Data download, evaluation of immune invasion and comparison of immune cells between cancer and adjacent tumor
In this study, we first from TCGA official website https://portal.gdc.cancer.gov/ We downloaded the mRNA and lncRNA data of osteosarcoma patients, miRNA and corresponding clinical data are UCSC xenahttps://xenabrowser.net/ It is downloaded directly from the database. We used four methods: single sample gene set environment analysis (ssgsea), microenvironment cell populations (MCP) - counter, cibersort and xcell to compare the differences of immune cells between cancer and adjacent tissues. The four methods included different numbers of immune cells (cibersort: 22, ssgsea: 28, MCP counter: 10, xcell: 67). In order to reduce possible errors, we included all four evaluation methods into the study. We used four methods to compare the difference of immune cells between cancer and paracancerous. The results showed that the number of most immune cells in cancer was significantly lower than that in paracancerous (Figure 1A-D), and the immune and matrix scores in cancer were lower than that in paracancerous (Figure 1E).
Correlation of immune cells
We analyzed the correlation of immune cells in tumor and paracancerous samples (Figure 2A-H). The results showed that the correlation of immune cells in tumor and paracancerous samples was significantly different, which may be due to the synergistic infiltration of immune cells activated by cancer antigen. In tumor tissue, the synergistic effect of different immune cells constitutes the tumor immune microenvironment, and plays an important role in tumor invasion and development.
Cell consensus clustering
We classified thyroid cancer samples into different subtypes according to the results of different immune infiltration. The cumulative distribution function (CDF) is drawn to identify the number of optimal subgroups (Figure 3A, B & C). Finally, we identified four different subtypes. We compared the distribution of cells and the expression of immune matrix score between different subtypes by mapping heat map. Among the four subtypes, we compared the differences of tumor purity, immune score and stromal score in innate and adaptive immune mechanisms. We observed that the tumorpurity of cluster3 and 4 was lower than that of the other two subtypes, while the immunescore and stromalscore were higher than those of the other two subtypes (Figure 3D).
Immune matrix scores and immune-related molecules among different subtypes
Immune infiltration in the tumor microenvironment can be assessed by the immune matrix score. We respectively compared the immune matrix scores and the expression levels of immune-related molecules in 4 different immune infiltrating subtypes (Figure 4A-F). According to the ESTINMATE algorithm, the matrix score is between -600 and 1700, and the immune score is between -800 and 3500. Among them, the immune scores of clusters 3 and 4 are significantly higher than those of clusters 1 and 2. This means that immune and matrix scores are meaningful in the classification of subtypes.
Differences in tumor size distribution and grade
The American Joint Committee on Cancer/International Cancer Control (AJCC/UICC) released the 8th edition of the TNM staging system.Where T represents the size of the cancer, N represents the lymph node metastasis or not, and M represents whether there is distant metastasis. However, the TNM staging system alone is not enough to accurately reflect the disease stage of the tumor, and other tumor-related factors have also been paid more and more attention, so the different grades of tumors (stage1-4) are named.We draw a pie chart to understand the difference in tumor size distribution and grading between different subtypes of thyroid cancer (Figure 5A-H). Among them, the ratio of cluster4 at T1 and stage1 is significantly higher than that of other clusters, which suggests that hot tumors have a lower pathological stage than cold tumors.
Analysis of differences between cold and hot tumors
We define clusters 3 and 4 with higher immune matrix scores as hot tumors, and clusters 1 and 2 with lower immune matrix scores as cold tumors. We drew a heat map and found that the immune cell types of thyroid cancer and adjacent samples were significantly different (Figure 6A). We used limma package (|Log2FC| > 2, adjusted P value < 0.05) to identify differentially expressed genes, and drew a volcano map to compare the molecular differences in gene expression between cold and hot tumors (Figure 6B).
Enrichment analysis of differential genes and construction of protein interaction network of differential genes
Through screening, we found 568 up-regulated differential genes and 412 down-regulated differential genes. We use the clusterProfiler package to perform GO enrichment and KEGG pathway enrichment analysis on differential genes related to cold and hot tumors. The up-regulated differential genes are mainly enriched in regulation of leukocyte activation (GO:0002694), T cell activation (GO:0042110), regulation of lymphocyte activation (GO:0051249) and leukocyte cell-cell adhesion (GO:0007159); down-regulated The genes are mainly enriched in hormone metabolic process (GO:0042445), thyroid hormone metabolic process (GO:0042403), hormone biosynthetic process (GO:0042446) and thyroid hormone generation (GO:0006590) (Figure 7A-D). KEGG pathway enrichment analysis showed that the up-regulated differential genes were mainly enriched in Hematopoietic cell lineage (hsa04640), Cytokine-cytokine receptor interaction (hsa04060) and Viral protein interaction with cytokine and cytokine receptor (hsa04061), and the down-regulated differential genes were mainly enriched in Thyroid hormone synthesis (hsa04918), Rap1 signaling pathway (hsa04015) and Cortisol synthesis and secretion (hsa04927) (Figure 7E-H). We used the STRING online tool to construct a protein interaction network (PPI) of differential genes related to cold and hot tumors. Among the up-regulated differential genes, we chose the node connection greater than 20 as the hub gene, and in the down-regulated differential gene we chose the node connection Those greater than 10 are defined as hub genes (Figure 7I: down-regulated differential gene protein interaction network; Figure 7J: up-regulated differential gene protein interaction network). We have 19 up-regulated hub genes and 9 down-regulated hub genes. In the protein interaction network, we have observed a wide range of links between the markers related to the matrix in the differential gene and the markers related to the immune in the differential gene, which may be related to the higher scores of immunity and matrix in hot tumors .
Looking for tumor-related regulatory molecules and enrichment analysis of tumor-related molecules
T cell-based cancer immunotherapy, such as checkpoint suppression or adoptive cell therapy, has greatly changed the way cancer is treated.We know that immunity plays a very important role in the occurrence and development of tumors. We first calculated the differential genes between cancer and adjacent cancers, and then intersected the genes up-regulated in cold tumors (adjpvalue<0.05) with the highly expressed genes in the tumors. A total of 717 genes were negatively regulated immune genes related to cancer. So these genes may be therapeutic targets. Similarly, the genes that are up-regulated in hot tumors (adjpvalue<0.05) are crossed with genes that are low-expressed in cancer. There are 1246 genes in total. These genes are positively regulated immunity, so agonists need to be added during the treatment. We enrich these differential genes. In GO enrichment analysis, the up-regulated differential genes are mainly enriched in: T cell activation (GO:0042110), regulation of T cell activation (GO:0050863), regulation of lymphocyte activation (GO:0051249), external side of plasma membrane (GO:0009897), cytokine activity (GO:0005125), etc. (Figure 8A&B). In the enrichment analysis of the KEGG pathway, the up-regulated differential genes were mainly enriched in: Cytokine-cytokine receptor interaction (hsa04060) and Viral protein interaction with cytokine and cytokine receptor (hsa04061) (Figure 8C&D). The down-regulated differential genes are not enriched in related pathways.
Differences between lncRNA and miRNA in cold and hot tumors
We will compare the expression differences of the downloaded lncRNA and miRNA between the two groups of cold and hot tumors (Figure 9A-D). We used limma package to identify differentially expressed genes, and drawn volcano maps to compare the differentially expressed genes between cold and hot tumors. A total of 629 differential lncRNAs (|Log2FC| > 1, adjusted P value < 0.05) and 114 differential miRNAs (|Log2FC| > 0.5, adjusted P value < 0.05) were obtained.
To better understand the effect of lncRNAs on mRNAs mediated by combination with miRNAs in DTC, we built a ceRNA network based on the above mentioned data and used the ggalluvial R package (Version: 0.9.1) to visualize the network. miRNA target gene prediction passed miRDB, Targetscan, and miRTarBase take the intersection. The circle represents lncRNA (adj<0.05), the diamond represents mRNA (adj<0.05, logFC>1, logFC<0.05), and the triangle represents miRNA (adj<0.05). Finally, the cytohub module calculates the first 5 networks defined as hubnet (Figure 10). Five miRNAs, hsa-mir-204, hsa-mir-128, hsa-mir-214, hsa-mir-150 and hsa-mir-338, are located in the central area of the network, and are of great significance to the immunity of thyroid cancer.