The discovery of gene expression signature in KRAS-oncogene-driven lung cancer
To uncover specific gene expression signature of KRAS-oncogene-driven lung cancer, we unbiased analyzed transcriptional expression profiles of normal lung tissues and KRAS-mutant lung tumor tissues based on GEO datasets (GSE18784, GSE49200), respectively, and identified differentially expressed genes (DEGs) between normal and tumor tissues. As shown in Fig. 1a-b, 155 upregulated DEGs and 120 downregulated DEGs were screened out based on GSE18784 dataset, using “degeR” R package. Using the same method, 25 upregulated DEGs and 45 downregulated DEGs were screened out based on GSE49200 dataset. The signatures between the two DEGs sets were different, indicating the heterogeneity of KRAS-driven tumors. Interestingly, SDPR was the only DEG that decreased in KRAS-mutant tumor tissues based on GEO datasets (GSE18784, GSE49200), which suggests the down-regulation of SDPR might be a specific signature during the development of KRAS-mutant lung cancer.
Structure and phylogenetic conservative analysis of SDPR
SDPR, also named CAVIN2, was a member of CAVIN family that located at chr 2, q32.3 (Fig. 1c). The structures of SDPR gene include 5’UTR exon, 2 exons, 3’UTR exon and 1 intron. Protein sequences were compared to explore the conservative of SDPR during molecule and species evolution, and the alignment results showed that Homo sapiens SDPR share 82.82%, 81.88%, 99.53%, 96%, 87.29% and 89.18% identity to Mus musculus, Rattus norvegicus, Pan troglodytes, Macaca mulatta, Sus scrofa and Felis catus, respectively, which indicates that SDPR is highly conserved in mammals (Fig. 1d).
CAV and CAVIN family members play important roles in the formation and stability of pulmonary alveoli (33). Moreover, CAVIN members could regulate the expression of CAV members. Thus, the phylogenetic conservative of CAV and CAVIN family members were analyzed. As shown in Fig. 1e, CAV and CAVIN family members divide into two major clusters, and CAVIN2 shares a closer evolutionary relationship with CAVIN3, compared with CAVIN1 and CAVIN4.
SDPR were down-regulated in human lung adenocarcinomas including KRAS-mutant group
To identify the SDPR expression level in mouse and human lung tissues and tumors, we established KRAS-oncogene-driven lung cancer models (30) and detected SDPR expression using real-time qPCR. As shown in Fig. 2a, higher SDPR expression was detected in pulmonary rather than bronchial tissue. Moreover, lower SDPR were expressed in KRAS-mutant tumor tissues (P < 0.05). We further confirmed SDPR expression in human tissues and found a similar result in KRAS-mutant tumors. As shown in Fig. 2b-e, SDPR significantly decreased in KRAS-mutant specimens as well as all lung tumors compared with normal tissues (P < 0.05). In addition, low SDPR expression was detected in KRAS-mutant and KRAS-wildtype NSCLC cell lines compared with the immortalized normal lung cells, MRC-5 (Fig. 2f-h).
Low expression of SDPR was associated with a Poor Prognosis for NSCLC Patients
As shown in Fig. 3a-c, low expression of SDPR was associated with shorter overall survival (OS) in NSCLC patients as well as KRAS-mutant group, based on GEO dataset and lung cancer microarray (GSE72094, HLugA180Su02, P < 0.05). Similar results were found in NSCLC patients using GEPIA (Fig. 3d, P < 0.05). Meanwhile, results using univariate survival analysis indicated that low SDPR expression was associated with the shorter OS in NSCLC patients as well as KRAS-mutant group (KRAS-mutant lung adenocarcinoma, P < 0.05, hazard ratio [HR] = 0.7; lung adenocarcinoma, P < 0.05, hazard ratio [HR] = 0.7; Table. 1). Moreover, results using multivariate survival analysis showed that SDPR expression and stage were independent predictors for prognosis in lung adenocarcinoma patients as well as in KRAS-mutant group (Table. 1). These data highlights the prognostic values of SDPR in human lung adenocarcinoma, especially in KRAS-mutant subgroups.
Construction of competing endogenous RNA (ceRNA) network of SDPR in KRAS-mutant lung adenocarcinoma pathway
To identify the upstream regulatory structure of SDPR in KRAS-mutant lung cancer, DEGs based on GSE72094 and three public predicted websites (TargetScan, miRDB and miranda) were used (Fig. 4a). Briefly, 139 expression profiles of KRAS-mutant patients with complete clinical information were collected (GSE72074), and DEGs sets between low and high SDPR group were screened out using “degeR” R package. Three public websites, TargetScan, miRDB and miranda, were used to predict potential combinations between SDPR and transcription factors. As shown in Fig. 4a, 2 transcription factors (DACH1, WT-1) were identified based on DEGs and TargetScan websites. Moreover, SDPR were positively correlated with DACH1 (R2 = 0.509, P < 0.01; Fig. 4b) while negatively correlated with WT-1 (R2=-0.218, P < 0.05; Fig. 4c).
Similar to the above screening method of transcription factors, a set of miRNAs were predicted, and 5 miRNAs (has-miR-1, has-miR-204, has-miR-144, has-miR-105 and has-miR-363) were ultimately screen out that both observed in the above 4 miRNA sets (Fig. 4d). All of them were down-regulated in KRAS-mutant lung adenocarcinomas compared with normal tissues (Fig. 4e). Interesting, we found that there are some potential complementary sequences between has-miR-1 and DACH-1 (Fig. 4f), which indicates the above miRNAs and TFs may form a complex network to regulate SDPR expression. Thus, we screened a series of miRNAs with potential combination sequence with SDPR-related TFs, and constructed a competing endogenous RNA (ceRNA) network of SDPR in KRAS-mutant lung adenocarcinoma (Fig. 4g).
Biological enrichment analysis of SDPR downstream pathway
To explore the downstream pathway of SDPR, DEGs based on GSE72094 were explored to identify biological differences between SDPR low and high tissues in KRAS-mutant lung cancer. The gene ontology analysis were performed using DAVID online software to unfold the biological function of biological process, cellular component and molecule function among the above DEGs. As shown in Fig. 5a-c, biological processes were mainly associated with cell mitosis and cell cycle, and the differences of cellular components were mainly located in extracellular such as extracellular space, exosome and matrix. In addition, there were a serial of members related to redox balance and energy transfer, which indicated the close interaction between SDPR expression and metabolism. Moreover, GSEA analysis results showed that G2 pathway, TGF-beta pathway were most likely associated with the above DEGs (Fig. 5d-e).
Correlation between SDPR, immune negative regulatory molecules and immune infiltration models
Recently, SDPR was reported to play a vital role in cancer progression and metastasis via epithelial mesenchymal transition (EMT) in gastric and breast cancer (25, 34). However, the function of SDPR on lung cancer, especially for KRAS-mutant group, remains unclear. Since different SDPR expression level are accompanied with changes of extracellular cellular components (Fig. 5c), we hypothesized that SDPR expression may be closely related with tumor environments. Thus, we explored the correlation between SDPR and immune checkpoint molecules and immune infiltration models.
As shown in Fig. 6a, SDPR was negatively correlated with PD-L1(CD274), GITR(TNFRSF18), 4-1BBR(TNFRSF9) and TDO2 (R2 =-0.247, -0.327, -0.183, -0.233, respectively, P < 0.05). Since the role of SDPR in immune infiltration is rarely to be understood, we analyzed the abundance of immune cells in lung cancers at different SDPR expression levels and copy number variation (CNVs) patterns. As for the KRAS-mutant subgroups, cancer tissues with lower expression of SDPR may accompanied with less infiltration of γ T cells and resting mast cells but higher abundance of plasma cells, CD4+ memory activated T cells and M1 macrophages (Fig. 6b). Meanwhile, SDPR expression in lung adenocarcinoma may be positively correlated with infiltration of memory B cells, endothelial cells, M1and M2 macrophages, myeloid dendritic cells, neutrophils, memory resting CD4+ T cells, CD8+ T cells, but negatively correlated with M0 macrophages, plasma B cell, CD4+ memory activated T cells based on TIMER 2.0 website (Table. 2). In addition, lung adenocarcinoma tumors with SDPR arm-level deletion suggests to have less infiltration CD4+ T cells, macrophages and neutrophils in TME (Fig. 6c).
These results illustrated close relationship between SDPR, PD-L1(CD274), GITR(TNFRSF18), 4-1BBR(TNFRSF9), TDO2, and abundance of immune cells in human lung adenocarcinoma, especially in KRAS-mutant subgroups.