Over-expression of RFCs family was found in Brain and Central Nervous System (CNS) cancer patients.
At the beginning of our study, we looked into the different mRNA expressions of RFCs between Brain and CNS cancer tissues and normal tissues via ONCOMINE database. A comprehensive view of RFCs expression in different types of cancers was shown in Fig. 1. Moreover, focusing on Brain and CNS cancer, several datasets in table 1 demonstrated that the mRNA of RFC1/2/3/4/5 was significantly over-expressed in cancer tissues rather than normal tissues. RFC1 not only showed elevated expression in Desmoplastic Medulloblastoma (fold change = 1.894) via Pomeroy Brain dataset(41), but also in Glioblastoma (fold change = 1.594) through TCGA Brain dataset. The mRNA expression of RFC2 raised in Glioblastoma in different datasets, including Bredel Brain 2 (fold change = 1.769)(42), Sun Brain (fold change = 1.679)(43), TCGA Brain (fold change = 1.706) and Murat Brain (fold change = 1.719)(44). RFC3 transcription was found increased in Glioblastoma in Liang Brain (fold change = 1.617)(45). In French Brain dataset(46), RFC3 was found increased in Anaplastic oligoastrocytoma (fold change = 3.709) and Anaplastic Oligodendroglioma (fold change = 2.454). Bredel et al.(42) also reported a raise of RFC3 both in Anaplastic Oligodendroglioma (fold change = 2.024) and Oligodendroglioma (fold change = 1.781). Glioblastoma exhibited higher expression of RFC4 in Murat Brain (fold change = 2.572)(44), Bredel Brain 2 (fold change = 1.634)(42), Sun Brain (fold change = 1.75)(43) and Liang Brain (fold change = 1.813)(45). Moreover, RFC4 also showed an enhanced expression in Anaplastic Oligodendroglioma (fold change = 2.293)(46), Anaplastic Astrocytoma (fold change = 1.543)(43) and Oligoastrocytoma (1.753)(45). Sun et al. discovered an increased expression of RFC5 in Glioblastoma (fold change = 1.800), Anaplastic Astrocytoma (fold change = 1.799) and Oligodendroglioma (fold change = 1.600). French et al. demonstrated that RFC5 mRNA expression was stimulated in Anaplastic Oligodendroglioma (fold change = 1.831) and Anaplastic Oligoastrocytoma (fold change = 2.51). What’s more, over-expressed RFC5 was also detected in Glioblastoma respectively through Murat Brain dataset (fold change = 2.431) and TCGA Brain dataset (fold change = 3.683). Pomeroy et al. also reported an increase of RFC5 in Desmoplastic Medulloblastoma (fold change = 1.894).
The mRNA expression of RFCs was elevated in low-grade glioma (LGG) patients.
To further explore the expression of RFCs in low-grade glioma (LGG) and normal tissues, we conducted an analysis via GEPIA and GTEx databases, which were entirely different bases from Oncomine. From the expression profile showed in Fig. 2, RFC1 and RFC3 showed significantly higher expression in LGG than normal tissues (p < 0.05). Although RFC2, RFC4, RFC5 got higher average expression in LGG tissues, the differences were lack of statistical significance (p > 0.05).
Then, the protein expression patterns of RFCs were analyzed on the Human Protein Atlas database. As the immunohistochemical photos displayed in Fig. 3, higher expression of RFC2, RFC4, RFC5 were detected in brain glioma than in normal brain tissues, while RFC1 and RFC3 were both considered high expression in brain glioma tissues and normal brain tissues.
In conclusion, the mRNA expression and protein expression of certain RFCs were enhanced in LGG patients.
Correlation between expression profiles of RFCs and the pathological grades in low-grade glioma.
After demonstrated the enhanced expression of RFCs in LGG, we wondered if there was a correlation between RFCs expression and higher pathological grade in LGG. To seek for the answer, we analyzed the data on Oncomine and GEPIA. As data presented in Fig. 4A, RFC1, RFC2 and RFC3 showed a significantly higher expressed in grade 3 LGG patients than in patients with grade 2 LGG, while the results in RFC4 and RFC5 were lack of statistical significance. Moreover, in Sun Brain dataset(43), higher mRNA transcriptions of RFC2, RFC3, RFC4 and RFC5 were detected in patients with grade3 or grade4 tumors than in grade2 tumors (Fig. 4B). In short, the expression profiles of RFCs showed a significant correlation with the pathological grades of LGG patients.
The prognostic value of RFCs expression in LGG patients.
To find out if higher RFCs expression were significantly correlated to the shorter overall survival (OS) and disease-free survival (DFS), we analyzed the expression profiles and the clinical data of LGG patients in GEPIA. As shown in Fig. 5, the cutoff of high expression group was 75% while the cutoff of low expression group was set for 25%. Surprisingly, except for the correlation between DFS and the expression of RFC4 was lack of statistical significance (p > 0.05), other expression of RFCs showed negative association with OS and DFS in patients with LGG. In other words, all 5 RFCs were associated with poorer prognosis. It was clear that higher expression of RFCs was correlated with shorter OS and DFS.
Prognostic significance of RFCs genetic mutations and the correlation between the expression of RFCs in LGG patients.
Next, we assessed the mutations of RFCs in LGG patients by the online tool cBioPortal for low-grade glioma based on TCGA. As Fig. 6A and 6B showed, RFCs were altered in 41.79% of patients with Anaplastic Astrocytoma, 32.26% of patients with Astrocytoma, 16.22% of patients with Oligoastrocytoma, 15.56% of patients with Anaplastic Oligoastrocytoma and in 9.09% of patients with Oligodendroglioma. Next, we analyzed the RFCs mutation with the patient’s prognosis in LGG. As in Fig. 6C, we discovered an evident correlation between the higher mutations of RFCs and the lower overall survival in patients with LGG (p = 0.0241) while the correlation between the mutations of RFCs and the disease-free survival was lack of statistical significance. Then we evaluated the correlation between RFCs one to one by GEPIA, the result showed significantly positive association between RFC1 and RFC2, RFC1 and RFC3, RFC1 and RFC4, RFC1 and RFC5, RFC2 and RFC3, RFC2 and RFC4, RFC2 and RFC5, RFC3 and RFC4, RFC3 and RFC5, RFC4 and RFC5 (P < 0.05). The correlation rates were shown in Fig. 6D.
Predicted functions and pathways of the mutations in RFCs and their 50 frequently altered neighbor genes in LGG patients.
In order to find out the correlation and functions of RFCs, we first analyzed the protein-protein interaction (PPI) network of different expressed RFCs on STRING (Fig. 7A). Then, we found out the 50 neighbor genes that were associated with RFCs mutations most frequently by GEPIA2 and constructed the PPI network with RFCs and their 50 neighbor genes with STRING and Cytoscape (Fig. 7B). Next, we enriched RFCs and their 50 frequently altered genes in GO and KEGG by R packages “clusterProfiler”, “GOplot” and “pathview”. As shown in Fig. 7C, biological process, such as GO:0007059 (chromosome segregation), GO:0006260 (DNA replication), GO:0098813 (nuclear chromosome segregation), GO:0000819 (sister chromatid segregation) and GO:0000070 (mitotic sister chromatid segregation) were evidently associated with RFCs and their 50 neighbor genes. In cellular components, GO:0098687 (chromosomal region), GO:0000775 (chromosome, centromeric region), GO:0000776 (kinetochore), GO:0000777 (condensed chromosome kinetochore) and GO:0005819 (spindle) were significantly influenced by the RFCs and their 50 neighbor genes. What’s more, molecular functions such as GO:0003678 (DNA helicase activity), GO:0140097 (catalytic activity, acting on DNA), GO:0017116 (single-stranded DNA helicase activity), GO:0008094 (DNA-dependent ATPase activity) and GO:0004386 (helicase activity) were closely related to RFCs and the 50 neighbor genes. Other enriched GO pathways were exhibited in Additional file 1. In KEGG pathways analysis with RFCs and frequently altered genes, 8 pathways were enriched as Fig. 8A showed, including hsa03030 (DNA replication), hsa04110 (Cell cycle), hsa03430 (Mismatch repair), hsa03420 (Nucleotide excision repair), hsa04114 (Oocyte meiosis), hsa04115 (p53 signaling pathway), hsa03440 (Homologous recombination), hsa03460 (Fanconi anemia pathway). The two most enriched pathways were shown in Fig. 8B and 8C. The p-value and enriched genes were precisely recorded in Additional file 2.
Kinase targets, miRNA targets and Transcription factor targets of RFCs in patients with LGG.
In order to explore more molecules and functions correlated to RFCs, we predicted the kinase targets, miRNA targets and Transcription factor targets through LinkedOmics database. The results were shown in table 2. ATM and CDK1 were the top two kinases related to RFC2, ATR and PLK1 showed association with RFC2, PLK1 and CDK1 were two kinase targets influenced by RFC3, PLK1 and ATR were evidently correlated to RFC4, ATM and ATR were top two kinases targeted by RFC5. Speaking of miRNA targets, only RFC1 had more than one closely correlated miRNA targets and MIR-144, MIR-381 were the top two. Moreover, only MIR-144 was significantly associated with RFC5, while other miRNA targets predicted were lack of statistical significance. The enrichment of transcription factor targets of RFCs were mainly related to E2F families, including V$E2F_Q4, V$E2F_Q6, V$E2F_Q3, V$E2F1_Q6, V$E2F4DP1_01.
The relation of Immune Cell Infiltration and the RFCs gene family in patients with LGG.
To assessed the RFCs related immune cell infiltration, we analyzed the data by TIMER, an online tool based on TCGA. The results were shown in Fig. 9A. RFC1 had a positive correlation with CD8 + T cells (Cor = 0.434, p = 2.01e-23), CD4 + T cells (Cor = 0.22, p = 1.30e-06), B cells (Cor = 0.396, p = 2.05e-19) and other innate immune cells. RFC2 also had a positive correlation with CD8 + T cells (Cor = 0.208, p = 4.44e-06), CD4 + T cells (Cor = 0.288, p = 1.61e-10), B cells (Cor = 0.35, p = 3.29e-15) and other innate immune cells. Although RFC3 had a positive correlation with CD8 + T cells (Cor = 0.376, p = 1.80e-17), B cells (Cor = 0.225, p = 6.43e-07) and other innate immune cells, the correlation rate was lower in innate immune cells infiltration. Moreover, the correlation between RFC3 and CD4 + T cells was lack of statistical significance. RFC4 had a positive correlation with CD8 + T cells (Cor = 0.115, p = 1.16e-02), B cells (Cor = 0.152, p = 8.85e-04), Macrophage cells (Cor = 0.117, p = 1.08e-02) and Dendritic cells (Cor = 0.136, p = 3.03e-03) while its correlation with CD4 + T cells and Neutrophil cells was not statistically significant. Like RFC1 and RFC2, the correlations between RFC5 and infiltrated immune cells were positive, including CD8 + T cells (Cor = 0.186, p = 4.27e-05), CD4 + T cells (Cor = 0.181, p = 7.42e-05), B cells (Cor = 0.219, p = 1.33e-06) and other innate immune cells. Apart from the correlation research, we also explored the prognostic significance of immune cell infiltration in LGG patients. As shown in Fig. 9B, the infiltration of B cells, CD8 + T cells, CD4 + T cells, Macrophage cells, Neutrophil cells and Dendritic cells were all associated with poorer prognosis in patients with LGG. Moreover, we also calculated the correlation between the expression of RFCs and the infiltration of immune cells by the Cox proportional hazard model on the TIMER online tool. As shown in Table 3, the model contains the confounding factors including B cells, CD4 + T cells, CD8 + T cells, Macrophage cells (p = 0.010), Neutrophil cells, Dendritic cells, RFC1, RFC2 (p < 0.001), RFC3, RFC4 (p = 0.006) and RFC5.