Pan-Cancer Analysis of the Prognostic and Immunological Role of Thymocyte-Expressed Positive Selection-Associated Protein 1 (TESPA1) in Human Tumors


 Background: Thymocyte-expressed positive selection-associated protein 1 (TESPA1) was identified playing a critical role responsible for T cell development in the thymus. Evidence has built the relationship between TESPA1 and cancers, but no pan-cancer analysis is available. Methods: We explored the expression patterns, prognostic values and immunological roles of TESPA1 across thirty-three cancers based on the datasets of TCGA and GEO via multiple databases and analysis tools, including Oncomine, TIMER 2, GEPIA 2, Kaplan–Meier Plotter, Prognoscan, UALCAN, cBioPortal web, STRING website, DNMIVD and GO/KEGG analysis. Results: We have demonstrated the different expression level of TESPA1 gene across pan-cancers and pathological stages. Furthermore, different expression level of TESPA1 gene correlated with prognosis of different cancer. A group of factors, such as DNA methylation, genetic alteration, protein phosphorylation and relevant cellular pathways are included differently compared with normal tissues, further related to prognosis. In addition, TESPA1 expression correlate positively with immune infiltrates, especially CD8 + T cells. Moreover, blood coagulation disorders were involved in the functional mechanisms of TESPA1. Conclusions: Our findings supported TESPA1 could serve as a pan-cancer prognostic and immune-related biomarker and play a vital role in tumorigenesis.


Background
Given the complex nature of tumorigenesis, pan-cancer expression analysis of meaningful gene to explore its relation with clinical prognosis and potential underlying molecular mechanisms should be valued highly [1][2][3][4]. Gene data bank The Cancer Genome Atlas (TCGA) and The Gene Expression Omnibus (GEO) database are two comprehensive databases applied to catalogue and discover major cancer-causing genomic alterations in large sample populations around the world in public, thus allow us to develop pan-cancer analysis [5][6][7].
Thymocyte-expressed positive selection-associated protein 1 (TESPA1) protein, also known as KIAA0748 or HSPC257, is a T cell-expressed protein which play a dominant and necessary role in development and maturation of T-cells and involved in the late stages of thymocyte development through the regulation of T-cell antigen receptor-mediated signaling [8,9]. TESPA1 affects the regulation of inositol 1,4,5trisphosphate receptor-mediated Ca 2+ release and mitochondrial Ca 2+ uptake via the mitochondriaassociated endoplasmic reticulum membrane compartment[8, [10][11][12]. Given the important role of these signaling pathways and immune in cancer, increasing focus is being placed on understanding the role of TESPA1 in cancer. TESPA1 has been shown credible prognostic value in lung adenocarcinoma (LUAD) patients in TCGA through GEPIA database online analysis and veri cation in the Kaplan-Meier plotter database [13].
In this study, we used the TCGA project and GEO databases to conduct a pan-cancer analysis of TESPA1 for the rst time. Based on analysis of the expression of TESPA1 in various tumors using Oncomine and the Tumor immune estimation resource, version 2 (TIMER 2). Then, the expression of TESPA1 and its correlation with cancer prognosis were analyzed via Gene Expression Pro ling Interactive Analysis, version 2 (GEPIA 2), Kaplan-Meier Plotter approach and Prognoscan. Moreover, we explored the relationship between TESPA1 expression and immune cells using the TIMER. A group of another factors, such as DNA methylation, genetic alteration, protein phosphorylation and relevant cellular pathways are included to investigate the mechanisms of TESPA1 in the pathogenesis or clinical prognosis of different cancers.

Gene expression analysis
We input "KIAA0748" in the "Gene DE" module of TIMER2 web (http://timer.cistrome.org/) to analyze the different expression of TESPA1 between tumor or speci c tumor subtypes and adjacent normal tissues of the TCGA project [14]. Given certain tumors without normal tissues, the expression difference between the these tumor tissues and the corresponding normal tissues of the Genotype-Tissue Expression (GTEx) database was obtained by the "Expression analysis-Box Plots" module of the GEPIA2 web server (http://gepia2.cancer-pku.cn/#analysis) in box plots form, under the settings of P-value cutoff = 0.01, log2FC (fold change) cutoff =1, and "Match TCGA normal and GTEx data" [15]. The expression difference between skin cutaneous melanoma (SKCM) tissues and the corresponding normal tissues of the GTEx database obtained by xiantao tools (https://www.xiantao.love/) [16].
Oncomine(https://www.oncomine.or) as a comprehensive database that collects all published cancer microarray data, was concurrently utilized to analyze the expression levels of the TESPA1 gene in various tumors [17]. "P-value 0.001 and fold change 1.5" as threshold values were used. Additionally, we obtained the different expression of TESPA1 expression in different pathological stages (stage 0, stage I, stage II, stage III, and stage IV) across all TCGA tumors through the "Pathological Stage Plot" module of GEPIA2 and shown in violin plots form [15]. Expression data after log2 [TPM (Transcripts per million) +1] transformed were applied for the violin plots and box.

TESPA1-related phosphorylation analysis
Clinical Proteomic Tumor Analysis Consortium (CPTAC) is a convenient but powerful dataset in UALCAN(http://ualcan.path.uab.edu/index.html)[18], provided proteomic characterization of more than 500 human cancers [19]. We also compared the differences in TESPA1 phosphorylation levels between normal tissues and primary tumor tissues in breast cancer, clear cell RCC and LUAD, were analyzed (No data was available for the other tumors).

Survival prognosis analysis
GEPIA 2 was a user-friendly tool used to analyze the effects of TESPA1 expression on survival including overall survival(OS) and disease-free survival(DFS) [15]. We analyzed OS and DFS signi cance map data of TESPA1 across all TCGA tumors by the "Survival Map" module of GEPIA2. We choose Cutoff-high (50%) and cutoff-low (50%) values as expression thresholds for separating the high-expression and lowexpression cohorts. The log-rank test was used in the hypothesis test. Furthermore, the survival plots were also obtained by the "Survival Analysis" module of GEPIA2. For more rigorous results, we analyzed the relationships between TESPA1 expression and survival in various cancer types using PrognoScan, Kaplan-Meier Plotter as well. PrognoScan (http://gibk21.bse.kyutech.ac.jp/PrognoScan/index.html) provides a powerful platform accessible for evaluating potential tumor markers and therapeutic targets [20]. Adjusted Cox P-value < 0.05 was as threshold value. Kaplan-Meier Plotter(https://kmplot.com/analysis/) is a gene expression data downloaded from GEO, EGA and TCGA, can be used for analyzed the correlations between relapse free or overall survival information and expression of genes [21]. Kaplan-Meier Plotter analyzed the relationship of TESPA1 expression with OS and RFS across different cancers, hazard ratio (HR) values with 95% con dence intervals and log-rank Pvalues were calculated.

Genetic alteration analysis
After logging into the cBioPortal web (https://www.cbioportal.org/) [22,23], we click the "TCGA Pan Cancer Atlas Studies" and next choose the "Query by gene" section and entered "TESPA1" for queries of the genetic alterations of TESPA1. Alteration frequency, mutation type and copy number alteration of TESPA1 across all TCGA tumors were shown in the "Cancer Types Summary" module. We also searched the "Comparison" module to explore the correlations between TESPA1 genetic alterations and overall, disease-free, progression-free, and disease-free survival differences for different TCGA cancer cases.
Kaplan-Meier plots with log-rank P-value were calculated as well.

Methylation analysis
DNA methylation patterns vary greatly between tumor and adjacent normal tissues. Identi cation signatures may provide potential cancer-speci c prognostic biomarkers for pan-cancer [24]. DNMIVD web (http://119.3.41.228/dnmivd/index/) serve as a user-preferred database to visualize the DNA methylation interactive action [25]. We chose the "gene symbol" in the "Quick Search" section and input "TESPA1" for searches of the genetic methylation characteristics of TESPA1. The results of differential methylation level of this gene in the promoter region of across all tumors were observed in the "DMG" module. We also used the "Survival" module to obtain the data on the overall, disease-free, progression-free, and disease-free survival differences for the TCGA cancer cases with or without TESPA1 genetic methylation using two methods to make prognostic grouping. One method was to group the patients according to the median value, the other method was to group based on pre-methylation data < 0.3 and >0.7. Kaplan-Meier plots with log-rank P-value were generated as well.
2.6 Immune in ltration analysis TIMER2.0 also served as a web server for comprehensive analysis of tumor-in ltrating immune cells [26]. We input the "TIMER2" in "Immune-Gene" module, and selected different types of in ltrating immune cells to explore the association between TESPA1 expression and immune in ltrates across all TCGA tumors. The immune cells of CD8+ T-cells, monocyte, Treg, T cell CD4+, CAF, NK cell, macrophage, DC cell, mast cell, B cell were selected. The TIMER, EPIC, MCPCOUNTER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ and XCELL algorithms were applied for immune in ltration assessments. Purity-adjusted Spearman's rank correlation test as the statistical methods to calculate P-values and partial correlation (cor) values, and visualized as a heatmap and a scatter plot.

TESPA1-related gene enrichment analysis
Search Tool for the Retrieval of Interaction Gene/Proteins (STRING) website (https://string-db.org/) analyzed protein-protein networks [27]. We explored TESPA1 internetworks by using the query of a single protein name ("TESPA1") and organism ("Homo sapiens"). We set the following main parameters in "setting" module: minimum required interaction score ["Low con dence (0.150)"], meaning of network edges ("evidence"), max number of interactors to show ("no more than 50 interactors" in 1st shell) and active interaction sources ("experiments"). Finally, the available visualized experimentally com rmed TESPA1-binding proteins were obtained. Subsequently, based on the datasets of all TCGA tumor and normal tissues, we used the "Similar Gene Detection" module of GEPIA2 to obtain the top 100 TESPA1correlated targeting genes. Then, we performed a pairwise gene Pearson to analyze the correlation analysis of TESPA1 and selected genes by the "correlation analysis" module of GEPIA2. Log2 TPM-based P-value and correlation coe cient (R) were indicated in dot plot. Furthermore, we utilized the "Gene_Corr" module of TIMER2 to visualize the heatmap data of the correlations between TESPA1 and selected genes. Partial correlation (cor) and P-value in the purity-adjusted Spearman's rank correlation test were calculated. Moreover, we combined the two sets of TESPA1-correlated targeting genes and TESPA1interrelated genes. Enriched pathways and Gene ontology (GO) enrichment analysis were nally conducted and visualized with the cnetplot function by Xiantao tools(https://www.xiantao.love/products) [28]. The data for biological process, cellular component, molecular function and Kyoto encyclopedia of genes and genomes (KEGG) pathway were visualized as cnetplots. Two-tailed P <0.05 was considered statistically signi cant.

Genetic alteration analysis data
Genetic alteration status of TESPA1 in different tumor samples of the TCGA cohorts were shown in Fig.   5a. Patients with skin cutaneous melanoma with "mutation" as the primary type accounts for the highest alteration frequency of TESPA1 (>6%). Patients with adrenocortical carcinoma with "ampli cation" type of copy number alteration as the primary and only type shown an alteration frequency above 4% (Fig.  6a). The types and sites of the TESPA1 genetic alteration were further displayed in Fig. 6b. Among these mutations, missense mutation of TESPA1 was the main type of genetic alteration, and R184Q alteration in the KRAP_ inositol 1,4,5-trisphosphate receptor (IP3R) _bind domain, which was detected in 5 cases of 1 case of GBM, 1 case of UCEC, 1 case of COAD and 2 cases of SKCM (Fig. 6b), is able to induce mutation of the TESPA1 gene, translation from R (Arginine) to Q (Glutamine) at the 184 sites of TESPA1 protein (Fig. 6b). Additionally, we analyzed the possible correlations between genetic alteration of TESPA1 and survival rate of cases with different types of cancer. The data of Fig. 6c indicate that esophageal adenocarcinoma patients with altered TESPA1 mutation were victims with worse prognosis in OS(P=0.0192), but not DSS (disease-speci c survival) (P=0.136), DFS (disease-free survival) (P=0.603), and progression-free survival (PFS) (P=0.652). However, adrenocortical carcinoma patients with altered TESPA1 mutation were victims with worse prognosis in PFS (P=0.0181) and DSS (P=0.0491), but not OS(P=0.0831) and DFS (P=0.359) (Fig. 6c).

Immune in ltration analysis data
Tumor-in ltrating immune cells, as prominent components of the tumor microenvironment, were closely linked to the almost all process of cancer. After a series of analysis, we observed a statistical positive correlation between the immune in ltration of CD8+ T cells and TESPA1 expression in the tumors of 12 tumors including BRCA, CESC, COAD, KIRC, KIRP, LUAD, MESO(Mesothelioma), SARC, SKCM, SKCM-Metastasis, STAD and TGCT (Fig. 8a). The detail scatterplot data of these tumors generated using one algorithm are presented in Fig. 8b. For example, TESPA1 expression level in KIRC is positively correlated with the in ltration level of CD8+ T cells using XCELL algorithm (Fig. 8b, cor=0.721, P=4.00e-75). The correlations between monocyte, Treg, CD4+ T cells, CAF, NK cell, macrophages, DC cells mast cell, B cell and TESPA1 expression shown in Fig. 8c.

Enrichment analysis of TESPA1-related partners
To further investigate the molecular mechanism of the TESPA1 gene in pathogenesis of cancer, we screened out TESPA1-binding proteins and TESPA1-correlated genes for a series of pathway enrichment analysis. We obtained a total of 22 experimented TESPA1-binding proteins and the interaction networks of them were shown via STRING tools in Fig. 9a. Concurrently, we obtained the top 100 TESPA1correlated genes used the GEPIA2 tools. Nucleotide-oligomerization domain-like receptor subfamily C3 (NLRC3) (R=0.72), solid-pseudopapillary neoplasm (SPN) (R=0.72), integrin alpha4 (ITGA4) (R=0.68), Protein tyrosine phosphatase nonreceptor type 7 (PTPN7) (R=0.68), CBFA2/RUNX1 partner transcriptional co-repressor 3(CBFA2T3) (R=0.67) genes (all P <0.001) were selected to analyzed the correlation with TESPA1 expression level in dot plot, which proved a positive relationship (Fig. 9b). The heatmap data also showed a signi cant positive correlation between TESPA1 and the above ve genes across almost all cancer types consistently (Fig. 9c). We combined the two datasets to perform KEGG and GO enrichment analyses. The KEGG data of Fig. 9d suggest that "coagulation", "hemostasis" and "blood coagulation" might be involved in the effect of TESPA1 on tumor pathogenesis. The GO enrichment analysis further indicated that most of these genes are linked to the pathways or cellular biology of coagulation, hemostasis, gap junction, growth hormone synthesis, secretion and action, oocyte meiosis and others. (Fig. 9e).

TESPA1, a key gene in thymocyte development, is a rare and potentially pathogenic variant in 14
systemic diseases, such as rheumatoid arthritis, and plays an important role in the pathogenesis of these diseases, such as autoimmune arthritis[8, 29,30]. In the process of double-positive (DP) thymocytes become mature single-positive CD4+ and CD8+ T cells, TESPA1 have been reported as a necessary gatekeeper of thymic-speci c TCR signaling regulator that are able to improve the sensitivity of the TCR signal to facilitate positive selection[8, 9,[31][32][33],The mechanism of Tespa1 in T cell development and the regulation of TCR is that Tespa1 interacts with a transmembrane Ca 2+ channel protein in endoplasmic reticulum inositol 1,4,5-trisphosphate receptor (IP3R), then induced subsequent calcium signaling and MAPK activation [11,34]. Interestingly, Tespa1 protein is phosphorylated in response to store-operated calcium entry [10]. For B cells, Tespa1 is essential for T cell-dependent (TD) B cell responses. However, Tespa1 does not in uence the development of B cells, but Tespa1-de cient has a signi cant reduced impact on antibody concentrations in serum due to inhibit the activation and proliferation of B cells induced by TD antigens [9]. Mast cell is also activated by Tespa1, which orchestrates by tuning the balance of LAT1 and LAT2 signalosome assembly [35]. Tespa1 also participate in mitochondrial Ca 2+ uptake in the MAM compartment [12]. Owing to the close relationship between TESPA1 and immune system, more and more attention focused on the role of TESPA1 in cancer. Novelty, TESPA1 has been discovered credible prognostic value in evaluating the survival/prognosis of patients, invasion and progression of tumors in LUAD patients [13]. Whether TESPA1 plays a role in the pathogenesis of different tumors through some common molecular mechanisms remains unclear. Therefore, based on TCGA, CPTAC and GEO database data, as well as molecular characteristics of gene expression, genetic changes, DNA methylation or protein phosphorylation, we comprehensively examined TESPA1 genes in a total of 33 different tumors. TESPA1 expression level is aberrantly high or aberrantly low in different tumors. However, the survival prognostic analysis data of TESPA1 gene showed that there were signi cant differences among different tumors. Our study rst used four independent datasets in PrognoScan, and TCGA data in GEPIA and Kaplan-Meier plotter approach to explore the expression of TESPA1 and its prognostic value across pancancers for a more rigorous conclusion. For breast cancer and liver hepatocellular carcinoma, the expression of TESPA1 shown no difference in cancers compared with normal tissues. But in TCGA data in GEPIA and Kaplan-Meier plotter displayed consistently TESPA1 was bene cial to OS, DFS and RFS. Overexpressed TESPA1 gene may be a new target for breast cancer and liver hepatocellular carcinoma treatment. For skin cutaneous melanoma, high expressed TESPA1 was correlated with OS, DFS but not RFS. Kaplan-Meier plotter approach shown that in skin cancers, high expressed TESPA1 was correlated with OS. The expression of TESPA1 in different stage of skin cutaneous melanoma is also shown obvious difference. TESPA1 may play an important role in the development and treatment of. For the three cancers in kidney, TESPA1was all highly expressed. High expressed TESPA1 was related to good OS and RFS in kidney renal clear cell carcinoma and DFS of kidney chromophobe, but had a detrimental effect on prognosis kidney renal papillary cell carcinoma. This contradictory conclusion needs to be uni ed in a large number of samples. For tumor of reproductive system, including cervical squamous cell carcinoma, Ovarian serous cystadenocarcinoma, uterine carcinosarcoma and uterine corpus endometrial carcinoma. TESPA1 expressed lowly compared with controls. OS and RFS of Ovarian serous cystadenocarcinoma, cervical squamous cell carcinoma and uterine corpus endometrial carcinoma bene t from high expressed TESPA1. The contradiction also exists in testicular germ cell tumors, TESPA1 was bene cial to RFS of TGCT, but was detrimental to OS of TGCT. For eye cancers, uveal melanoma, contradictory conclusion that higher TESPA1 was bene cial DMFS of eye cancer via Prognoscan, but higher TESPA1 was related to poor survival of uveal melanoma via GEPIA 2. For lung cancer, we analyzed the datasets of the TCGA-LUSC and TCGA-LUAD projects and found a correlation between TESPA1 high expression and good overall survival prognosis of lung adenocarcinoma but not lung squamous cell carcinoma. The expression of TESPA1 in every stage of lung adenocarcinoma is also shown obvious difference. Previous study has obtained the consistent results and proposed prognostic value of TESPA1 [13]. Our results demonstrated the prognostic value of TESPA1 in lung adenocarcinoma, and the possible of its value in lung adenocarcinoma treatment. Larger sample sizes and experiments are required to con rm these results. In blood cancers, results from Oncomine and TIMER2 consistently shown that TESPA1 is high expressed in leukemia, but the survival analysis by Prognoscan has demonstrated that higher TESPA1 expression could led to poor survival in blood cancers. The prognostic value of TESPA1 in blood cancer is prospective. For tumor of digestive system, TESPA1 is has no different expression in cholangiocarcinoma esophageal carcinoma, stomach adenocarcinoma compared with controls. But the expression level of TESPA1 had an in uence on the survival of tumor of digestive system. Moreover, TESPA1 has been demonstrated to be associated with the development of gastric carcinoma [36]. For brain cancers, TESPA1 expression is reduced in glioblastoma and low-grade glioma, The Prognoscan demonstrated high-expressed TESPA1 is related the good OS of brain cancer. Upregulated TESPA1 maybe a new treatment of brain cancers. In summary, considering the above contradiction between TESPA1 expression level and the prognosis of some cancers. There are three possible reasons. First, a larger sample size is needed to further verify the above conclusions. Second, other clinical features should also be fully considered. Finally, further in vitro and in vivo molecular experimental evidence is needed to determine whether the high expression of TESPA1 plays an important role cancer mentioned above or is merely an accompanying result of the immune response [1].
We found a previously uncharacterized and closed correlation between TESPA1 expression and immune cell in ltration across pan-cancers and reveal its critical mechanisms and immunological role in tumor microenvironment. After the analysis by TIMER2, TESPA1 expression levels in cancers were signi cant positively correlated with in ltration of immune cells, including CD8 + T cells, CD4 + T cells, B cells, macrophages, mast cells, CAF, Treg, monocytes and dendritic cells. Particularly, TESPA1 correlation with B cells, T cells, dendritic cells are almost comparable high, which con rmed that TESPA1 possibly involved in tumor antigen presentation and tumor killing critically. Not only B cells, T cells, dendritic cells, but macrophage and Treg is also correlated with expression level of TESPA1. Due to the complex of tumor microenvironment, the mechanism of TESPA1 affects cancers through the regulation of the immune microenvironment is unclear, which is need further research to explore.
Furthermore, we integrated the information on TESPA1-binding components and TESPA1 expressionrelated genes across all tumors for a series of enrichment analyses and identi ed the potential impact of "coagulation", "hemostasis" and "blood coagulation" in the etiology or pathogenesis of cancers.
Interestingly, previous studies have demonstrated that cancer and the hemostatic system interact with each other and trigger coagulation abnormalities [37]. Hemostatic factors have been reported play a critical role tumor progression, growth and metastasis through effecting on the key event of neovascularization [37][38][39]. But the mechanism is not clear, our study may provide a new sight to solve this problem. Also, the treatments of cancer aimed at hemostatic system are bifunctional therapeutic approaches that are both able to attack the malignant process and resolve the coagulation impairment [37]. Last but not least, the coagulation disorders of tumor patients are different from those of other diseases. Finding cancer speci c biomarkers can better guide the treatment of tumor patients [40].
We also found signi cant differences in TESPA1 DNA methylation compared with normal tissues, and different levels of TESPA1 DNA methylation were also associated with different survival outcomes, including OS, DFI and PFI. It's a pity that the data of total protein of TESPA1 is not clear. We only used the CPTAC dataset to analyzed of the TESPA1 phosphorylated protein in breast cancer, clear cell renal cell carcinoma, lung adenocarcinoma. We observed high expression level of phosphorylated TESPA1 protein level at the S311 locus in the primary tumors compared with normal controls in breast cancer and clear cell renal cell carcinoma, but low expression level in lung adenocarcinoma. We also found that S454 phosphorylation of TESPA1 is increased in primary tumor. Additional experiments are required to further evaluate the total protein level of TESPA1 and more phosphorylated locus in other types of cancer, and their role in tumorigenesis.
There are some limitations still exist in our study [3]. Firstly, systematic bias may be introduced into our analysis given that the data were collected by analysis of a large number of tumor tissues from different types of gene chip and methods of sequencing. Higher-resolution methods such as single-cell RNA sequencing is very necessary [3,[41][42][43]. Secondly, due to the complex disease-related clinicopathological characteristics of cancers, more clinical data need to be involved into the correlation analysis between TESPA1 expression and clinical outcome [44]. Finally, in vivo or in vitro experiments were essential to validate the results of the prognostic value and immunological role in cancer obtained by bioinformatics analysis. Finally, owing to the complexity of tumor microenvironment and hallmarks of cancer [45], we just observed a phenomenon of the close correlation between TESPA1 expression and immune cell in ltration and patient survival in cancer, we could not directly conclude whether TESPA1 affects patient survival via immune cell in ltration. Our study laid the foundation for further exploration of the mechanism of interactions between the expression of TESPA1 and the in ltration of tumor immune cells. Future studies on up or down TESPA1 expression and immune cell in ltration in cancer populations will help to provide a clear answer to this question [3].

Conclusions
In conclusion, our pan-cancer analysis con rmed the prognostic value of level of TESPA1 expression, DNA mutation and methylation and protein phosphorylation in various of cancers. We also observed the close correlation between TESPA1 with immune cell in ltration, which uncovered its immunological role in tumor. Novelty, we also further founded that TESPA1 could also involve tumor progression by in uencing blood coagulation. This work builds a foundation for us to explore TESPA1 in tumorigenesis.      Mutation characters of TESPA1 in different cancers of TCGA. Alteration frequency with (a) mutation type and (b) mutation site of TESPA1 of cancers in TCGA using the cBioPortal tool. (c) Correlation between mutation status and overall, disease-speci c, disease-free and progression-free survival of esophageal adenocarcinoma and adrenocortical carcinoma analyzed using the cBioPortal tool.   Expression correlation between TESPA1 and 5 targeting genes selected from top 100 TESPA1-correlated genes, including NLRC3, SPN, ITGA4, PTPN7, and CBFA2T3 using the GEPIA2 approach in dot plot. (c) The corresponding heatmap data in the detailed cancer types. (d) Combined the TESPA1-correlated and TESPA1-interacted genes, KEGG pathway analysis was performed. (e) The cnetplot for the molecular function data in GO analysis is also shown.