Expression of ATG101 in pan-cancer
First, we compared tumour samples and paracarcinoma tissues from the TCGA database, normal samples from the GTEx database and cancer cells from the CCLE database to evaluate the mRNA expression characteristics of ATG101 in humans. We found that the expression of ATG101 varied in different normal tissues (Figure 1A) and cancer cells (Figure 1B) after collating the tumour tissues and paracarcinoma tissues from the TCGA database. We found that the expression of ATG101 was upregulated in BRCA, CHOL, COAD, ESCA, HNSC, LIHC, LUAD, LUSC, PRAD, READ, STAD, and UCEC. ATG101 was downregulated only in KIRP (Figure 1C). Then, we analyzed the difference in ATG101 expression between normal samples from the GTEx database and tumour tissues from the TGGA database and found that ATG101 expression was upregulated in BRCA, CESC, CHOL, COAD, GBM, HNSC, KICH, LGG, LIHC, LUSC, OV, PAAD, PRAD, READ, SKCM, UCEC and UCS. However, its expression was found to be downregulated in ESCA, LAML, LUAD, SKCM, STAD and TGCT (Figure 1D).
Correlation analysis between ATG101 expression level and prognostic value
The characteristics of ATG101 expression at the mRNA level suggested that ATG101 may be a valuable target for pan-cancer. Therefore, we further used Kaplan–Meier analysis to explore the correlation between ATG101 mRNA expression levels and the survival outcomes (including the OS, DSS, DFS and PFS rates) of different cancers from the TCGA database. Our results demonstrated that upregulated ATG101 expression was associated with a shorter OS rate in ACC, CHOL, LGG, LIHC and MESO (Figure 2 A-E). In addition, upregulation of ATG101 expression was related to a shorter DSS rate in ACC, COAD, KIRP, LGG, LIHC, MESO and READ (Figure 2 F-L). High ATG101 expression was associated with a poor DFS rate in ACC, CHOL and LUSC, while low ATG101 expression was associated with a poor DFS rate in USC (Figure 2 M-P). Furthermore, the high expression of ATG101 was also associated with a poor PFS rate in ACC, COAD, KIRP and LIHC (Figure 2 Q-T). These results confirmed that the high expression of ATG101 is negatively correlated with the prognosis of most malignant tumours.
Correlation analysis between ATG101 expression and immune cells
We explored the relationship between ATG101 expression and immune infiltration in different tumours after investigating the relationship between ATG101 expression and cancer prognosis. An analysis of six tumour-infiltrating immune cells (B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages and dendritic cells) was conducted. With the cut-off of p value of 0.05, ATG101 expression was positively correlated with tumour-infiltrating immune cells. In LIHC (Figure 3 A), ATG101 expression was positively correlated with B cells (correlation coefficient=0.37, p value=1.4e-13), CD4+ T cells (correlation coefficient=0.313, p value=6.31e-10), CD8+ T cells (correlation coefficient=0.284, p value=2.5e-08), dendritic cells (correlation coefficient=0.416, p value=4.5e-17), macrophages (correlation coefficient=0.407, p value=2.75e-16) and neutrophils (correlation coefficient=0.398, p value=1.31e-15). In LUSC (Figure 3 B), ATG101 expression was positively correlated with B cells (correlation coefficient=0.098, p value=0.0284), CD4+ T cells (correlation coefficient=0.146, p value=0.00107), CD8+ T cells (correlation coefficient=0.142, p value=0.00149), dendritic cells (correlation coefficient=0.152, p value=0.000633), macrophages (correlation coefficient=0.156, p value=0.000472) and neutrophils (correlation coefficient=0.214, p value=1.34e-06). In PCPG (Figure 3 C), ATG101 expression was positively correlated with B cells (correlation coefficient=0.295, p value=5.31e-09), CD4+ T cells (correlation coefficient=0.243, p value=0.000931), CD8+ T cells (correlation coefficient=0.164, p value=0.0265), dendritic cells (correlation coefficient=0.413, p value=8.41e-09), macrophages (correlation coefficient=0.38, p value=1.41e-07) and neutrophils (correlation coefficient=0.368, p value=3.7e-07).
Relationship between ATG101 expression and the tumour immune microenvironment
Based on the correlation between immune cells and ATG101 expression, we also explored the relationship between ATG101 expression and the tumour immune microenvironment. Our results showed that ATG101 expression was significantly positively correlated with the immune score in BLCA, BRCA, CESC, COAD, DLBC, ESCA, GBM, HNSC, KICH, KIRC, LAML, LGG, LIHC, LUAD, LUSC, PCPG, SARC and UVM (Figure S1). ATG101 expression was significantly positively correlated with stromal score in BLCA, BRCA, CESC, COAD, GBM, HNSC, KICH, KIRP, LGG, LIHC, LUAD, LUSC, MESO, PCPG, PRAD, SARC, STAD, TGCT, THCA, THYM, UCEC and UVM (Figure S2). Moreover, ATG101 expression was significantly positively correlated with the ESTIMATE score in BLCA, BRCA, CESC, COAD, ESCA, GBM, HNSC, KICH, LAML, LGG, LIHC, LUAD, LUSC, MESO, PCPG, SARC and UVM (Figure S3). ATG101 expression was found to be significantly positively correlated with all three scores in BLCA, BRCA, CESC, COAD, GBM, HNSC, KICH, LGG, LIHC, LUAD, LUSC, PCPG, SARC and UVM. Overall, ATG101 expression had the third highest correlation with stromal score in LUSC (r = -0.212, P < 0.001), TGCT (r = -0.353, P < 0.001), and PCPG (r = 0.315, P < 0.001). ATG101 expression had the third highest correlation with immune score in LUAD (r = -0.174, P < 0.001), LUSC (r = -0.212, P < 0.001), HNSC (r = -0.142, P < 0.001) and the third highest correlation with the ESTIMATE score in LUSC (r = -0.212, P < 0.001), LUAD (r = -0.174, P < 0.001), PCPG (r = 0.315, P < 0.001) (Figure 4).
Relationship between ATG101 expression and immune checkpoint gene expression in various cancers
The immune checkpoint is an inhibitory signalling pathway in the immune system that regulates the intensity and persistence of the immune response in peripheral tissue, prevents tissue injury and plays an important role in maintaining self-antigen tolerance. We used the mRNA sequence database to assess whether there was an association between the expression level of ATG101 and 47 common immune checkpoint genes. We found that ATG101 was highly correlated with immune checkpoint genes in various cancers. In BRCA, COAD, LIHC, LUAD and PCPG, we found that ATG101 and other immune checkpoint genes were significantly coexpressed. However, the expression of ATG101 was negatively correlated with all forty-seven immune checkpoint genes in CHOL, DCBC, TGCT and UCS (Figure 5 A).
Relationship between ATG101 expression and TMB and MSI in various cancers
Classic immune checkpoint therapeutic targets, including PD-1, PD-L1 and CTLA-4, are widely targeted clinically and these therapies have achieved satisfactory effects. TMB and MSI are important evaluation indexes directly related to the efficacy of immune checkpoint therapies such as PD-1 blockade. We found that TMB was significantly correlated with the expression levels of ATG101 in COAD, HNSC, LGG, LUAD, SARC, SKCM, STAD, THCA and THYM (P < 0.05). In addition, UCEC had the lowest TMB score, and SKCM had the highest. (Figure 5 B). The results showed that ATG101 expression is negatively correlated with the hypermutation state in UCE but positively correlated with the hypermutation state in SKCM. We also researched the correlation between ATG101 expression and MSI in various cancers. We found that ATG101 expression was significantly correlated with MSI in COAD, DLBC, HNSC, KICH, KIRC, KIRP, READ, SARC and SKCM (P<0.05), while DLBC had the highest coefficient and CHOL had the lowest score (Figure 5 C).
Relationship between ATG101 expression and neoantigens in various cancers
Neoantigens are new antigens produced by tumour cells after tumour mutation and are different from those expressed by normal cells. The more neoantigens are produced, the easier it is for tumour cells to be recognized and attacked by immune cells, which is more conducive to the development of new targeted drugs. The development of bioinformatics technology has accelerated the identification of neoantigens. Here, we further analyzed the relationship between ATG101 expression and pan-cancer neoantigens to provide a reference for new immunotherapies for tumours. Our results showed that ATG101 expression was positively correlated with neoantigens in BRCA, KIRC, READ, HNSC, LIHC, SKCM and CESC (P < 0.05). However, in GBM, OV, LUAD, LUSC, KIRP, UCEC, COAD, STAD, THCA, BLCA, PRAD and LGG, ATG101 expression was negatively correlated with neoantigens (Figure S4).
Relationship between ATG101 expression and the expression of four methyltransferases in various cancers
DNA methylation is a physiological process that changes chromatin structure, DNA conformation, DNA stability, and the interaction between DNA and protein through the action of DNA methyltransferase. It is one of the main mechanisms by which gene expression is regulated. DNA methylation is considered to be one of the main factors affecting tumour occurrence and development. In our research, we explored the correlation between ATG101 expression and the expression of four methyltransferases (DNMT1, DNMT2, DNMT3A and DNMT3B) (Figure 6). In STAD, THCA, THYM, UCEC, UVM, ACC, BLCA, BRCA, CESC, CHOL, COAD, DLBC, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LGG, LIHC, LUAD, LUSC, MESO, OV, PAAD, PCPG, PEAD and SKCM, ATG101 expression was positively correlated with DNA methylation (P < 0.05). In contrast, ATG101 expression was negatively correlated with DNA methylation in UCS, SARC, and TGCT (P < 0.05).
Gene set enrichment analysis of ATG101 in COAD and LIHC
After preliminary exploration of the genetic and epigenetic mechanisms of ATG101, we used GSEA to research the function of ATG101 in COAD and LIHC. In COAD, ATG101 can inhibit proteasome, RNA polymerase, BASE excision repair, DNA replication, and ribosome (Figure 7 A), but promote Wint signaling pathway, ERBB signaling pathway, Adherens junction, and aldosterone regulated sodium reabsorption (Figure 7 B). While in LIHC, ATG101 can inhibit Base excision repair, spliceosome, RNA degradation, cytosolic DNA sensing pathway, and epitheliak cell signaling in helicobacter pylor (Figure 7 C), but promote complement and coagulation cascades, butanoate metabolism, valine leucine and isoleucine degradation, drug metabolism cytochrome P450, and fatty acid metabolism (Figure 7 D). As for transcription factor level, ATG101 can promote the function of transcription factor 4, androgen receptor, signal transducer and activator of transcription 5A and YY1 transcription factor (Figure 7 E). In addition, ATG101 can inhibit the function of members of the ETS oncogene family, v-myc avian myelocytomatosis viral oncogene homologue, cAMP responsive element binding protein 1, v-ets avian erythroblastosis virus E26 oncogene homologue and early growth response 1 (Figure 7 F). In LIHC, ATG101 can affect the function of v-myc avian myelocytomatosis viral oncogene homologue, GA binding protein transcription factor alpha subunit, ETS oncogene family, v-myc avian myelocytomatosis viral oncogene homologue and v-myc avian myelocytomatosis viral oncogene homologue (Figure 7 G). The transcription factor binding information for ATG101 was then downloaded from ChIPBase, and the genes enriched by GSEA were intersected with the genes identified from ChIPBase. Twenty-one common genes related to ATG101, COAD and LIHC were obtained (figure 7H).