At last, we constructed a nomogram combining the risk type and other clinical features including age, gender, stage and TNM stage, which can predict an individual’s clinical outcome quantitatively. By using the nomogram, every patient will get total points based on his or her various indicators respectively. The total points will predict the patient’s survival probability in 1, 3-, and 5-year. Obviously, the higher the total points are, the lower the survival probability is.