Our methodological roadmap for this study is presented in Figure 1.
3.1. The mRNA expression levels of LY9 in pan-cancer
To determine whether LY9 expression levels differed between cancerous and paraneoplastic tissues in various tumors, we performed analyses using the Oncomine database. We found that the LY9 mRNA was expressed at low levels in most tumor tissue types compared to corresponding normal tissues, including bladder and other cancers (marked in blue in Fig. 2A). On the other hand, LY9 was highly expressed in cancerous tissues compared to normal tissues in several other tumors (marked in red in Fig. 2A). The risk volcano map results showed that LY9 was a risk factor for Uveal Melanoma (UVM). Meanwhile, LY9 can play a protective role in SKCM, SARC, LUAD, HNSC, CESC, BRCA, and ACC (Fig. 2B). Additionally, we retrieved transcriptomic and clinical data of 33 tumors from the UCSC database for further analysis and validation. Results showed that tumors with higher LY9 expression in normal tissues than in cancerous tissues were BLCA, COAD, LUSC, READ, and THCA (Fig. 2C). However, the tumors with higher LY9 expression, compared to normal tissues, were GBM, KIRC, KIRP, and LUAD (Fig.2 C).
3.2. Prognostic value of LY9 in pan-cancer
We explored the association of LY9 with the overall survival (OS) in patients with various tumors using the Kaplan-Meier plotter (Fig. 3A-F) and GEPIA (Fig. 3G-L) databases. Results showed six tumors significantly associated in both databases, including BRCA, HNSC, LUAD, CESC, LIHC, and OV. Among these tumors, patients with high LY9 expression had longer OS. These results suggested that LY9 was a protective factor for these six cancers.
3.3. Gene Set Enrichment Analysis (GSEA) and correlation analysis of clinical characteristics in LUAD
Further, the Wilcox Tests for LY9 expression in samples from the LUAD dataset (TCGA database) indicated that LY9 was indeed expressed at a higher level in cancerous tissues (Fig. 4A, p = 0.013; B, p = 0.0022). Also, the survival analysis confirmed that patients with high LY9 expression had longer OS (Fig. 4C, p = 0.005). The results of correlation analysis with other clinical characteristics showed that, in LUAD patients, the LY9 expression tended to increase with age (Fig. 4D, p = 0.003). Also, the LY9 expression level was higher in females than in males (Fig. 4E, p = 0.02). Additionally, the overall trend of clinical and TMN staging showed a decreasing LY9 expression trend in early to advanced patients (Fig. 4F-I), consistent with the result that patients with higher LY9 expression in tumors have better prognoses. The univariate independent prognostic analysis results showed that the clinical stage, T-stage, and N-stage were high-risk factors for LUAD patients, while the LY9 expression level was a low-risk factor (Fig. 4J). Similarly, the results of the multifactorial independent prognostic analysis suggested that the expression level of LY9 in LUAD is a protective factor (Fig. 4K). Moreover, we classified the LUAD transcriptome, retrieved from the TCGA database, into high- and low-expression groups, based on the expression of LY9, then performed the GSEA. Results indicated that LY9 was involved in many immune-related and non-small cell lung cancer pathways in the high expression group. We selected the top 11 pathways to draw enrichment maps (Fig. 4L). In the low expression group, LY9 was mainly involved in the biosynthesis of unsaturated fatty acids. These results implied that the prognosis of LUAD patients can be assessed by LY9 expression levels.
3.4. Construction of the lncRNA-miRNA-LY9 ceRNA network
To explore the potential lncRNA-miRNA-mRNA ceRNA action network of LY9, we conducted an analysis using online databases. First, we retrieved LY9-related miRNAs from the targetscan7.2 database for correlation and differential expression analyses and filtered the predicted upstream miRNAs of LY9 according to correlation > 0.1 and p < 0.05. Results showed that seven miRNAs, including hsa-miR-141-3p, hsa-miR-4746-5p, hsa-miR-151a-5p, hsa-miR-17-3p, hsa-miR-301b-5p, hsa-miR-4786-3p, and hsa-miR-629-5p might be potential targets for LY9 (Table 1). Also, we constructed correlation networks for these seven miRNAs (Fig. 5L). Considering that the miRNA with the strongest correlation with LY9 was hsa-miR-141-3p, we further performed correlation, differential, and prognostic survival analyses for hsa-miR-141-3p. Results showed that the expression of LY9 was negatively correlated with hsa-miR-141-3p (Fig. 5A, p = 9.8e-06). Moreover, hsa-miR-141-3p was expressed at higher levels in LUAD tissues than in normal ones (Fig. 5B, p = 2.22e-16), and patients with high hsa-miR-141-3p expression had longer OS (Fig. 5C, p = 0.012). Thereafter, we downloaded hsa-miR-141-3p-related lncRNAs from the DIANA-tools database LncBase Experimental v.2 and performed correlation and differential expression analyses. We found that RNF216P1 and LINC00943 had a correlation > 0.2 and p < 0.05 (Table 2). Then, we performed correlation, differential expression, and survival analyses of RNF216P1 and LINC00943 with LY9 expression. Results showed that the correlation between LY9 and RNF216P1 was not strong and not statistically significant (Fig. 5I, r = 0.055, p = 0.23). Meanwhile, a stronger and significant positive correlation was found between LINC00943 and LY9 (Fig. 5E, r = 0.44, p < 2.2e−16). LINC00943 was also expressed at higher levels in LUAD tissues (Fig. 5F, p = 2.8e-07). This higher expression was associated with better OS (Fig. 5G, p = 0.018). Therefore, we hypothesized that the existence of a ceRNA regulatory network of LINC00943 - hsa-miR-141-3p - LY9 and constructed the lncRNA-miRNA network of LY9 (Fig. 5L). Finally, we constructed the PPI network of LY9 using the GeneCards database (Fig. 5M).
3.5. Correlation of LY9 expression levels in LUAD with immune cell infiltration and immune checkpoints
Through differential expression analysis and exploration of prognostic value in the above databases, we found that, in LUAD, LY9 was differentially expressed in cancerous and paraneoplastic tissues and significantly correlated with prognostic value. Therefore, we selected the TIMER 2.0 database to explore the correlation between the LY9 expression level in LUAD, and the infiltration of various immune cells in the TME. Results suggested a significant positive correlation with the level of B cells, CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells (Fig. 6E). We also used the CIBERSORT algorithm to calculate the relative expression levels of 23 immune cells in each LUAD patient and to calculate the correlation between these immune cells. Then, we used a differential expression approach (i.e., we divided the patients into high and low groups according to the LY9 expression level), then calculated whether there was a difference in immune cell expression between high and low LY9 expression groups. We used a violin plot to show the results (Fig. 6A). Then, we analyzed the correlation between LY9 expression and these 23 immune cells using the Spearman correlation test. Results are shown in the correlation coefficient bar graph (Fig. 6B). These two methods showed that LY9 expression in LUAD was associated with 13 immune cells (Fig. 6C). They were Plasma cells, T cells CD8, T cells CD4 memory resting, T cells CD4 memory activated, T cells regulatory (Tregs), NK cells activated, Monocytes, Macrophages M0, Macrophages M1, Macrophages M2, Dendritic cells activated, Mast cells activated and Neutrophils. We also found that LY9 was significantly positively correlated with the B cell markers CD19 and CD79A, the CD8+T cell marker CD8, the CD4+T cell marker CD4, and the neutrophil marker CCR7 (Fig. 6D). This result indicated that LY9 might be a LUAD biomarker and can be used as a target for immunotherapy. Similarly, the TIMER database results showed that the LY9 expression in LUAD was strongly and positively correlated with the expression levels of CD8 and CD4 T cells (Fig. 6E). Additionally, the GEPIA (Fig. 6F-H) and TIMER (Fig. 6I-K) databases results suggested that the LY9 expression in LUAD was significantly and positively correlated with the immune checkpoints CD274, PDCD1, and CTLA4. Therefore, we hypothesized that the expression status of LY9 might affect the therapeutic effect of immune checkpoint inhibitors in LUAD patients.
3.6. Validation of qRT-PCR experiments
After averaging the Ct value obtained from the three amplifications, the relative quantification was performed by the 2-ΔΔCt method. The results of PCR experiments showed that the expression of LY9 in LUAD tissues was significantly and positively correlated with CD8 and CD4 expressions (Fig. 7A, CD8: R2 = 0.8541, p < 0.001; Fig. 7B, CD4: R2 = 0.4325, p = 0.003). Results showed that the expression of LY9 in LUAD was closely and positively associated with CD8 and CD4 expressions.