Elevated expression levels of ARNTL2 in ccRCC
We preliminarily investigated the expression level of ARNTL2 in pan-cancer by analyzing TCGA and GTEx database. ARNTL2 was observed to be upregulated in various cancer types, including ccRCC (Fig. 1a, b), a paired line graph of 72 pairs of ccRCC samples and matching nontumorous samples indicated that most ccRCC tissues possessed higher ARNTL2 expression compared to matching normal tissues (Fig. 1c). Three independent datasets from GEO database were utilized to externally illustrated the expression of ARNTL2 in ccRCC, which also demonstrate the highly expressed level of ARNTL2 in ccRCC (Fig. 1d-e), and increased ARNTL2 expression significantly associated with advanced ccRCC clinical stage (Fig. 1f, Table 1) and tumor histologic grade (Table 1). Additionally, we demonstrated that most ccRCC tissues possessed higher ARNTL2 expression compared to adjacent nontumorous in 20 pairs ccRCC tissues via qRT-PCR method (Fig. 1g). These results revealed that ARNTL2 was significantly elevated in ccRCC.
Table 1
Clinicopathological correlation of ARNTL2 expression in human ccRCC.
Characteristic | Low expression of ARNTL2 | High expression of ARNTL2 | pa |
n | 269 | 270 | |
Age, n (%) | | | 0.518 |
<=60 | 130 (24.1%) | 139 (25.8%) | |
> 60 | 139 (25.8%) | 131 (24.3%) | |
Gender, n (%) | | | 0.903 |
Female | 94 (17.4%) | 92 (17.1%) | |
Male | 175 (32.5%) | 178 (33%) | |
T stage, n (%) | | | 0.027 |
T1-T2 | 183 (33.9%) | 166 (30.8%) | |
T3-T4 | 86 (16%) | 95 (19.3%) | |
N stage, n (%) | | | 0.034 |
N0 | 119 (46.3%) | 122 (47.5%) | |
N1 | 3 (1.2%) | 13 (5.1%) | |
M stage, n (%) | | | 0.118 |
M0 | 220 (43.5%) | 208 (41.1%) | |
M1 | 32 (6.3%) | 46 (9.1%) | |
Pathologic stage, n (%) | | | 0.031 |
Stage I-II | 174 (32.5%) | 157 (29.3%) | |
Stage III-IV | 95 (17.7%) | 110 (20.6%) | |
Histologic grade, n (%) | | | 0.012 |
G1-3 | 233 (43.9%) | 223 (42%) | |
G4 | 31 (5.8%) | 44 (8.3%) | |
ap-values were derived with chi-square test. |
Multivariate cox regression analysis of ARNTL2 and construction of nomogram model
We then evaluated the prognostic values of ARNTL2 in ccRCC, as plotted in Fig. 2a and b, elevated expression of ARNTL2 indicated poor OS in both TCGA (HR = 1.67, P = 0.001) and Kaplan-Meier plotter (HR = 1.91, P = 2.2e-05) database. The cox analyses were also applied to explore associations between ARNTL2 expression and OS in ccRCC, univariate analysis indicated that ARNTL2 expression (HR = 1.45853, p = 0.00029), age (HR = 1.02888, p = 1e-05), pathological TNM stage (HR = 1.86653, p < 0.0001) and tumor grade (HR = 2.29073, p < 0.0001) are significantly correlated with OS of ccRCC (Fig. 2c). Multivariate analysis, depicted as a forest boxplot in Fig. 2d, revealed that ARNTL2 expression (p = 0.01016), as well as the age (p = 1e-05), pathological TNM stage (p < 0.0001) and tumor grade (p = 0.00083) were independent predictor of ccRCC patient OS. ARNTL2 expression also showed preferable predective ability as the ROC curve exhibited that the AUC of ARNTL2 expression for predicting OS was 0.773 (Fig. 2e). Furthermore, we developed a nomogram model by integrating ARNTL2 and other independent prognostic variables base on the multivariate cox regression analysis results (Fig. 2f), the nomogram can contribute to quantitatively assess ccRCC patients 1-y, 3-y and 5-y survival probability.
Mutation patterns of SMGs in different ARNTL2 expression level of ccRCC
In order to investigate mutation patterns of significantly mutated genes (SMGs) in different ARNTL2 expression level of ccRCC, we downloaded and analyzed the genetic mutation data of ccRCC patients from TCGA database. We identified 30 SMGs in ccRCC cohort, among these, VHL, PBRM1, TTN, SETD2, BAP1, MUC16 were the top six frequently mutated genes in ccRCC (Fig. 3a). Subsequently, we divided the ccRCC patients with genetic mutation data into two groups based upon the median expression value of ARNTL2, namely ARNTL2 high and ARNTL2 low group. As shown in Fig. 3b-g, we explore the mutation patterns of top six SMGs in two ARNTL2 groups. Significantly higher mutation frequency of PBRM1 was observed in ARNTL2 low expression group(P < 0.001) (Fig. 3b), while other SMGs were found no differences in two ARNTL2 groups (P > 0.05) (Fig. 3c-g).
Gsea Analysis Of Arntl2 In Ccrcc
To investigate the potential functions and mechanisms of ARNTL2 in ccRCC, we utilized GSEA analysis to explore ARNTL2-related pathways in ccRCC carcinogenesis, GSEA analysis contribute to reveal significantly enriched KEGG pathways in highly expressed ARNTL2 samples with a high accuracy. The results revealed that renal cell carcinoma (NES = 1.96614, P-adjust < 0.001), focal adhesion (NES = 2.06698, P-adjust < 0.001), Toll-like receptor signaling pathway (NES = 1.965199, P-adjust < 0.001), JAK-STAT signaling pathway (NES = 1.999494, P-adjust < 0.001), T cell receptor signaling pathway (NES = 2.036177, P-adjust < 0.001) and cell cycle pathways (NES = 1.551655, P-adjust < 0.005) (Fig. 4a-f, Table 2) were significantly enriched in upregulated ARNTL2 samples. These results indicated that the immune response and cell cycle related pathways were strongly correlated with abnormal expression of ARNTL2 in ccRCC.
Table 2
Gene set enrichment analysis (GSEA) of ARNTL2 in ccRCC.
GeneSet name | NES* | p-adjust | q-values |
KEGG_FOCAL_ADHESION | 2.06698 | 0.000503 | 0.000288 |
KEGG_JAK_STAT_SIGNALING_PATHWAY | 1.999494 | 0.000503 | 0.000288 |
KEGG_T_CELL_RECEPTOR_SIGNALING_PATHWAY | 2.036177 | 0.000503 | 0.000288 |
KEGG_TOLL_LIKE_RECEPTOR_SIGNALING_PATHWAY | 1.965199 | 0.000503 | 0.000288 |
KEGG_RENAL_CELL_CARCINOMA | 1.96614 | 0.000503 | 0.000288 |
KEGG_CELL_CYCLE | 1.551655 | 0.002661 | 0.001522 |
*NES: Normalized Enrichment Score. |
Significant correlations between the expression of ARNTL2 and its co-expressed genes and tumor infiltrating immune cells in ccRCC
In order to further explore the potential functions of ARNTL2 in the ccRCC carcinogenesis, we employed R software “stat” package to identify ARNTL2 positively co-expressed genes, and the strongly co-expressed genes (spearman analysis, r > 0.70, p < 0.001) were selected to further analysis, as shown in Fig. 5a-5b, the actin related protein 2/3 complex subunit 2 (ARPC2), guanine nucleotide binding protein beta polypeptide 4 (GNB4) and capping-protein muscle Z line alpha 1 (CAPZA1) were the only three positively co-expressed genes with spearman coefficient higher than 0.7 (p < 0.001). Subsequently, we applied ssGSEA algorithm in R package GSVA to preliminarily estimate the relationships between the expression of ARNTL2 and its top 3 co-expressed genes and tumor infiltrating immune cell types, as shown in Fig. 5c, ARNTL2 and its co-expressed genes ARPC2, GNB4, CAPZA1 were remarkably associated with the infiltrating level of T helper cells, macrophages, T cells, B cells and dendritic cells (DC) (P < 0.001), while showed weak associations with neutrophils and mast cells (P < 0.01). Further correlation analysis demonstrated that ARNTL2 expression significantly associated with the enrichment of T cells (r = 0.440, P < 0.001), T helper cells (r = 0.580, P < 0.001), Macrophages (r = 0.460, P < 0.001), B cells (r = 0.350, P < 0.001) and DC (r = 0.300, P < 0.001) (Fig. 5d). In summary, these results indicated that ARNTL2 might be participated in the immune response in ccRCC TIME.
The xCell algorithm was further employed to calculate the fraction of diverse tumor-infiltrating immune cells in the TIME of high and low ARNTL2 expression ccRCC samples. Distinct infiltrating levels of immune cells were observed in two ARNTL2 groups (Fig. 6a). With the rise of the ARNTL2 expression, the immune score (P < 0.001), microenvironment score (P < 0.001) and stroma score (P < 0.05) in ccRCC TIME were enhanced (Fig. 6b), high expression level of ARNTL2 tightly linked to the infiltrating levels of CD8 + T cell, CD4 + memory T cell, Myeloid dendritic cell, macrophage and CD4 + Th2 T cell, while CD4 + Th1 T cell, B naive cell and NK T cell were downregulated in highly ARNTL2 expression group (Fig. 6c). Additionally, the immune checkpoint-related genes such as PD-L1, PD-L2, CTLA4, PD-1, TIM3, LAG3 and TIGIT were also tightly related to the expression level of ARNTL2 (Fig. 6d). These results implied that ARNTL2 might be related with T cell exhaustion and immune evasion in ccRCC.
Associations of ARNTL2 with PD-L1, TMB, MSI and mismatch repair genes
We observed that in addition to ccRCC, ARNTL2 was also highly expressed in various tumor types compared to their corresponding normal tissues (Fig. 1a), we subsequently desired to assess whether ARNTL2 has a universal role in immune response in pan-cancer. Considering the GSEA results indicated the potential KEGG pathway of ARNTL2 in regulation of T cell receptor signaling pathway (Fig. 4e), we firstly explore the correlations between PD-L1 and ARNTL2 expression in pan-cancer, as shown in Fig. 7a, ARNTL2 expression remarkably correlated with the expression level of PD-L1 in various cancer types, including ccRCC. Meanwhile, ARNTL2 expression showed positive associations with the TMB of Uterine Carcinosarcoma (UCS), Sarcoma (SARC), Colon adenocarcinoma (COAD), Pancreatic adenocarcinoma (PAAD), Brain Lower Grade Glioma (LGG), Stomach adenocarcinoma (STAD), Breast invasive carcinoma (BRCA), Bladder Urothelial Carcinoma (BLCA), Lung adenocarcinoma (LUAD) and Ovarian serous cystadenocarcinoma (OV) (P < 0.01), while possessed negative associations with the TMB of Uveal Melanoma (UVM), Esophageal carcinoma (ESCA) and Lung squamous cell carcinoma (LUSC) (P < 0.01) (Fig. 7b). For MSI, another important prognostic indicator besides the PD-L1 and TMB in cancer immunotherapy, ARNTL2 also showed positive associations with the MSI of Mesothelioma (MESO), Adrenocortical carcinoma (ACC), Testicular Germ Cell Tumors (TGCT), SARC, Rectum adenocarcinoma (READ), UVM, UCS, Acute Myeloid Leukemia (LAML), Stomach adenocarcinoma (STAD) and OV (P < 0.01), while negatively associated with the MSI of Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBC),Cholangiocarcinoma
(CHOL), HNSC, LUAD, and Pheochromocytoma and Paraganglioma (PCPG) and GBM (P < 0.01) (Fig. 7c). Furthermore, we also explore the relationships between ARNTL2 expression and mismatch repair genes such as MLH1, MSH2, MSH6 and PMS2, the results demonstrated that ARNTL2 tightly related with the expression of mismatch repair genes, especially the expression of MSH2 and MSH6 (Fig. 7d). In summary, ARNTL2 expression showed significant correlations with the level of three vital prognostic indicators (PD-L1, TMB and MSI) for cancer immunotherapy in various cancer types.
Associations Of Arntl2 With Time In Pan-cancer
To further investigate the influence of ARNTL2 on TIME of pan-cancer, xCell algorithm was employed to explore the links of ARNTL2 expression with cancer- infiltrating cells in pan-cancer. The results showed that ARNTL2 was tightly related to the enrichment of immune cells, especially the Th2 CD4 + T cell, CD4 + memory T cell, Macrophages and (Fig. 8a), while remarkably correlated with the decrease of NK T cell and CD4 + central memory cell. Immune checkpoint-related genes had been demonstrated to be involved in immune escape in carcinogenesis, among these, PD-1, PD-L1, PD-L2, CTLA4, LAG3, TIM3, TIGIT were the seven most common immune checkpoint related genes, our results also indicated that ARNTL2 correlated with the expression level of these immune escape markers in various cancers, including ccRCC (Fig. 8b). These results further illustrated the vital role of ARNTL2 in the TIME of pan-cancer.
Overexpression of ARNTL2 predicted unfavorable overall survival in multiple cancer types
The relationships between ARNTL2 expression and OS of cancer patients were also analyzed both in TCGA and GEO database, as shown in Fig. 9a-9h, highly expressed ARNTL2 was tightly associated with the dismal OS in LUAD (HR = 1.62, P < 0.01) (Fig. 9a), MESO (HR = 2.77, P < 0.001) (Fig. 9b), Glioblastoma multiforme and Lower Grade Glioma (GBMLGG) (HR = 4.83, P < 0.001) (Fig. 9c), LGG (HR = 2.10, P < 0.001) (Fig. 9d), UVM (HR = 4.63, P = 0.003) (Fig. 9e), UCEC (HR = 1.66, P = 0.016) (Fig. 9f), PAAD (HR = 1.87, P = 0.004) (Fig. 9g) and LIHC (HR = 2.11, P = 0.004) (Fig. 9h) in TCGA database. ARNTL2 also showed poor OS in Glioma (HR = 3.25, P < 0.001) (Fig. 9i), Astrocytoma (HR = 1.73, P = 0.004) (Fig. 9j), Lung Adenocarcinoma (HR = 1.36, P < 0.01) (Fig. 9k) and AML (HR = 2.38, P < 0.01) (Fig. 9l) in GEO database. In summary, highly expressed ARNTL2 showed unfavorable OS and can serve as an adverse prognostic biomarker in multiple prevailing cancers.