TRIB3 As an Emerging Biomarker and Potential Target for Cholangiocarcinoma: Evidence from Experiments and Bioinformatics

doi:10.21203/rs.3.rs-4063586/v1

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TRIB3 As an Emerging Biomarker and Potential Target for Cholangiocarcinoma: Evidence from Experiments and Bioinformatics

https://doi.org/10.21203/rs.3.rs-4063586/v1

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Cholangiocarcinoma (CHOL) is an aggressive tumor originating from the epithelium of the bile duct, with increasing incidence and mortality rates. Cholangiocarcinoma, a malignant tumor that is difficult to detect in the early stages, has limited treatment options. There is an immediate requirement to identify biomarkers for earlier screening, prognostic analysis, and targeted therapy for CHOL. Studies have demonstrated that tribbles homolog 3 (TRIB3) is highly expressed in 16 different cancer types and is strongly associated with worse prognosis. However, the effects and mechanisms of TRIB3 expression in CHOL are not clear. Analysis of multiple databases and experiments suggests that TRIB3 is overexpressed in CHOL and positively correlates with bad prognosis compared to neighboring normal tissues. TRIB3 demonstrates high accuracy in predicting the diagnosis of CHOL (AUC=0.876). Bioinformatics analysis showed that TRIB3 was related to immunocyte infiltration in CHOL. Silencing of TRIB3 reduced proliferation, invasion and migration of CHOL cell lines RBE and HuccT1, while promoting apoptosis. In summary, TRIB3 is overexpressed in CHOL and promotes cell proliferation, invasion and migration, whereas silencing TRIB3 promotes apoptosis. TRIB3 is positively correlated with poor prognosis and accurately predicts the diagnosis of CHOL.TRIB3 may be an emerging biomarker and a potiential target for CHOL.

TRIB3

Cholangiocarcinoma

Biomarker

tribbles homolog 3

Cancer

Cholangiocarcinoma (CHOL) is a heterogeneous group of bile duct tumors and is the 2nd most frequent type of liver cancer after hepatocellular carcinoma (1, 2). CHOL poses a significant challenge in clinical oncology due to its aggressive nature, limited treatment options, and often late diagnosis (3, 4). The insidious progression of bile duct cancer and its resistance to conventional therapies render it one of the most formidable malignancies to treat (5). Furthermore, the absence of reliable diagnostic markers presents a major obstacle to early detection and intervention.

Currently, the characterization of emerging biomarkers and therapeutic targe is imperative to enhance the management of CHOL (6). Tribbles homolog 3 (TRIB3), a precursor to the Tribbles family of proteins, has emerged as a potential candidate in this regard (7). The TRIB3 protein is crucial for apoptosis, cell proliferation, and metabolic regulation (8, 9). The dysregulation of TRIB3 is associated with a number of pathological conditions, ranging from cancer (10).

Notably, some evidence suggests an association between TRIB3 and various cancers, with high expression observed in CHOL (11). However, its specific relationship with CHOL and its mechanism of action remain unclear. Moreover, the complex interactions between TRIB3 and the tumor microenvironment, particularly its involvement in regulating immune cell responses, underscore its significance in cancer immunology (12–14).

Given the pressing need for new diagnostic and therapeutic strategies for CHOL, it is promising to explore the functional properties of TRIB3 and its potential connection to the pathogenesis of this malignancy. Therefore, this study was designed to elucidate the significance of TRIB3 in CHOL and its potential as a prognostic and diagnostic marker. Through a comprehensive bioinformatics profiling of TRIB3 and its interactions with immunocells and immune-related genes, this study aims to unravel the intricate mechanisms underlying CHOL, thereby offering innovative insights into the diagnosis, prognosis, and targeted therapy of CHOL.

Tumor Immunoassessment Resource (TIMER)

Gene expression levels of TRIB3 have been calculated using the TIMER database (http://timer.comp-genomics.org/). We selected the Gene_DE module and entered TRIB3 to obtain its mRNA expression in a pan-cancer landscape. Gene expression levels have been expressed as log2 TPMs (15, 16).

TRIB3 Expression Patterns in Human Pancarcinomas

Dysregulation of TRIB3 expression between various cancer types and normal tissues was investigated by combining normal tissue data from the GTEx database with data from The Cancer Genome Atlas (TCGA) (17, 18). The following cancers were obtained from the TCGA database: Head and Neck Squamous Cell Carcinoma (HNSC), Thyroid Carcinoma (THCA), Thymoma (THYM), Breast Invasive Carcinoma (BRCA), Oesophageal Carcinoma (ESCA), Lung Adenocarcinoma (LUAD), Lung Squamous Cell Carcinoma (LUSC), Mesothelioma (MESO), Stomach Adenocarcinoma (STAD), Colorectal Adenocarcinoma (COAD), Rectal Adenocarcinoma (READ), Liver Hepatocellular Carcinoma (LIHC), Pancreatic Anaplasmacytosis (PAAD), Pancreatic Adenocarcinoma (PAAD), Kidney Clear Cell Carcinoma (KIRC), Kidney Renal Pleomorphic Cell Carcinoma (KIRP), Kidney Eosinophilic Cell Carcinoma (KICH), Bladder Urothelial Carcinoma (BLCA), Prostate Adenocarcinoma (PRAD), Testicular Germ Cell Tumor (TGCT), Uterus Carcinoma of the Uterus Body and Endometrium (UCEC), Uterine Carcinoma (UCS), Ovarian Volvulus Cystic Carcinoma (OV), Adrenocortical Carcinoma (ACC), Squamous Cell Carcinoma of the Cervix (CESC), Skin Melanoma (SKCM), Sarcoma (SARC), Acute Myeloid Leukaemia (LAML), Diffuse Large B-cell Lymphoma (DLBC), Glioblastoma (GBM), Low Grade Glioma of the Brain (LGG), Pheochromocyte Cancer (PAC), Pheochromocytes (PA), and Pheochromocytoma and Paraganglioma (PCPG). All expression data underwent normalization using the transformation Log_2(TPM + 1).

Genomic Heterogeneity Score

We derived TRIB3 gene expression data from various types of sources and further screened primary blood-derived cancers-peripheral blood and primary tumor samples. In addition, we downloaded level 4 simple nucleotide variant datasets instrumented by MuTect2 software (19) from all TCGA samples on GDC (https://www.portal.gdc.cancer.gov/), as well as the Neoantigen or purity score for each tumor (20). We integrated the samples' TMB, NEO, and purity scores with the gene expression data and filtered out samples with an expression level of 0 (21, 22).

Construction of ROC

We also obtained pan-cancer statistical clinical data from the TCGA database and built receiver operating characteristic curves (ROCs) utilizing the "pROC" R package to evaluate the diagnostic ability of TRIB3 for 24 cancers (23).

Expression and localization characteristics of TRIB3

The mRNA expression spectrum of CHOL was obtained from the TCGA database and transformed into Log2 (TPM + 1) form after normalization. To analyze TRIB3 expression differences between tumor and neighboring non-tumor tissues, the R packages "stats" (version 4.2.1) and "car" (version 3.1-0) were utilized. Finally, the data were visualized using the R package "ggplot2" (version 3.3.5). In an attempt to further explore the localization of TRIB3 in CHOL, A single-cell RNA sequence analysis based on TRIB3 mRNA expression was conducted on the Tumor Immune Single-Cell Hub (Tisch, http://www.tisch.comp-genomics.org/) (24). In addition, CHOL_GSE138709 dataset was analyzed for TRIB3 levels of expression in various cell cluster.

Immune infiltration analysis

The CIBERSORT algorithm was applied in the evaluation of the immunologic infiltration of 22 immune cells using the "immuneeconv "R software package. The levels of immunologic infiltration were then compared between the high and low TRIB3-expressing cohorts. Subsequently, the Spearman method was utilized to determine the correlation among the level of immmunocyte infiltration and TRIB3 expression in the samples. The visualization were performed with the “ggplot2” and “ggClusterNet” in R. P-value < 0.05 was considered statistically significant.

We also utilized the online bioinformatics analysis platform BEST to integrate two datasets related to CHOL, such as GSE107943 and TCGA_CHOL, to detect the link between immmunocyte infiltration and immune-related gene sets. Specifically, we employed seven distinct algorithms (cibersort, epic, estimate, mcpcounter, quantiseq, timer, and xCELL) to assess immmunocyte infiltration patterns. Furthermore, we scrutinized the correlation between TRIB3 level and five categories of Immunomodulators separately, namely Antigen Presentation, Immunoinhibitor, Immunostimulator, Chemokine, and Receptor, within the context of the CHOL datasets analyzed.

Protein-protein interactive network construction and enrichment analysis

STRING website (https://www.string-db.org/) was utilized to create the protein–protein interaction (PPI) network of TRIB3 containing 20 related proteins. To examine the correlation between TRIB3 and these top 20 related-genes, the correlation analysis was performed with the Gene Expression Profiling Interactive Analysis 2nd Edition (GEPIA2, http://www.gepia2.cancer-pku.cn/#analysis) (25). Those genes with Pearson's coefficient greater than 0.3 and p-value less than 0.05 were chosen for Kyoto Encyclopedia of Genomes (KEGG) and Gene Ontology (GO) analyses. The results were visually processed with the "gplot2" (version 3.3.0) and "clusterProfiler" (version 3.14.2) R packages.

Differential expression and enrichment analyses

CHOL patients were categorized into highly-expressed and low-expressed groups based on the median expression level of TRIB3. The “limma” R oftware package was utilized to screen out the differentially expression genes (DEGs) between these two subsets, with the filtering threshold of DEGs set as p value less than 0.05 and |log2(Fold change)| >1.5(26). The heatmap displays the TOP 50 most significantly upregulated and downregulated genes. Following this, the DEGs were characterized by GO and KEGG analysis. The dataset "c2.cp.kegg.v7.4.symbols.gmt" containing gene sets was acquired from the Gene Set Enrichment Analysis (GSEA) (http://www.gsea-msigdb.org/gsea/index.jsp) to false discovery rates (FDR) for Differentially Expressed Genes (DEGs) between high- and low- TRIB3 level groups. GSEA also presents the calculation of the normalized enrichment score (NES), the results include the display of the Top 10 most significantly enriched pathways (27, 28).

Drug sensitivity analyses

We utilized the online bioconfidence analysis platform BEST (https://www.rookieutopia.com/app_direct/BEST/) to scrutinize various CHOL datasets, encompassing GSE107943 and TCGA_CHOL, within multiple drug sensitivity prediction repositories. The relationship between TRIB3 and drug sensitivity was explored utilizing three databases: GDSC (https://www.cancerrxgene.org/), PRISM (https://www.theprismlab.org/), and CTRP (https://www.portals.broadinstitute.org/ctrp/), with the results visualized through heatmaps.

Proteomic correlation analysis

The TCPA database was used to take samples from the TCGA project for tumor proteomic studies using reverse-phase protein arrays (RPPA) technology (http://tcpaportal.org/tcpa/), which we used to explore the mRNA expression of TRIB3 in CHOL in relation to the proteomic Correlation. We screened proteins with an absolute value of the correlation coefficient with TRIB3 greater than 0.3 and a p-value less than 0.05.

Culture of cells and transfection of siRNA

Cells used for the experiment at 37°C and 5% CO₂ were grown in a humidified environment with nutrient solution selected from RPMI-1640 or high-sugar DMEM containing 10% fetal bovine serum (Gibco, USA).

TRIB3-siRNA#1, #2 and #3 for gene transfection were obtained from Shanghai GenePharma (Shanghai, China). The CHOL cell lines RBE and Hucc T1 (from Shanghai Cell Culture Preservation Center, China) were seeded into 6-well plates at a concentration of 3 × 10⁵. The next day, when the cell fusion reached 60–70%, TRIB3-siRNA (100 nM) was transfected into the cells with TurboFect Transfection Reagent (Thermo Scientific, Waltham, MA, USA) (Table S1). 48 h later, we extracted the RNA from the cells and detected the relatively expressed level of TRIB3 mRNA.

Real-time Quantitative Reverse Transcription-PCR

RNA was isolated from cultivated cells with Trizol reagent (Invitrogen, Carlsbad, CA, USA) and reverse-transcribed to cDNA with an RNA reverse-transcription kit (Thermo Scientific, Waltham, MA, USA). qPCR was performed based on a qPCR kit (Takara SYBR Premix Ex Taq I, Tokyo, Japan) and Sangon Biotech (Shanghai, China) synthesized primers (see Table S2) for qPCR. We analyzed the results of the data with 2-ΔΔCT and normalized the TRIB3 expression levels.

Cell Counting Kit-8 Proliferation Assay

In each well of a 96-well plate, 1000 cells were placed. CCK-8 reagent (Dojindo Seed, Japan) was added directly into the culture medium at the specified times (24, 48, 72, 96 h). After that, the cells were incubated at 37°C for 2 h and the optional density (OD) value was calculated at 450 nm by an enzyme marker (BioTek Instruments, USA).

Flow cytometry apoptosis assay

All transfected cells were collected and made into single cell suspension cells/mL at a concentration of 1 × 10⁶ using 1× binding buffer. Then, 100 µL of the cell suspension was mixed with 5 µL of PE and 5 µL of 7AAD stain for 15 minutes. Next, 400 µL of 1× binding buffer was added to each tube and the data was analysed by flow cytometry using FlowJo V10.

Wound Healing Assay

We inoculated a total number of 1 × 10⁶ cells into a 6-well plate for 24 hours. The cells were then scratched onto a unilayer with a 200 µL pipette blade. Cell migration images captured at 0-, 24-, and 48-hours post-injury are representative. The decremental separation of the region of evoked damage was calculated and expressed as relative mobility, normalized to 0 hr power.

Transwell cell migration and invasion tests

Cell invasion and migration experiments were performed with Transwell chambers with or without matrix gel (BD Biosciences, San Jose, CA, USA). After transfection, cells were injected in the Transwell upper chamber, while the bottom chamber contained medium containing 20% FBS. Following 48 hours of incubation, cells in both chambers were immobilized with 4% paraformaldehyde solution, colored with 0.1% crystal violet, and imaged with a microscope. Migrating or invading cells were quantified using ImageJ platform.

Statistical analysis

Each experiment was repeated independently three times at least, and quantitative values were expressed in mean ± standard deviation. Statistical comparisons between the two groups were made by independent samples or paired t-tests, and multiple comparisons were made by one-way ANOVA when the data showed normal distribution and homoscedasticity. If these assumptions were not met, nonparametric tests were used. Visualization was performed using GraphPad Prism 8.0 and R Studio software, and statistical analysis was performed with SPSS 22.0 software. A level of significance at P < 0.05 was regarded as statistical significance.

TRIB3 in a pan-cancer landscape

In order to examine the expression of TRIB3 in a variety of tissues adjacent to various human tumors and their associated tissues, we analysed TCGA using the TIMER2.0 (Fig. 1A). Among them, TRIB3 is expressed highly in 16 types of tumors, including BLCA, BRCA, CHOL, COAD, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LUAD, LUSC, PCPG, PRAD, STAD and UCEC. Tumors are often heterogeneous, and a number of scores, such as TMB, a score that can be used to predict the heterogeneity of tumors in relation to the effect of novel treatment regimens, are widely. This study showed that TMB, NEO, and purity scores were highly positively correlated with CHOL (Fig. 1B-D), and that the correlation of TRIB3 with TMB and purity scores was the highest among all cancers. Expression profiling of tumor samples based on the GTEx also showed that TRIB3 had highly expressed levels in CHOL (Fig. 1E). When we used TRIB3 for tumor prediction diagnosis in pan-cancer and derived AUC values for ROC curves, 13 cancers, including CHOL, demonstrated good accuracy (AUC > 0.8) (Fig. 1F).

Characterization of TRIB3 expression and localization in CHOL

Since TRIB3 is overexpressed in CHOL and is associated with a variety of risks, we then delved into the expression and function of TRIB3 in CHOL. TRIB3 was markedly over-regulated in tumoral organizations as compared to whole and normal tissues and paired normal counterparts (Fig. 2A and B). AUC measures both sensitivity and specificity and is often used to indicate the internal validity of a diagnostic test. TRIB3 predicted the diagnosis with good accuracy (AUC > 0.8) based on the AUC value of the ROC curve for CHOL (AUC = 0.876, 95%CI: 0.770–0.982) (Fig. 2C). TRIB3 significantly predicted overall survival (OS), progression-free survival (PFS), and disease-free survival (DFS) in CHOL, and TRIB3 is positively associated to worse prognosis (Fig. 2D-F).

The analysis of single-cell sequencing datasets from three CHOL datasets revealed the cellular profile of TRIB3 in CHOL tumorous organizations, and the results showed that TRIB3 is expressed in malignant tumor cells and most types of immune cells (Fig. 2G). Data from the CHOL_GSE138709 dataset indicate TRIB3 is predominantly expressed from malignant tumor cells and endothelial cells (Fig. 2H-J).

TRIB3 influences the immunocyte infiltration

Data from single-cell RNA sequencing revealed the expression of TRIB3 in a variety of immune cells, and we next used an immune infiltration algorithm to perform a detailed analysis of TRIB3's impact on the tumor immune microenvironment. High expression of TRIB3 significantly reduced the level of infiltration of CD8 + T cells and follicular helper T cells (Fig. 3A and B). Correlation assays of TRIB3 with immunocyte infiltration showed that the expression of TRIB3 was correlated positively with M2 macrophages, but correlated negatively with B cells, CD4 + T cells, and endothelial cells (Fig. 3C and D).

To further analyze the correlation of TRIB3 in CHOL with immune infiltration as well as immune-related molecules, we used a dataset sourced from the GEO database in conjunction with a dataset from the TCGA, and various algorithms were used to assess immune infiltration. We utilized 7 kinds of immune infiltration algorithms to analyze the correlation between the two CHOL datasets. The findings revealed a significant negative relevance between increased TRIB3 level and all three immune infiltration scores - StromalScore, ESTIMATEScore, and ImmuneScore (Fig. 4A and B). This observation implies that heightened TRIB3 expression might be linked to reduced immunocyte infiltration within the tumor microenvironment. The heatmap illustrated that a majority of immunocyte infiltration was inversely associated with TRIB3 expression (Fig. 4A). The negative relevance between CD8⁺ T cell infiltration and TRIB3 expression was consistent across multiple algorithms, which is in keeping with our previous analysis. Furthermore, analysis with the xCELL algorithm demonstrated a negative relevance between TRIB3 expression and the infiltration of iDC, pDC, and DC (Fig. 4C). Notably, TRIB3 level exhibited a negative correlation with various immune checkpoints, notably CTLA4, LAG3 and TIGIT (Fig. 4D).

Drug sensitivity analysis

Bioinformatics allows prediction of new mRNA expression matrices based on known cell line expression matrices and drug sensitivity information as a means of determining the magnitude of the correlation between mRNA expression of TRIB3 and the IC50 value of a drug, and thus making an initial guess about the drug regimen for a patient with TRIB3-specific expression. To discover the interaction between TRIB3 and drugs, we utilized three databases specializing in drug sensitivity analysis. Analysis from GDSC revealed a positive relevance between the IC50 values of FMK, Enzastaurin, Veliparib, Methotrexate, and Dacinostat with TRIB3 expression (Fig. 5A and D). This suggests that as TRIB3 expression increases, patients may exhibit greater tolerance to these drugs. Additionally, GDSC indicated a positive association between TRIB3 expression and AZD8931, Trametinib, FH535, Dyrk1b_0191, and FGFR_0939, indicating that elevated TRIB3 levels could enhance sensitivity to these drugs. Analysis from the CTRP database demonstrated a positive relevance between TRIB3 expression and the IC50 values of bexarotene, crizotinib, BRD9647, BRD-KO9587429, BRD K02492147, and ML083 (Fig. 5B and D). Moreover, analysis from the PRISM database revealed a positive relevance between TRIB3 expression and the IC50 values of altrenogest, tie2-kinase-inhibitor, and CGP-37157, while showing a negative correlation with gallopamil, fleroxacin, and setiptiline (Fig. 5C and D).

Enrichment analyses of differential expression genes

To further explore the functions played by TRIB3 in CHOL and the pathways it may mediate, we performed pathway enrichment analysis. Comparison of DEGs in the high and low TRIB3 expression groups yielded a total of 8,487 up-regulated and 888 down-regulated genes included (Fig. 6A). Heatmaps showed the top 50 up- and down-regulated genes, separately (Fig. 6B). GO and KEGG analyses indicated that the major DEGs were enriched for multiple metabolic and biosynthetic pathways, Biological processes (BP): organic acid biosynthetic process, olefinic compound metabolic process and carboxylic acid biosynthetic process; Cellular component (CC): blood microparticle, high-density lipoprotein particle and plasma lipoprotein particle; Molecular function (MF): iron ion binding, heme binding, monooxygenase activity. KEGG analysis revealed that the main enriched pathways were Metabolism of xenobiotics bycytochrome P450, Drug metabolism – cytochrome P450, and Retinol metabolism (Fig. 6C). GSEA analysis showed that GnRH signaling pathway, snare interactions in vesicular transport, regulation of actin cytoskeleton, adherens junction, glycosphingolipid biosynthesis ganglio series, focal adhesion, and RNA polymerase were notably enriched in the highly TRIB3-expressing cohort (Fig. 6D).

Enrichment analyses of TRIB3-related proteins

Using STRING database, the PPI network of TRIB3 was conducted containing 20 related proteins (Fig. 7A). Among these top 20 related genes, ATF4, AKT1, CEBPG, CHAC1, DDIT3, and SMAD3 were considered to be the most relevant genes, with Pearson correlation coefficients of greater than 0.3 and p values of less than 0.05, and all of them were upregulated significantly in tumoral tissues in comparison with the normal tissues (Fig. 7B and D). GO and KEGG analysis showed that the TRIB3-related genes were enriched in apoptotic related pathways, BP: regulation of binding, intrinsic apoptotic signaling pathway in response to endoplasmic reticulum stress and intrinsic apoptotic signaling pathway; CC: transcription regulator complex, cell leading edge; and MF: RNA polymerase Il transcription regulator complex, DNA-binding transcription activator activity, RNA polymerase Ⅱ-specific and leucine zipper domain binding. KEGG analysis also revealed that the main enriched pathways included Non-alcoholic fatty liver disease, Hepatitis B and apoptosis (Fig. 7C and Table S3). Ultimately, we used the network to visualise the links between these genes and the GO as well as KEGG pathways (Fig. 8A and B).

Proteomic correlation analysis of TRIB3

TRIB3 has multiple biological regulatory mechanisms that not only affect mRNA expression, but also have noteworthy effects on the protein level. We therefore explored the correlation of TRIB3 mRNA expression with the proteome using CHOL samples from the TCPA database. We identified 25 proteins from 217 proteins in the CHOL proteome that were significantly correlated with TRIB3 gene expression (Fig. 9A). We plotted a regression scatter plot of top10 correlated proteins against TRIB3 mRNA, in which proteins such as LCK, HSP70, LKB1, STATHMIN, and STAT3_pY705 were negatively correlated with TRIB3 expression, and CYCLINE2, JAB1, CJUN_pS73, MSH2, the ACETYLATUBULINLYS40 and other proteins were positively correlated with TRIB3 expression (Fig. 9B-K).

Effects of silencing TRIB3 on CHOL cell biology

For investigating the effect from TRIB3 on the biology behavior of cells, we first selected RBE and HuccT1 cell lines with high TRIB3 expression to be transfected with TRIB3-targeting siRNAs, and quantitatively analyzed the TRIB3 transcripts after transfection with two different siRNAs (TRIB3 siRNA#3 could not achieve the knockdown of TRIB3 expression), and every siRNAs all specifically targeted the binding site on TRIB3, and TRIB3 expression was significantly decreasing in comparison with the negative control (NC) group (Fig. 10A).

In the CCK-8 assay, down-regulation of TRIB3 significantly inhibited the proliferative viability of both CHOL cells with both siRNAs used (Fig. 9B). We detected apoptosis using a flow-through detector. The results showed that apoptosis was significantly increased after TRIB3 knockdown in RBE and HuccT1 cells (Fig. 10C). Afterwards, we performed wound healing and transwell assays to assess the effect of TRIB3 on invasion and migration. The research results demonstrated that down-regulation of TRIB3 in RBE and HuccT1 cells inhibited the migratory and invasive abilities of CHOL cells significantly (Fig. 10D and E). These results suggested TRIB3 promoted invasive proliferation, invasion and migration of CHOL cells.

An important challenge for strategies to improve diagnosis, prognostic assessment, prediction of recurrence, and response to therapy for a variety of diseases, including cancer, is the discovery of new biomarkers (29). Biomarkers are indications of normal bio-processes, disease processes, or pharmacologic responses to treatment interventions, and can be utilized as diagnostic or prognostic predictors, as well as potential targets (30). Our results showed that TRIB3 exhibited high-expression in CHOL and was link with worse prognosis and may could predict CHOL development with high accuracy. Our study also suggested that TRIB3 may promote cell proliferation, invasion and migration in CHOL, thereby promoting CHOL development, making it a possible biomarker for CHOL. Pathway enrichment analyses revealed that it may have function by inhibiting the intrinsic apoptotic signalling pathway (31), a conjecture corroborated by the increased apoptosis rate in RBE and HuccT1 cells with TRIB3 knocked out.

TRIB3 was first identified as having a regulatory role in insulin resistance in diabetes mellitus. TRIB3 is inducibly expressed in the liver, and TRIB3 disrupts insulin signaling by directly binding to Akt and blocking activation of the kinase (32). Subsequent studies have shown that TRIB3 promotes cancer progression in COAD and BRAC (7, 33), but its role in CHOL is unknown. Through a combined analysis of multiple databases, we found that TRIB3 is highly expressed in a variety of tumors, especially CHOL. And in CHOL, TRIB3 expression was highly positively correlated with TMB, NEO, and purity scores. This implies that high expression of TRIB3 in CHOL is associated with higher heterogeneity and may also be a marker for immunotherapy or novel antigen therapy. We further analysed the role of TRIB3 in CHOL and found that its expression level was positively correlated with poor prognosis and could be used with good accuracy as a diagnostic marker for CHOL. The results of qRT-PCR on two types of CHOL cells (RBE and HuccT1 cells) were consistent with the bioinformatics analysis. Therefore, we conclude that the role of TRIB3 is similar to that reported in previous studies: it promotes CHOL progression and serves as a poor prognostic factor for patients as well as a diagnostic marker.

Through bioinformatics analysis, we found a significant negative correlation between TRIB3 and CD8 + T cells, iDC cells, pDC cells, and DC cells in the CHOL dataset of TCGA and GEO, which revealed that the expression of TRIB3 may inhibit the tumor infiltration of these cells. It has been shown that TRIB3 reduces CD8⁺ T cell infiltration through inhibition of the STAT1-CXCL10 axis in colorectal cancer and produces resistance to immune checkpoint blockade therapy(7). In RCC, TRIB3 also showed a significant negative correlation with CD8 + T cells (34). The relationship between TRIB3 expression in CHOL and DC cells has not yet been revealed by other studies, and DC cells, as antigen-presenting cells that can present tumor antigens to a variety of immune cells, play a major role in cancer immunity, and the study of the crosstalk between TRIB3 and DC cells may have the potential to improve "cold" tumors. TRIB3 and resistance to immune checkpoint blockade therapy was also demonstrated in bioinformatics analysis, with TRIB3 negatively correlating with a variety of immune checkpoints, including LAG3, CTLA4, and TIGIT. However, there is no experimental evidence on the mechanism of TRIB3 interaction with immune checkpoints.

The results of drug sensitivity analysis showed that overexpression of TRIB3 may lead to the occurrence of tolerance to drugs such as FMK, Veliparib, methotrexate, bexarotene, crizotinib, atenogestrel, and tie2-kinase inhibitors, but it has not yet been by the information about how binding of the TRIB3 protein occurs with these small-molecule inhibitors, which may be a direction for research to reveal heterogeneity of dosing choices and the possibility of combinations.

In this paper we found that TRIB3 is associated with a variety of mRNAs and proteins by analyzing the TCGA and TCPA databases. For example, we found that TRIB3 is positively associated with ATF4 expression in CHOL, which is consistent with existing experimental findings. It was shown that TRIB3 can localize to the chromatin of ATF4 and bind to genomic regions containing C/EBP-ATF motifs, subsequently activating endoplasmic reticulum stress response and cell death in LIHC(35). In addition, the relationship between TRIB3 expression and AKT1, DDMT3 was found to be accompanied by endoplasmic reticulum stress and autophagy (36). In a study using metformin to treat melanoma, it was shown that TRIB3 acts on KAT5 (lysine acetyltransferase 5) to bind to SMAD3, ultimately enhancing SMAD3 K333 acetylation(37). A study based on mouse embryonic fibroblasts showed that Trib3 inhibited the onset of cell death caused by Chac1 during the period when cells were under arsenite stress, which is contrary to the conclusion drawn herein that TRIB3 is positively correlated with CHAC1 expression, which may be due to the differences in the cellular models with the complexity of the tumor regulatory mechanisms(38). In proteomic analysis, the interaction between TRIB3 and CJUN has also been experimentally demonstrated: TRIB3 can directly bind to and promote the phosphorylation of pro-c-Jun N-terminal kinase (JNK)(39). It is worth noting that most of the proteomic analysis results have not yet been validated by existing experiments, and further studies are needed to explain the molecular mechanisms.

It has been shown that TRIB3 promotes the proliferation and invasion characteristics of cancer cells such as colorectal, breast, and hepatocellular carcinomas (33, 40, 41). In the present study, we demonstrated by multiple proliferation assays that silencing TRIB3 notably inhibited the proliferation of RBE and TuccT1 CHOL cells and significantly suppressed their invasion and migration. Consistent with KEGG and GSEA enrichment analyses and reports from other cancer species, these results suggest that TRIB3 might play a crucial role in the proliferation, invasion, and migration of developing CHOL cells.

Our findings suggest that TRIB3 holds promise as a biomarker and therapeutic target for CHOL, and that it is associated with may be related to the abundance of infiltration with a wide range of immune cells as well as drug sensitivity to certain small molecule inhibitors. However, more studies are needed to elucidate the molecular mechanisms by which TRIB3 plays a role in CHOL progression and to develop more effective therapies for targeted treatment of CHOL patients. At the molecular and cellular level, the downstream pathways regulated by TRIB3 through which pathways and how they affect the sensitivity of small molecule drugs are all questions worth exploring. At the clinical level, subsequent studies could focus on exploring TRIB3-targeted therapies in preclinical models and evaluating their efficacy and safety in clinical trials.

Funding

None.

Ethics approval and consent to participate

Not applicable.

Availability of data and material

Data in support of the findings of this study are available from the corresponding author by request.

Consent for publication

Not applicable.

Author contributions

Shen Chen: Conceptualization, Methodology, Software, Investigation, Writing - Original Draft, Visualization. Yijie Jiao: Methodology, Validation, Writing - Review & Editing. Yuping Lai: Methodology, Software, Writing - Original Draft. Jiarui Cao: Data Curation, Resources. Rui Cao: Validation, Writing - Review & Editing. Ke Li: Investigation, Writing - Review & Editing. Zhiheng Cheng: Formal analysis, Writing - Review & Editing. Siqian Liao: Writing - Review & Editing. Xiaoqiang Niu: Resources, Data Curation. Maopu Tu: Validation, Formal analysis. Shengxun Mao: Supervision, Project administration. Huizi Li: Supervision, Project administration, Conceptualization.

Declaration of competing interest

The authors disclaim any known financial interests or individual relationships that might affect the work reported in this article.

Acknowledgments

None.

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No competing interests reported.

TableS1.SequencesforsiRNA.xlsx
Table S1. Sequences for siRNA
TableS2.PrimersequencesforqRTPCR.xlsx
Table S2. Primer sequences for qRT-PCR
TableS3.KEGGandGOpathwayenrichmentanalysis.xlsx
Table S3. KEGG and GO pathway enrichment analysis

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TRIB3 As an Emerging Biomarker and Potential Target for Cholangiocarcinoma: Evidence from Experiments and Bioinformatics

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Abstract

Figures

Introduction

Methods

Tumor Immunoassessment Resource (TIMER)

TRIB3 Expression Patterns in Human Pancarcinomas

Genomic Heterogeneity Score

Construction of ROC

Expression and localization characteristics of TRIB3

Immune infiltration analysis

Protein-protein interactive network construction and enrichment analysis

Differential expression and enrichment analyses

Drug sensitivity analyses

Proteomic correlation analysis

Culture of cells and transfection of siRNA

Real-time Quantitative Reverse Transcription-PCR

Cell Counting Kit-8 Proliferation Assay

Flow cytometry apoptosis assay

Wound Healing Assay

Transwell cell migration and invasion tests

Statistical analysis

Results

TRIB3 in a pan-cancer landscape

Characterization of TRIB3 expression and localization in CHOL

TRIB3 influences the immunocyte infiltration

Drug sensitivity analysis

Enrichment analyses of differential expression genes

Enrichment analyses of TRIB3-related proteins

Proteomic correlation analysis of TRIB3

Effects of silencing TRIB3 on CHOL cell biology

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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