Clear cell renal cell carcinoma (ccRCC) is the most common histological type of RCC accounting for 90% of kidney neoplasms [23]. We plan to explore new reliable method for survival prediction and treatment of ccRCC. Nowadays, many studies have reported that the prognosis of ccRCC has connections with genetic factors, and some genes may provide method for predicting prognosis and selecting treatments [24–28]. Today, with the development of microarray technology, some potential prognostic and therapeutic targets have been found. We find CLOGAT1 may has connection with some metabolism pathways and immune infiltrations which have impacts on tumor. Hence, our present study focused on the prognostic role of COLGALT1 in ccRCC.
In this study, we identified the COLGALT1 expression between the ccRCC tissues and the adjacent normal tissues from the TCGA database. Based on qRT-PCR, the COLGALT1 mRNA expression was high in ccRCC cell line Caki-1 compared with human kidney cell. These results revealed that COLGALT1 shows obviously higher expression in ccRCC tissues than that in adjacent normal tissues. Furthermore, we explored the association among COLGALT1 expression and patient survival based on the database showing that high expression of COLGALT1 indicated a bad prognosis of ccRCC. Additionally, high COLGALT1 expression was significantly associated with better pathologic stage, histological grade, T stage, M stage, and satisfactory survival time. The COLGALT1 expression level was proved to be an independent predictive factor of overall survival for ccRCC patients. In order to explore how COLGALT1 was involved in ccRCC pathogenesis, we carried out GSEA between tissues with different COLGALT1 expression levels and found that some pathways including butanoate metabolism, fatty acid metabolism, histidine metabolism, ppar signaling pathway, propanoate metabolism, pyruvate metabolism and tryptophan metabolism are differentially enriched in COLGALT1 high expression phenotype. We further investigated the relationship of COLGALT1 and related genes based on TCGA dataset and discovered that the five most possitively relevant genes are BMP1, CD276, IMPDH1, PPP1R18 and RHBDF2. In contrast, the five most negatively relevant genes are AUH, FDX1, MICU2, MOCS2 and RBM47. Also, we find the relationship between the COLGALT1 with ten mostly relevant other genes through the PPI network. And the COLGALT1 is related to Microsatellite Instability, Tumor Mutational Burden in ccRCC based on the TCGA databse. And, we explore the potential connection between the COLGALT1 with the tumor microenvironment and immune infiltrations in various tumors. Finally ,we find the COLGALT1 is significantly related to these aspects including immune cell infiltration, DNA methyltransferase, immune checkpoint molecules immune cells and mismatch repair protein among ccRCC.
We search some relevant literature for the progress of the COLGALT1. The galactosyltransferases COLGALT1 and COLGALT2 in the endoplasmic reticulum initiate the glycogen glycosylation reaction. Mutations in the COLGALT1 gene can cause abnormalities in cerebral small blood vessels and malformations of the nostrils, which are common in type IV collagen deficiency. These advances are focused on muscle and small vessel diseases [29–31]. As we all know, alterations in mitochondrial metabolism have been described as one of the major factor of both ageing cells and cancer [32]. This may be one of the reasons that the COLGALT1 are related to ccRCC. However, We still need further study and analysis to explore the possible relationship.
In our study, we discovered that high COLGALT1 expression had association with butanoate metabolism, fatty acid metabolism, histidine metabolism, ppar signaling pathway, propanoate metabolism, pyruvate metabolism and tryptophan metabolism by GSEA. These pathways are crucial biological processes in tumorigenesis and development of tumor. We can find that these pathways are mainly focused on metabolic pathways. Over these years, many studies have begun to explore the molecular and cellular mechanisms that constitute the interaction between nutrition, amino acid, fat metabolism and effects on cancer [33, 34]. Given the research background of metabolism, there are many articles focused on finding potential ways to treat or even cure cancer [35–37]. COLGALT1 as one gene enriched in these metabolic pathways, which may play a certain role in tumor metabolism and the body's immune process. However, further experiments are needed to verify it.
Ten most significantly relevant geneswere discovered in this study. Five genes (BMP1, CD276, IMPDH1, PPP1R18 and RHBDF2) were possitively correlated with GOLGALT1 expression. Recently, outstanding research has provided new clues to the function of CD276 (B7H3) in cancer, and determined that CD276 is the key to tumor cell proliferation, migration, invasion, epithelial-mesenchymal transition, cancer stemness, drug resistance and Warburg effect Promoter.[38]. Also, one article reports that RHBDF2 may be linked to the esophageal cancer [39]. On contrary, Five genes (AUH, FDX1, MICU2, MOCS2 and RBM47) were negatively correlated with GOLGALT1 expression. Among them, three genes (AUH, FDX1, MICU2) are related to mitochondrial energy metabolism [40–42]. As we mentioned earlier, mitochondria are closely related to tumor progression. This again proves that COLGALT1 may act on tumors through metabolic pathways. Also, we find the relationship between the COLGALT1 with ten mostly relevant other genes through the PPI network. Further verification need to be carried out to confirm the relationship between them.
Immune response were shown to be linked with various tumors importantly. In COAD, a mouse model showed depletion of CD25+, CD4 + regulatory T cells able to enhance the anti-tumor immunity induced by interleukin2 [43]. CD4 + T cells were found to play significant role in the progression and metastasis of lung cancer [44]. Tumor-infiltrating naive CD4 + T cells were proved to be conneted with poor survival in bladder cancer [45]. Here, We explorer the connection between the GOLGALT1 and six different immune cells including B cell infiltration, CD8 + T cell infiltration, CD4 + T cell infiltration, macrophage infiltration, neutrophil infiltration, and dendritic cell infiltration, respectively in ccRCC. The COLGALT1 was indicated to be positively correlated with these immune cells (P < 0.01). The methyltransferase including DNMT1, DNMT2, DNMT3a and DNMT3b was positively correlated with the COLGALT1 in different tumors (P < 0.01).
Immune checkpoints provide a general mechanism for various cancers to avoid immune surveillance and play a significant role in the immune system. In lung cancer, anti-CTLA-4 and anti-PD-1/PD-L1 blocking antibodies have shown to be successful for treatment. In addition, in lung cancer, there are some recognition markers for early response, such as TCR library, CD4 + / CD8 + T cell profile, cytokine markers and the expression of immune checkpoint molecules in tumor cells, macrophages or T cells [46, 47]. Here,we find COLGALT1 is related to MSI, TMB in ccRCC. Also, we explored immune microenvironment in tumor tissue through three aspects including immune checkpoint molecules, immune cells and mismatch repair protein respectively by using the CIBERSORT algorithm. The COLGALT1 is significantly connected with the immune checkpoint molecules like BTLA, CD28, CD40, NRP1 ect in ccRCC. Aslo, the COLGALT1 is linked to relevant immune cells including Actived memory B cell, Actived CD4 T cell, Actived CD8 T cell ect. The relationship between the mismatch repair protein including MLH1, MSH2, MSH6, PMS2 with the COLGALT1 was investigated showing potential connection among them in ccRCC. These results may offer help in the development of ccRCC treatments.
Our study has some limitations. First, our results come from the TCGA database and generated by bioinformatic analysis. Considering various factors (region, age, gender, race, etc.) and the heterogeneity in the analysis process, the sample size of renal clear cell carcinoma cannot guarantee sufficient. Therefore, the results of our study need to be verified with enough clinical samples. Also, we need further investigations to validate our results based on ccRCC samples and clinical data. Second, the relationship between COLGALT1 expression with the ccRCC and these signaling pathways is the first to be reported, and the regulatory mechanism needs to be further investigated. Therefore, further experiments need to be carried out to explore whether progression in ccRCC should be affected by COLGALT1 through metabolic pathways or other possible pathways.