Expression analysis data
First, we analyzed the expression profile of lncRNA FAM99A gene in the different cancers, enrolled in the TCGA project. As shown in Fig.1A, FAM99A was specifically expressed in the tissue samples of TCGA-LICH or TCGA-CHOL cohorts. Then, we compared the expression difference between the tumor tissues and corresponding controls in the TCGA-LICH/CHOL cohorts. The data indicated the reduced expression level of FAM99A gene in the hepatocellular carcinoma (Fig.1B, P<0.05) or cholangio carcinoma (Fig.1C, P<0.05) tissues, compared with the control tissues. Moreover, we observed the correlation between the FAM99A expression and the pathological stages of liver hepatocellular carcinoma cases (Fig.1D), but not cholangio carcinoma cases (Fig.1E). Therefore, these suggested the potential role of lncRNA FAM99A gene in the etiology of hepatic cancer or cholangio carcinoma.
Survival curve analysis data
Next, we tried to analyze the association between FAM99A expression status and clinical prognosis for hepatocellular carcinoma and cholangio carcinoma. Due to the lack of survival data for the cholangio carcinoma cases, we only focused on the hepatocellular carcinoma. We observed the lower rates of overall survival (Fig.2A, HR=0.56, P=0.0014), relapse free survival (Fig.2B, HR=0.63, P=0.011), progress free survival (Fig.2C, HR=0.62, P=0.0035), disease specific survival (Fig.2D, HR=0.56, P=0.015), in the FAM99A high expression group, compared with the high expression group. In addition, we fully considered the effect of different clinical factors, such as the pathologic stages, grade, vascular invasion, or sorafenib treatment, in the above correlation. We performed survival curve analysis after grouping the samples by different clinical factors. As shown in Table 1 and Table S1-S3, we observed the relationship between FAM99A low expression and the worse survival in the subgroups of “pathologic stage 3”, “grade 3”, “AJCC_T3”, and “male” (all HR<1, P<0.05), but not female subgroup (all P>0.05). These results provide evidence regarding the association between FAM99A low expression and poor clinical outcomes of hepatocellular carcinoma, which warrants a more in-depth molecular mechanism investigation.
CNV analysis data
Herein, we analyzed the CNV status of the lncRNA FAM99A gene. lncRNA FAM99B was also examined. As shown in Fig.3A-B, we did not observe the copy number variations in the majority of hepatic cancer cases, and the heterozygous amplification/heterozygous deletion in the limited cancer cases. Furthermore, we did not detect a strong correlation between CNV and expression of the lncRNA FAM99A gene (Fig.3C). Thus, copy number variations of the FAM99A gene may not play an essential role in hepatic tumorigenesis.
DNA methylation analysis data
We attempted to exploit the potential molecular mechanism from the point of FAM99A DNA methylation. Based on the methylation data of TCGA-LIHC, we found that FAM99A gene expression were negatively correlated with the methylation signal values of five methylation probe sites, including cg24218935 (Fig.4, r=-0.397, P<0.001), cg01745044 (r=-0.359, P<0.001), cg04353359 (r=-0.564, P<0.001), cg04938738 (r=-0.421, P<0.001), cg25356611 (r=-0.395, P<0.001). This suggested the potential role of FAM99A DNA methylation in the hepatic tumorigenesis.
Immune cell infiltration analysis data
Also, we aimed to investigate whether the FAM99A gene is involved in the etiology of hepatic cancer through immune cell infiltration by GEPIA2 tool. As shown in Fig.5, the expression of FAM99A gene was slightly correlated with the infiltration level of native T cells (P=3.8e-05, r=-0.20), Th1 like cells (P=0.0074, r=-0.13), native T cells (P=0.0038, r=-0.14), but not others.
Enrichment analysis data
Finally, we utilized the LinkedOmics approach to screen out a group of FAM99A expression-correlated negatively genes (e.g., SLC2A1, BZW2, TSN, KIAA0114, and CCT4, etc.) and positively related genes (e.g., FAM99B, SLC22A7, HSD17B13, C14orf68, HAO2, etc.) in Fig.6A. We then performed the GSEA for the category of reactome pathway. As shown in Fig.6B, positively related pathways (e.g., complement cascade, fatty acid metabolism, etc.) and negatively related pathways (e.g., metabolism of RNA, M phase, etc.) were obtained. Enrichment plots of complement cascade and metabolism of RNA were shown in Fig.6C as examples.
Furthermore, GO analysis data (Fig.7) presented a series of FAM99A-correlated issues of biological process (e.g., protein activation cascade, drug metabolic process, etc.), cellular component (e.g., extracellular organelle, mitochondrion, etc.), and molecular function (e.g., oxidoreductase activity, RNA binding, etc.). KEGG analysis (Fig.8) further showed the enriched pathways, such as metabolic pathways, PPAR signaling pathway, cell cycle.