3.1 Gene expression analysis data
In this study, we aimed to conduct a comprehensive analysis of the oncogenic role of human NUSAP1 (mRNA, NM_001243144.2; protein, NP_001230073.1; Figure S1). We analyzed NUSAP1 expression patterns in various cell lines and non-tumor tissues. Additionally, we acquired data from HPA, GTEx, and Function Annotation of the Mammalian Genome 5 (FANTOM5) datasets, which are shown in Figure S2A. Based on this analysis, NUSAP1 has its highest expression in the thymus, followed by bone marrow and the appendix, and its mRNA tissue specificity showed group enrichment (bone marrow and lymphoid tissue). HPA/Monaco/Schmiedel datasets were used to analyze NUSAP1 expression in different blood cells, and Treg cell RNA specificity was enhanced (Figure S2B).
We then applied the TIMER2 method to compare NUSAP1 expression levels between tumor and adjacent normal tissues in the TCGA dataset. As shown in Fig. 1A, NUSAP1 expression levels in the tumor tissues of bladder urothelial carcinoma (BLCA),breast invasive carcinoma (BRCA),cholangiocarcinoma (CHOL),colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), LIHC, LUAD, lung squamous cell carcinoma (LUSC), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC) were higher than those in the corresponding control tissues. Only kidney chromophobe (KICH) did not show differential expression.
Using the GTEx dataset for tumors lacking normal tissue data, we further evaluated NUSAP1 expression differences between tumor and normal tissues. We found significant differences between tumor and normal tissues for cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), glioblastoma multiforme (GBM), brain lower grade glioma (LGG), OV, pancreatic adenocarcinoma (PAAD), sarcoma (SARC), skin cutaneous melanoma (SKCM), testicular germ cell tumors (TGCT), THYM, and uterine carcinosarcoma (UCS) (Fig. 1B, P < 0.05). In contrast, we found no significant differences for adrenocortical carcinoma (ACC), LAML, and pheochromocytoma and paraganglioma (PCGC) (Figure S3). Overall, we found that NUSAP1 expression is elevated in most human tumors.
In addition, using the CPTAC dataset, we evaluated NUSAP1 at the protein level. The expression level of NUSAP1 total protein in breast cancer, clear cell renal cell carcinoma (RCC), colon cancer, LUAD, ovarian cancer, and UCEC tissues was higher than that in normal tissues (Fig. 2A, P < 0.001).
The “Pathological Stage Plot” module of HEPIA2 tool was used to analyze the relationship between NUSAP1 expression and tumor pathological staging. We found that NUSAP1 expression underwent stage-specific changes in some tumor types, including ACC, BRCA, KICH, KIRC, KIRP, LIHC, LUAD, SKCM, and THCA (Fig. 2B, all P < 0.05), but not in other tumor types (Figure S4).
Moreover, the IHC results, provided by the HPA database, were compared to TCGA NUSAP1 gene expression data. The data analysis results for the two databases were consistent. NUSAP1 IHC staining in normal liver, lung, and ovary tissues was negative or medium, while medium or strong staining was observed in corresponding tumor tissues (Figs. 3A–C).
3.2 Survival analysis data
We aimed to understand the relationship between NUSAP1 expression, prognosis, and OS. Based on the NUSAP1 expression level, tumor cases were divided into high-expression and low-expression groups, and the correlation between NUSAP1 expression and prognosis in patients with different tumors was determined, mainly using the TCGA and GEO datasets. High NUSAP1 expression was related to a poor OS prognosis for ACC (P = 0.038), KIRP (P = 0.00012), LGG (P = 3.6e-07), LIHC (P = 0.0063), LUAD (P = 0.00051), mesothelioma(MESO) (P = 7e-05), PAAD (P = 0.0046), and PRAD (P = 0.02) (Fig. 4A). High NUSAP1 expression was related to a poor DFS prognosis for ACC (P = 5e-04), KIRP (P = 0.00016), LGG (P = 1.3e-05), LIHC (P = 7e-04), LUAD (P = 0.017), PAAD (P = 0.0049), PRAD (P = 0.0018), SARC (P = 0.03), and uveal melanoma (UVM) (P = 0.00035) (Fig. 4B). Additionally, low NUSAP1 expression was related to a poor OS prognosis for THYM (P = 0.0049) (Fig. 4A).
Next, we analyzed the survival data using the Kaplan-Meier plotter tool and noted a relationship between high NUSAP1 expression and poor OS and post-progression survival (PPS) for gastric cancer; poor OS, first progression (FP) and PPS for lung cancer; poor OS for ovarian cancer; poor OS, relapse-free survival (RFS), distant metastasis-free survival (DMFS), and PPS for breast and liver cancers (Figure S5). In contrast, we failed to detect the correlation between expression of NUSAP1 and the prognosis of gastric cancer FP, ovarian cancer FP and PPS.
3.3 Genetic alteration analysis data
We explored NUSAP1 genetic alterations in human tumor samples. With “mutation” as the main type, the highest alteration frequency of NUSAP1 (> 3%) occurred in patients with uterine tumors. In the “amplification” type of CNA, sarcoma had the highest alteration frequency (approximately 1%). All cases of mesothelioma, DLBC, UCS, and esophageal adenocarcinoma had NUSAP1 copy number deletions, with genetic alteration frequencies of more than 4% for the first two tumors (Fig. 5A). Figure 5B shows the types, locations, and numbers of NUSAP1 genetic alterations. We did not find major types of genetic alterations, and the missense mutation, R249*/Q alteration, was exclusively detected in two cases of UCEC. In addition, we observed the R249 site in the 3D structure of NUSAP1 (Fig. 5C). Furthermore, we systematically evaluated the potential link between NUSAP1 genetic alterations and the clinical survival prognosis of patients with various types of cancer. Figure 5D shows that the PFS prognosis (P = 0.0306) in patients with UCEC with a NUSAP1 alteration was better, but the OS (P = 0.0850), PFS (P = 0.118), and disease-specific survival (P = 0.228) were not significantly different, than that in patients with no NUSAP1 alterations (Fig. 5D).
We went a step further to probe the correlation between NUSAP1 expression and the tumor mutational burden (TMB) and microsatellite instability (MSI) for all tumors in the TCGA. We found a positive correlation between NUSAP1 expression and TMB for BLCA (P = 1.9e-06), BRCA (P = 2.3e-019), COAD (P = 6e-06), KICH (P = 0.0038), KIRC (P = 0.025), LGG (P = 1.5e-17), LUAD (P = 3.3e-19), PAAD (P = 7e-08), PRAD (P = 6.2e-17), SARC (P = 0.00021), SKCM (P = 1.3e-05), COAD (P = 1.6e-23), and UCEC (P = 2.1e-06), and a negative correlation for ESCA (P = 0.025) and THYM (P = 2.5e-12) (Figure S6). In addition, NUSAP1 expression was positively correlated with MSI for CHOL (P = 0.0076), COAD (P = 1.2e-07), LUSC (P = 0.0013), READ (P = 2e-04), SARC (P = 0.00019), STAD (P = 5.5e-08), and UCEC (P = 2.1e-13), and was negatively correlated with MSI for DLBC (P = 0.00014) (Figure S7). In summary, these findings indicate that NUSAP1 genetic alterations may be viewed as possible drivers of the tumors mentioned above.
3.4 Protein phosphorylation analysis data
The phosphorylation-dephosphorylation cascade plays a key role in tumorigenesis. We compared the NUSAP1 phosphorylation levels between normal and primary tumor tissues. The CPTAC database, which includes breast and ovarian cancers, was used to analyze two types of tumors in detail. The NUSAP1 phosphorylation sites were at S134, T181, and T312, which showed significant differences in ovarian, but not breast, cancer (Fig. 6A). The NUSAP1 phosphorylation level at the T181 locus in breast cancer tissues was not different from that in normal tissues (Fig. 6B). The phosphorylation level was significantly increased in ovarian cancer at the T181 and T312 loci, while it was reduced at the S134 locus (Fig. 6C).
Immune infiltration analysis data
It is well known that tumor-infiltrating immune cells, an important part of the tumor microenvironment, are closely associated with cancer occurrence, development, and metastasis(Fridman et al. 2011, Steven and Seliger 2108). In tumor microenvironmental stroma, tumor-associated fibroblasts are involved in regulating the functions of all kinds of tumor-infiltrating immune cells (Chen and Song 2019, Kwa et al. 2019). NUSAP1 overexpression causes the bundling of cytoplasmic microtubules (Raemaekers et al. 2003); therefore, we predicted that the NUSAP1 expression level or genetic changes in NUSAP1 may affect the response of tumor-infiltrating immune cells. Based on this, we explored the correlation between the level of infiltration of various immune cells and NUSAP1 expression in a variety of tumor types in the TCGA dataset. It should be noted that NUSAP1 expression was positively correlated with the estimated infiltration value of six immune cell types (B cells, CD8 + T cells, CD4 + T cells, macrophages, neutrophils, and DCs) for HNSC, LGG, and LIHC (Fig. 7A). Figure 7B shows the correlation between the NUSAP1 expression level and the infiltration of four immunosuppressive cells (CAFs, M2-TAMs, MDSCs, and Treg cells) that are known to promote T-cell exclusion. NUSAP1 expression was positively correlated with MSDC tumor infiltration in almost all tumors (except CESC, DLBC, HNSC-human papilloma virus-positive, TGCT, and THCA), Treg tumor infiltration in PRAD, and CAF tumor infiltration in BRCA, TGCT, and THYM. However, NUSAP1 expression was not correlated with M2-TAM tumor infiltration in any cancer. We assessed the relevance of NUSAP1 biomarkers by comparing the response results of NUSAP1 and standardized biomarkers to immune checkpoint blocking (ICB) subgroups, as well as the predictive power for OS. Custom exhibited a higher predictive value than TMB, T. Clonality, and B. Clonality, which had areas under the curve (AUCs) > 0.5 in eight, nine, and seven ICB sub-cohorts, respectively. Additionally, the predictive power was comparable to that of Merck 18, but lower than that of TIDE, MSI.Score, CD274, CD8, and interferon-γ(IFNG) (Fig. 8A). Furthermore, we evaluated the influence of genetic and epigenetic changes in NUSAP1 on the phenotype of dysfunctional T cells. The results showed high NUSAP1 expression as related to a poor prognosis for programmed death 1 protein (PD1) in melanoma (ICB_Gide2019_PD1 + CTLA4), adoptive cell transfer (ACT) in melanoma (ICB_Lauss2017_ACT), and PD1 in melanoma (ICB_Riaz2017_PD1). Additionally, in an analysis of gene knockout phenotypes, we noted that NUSAP1-knockout had a significant impact on lymphocyte-mediated tumor killing in Mel624 melanoma (Patel 2017 1) and T-cell (Shifrut 2018 Average) models (Fig. 8B).
3.5 Enrichment analysis of NUSAP1-related partners
Finally, we screened for NUSAP1 interacting proteins and NUSAP1 expression-related genes, and performed a series of pathway enrichment analyses to further investigate how the molecular mechanisms of NUSAP1 are involved in tumorigenesis and development. By applying the STRING tool, we acquired data on 30 NUSAP1 binding proteins detected in experiments. The interaction network of these 30 proteins is shown in Fig. 9A. We also used the GEPIA2 tool to combine all tumor expression data from the TCGA dataset and obtain the top 100 genes related to NUSAP1 expression. NUSAP1 expression was positively correlated with that of cyclin F (CCNF; R = 0.73), chromatin assembly factor 1 subunit A (CHAF1A; R = 0.71), kinesin family member 14 (KIF14; R = 0.77), kinesin family member 20A (KIF20A; R = 0.78), and thymopoietin (TMPO; R = 0.78) (Fig. 9B). The heatmap showed that in most cancer types, NUSAP1 was strongly and positively correlated with the above five genes (Fig. 9C). The intersection analysis of the above two sets of data also indicated the above five common members (Fig. 9D).
We integrated these two datasets for KEGG and GO enrichment analyses. The KEGG analysis showed that the role of NUSAP1 in tumor pathogenesis may be connected with the "Cell cycle" and "Mismatch repair" (Fig. 9E). Furthermore, the GO enrichment analysis data showed that NUSAP1 mostly acted on chromosomal regions, chromosomal segregation, and mitotic nuclear division (Fig. 9F).