BYSL: A Novel Biomarker for Cancer Treatment

doi:10.21203/rs.3.rs-1643744/v1

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BYSL: A Novel Biomarker for Cancer Treatment

https://doi.org/10.21203/rs.3.rs-1643744/v1

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Background: BYSL, Bystin like, was previously characterized as an important gene for cell proliferation, migration, invasion, and mesenchymal transformation in human carcinoma. However, systematic pan-cancer analysis of BYSL has never been developed to comprehend its regulation in cancer so far.

Methods: The potential carcinogenic role of BYSL in 33 carcinomas based on the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) dataset, Tumor Immune Estimation Resource (TIMER), Human Protein Atlas (HPA), and Gene Set Enrichment Analysis (GSEA) datasets.

Results: In most tumors, BYSL expression is high and correlated with prognosis. Furthermore, BYSL gene alterations, bystin like protein phosphorylation, and bystin DNA methylation are linked to many types of tumorigenesis and immune invasion. Thirdly, translation and viral carcinogenesis may contribute to BYSL's tumor-pathogenic effects.

Conclusions: Based on the results of the bioassay used in this study, BYSL may have potential as a biomarker to differentiate tumor tissue from normal tissue in most cancer patients, providing new research clues of how BYSL is regulated and thus the development of targeted therapies.

BYSL

cancer

prognosis

biomarker

bioinformatics

BYSL encodes bystin-like protein, which is located in nucleoli and cytoplasm, and is one of the highly conserved genes of humanity [1]. For BYSL to function properly, the 20S pre-rRNA precursors and the 40S ribosomal subunits are required. During implantation of human embryos, trophinin-dependence may be required to regulate cell adhesion. Ayala et al. found that BYSL was a crucial therapeutic target for neurotropic cancer in studying the infiltration effect of BYSL around the nerves in prostate cancer [2]. BYSL is upregulated in hepatocellular carcinoma and mediates early embryo implantation which necessary for cancer progression [3]. Furthermore, GSK-3β/β-catenin is also a signaling pathway actived by BYSL to promote glioblastoma cell migration and invasion [4]. However, the existing studies on BYSL for specific tumor type respectively are still remaining elusive. Further comprehensive analysis of BYSL is an urgent need for guiding the treatment of carcinoma in clinic.

To investigate the expression profiling of BYSL of various tumor types, we performed the first pan-cancer analysis of BYSL using TCGA, GEO, Clinical Proteomics Tumor Analysis Consortium (CPTAC) datasets, etc. The expression profile of BYSL was compared across tumor types based on survival status, genetic changes, DNA methylation, protein phosphorylation, immune infiltration, as well as associated cell signaling pathways. Based on the comprehensive analysis, we found that BYSL may be involved in both the pathogenesis and clinical outcome of patients with tumors. It is noteworthy that BYSL might be a reliable biological checkpoint for distinguishing tumor tissue from normal tissue in tumor patients and is expected to become an effective target for searching for new anti-tumor biomedicals.

Gene expression analysis of BYSL

The Tumor Immune Estimation Resource (TIMER2, version 2, http://timer.cistrome.org/) web server was applied for tumors samples and its adjacent normal tissue samples to analyze the expression of BYSL. In addition, the Gene Expression Profiling Interactive Analysis (GEPIA2, version 2, http://gepia2.cancer-pku.cn/#analysis) [5] webserver was employed since certain tumors typically without normal tissue [e.g., Brain Lower Grade Glioma (LGG), Skin Cutaneous Melanoma (SKCM), Testicular Germ Cell Tumors (TGCT), and etc.], P = 0.01, log2FC (fold change) cutoff = 1, and “Match TCGA normal and Genotype-Tissue Expression Project (GTEx) data”. Data base GEPIA2 was also utilized to analyze the BYSL expression of tumors in pathological stage I, II, III, and IV separately. The expression data was shown by violin plots.

The comparison of total BYSL levels between primary tumor and normal tissues was performed using the UALCAN tool (http://ualcan.path.uab.edu/index.html) to analyze cancer Omics data. The protein expression analysis of CPTAC dataset [6] was also employed. Furthermore, IHC analysis of normal tissues and four tumor tissue data were downloaded from the Human Protein Atlas (HPA, https://www.proteinatlas.org/).

Survival prognosis analysis of BYSL

The Overall Survival (OS) and Disease-Free Survival (DFS) significance mapping data together with survival plots of BYSL across all TCGA tumors were analyzed by GEPIA2 webserver. Next, the survival results of high and low expression groups were obtained by setting Cutoff-high (50%) and Cutoff-low (50%). Kaplan-Meier plots and log-rank P value were generated. Also, a ROC curve analysis using the pROC package was used to detect the cutoff value of BYSL.

Genetic alteration analysis of BYSL

The cBioPortal (https://www.cbioportal.org/) webserver was utilized to collect alternated frequency data, mutation type, mutated site information, as well as copy number alteration (CNA) of the protein structure across all TCGA tumors. We used Kaplan–Meier plots with a log-rank P-value to compare the patients' survival rate with or without BYSL alteration, the data we analyzed included overall, disease-specific, progression-free, and disease-free survival.

Protein phosphorylation analysis of BYSL

Bystin phosphorylation expression from CPTAC dataset was analyzed by the UALCAN webserver. In detail, the expression levels of bystin protein phosphorylation between primary tumor and normal tissues were investigated. Available databases for 5 tumors were selected, namely Uterine Corpus Endometrial carcinoma (UCEC), Colon adenocarcinoma (COAD), Ovarian serous cystadenocarinoma (OV), Breast invasive carcinoma (BRCA), Kidney Renal Clear Cell carcinoma (KIRC), and Lung adenocarcinoma (LUAD).

DNA methylation analysis of BYSL

DNA methylation level of BYSL of multiple probes was also analyzed by the MEXPRESS (https://mexpress.be/) webserver, highlighting the promoter region probes and showing the Pearson correlation coefficient (R) of methylation. The TISIDB (http://cis.hku.hk/TISIDB/) webserver was utilized to analyze tumor-immune system interaction to determine the expression of BYSL methylation and TILs in human cancer. The relative abundance of TILs was analyzed by gene set variation based on the gene expression profile. Correlation within BYSL DNA methylation and TILs presented was set by Spearman’s test.

Immune infiltration analysis of BYSL

The correlation within BYSL expression and immune infiltrates was analyzed by the TIMER2 webserver across all TCGA tumors. The immune cells of CD8 + T cell, Treg cell, neutrophilic, cancer-associated fibroblast, endothelial, and T cell follicular helper cell were selected, and the data were visualized as a heatmap. Infiltration of the immune system was assessed using CIBERSORT, Cibersort-ABS, and other algorithms. Following purity adjustment, the P and Cor (partial correlation) values were obtained using the Spearman's rank correlation test.

BYSL-related gene enrichment analysis

The subsequent analysis of the protein-protein interaction network data was obtained via STRING (https://string-db.org/) website. During analysis, we adjusted the following parameters: meaning of network edges (“evidence”), active interaction sources (“experiments”), minimum required interaction score with [“Low confidence (0.150)”], max number of interactors to show (“no more than 50 interactors” in 1st shell). The top 100 BYSL-correlated targeting genes were obtained by GEPIA2, and the data from tumors and normal tissues in TCGA datasets.

The interactive Venn diagram viewer, Jvenn [7], was employed to perform crossover analysis to contrast the BYSL-binding genes and interacted genes. In addition to Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, Gene Ontology (GO) enrichment analysis was also conducted on these two sets of data. The R packages “tidyr”, “ggplot2” and "clusterProfiler" were used to visualize the enriched pathways. The R language software [R-4.0.4, 64-bit] (https://www.r-project.org/) was used for this analysis. P < 0.05 was taken as statistic ally significant.

Gene expression analysis

The expression pattern of BYSL in different non-tumor cell lines was assessed. Apparently, datasets including HPA, GTEx, and FANTOM5 (function annotation of the mammalian genome 5) showed high expression of BYSL in the skeletal muscle (Figure S1A), yet with low tissue specificity. In the HPA/Monaco/Schmiedel datasets, BYSL expression level was the highest in plasmacytoid DC cells (Figure S1B).

The TIMER2 webserver was further utilized to investigate the differential BYSL protein level between tumors and normal tissues adjacent to tumors within TCGA database. In Fig. 1A, we can see the BYSL protein level in the tumor tissues, such as Bladder Urothelial Carcinoma (BLCA), Kidney Chromophobe (KICH), KIRC, Prostate Adenocarcinoma (PRAD), and UCEC of urinary system, Liver hepatocellular carcinoma (LIHC), Cholangiocarcinoma (CHOL), Esophageal carcinoma (ESCA), COAD, Rectum adenocarcinoma (READ), and Stomach adenocarcinoma (STAD) of digestive system, LUAD and Lung squamous cell carcinoma (LUSC) of respiratory system, Glioblastoma Multiforme (GBM), Head and Neck squamous cell carcinoma (HNSC), and BRCA of brain and thoracic tumor, was higher than the corresponding control tissue (P < 0.05). Notably, only four tumor types did not show such significant differences, including Thyroid carcinoma (THCA), Pancreatic adenocarcinoma (PAAD), Kidney Renal Papillary Cell carcinoma (KIRP), and Pheochromocytoma and Paraganglioma (PCPG) (P > 0.05).

Furthermore, for some tumors without normal tissue data in TCGA, the GTEx dataset was applied to evaluate the divergence of BYSL upon tumor and normal tissues. BYSL expression was universal higher in a series of tumor tissues, such as LGG, Lymphoid Neoplasm Diffuse Large B-cell Lymphoma (DLBCL), Uterine Carcinosarcoma (UCS), TGCT, and SKCM (See Fig. 1B and Figure S2A, P < 0.05).

On the other hand, we also evaluated the protein level of BYSL by the CPTAC database. There was a higher protein levels of bystin in a series of tumor tissues e.g., BRCA, COAD, KIRC, LUAD, and UCEC compared with non-tumor tissues (P < 0.05). Nevertheless, we did not see significant difference in the total protein expression of BYSL in OV compared with normal controls (P > 0.05) (see Fig. 1C ).

GEPIA2 was employed to observe the correlation between BYSL expression and patient survival. We found that the expression of BYSL is stage-dependent in the case of a few tumor types, including KICH, KIRP, LIHC, LUAD, OV, PAAD, and UCS (Fig. 1D, Figure S2B, P < 0.05).

Finally, bystin expression was analyzed by the IHC results from the HPA database and TCGA, respectively. As expected, normal cervical, glial, prostate, renal, and urothelial tissues showed negative bystin IHC staining, while tumor tissues showed highly positive staining (Fig. 2A-E, P < 0.01).

Survival analysis

To uncover characterize the relationship of BYSL expression between prognosis and overall survival. On the basis of the expression level of BYSL protein, tumor cases were divided into high and low expression groups. OS analysis data (Fig. 3A) showed that high BYSL expression is positive correlated with poor prognosis of Adrenocortical carcinoma (ACC, P = 0.0061), BRCA (P = 0.0026), Acute Myeloid Leukemia (LAML, P = 0.022), LIHC (P = 0.015), Sarcoma (SARC, P = 6.4e-05), and SKCM (P = 0.0027). Correspondingly, DFS analysis data (Fig. 3B) also proved that high BYSL expression was significantly associated with poor prognosis of ACC (P = 0.0028), KIRP (P = 0.046), LIHC (P = 0.0042), and STAD (P = 0.037).

Next, the AUC value of BYSL was obtained via ROC curve analysis in Fig. 4 to distinguish various tumors from corresponding normal samples. And it showed that the AUC value of BYSL in CHOL (95% CI: 1.000–1.000), LGG (95% CI: 0.981–0.992), TGCT (95% CI: 0.974–0.997), UCS (95% CI: 0.967-1.000), READ (95% CI: 0.951-1.000), GBM (95% CI: 0.964–0.985), and COAD (95% CI: 0.948–0.982) were all greater than 0.965, meanings high accuracy. Tumors with an AUC value of BYSL between 0.7 and 0.9 (with some accuracy) included STAD, BRCA, SKCM, LIHC, ESCA, PRAD, KICH, HNSC, BLCA, UCEC, and OV. These findings suggest that BYSL might be able to be marked to distinguish malignant cancer.

Genetic alteration analysis

Since the accumulation of altered genes will mediate the malignant progression of human carcinoma and poor diagnose in clinic, the genetic alterations of BYSL in human tumors were analyzed. According to our findings (Fig. 5), amplification of BYSL was the most commonly change for STAD (Amplification = 6.14%). What is noteworthy is that in cases such as ESCA, DLBC, CHOL, Uveal Melanoma (UVM), PAAD, KIRP, UCS, Mesothelioma (MESO), ACC, LGG, TGCT, GBM, and KIRC, “amplification” was found to be the only form of BYSL alteration (Fig. 5A). The specific types and sites of the BYSL genetic alteration was shown in Fig. 5B. The most prominent genetic alteration was the missense mutation. Alterations in R406W/Q were detected in COAD, LUSC, and SKCM. Additionally, the link between clinical survival prognosis of COAD, LUSC, as well SKCM and BYSL gene alteration was also discussed. In contrast, there were no significant differences in OS, disease-specific survival, disease-free survival, and PFS in COAD, LUSC, and SKCM cases in the BYSL alternated group compared with unaltered BYSL group (Fig. 5C, P > 0.05).

Protein phosphorylation and DNA methylation analysis

As proteins are phosphorylated during tumorigenesis, their dephosphorylation also plays a role in tumor progression. Therefore, we compared BYSL differences in tumor and non-tumor tissues at the level of phosphorylation modification. Six types of tumors (BRCA, OV, COAD, KIRC, UCEC, and LUAD) were analyzed in a more detailed way by using the CPTAC dataset. As is showed in Fig. 6A-B, the phosphorylation level of the S98 locus significantly increased in KIRC, UCEC, colon cancer, breast cancer and LUAD, while it significantly decreased in ovarian cancer. The phosphorylation level of the S167 locus significantly increased in breast cancer, while decreased in colon cancer. Besides, the S170 locus exhibits a lower phosphorylation level in colon cancer (P < 0.05).

Next, in this study, we also explored the relationship between BYSL DNA methylation and the pathogenesis of different tumors in the TCGA. We used the MEXPRESS Approach to analyze the data, the specific results can be seen in Figure S3. We found that BYSL DNA methylation related with multiple probes rather than unitary point, and it was exhibited a negative related manner with gene expression such as cg14707898 (r = -0.380, P < 0.001) and other genes which not in the promoter region. Taking COAD as a significant negative correlation, hypomethylation of BYSL at CG01526854 3’UTR site was prominent related with a poor prognosis, and P value was showed less than 0.05 (See Figure S4A).

In Figure S4B, we additionally showed an obviously relationship between methylation of BYSL and TILs across human cancers which only 28 kinds of data that can be analyzed and displayed. As shown in Fig. S4C, like COAD, the BYSL protein expression level was found to be connected with abundance of CD8 + Tem cells (r = 0.517), NK cells (r = 0.484), Follicular helper T cells (Tfh) (r = 0.525), and macrophage cells (r = 0.496). The P value above all analysis was less than 2.2e-16. Data in this part suggested that BYSL DNA methylation might be able to take on very important responsibilities in immune infiltration across multiple human cancers.

Immune infiltration analysis

Tumor microenvironment is closely related to the development and progression of TILs, which are crucial elements in tumor microenvironment [8–10]. In the HPA/Monaco/Schmiedel datasets, BYSL RNA expression was highest in NK cells, based on total PBMC (Figure S5). Herein, CIBERSORT, CIBERSORT-ABS, EPIC, MCPCOUNTER, QUANTISEQ, TIDE, TIMER, and XCELL algorithms were used to investigate the correlation between BYSL expression and the infiltration level of different immune cells in multiple tumor types of TCGA. BYSL expression in HNSC-HPV, SKCM, and SKCM-metastasis negatively correlated with CD8 + T cells. Similarly, DLBC, SKCM, and SKCM-metastasis negatively correlated with Treg cells, and BRCA, PRAD and TGCT negatively correlated with tumor-associated fibroblasts (Fig. 7A-C, P < 0.05). Exceptionally, neutrophils positively correlated with KIRC (Fig. 7D, P < 0.05).

Enrichment analysis of BYSL-related partners

According to existing analysis results, BYSL is closely related to the occurrence and development of tumors, to further investigate the molecular mechanism of which, we filtered out the known BYSL interacting proteins and the BYSL expression-related genes for a series of pathway enrichment analyses. We obtained 50 BYSL-binding proteins by applying the STRING tool, final data was shown in Fig. 8A, and the interaction network of these 50 proteins were presented visually. Then the top 100 genes associated with BYSL were acquired by GEPIA2 through all tumor expression data from TCGA banks. The intersection analysis of the above two groups revealed three interesting members: Activator of basal transcription 1 (ABT1), DDB1 and CUL4 Associated Factor 13 (DCAF13), and RNA-binding protein PNO1 (PNO1), which are involved in ribosomal biogenesis and protein ubiquitination (Fig. 8B).

The enrichment analyses data was mainly from GO and KEGG datasets. Specifically, the GO enrichment bar plot analysis indicated that BYSL plays a predominate role in biological processes, cell composition, and molecular function (Fig. 8C). In addition, the GO chord plot directly shows the corresponding interaction genes of BYSL in the functions of nuclear division, chromosome separation, and organelle division (Figure S6). The KEGG enrichment bar plot and scatter plot indicated that the translation and viral carcinogenesis might participate in the influence of BYSL on tumor pathogenesis (See Fig. 8D).

Bystin, as a cytoplasmic protein encoded by BYSL, binds directly to trophinin and tastin [1], that has been defined as a sensitive marker for astrocyte proliferation during brain damage and inflammation. BYSL has been reported to mediates homogenous adhesion between trophoblast and endometrial epithelial cells [11, 12], and participates in a series of biological processes such as embryo implantation [13], cell population proliferation [3, 4, and 14], ribosomal biogenesis [13], and ribosomal biogenesis [13]. Studies have continuously reported the functional relationship between BYSL and tumors [3, 14, and 15]. However, no literature on the pan-cancer analysis of BYSL from the perspective of the whole tumor has been retrieved. Therefore, we conducted the analyzation of BYSL gene in 33 tumors based on TCGA, CPTAC, GEO database, with a series of bioinformatics tools. These comprehensive tests include gene expression, survival prognosis, genetic changes, protein phosphorylation, DNA methylation, immune infiltration, and associated cellular pathways.

Many studies have shown that BYSL upregulates in a series of tumors. Ayala et al. found that Bystin overexpressed in human prostate cancer cells with perineural infiltration, and showed a gradient increase [16]. The expression levels of BYSL mRNA significantly increased in human hepatocellular carcinoma (HCC) specimens. BYSL siRNA in HCC in vitro and vivo showed cell growth stagnation and inhibition of tumor formation, and suggesting that the up-regulation of BYSL expression may lead to the occurrence of HCC [17]. BYSL expression is also enhanced in glioma tissues. siBYSL could inhibit the proliferation of glioma cells, and the progress of the cell cycle, and induce apoptosis. Furthermore, studies showed that a signaling pathway connected to GSK-3β/β-catenin was necessary for BYSL to promote GBM cell migration, invasion, and EMT [4]. Consistent with our results, BYSL expression was higher in most tumor tissues except for the LAML.

High BYSL level has been found to shorten the progression-free survival in cancer patients [18]. By exploring the TCGA database, we also found that high expression of BYSL corresponds to the poor prognosis of ACC and LICH. Previous studies have demonstrated that high trophinin expression in COAD patients is associated with poor prognosis [19], and BYSL plays an important role in directly binding trophinin and tastin [15], so maybe high BYSL expression is related to TNM staging. BYSL over-expressed in STAD. The overexpression of BYSL is associated with an improved prognosis in STAD, but additional molecular experimental evidence is needed to confirm that it plays a critical role in STAD development. BYSL may be a potential biomarker for predicting prognosis for cancer patient in clinic. To verify the clinical value of BYSL in tumor diagnosis, we further performed a ROC curve analysis. The results also showed that BYSL had a significantly higher AUC value in a variety of tumors. Therefore, we conclude that BYSL can be a potential diagnostic biomarker to distinguish tumors from corresponding normal tissues.

The types of genetic variation include mutation, structural variation, amplification, deep deletion, and multiple changes. We found that "amplification" is the dominant form of BYSL variation in all tumors. "Amplified" is the characteristic variant form of all ESCA, DLBC, CHOL, UVM, PAAD, KIRP, UCS, MESO, ACC, LGG, TGCT, GBM, and KIRC.

Phosphorylation is essential for protein regulation [20], which usually occurs at residues of serine (S), threonine (T), and tyrosine (T) substrates, and induces conformational changes in many proteins, leading to activation or inhibition of proteins, resulting in different biological functions [21]. CPTAC dataset analysis revealed the molecular mechanisms of bystin in tumors from the perspective of protein phosphorylation. Additionally, the phosphorylated level at the S98 locus was higher in most primary tumors than normal issues. Those results indicate that BYSL total protein phosphorylation is closely related to tumorigenesis and development. However, the potential role of specific site phosphorylation in tumorigenesis needs further experimental investigation.

In TCGA patients, we observed a negative correlation between BYSL DNA methylation in the non-promoter region and the high expression of BYSL in most tumors. Hypermethylation of BYSL at the CG01526854 3 'UTR site was significantly associated with poor prognosis in COAD patients. BYSL methylation significantly correlated with TILs in human tumors. Therefore, BYSL DNA methylation may play a specific role in the immune invasion of various human tumors, and the potential role of BYSL DNA methylation in tumorigenesis deserves further study.

CD8 + T cells (TC or CTL cells) is a sub clones of T lymphocyte cells, which has been found can kill target cells expressing the antigen and play a crucial role in immune defense against intracellular pathogens such as bacteria, viruses, and tumors [22]. Loss of CD8 + T cells disrupts antitumor immunity and increases sensitivity to tumor growth [23]. Early studies have shown that effector CD8 + T cells in the TME can generate IL-2, IL-12, and IFN-γ, which enhance the cytotoxicity of CD8 + T cells and lead to targeted tumor cell killing [24, 25]. Elevated levels of cytotoxic CD8 + T cells in the TME are associated with antitumor effects and improved prognosis of numerous cancers [26, 27]. Currently, no studies have been conducted on BYSL and TME. For the first time, our results show a negative correlation between BYSL expression and CD8 + T cells in HNSC-HPV, SKCM and SKCM-metastasis, and high BYSL expression is associated with low CD8 + T cell levels. It is likely that this will result in a reduced anti-tumor immunity and weakened ability to target and kill tumors, which is consistent with the above analysis results showing that high BYSL expression correlates with poor prognosis of cancer patients.

BYSL plays an important role in ribosome production and cell growth. BYSL can interact with mTORC2 and increase the synthesis of ribosomal proteins (such as S6) through the Akt-MTORC1-P70S6K pathway, mediate the survival and proliferation of glioma cells [14]. BYSL is present at multiple stages of nucleogenesis, including nucleolus-derived foci (NDF), perichromosomal regions, and the prenucleolar body (PNB) during mitosis [1, 28]. BYSL consumption significantly inhibits the formation of NDF and PNB, and destroys nucleolar assembly after mitosis, leading to increased apoptosis [3]. GBM tissues expressed highly WNT and β-catenin [29], and which is involved in glioma cell invasion and EMT [30, 31]. As a result of its overexpression, BYSL increases the phosphorylation level of GSK-3β and the nuclear distribution of β-catenin in GBM cells, and promotes the expression of EMT markers and cell migration or invasion of these cells [4]. BYSL, in combination with these previous studies and the findings of our analysis, may prove to be a promising biomarker for diagnosing most tumors, distinguishing them from healthy tissue, and serving as an effective target for the development of anti-tumor drugs. Novel small molecules or anti-body drug can be designed and synthesized by its high selectivity and better binding properties that target the pathway's upstream driver, such as BYSL. It may mechanically regulate the cell cycle and result in tumor cell death through AKT-mTOR, GSK-3β/β-catenin, and other molecular mechanisms. Furthermore, biomedical treatments may reduce toxicity resulting from molecular combinations, cancer patients may be treated in a more precise manner.

Our comprehensive pan-cancer analysis of BYSL provided a statistical association between BYSL expression and clinical prognostic, protein phosphorylation, DNA methylation, immune cell infiltration. BYSL can be used as a potential diagnostic biomarker to distinguish tumors from corresponding normal tissues. This study supplied detailed data for further understanding the relationship between BYSL and different tumors, provides theoretical basis for further understanding the regulation mechanism of BYSL in tumorigenesis, and provides new research clues for the development of targeted therapy in clinic.

ABT1, Activator of basal transcription 1; BLCA, Bladder Urothelial carcinoma; BRCA, Breast invasive carcinoma; BYSL, Bystin like gene; CAN, copy number alteration; CESC, Cervical squamous cell carcinoma and Endocervical adenocarcinoma; CHOL, Cholangiocarcinoma; ESCA, Esophageal carcinoma; COAD, Colon adenocarcinoma; CPTAC, Clinical Proteomics Tumor Analysis Consortium; DCAF13, DDB1 and CUL4 Associated Factor 13; DFS, Disease-Free Survival; DLBC, Lymphoid Neoplasm Diffuse Large B-cell Lymphoma; FANTOM5, function annotation of the mammalian genome 5; GBM, Glioblastoma Multiforme; GEO, Gene Expression Omnibus; GEPIA2, Gene Expression Profiling Interactive Analysis, version 2; GO, Gene Ontology; GTEx, Genotype-Tissue Expression Project; HCC, hepatocellular carcinoma; HNSC, Head and Neck squamous cell carcinoma; HPA, Human Protein Atlas; KEGG, Kyoto encyclopedia of genes and genomes; KICH, Kidney Chromophobe; KIRC, Kidney Renal Clear Cell carcinoma; KIRP, Kidney Renal Papillary Cell carcinoma; LGG, Brain Lower Grade Glioma; LIHC, Liver hepatocellular carcinoma; LUAD, Lung adenocarcinoma; LUSC, Lung squamous cell carcinoma; MESO, Mesothelioma; NDF, nucleolus-derived foci; OS, Overall Survival; OV, Ovarian serous cystadenocarinoma; PAAD, Pancreatic adenocarcinoma; PCPG, Pheochromocytoma and Paraganglioma; PNB, prenucleolar body; PNO1, RNA-binding protein PNO1; PRAD, Prostate Adenocarcinoma; READ, Rectum adenocarcinoma; SKCM, Skin Cutaneous Melanoma; STAD, Stomach adenocarcinoma; TCGA, The Cancer Genome Atlas; Tfh, Follicular helper T cells; TGCT, Testicular Germ Cell Tumors; THCA, Thyroid carcinoma; THYM, Thymoma; TILs, Tumor-infiltrating immune cells; TME, the tumor microenvironment; TIMER2, The Tumor Immune Estimation Resource, version 2; UCEC, Uterine Corpus Endometrial carcinoma; UCS, Uterine Carcinosarcoma; UVM, Uveal Melanoma.

- Ethics approval and consent to participate

Ethics are not in dispute.

- Consent to publish

Not applicable.

- Availability of data and materials

The data that support the findings of this study are openly available in TIMER2 at http://timer.cistrome.org/, GEPIA2 at http://gepia2.cancer-pku.cn/#analysis, UALCAN at http://ualcan.path.uab.edu/index.html, HPA at https://www.proteinatlas.org/, cBioPortal at https://www.cbioportal.org/, MEXPRESS at https://mexpress.be/, TISIDB at https://mexpress.be/, STRING at https://string-db.org/. The R language software [R-4.0.4, 64-bit] (https://www.r-project.org/) was also used for this analysis with the “tidyr”, “ggplot2” and "clusterProfiler" R packages.

- Competing interests

The authors declare that they have no competing interests.

- Funding

This study was supported by National Natural Science Foundation of China (82073864, 82073895), Guangdong Natural Science Funds for Distinguished Young Scholar (2017A030306008) and Guangzhou Science and technology planning project (202002030294).

- Acknowledgement

Thanks are given to all contributors in this field.

- Authors Contributions

XF, and LZ conceived and designed the study. YL, KL, and JZ analyzed the data. YL, and AR drafted the initial paper. XF revise the article. All authors contributed to the article and approved the submitted version.

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No competing interests reported.

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BYSL: A Novel Biomarker for Cancer Treatment

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Abstract

Figures

Background

Methods

Gene expression analysis of BYSL

Survival prognosis analysis of BYSL

Genetic alteration analysis of BYSL

Protein phosphorylation analysis of BYSL

DNA methylation analysis of BYSL

Immune infiltration analysis of BYSL

BYSL-related gene enrichment analysis

Results

Gene expression analysis

Survival analysis

Genetic alteration analysis

Protein phosphorylation and DNA methylation analysis

Immune infiltration analysis

Enrichment analysis of BYSL-related partners

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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