Abstract
The TBC (Tre-2/Bub2/Cdc16, TBC) structural domain is now considered as one of the factors potentially regulating tumor progression. However, to date, studies on the relationship between TBC structural domains and tumors are limited. In this study, we identified the role of TBC1 domain family member 8 (TBC1D8) as an oncogene in colorectal cancer (CRC) by least absolute shrinkage and selection operator (LASSO) and Cox regression analysis, showing that TBC1D8 may independently predict CRC outcome. Functional enrichment and single-cell analysis showed that TBC1D8 levels were associated with hypoxia. TBC1D8 levels were also positively correlated with M2 macrophage infiltration, which may have a complex association with hypoxia. Taken together, these results show that the TBC1D8 gene is involved in colorectal carcinogenesis, and the underlying molecular mechanisms may include hypoxia and immune cell infiltration.
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Introduction
Colorectal cancer (CRC) is a tumor of the gastrointestinal system, ranking third in incidence among malignancies and second in lethality among cancers1. Its pathogenesis appears to involve multiple factors and steps2, including genetics, diet, and lifestyle habits3,4. Statistics show that the tumor frequently recurs and metastasizes, spreading especially to the liver, lung, bone, ovaries, peritoneal and the five-year survival rate for such patients is approximately 10%5,6. Treatment methods for CRC include chemotherapy, surgery, radiotherapy, and molecular-targeted therapy7,8. Molecular-targeted therapy for CRC has become a hot topic9,10. The number of molecularly stratified treatment options is increasing, as is the use of biomarkers to guide prediction and treatment decisions11. Therefore, the identification of novel markers and targets for treating CRC is necessary for improving the disease prognosis.
The TBC (Tre-2/Bub2/Cdc16, TBC) structural domain is a conserved amino acid sequence consisting of the oncogene Tre-2 (gene symbol: USP6) and the structural domains of the yeast cell circulation regulator genes Bub2 and Cdc1612. TBC-domain family members are strongly conserved with high levels of homology13. TBC structural domains are often located in tandem with other structural domains associated with cell membrane functions, suggesting that the biological functions of TBC structural domain proteins are closely linked to cell membranes and may mediate the onset and progression of cancer or other diseases14,15,16 and, indeed, an association with tumor progression has been found for a number of these proteins. High TBC1D7 levels have been observed in several lung cancers and have been correlated with poor patient outcomes. RNAi interference of TBC1D7 in lung cancer cells inhibited cell growth; conversely, overexpression of TBC1D7 promoted lung cancer cell proliferation and promoted tumor formation in mice17. TBC1D16 also appears to be the driving gene in melanoma, and its overexpression can promote cell growth and gene expression, affecting vesicle transport and further influencing melanoma progression18. It has been shown that TBC1D8 is significantly up-regulated in invasive ovarian cancer cells, which drives the occurrence and metabolic reprogramming of ovarian cancer19. However, there are few studies on its role in CRC. The present study seeks to fill this lacuna.
In this study, human CRC specimens were analyzed by immunohistochemistry. At the same time, using bioinformatics software and methods, we performed an initial exploration of the potential function and mechanisms of TBC1D8.
Materials and methods
Reagents
The reagents and antibodies used are listed in the Supplementary material (Supplementary Table 1). The concentrations used were based on those used in previous research or on the manufacturers’ recommendations. Experimental procedures are also provided in the Supplementary material.
Cell culture
The human CRC cell lines, SW620, SW480, RKO, HCT116, HT-29, and LoVo, as well as the human normal human colonic epithelial cell line, NCM460, and THP-1 cells (human monocytic cells) were acquired from the cell bank of the Chinese Academy of Sciences (Shanghai, China). SW620 cells were grown in Leibovitz's L-15 medium with 10% fetal bovine serum (FBS), while the other lines were grown in DMEM with 10% FBS at 5% CO2 and 37 °C. All media were supplemented with 1% penicillin/streptomycin. Cells passed regular mycoplasma contamination testing.
Ethics statement and specimen collection
The study’s protocol was approved by the ethics committee of the Jiangsu Province Hospital of Chinese Medicine, and informed consent was obtained from clinicians and patients (2020NL-107-01). All experiments were conducted in accordance with the Declaration of Helsinki and in accordance with the relevant designated guidelines and regulations. Written information was provided and written consent was given to all participants and informed consent was obtained from all participants prior to collection of specimens20. Thirty CRC tissues and paracancerous tissues (5-cm margin) were obtained intra-operatively from untreated patients at the Jiangsu Province Hospital of Chinese Medicine. Tumors were scored using the 8th edition of the “American Joint Committee on Cancer tumor-node-metastasis” (TNM) system21. Samples were washed in chilled PBS and quick-frozen in liquid nitrogen after which they were stored at − 80 °C until analysis.
Immunohistochemical (IHC) staining
IHC was conducted according to a previously published protocol22. Sections were blocked with blocking solution and incubated with anti-TBC1D8 antibodies. The extent and intensity of staining were assessed by two independent investigators. Staining intensities were graded as 0 (none), 1 (weak), 2 (moderate), and 3 (strong). Staining extent was graded as 0 (no positively-stained cells), 1 (less than 10%), 2 (10–50%), and 3 (over 50%). The histochemistry score (H-SCORE), representing both the proportion of stained cells and the degree of staining, was determined as: “H-SCORE = ∑ (PI × I) = (percentage of cells with weak intensity × 1) + (percentage of cells with moderate intensity × 2) + (percentage of cells with strong intensity × 3)”, where PI represents the percentage of positive cells to the total number of cells in a particular field and I represent the intensity of staining. The H-SCORE ranged between 0 and 300, with higher scores indicating more intense staining.
Establishment of hypoxia model
A stock solution of CoCl2 was prepared by dissolving solid CoCl2 in serum-free DMEM. Using a previously published method23, we pretreated cells with 300 µmol/L CoCl2 for 30 h. CoCl2 induces hypoxia through blocking degradation of HIF-α, activating the hypoxia cascade24.
TBC1D8 expression assessment
The Western blotting method followed previous descriptions25. Proteins were lysed in RIPA buffer and protein concentrations were measured by the Bradford assay. Samples of 20 µg each were separated on 10% or 8% SDS-PAGE, transferred to polyvinylidene fluoride (PVDF) membranes, and blocked with 5% bovine serum albumin. The blots were then probed with the relevant primary antibodies at 4 °C overnight. Blots were washed in Tris-buffered saline containing 0.05% Tween-20 and were incubated with the corresponding secondary antibodies with an Electrochemiluminescence (ECL) detection kit used to measure densities. The protein bands (including β-actin as the loading control) were visualized with a gel image processing system (ChemiDoc XRS +) and relative concentrations were determined. The expression levels of each protein were normalized relative to β-actin protein expression and are shown as relative protein levels. Relative protein expression = gray value of target protein band/gray value of β-actin band. TBC1D8 expression in CRC was first investigated using the Tumor Immune Estimation Resource (TIMER) web tool (https://cistrome.shinyapps.io/timer/) Cancer Cell Line Encyclopedia (CCLE) database (https://portals.broadinstitute.org/ccle); The Human Protein Atlas (HPA) (https://www.proteinatlas.org/) and the Gene Expression Profiling Interactive Analysis (GEPIA) database (https://www.oncomine.org/).
RNAi plasmids construction and transfection
HCT116 and HT-29 cells which express relatively high levels of TBC1D8 were selected for subsequent experiments. The plasmids described below were all constructed by GeneChem (Shanghai, China). The procedures for constructing RNAi plasmids are described in the Supplementary material. From three short hairpin interfering RNA (shRNA) targeting TBC1D8, we selected the one with the highest inhibitory efficiency for the subsequent experiments. The shRNA-TBC1D8 plasmid (sh-TBC1D8) and the control (NC) non-targeting sequence plasmid were transfected into 70%-confluent CRC cells using Lipofectamine 3000 per provided protocols. The transduction efficiencies were examined by western blotting and Green fluorescent protein (GFP) expression.
Colony formation assays
The ability of cells to form colonies was measured as previously described26. Five hundred cells per well were plated in 6-well plates and grown for approximately 14 days. Colonies (a minimum of five cells) were stained with 0.5% crystal violet at room temperature (20–25 °C) for 10 min and counted under light microscopy (Olympus BX53, Japan).
Xenograft tumor model
All animal experiments were performed according to the “Guide for the Care and Use of Laboratory Animals” (NIH Publications No. 80‐23, revised 1996) and approved by the ethics committee of the Jiangsu Province Hospital of Chinese Medicine (2021-11-032). 4-week-old male BALB/c nude mice were acquired from the Beijing Institute of Biomedicine (Beijing, China) (Certificate No. SYXK2019-0010). Cells (HCT-116 cells transfected with sh-TBC1D8 and NC cells; 1 × 107 cells/mouse) were injected subcutaneously into the right armpit region (n = 6 per group). Tumors were visible after seven days. Tumor diameters (maximum and minimum) were measured twice a week. The mice were euthanized under CO2 on day 35, and the sera and tumors were excised. Euthanasia was fully consistent with the recommendations of the Guidelines on Euthanasia of the American Veterinary Medical Association. Tumor volumes were calculated as V = 1/2ab2, and tumor growth curves were drawn.
Sphere formation
Sphere formation was examined as previously described27. Single-cell suspensions were plated in 6-well ultralow attachment plates (5 × 103 cells/well) and grown in serum-free medium with Human recombinant Fibroblast growth factor (FGF) (20 ng/ml), Human recombinant Epidermal Growth Factor (EGF) (20 ng/ml), and 2% B27. The numbers and sizes of the spheres were determined after seven days under light microscopy.
Co-culture system
Cell slides were placed at the bottom of the 6-well plates at the beginning of the co-culture experiment. THP-1 monocytic cells (1 × 105 cells/mL) were incubated with 10 ng/mL PMA for 48 h to allow differentiation into M0 macrophages27 The medium was then replaced with serum-free DMEM and the cells were allowed to grow for 24 h. Co-cultures between M0 and CRC were conducted using Transwell inserts (0.4 µm) in which culture medium was diffusible but cells were not permeable. To mimic the interactions between macrophages and tumors, 6 × 105 CRC cells were placed in the upper chamber of a 6-well transwell apparatus and the M0 macrophages were placed in the lower chamber.
Immunofluorescence
Immunofluorescence staining was conducted according to a previously published protocol28. Briefly, the cells cultured on cover slips were fixed, permeabilized with 0.5% Triton X-100, and incubated with anti-CD163 and anti-CD206 polyclonal antibodies overnight at 4 °C. Subsequently, the slides were incubated with secondary antibodies. Nuclei were stained using 4',6-diamidino-2-phenylindole (DAPI) for 3 min, and the cells were incubated in the dark for 3 min. The slides were washed with PBS four times, for 5 min each. Then, the slides were sealed with sealing solution containing a fluorescence quencher and observed and imaged under a fluorescence microscope. Immunofluorescence was examined under an epi-fluorescence microscope (Olympus CKX-41,). By changing the filters, the different secondary antibodies could be identified in double-stained sections. Images were captured with a digital camera (Olympus, DP50).
Transcriptomic expression and survival analysis
TBC1D8 expression was investigated using the The Cancer Genome Atlas (TCGA)-Colon Adenocarcinoma (COAD) cohort and the GSE10950 and GSE37182 datasets29. Expression levels were analyzed in terms of different parameters, namely, “T (Tumor)”, “N (Node)”, and “M (Metastasis)” stages using the TCGA-COAD data.
Survival analysis, specifically, the Overall Survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) were performed by log-rank test using the TCGA-COAD data.
LASSO and cox regression
Raw RNA-seq and clinical data were acquired from the TCGA dataset in January 2020. LASSO regression analysis was conducted using the R package ‘glmnet' to determine the research objective. Univariate and multivariate Cox regression was performed to determine the independent prognostic factors of TBC1D8. The forest plot representing the P-value, hazard ratios (HRs), and 95% confidence intervals (CIs) of each variable were determined using the “forestplot” package in R.
Function analysis of TBC1D8-associated genes
The median cutoff of TBC1D8 expression in the TCGA-COAD was used to differentiate high and low TBC1D8 groups. The R “limma” package was used to identify TBC1D8-associated genes30. Enrichment analysis was conducted with the “clusterProfiler” package in R to examine possible TBC1D8 functions31.
Gene-Set Enrichment Analysis (GSEA) was conducted with the Broad Institute GSEA software 3.032. The gene set “Gene Ontology (GO)” was downloaded from the Molecular Signatures Databases (http://www.gsea-msigdb.org/gsea/msigdb/index.jsp) and was used for GO enrichment. A False Discovery Rate (FDR) < 0.1 represented statistical significance.
For Gene Set Variation Analysis (GSVA)33, the R package “GSVA” was used to conduct enrichment analysis and “Limma” was used to identify differentially enriched genesets based on TCGA-COAD and GSE37182. To further explore the association between TBC1D8 and hypoxia, we defined 29 prognosis-related hypoxia genes with reference to the work of Joon-Hyop Lee et al.34. and calculated the correlation between them and TBC1D8.The Tumor Immune Single Cell Hub (TISCH)35 based GSE146771 data and the CancerSEA36 were used for the single-cell level comprehensive exploration for cancer cell function37.
Immune analysis
The association between TBC1D8 level and immune infiltration was examined with the Estimating the Proportion of Immune and Cancer cells (EPIC) algorithm based on TCGA-COAD38. TIMER (TCGA-COAD) was used for reliable assessment of infiltration, pairwise comparisons were quantified by Spearman’s rank correlation, with P < 0.05 representing significance. CIBERSORT was used to confirm the relationship between TBC1D8 levels and macrophage polarization39 based on. GSE10950 and GSE37182 All results were processed using the “ggplot2” and “pheatmap” packages in R.
DNA methylation and genetic alternation analysis
The analysis of TBC1D8 DNA methylation and the correlation between disease prognosis and TBC1D8 methylation values was performed using the Methsurv and MEXPRESS databases (http://mexpress.be). We also used the SurvivalMeth database40 to analyze the differential methylation levels of different probes for TBC1D8 between normal and tumor tissues. The alteration frequency of the TBC1D8 gene in several studies of CRC, including the Colon Cancer (CPTAC-2 Prospective, Cell 2019), Colorectal Adenocarcinoma (Genentech, Nature 2012), Colorectal Adenocarcinoma (DFCI, Cell Reports 2016), Colorectal Adenocarcinoma (TCGA, PanCancer Atlas), Colorectal Adenocarcinoma (TCGA, Nature 2012), Colorectal Adenocarcinoma (TCGA, Firehose Legacy), Colon Adenocarcinoma (CaseCCC, PNAS 2015), and Metastatic Colorectal Cancer (MSKCC, Cancer Cell 2018)) studies, was analyzed via the cBioPortal database (http://www.cbioportal.org). We provided data of genomic alteration type, mutation site profile analyses. In addition, we analyzed the differential expression of macrophage markers in the wild-type and mutant TBC1D8 groups using the TIMER database (https://cistrome.shinyapps.io/timer/).
Statistical analysis
Data were expressed as means ± SEM. Comparisons between two groups and multiple groups were assessed by t-tests and one-way ANOVA, respectively. All data were analyzed with SPSS 29.0 (IBM Corp., Armonk, NY, USA) and visualized with GraphPad Prism 8.0 (GraphPad Software, Inc., USA). Experiments were conducted a minimum of three times. ****P < 0.0001, ***P < 0.001, **P < 0.01 and *P < 0.05 were defined to be statistically significant.
Adherence to ARRIVE guidelines
Our study complies with the ARRIVE guidelines (https://arriveguidelines.org).
Results
Prognostic value of TBC domain family members
A total of 44 TBC structural domain family members were used for LASSO regression to identify robust markers for follow-up studies. Cross-validation was performed in ten rounds to prevent overfitting (Fig. 1A,B). Sequential Cox regression (both univariate and multivariate) indicated that TBC1D8 was significantly correlated with CRC prognosis, identifying it as the research target (Fig. 1C–F). Finally, Kaplan–Meier survival curves showed that patients with higher TBC1D8 levels had shorter OS and PFI (P < 0.05, Fig. 1G–H) and we did not observe an association between TBC1D8 and DSS (P > 0.05, Figure S1). These results suggested that TBC1D8 is closely associated with the progression of CRC and had potential research value.
TBC1D8 expression as an indicator of overall survival and progression-free interval determined by Least absolute shrinkage and selection operator (LASSO) and Cox regression analysis in the The Cancer Genome Atlas (TCGA) cohort. (A) In the LASSO-Cox model based on TCGA-Colon Adenocarcinoma (COAD), the minimum standard was adopted to obtain the value of the super parameter λ by tenfold cross-validation, where the optimal lambda resulted in 2 non-zero coefficients (n = 480). (B) Cross-validation for selecting tuning parameters. (C–F) Univariate (C,E) and multivariate (D,F) Cox regression analysis for overall survival (C,D) and progression free interval (E,F) of the relationship between TBC1D8 and TBC1D17 levels and clinicopathological parameters related to CRC prognosis in TCGA-COAD (n = 480). (G,H) Overall survival (OS) (G), Disease-specific survival (DSS) (H), and Progression-free interval (PFI) (I) based on median TBC1D8 levels in TCGA-COAD (n = 480).
Analysis of TBC1D8 expression
Data from TCGA-COAD indicated higher levels of TBC1D8 transcription in CRC tissue (Fig. 2A). Moreover, information from the GSE10950 and GSE37182 datasets also showed higher TBC1D8 levels in CRC compared to paracancerous tissues (Fig. 2B,C). It was found that there was no significant link between TBC1D8 levels and TNM staging (Fig. 2D–F). Receiver operating characteristic (ROC) curves measured the ability of TBC1D8 to differentiate CRC and normal tissue, with carcinoembryonic antigen (CEA) used as a positive reference. The areas under the curves are shown in Fig. 2G–I. The Human Protein Atlas (HPA) revealed that TBC1D8 was mostly cytoplasmic or membrane-associated (Fig. 2J). The results of the CCLE database are shown in Fig. 2K and the transcriptome expression levels of TBC1D8 at cell level were, in descending order (HT-29, HCT 116, LoVo, SW620, RKO and SW480). The western blotting, shown in Fig. 2L,M, yielded consistent results. Subsequently, IHC demonstrated overexpression of TBC1D8 in CRC tissues compared to healthy tissues. We used our own CRC samples to detect TBC1D8 protein expression. The mean H-SCOREs for TBC1D8 in CRC and paracancerous tissues were 104.17 ± 5.79 and 52.58 ± 3.54, respectively (Fig. 2N). Results strongly indicated that an imbalance in the TCB1D8 gene expression can cause CRC.
TBC1D8 levels in colorectal cancer (CRC) and their association with clinicopathological parameters. (A) Expression level of TBC1D8 between CRC tissues and paired paracancerous tissue in the TCGA-COAD. Wilcoxon test was performed (n = 41). (B,C) The differences in TBC1D8 gene expression between CRC cases and normal controls in GSE10950 (n = 48) (B) and GSE37182 (n = 172) (C). Wilcoxon test was performed. (D–F) Association of TBC1D8 mRNA levels with (D), T stage (n = 477) (E), N stage (n = 478) (F), and M (n = 411) stage in CRC patients based on TCGA-COAD. (G–I) Diagnostic Operating Characteristic (ROC) curves based on TBC1D8 and Carcino embryonic antigen (CEA) levels [H, TCGA (n = 521); I, GSE10950 (n = 48); J, GSE37182 (n = 172)]. (J) Immunohistochemical (IHC) analysis of TBC1D8 in CRC tissues based on Human Protein Atlas (HPA) (Magnification, × 2, scale bars = 500 μm). (K–M) TBC1D8 expression in different CRC cell lines based on (K) the Cancer Cell Line Encyclopedia (CCLE) and (L,M) western blot. One-way ANOVA was conducted. Data in the bar chart are the means ± SME from three independent experiments. *P < 0.05. (N) Quantifications of TBC1D8 IHC staining based on our own samples (n = 30) show the differential expression between paracancerous and colonrectal carcinoma tissue. The t-test was performed (Magnification × 200, scale bars = 50 μm; Magnification × × 400, scale bars = 20 μm). (NS: non-significant, *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001).
Prediction of TBC1D8 functions and interactions
The network linking neighboring genes with TBC1D8 was compiled by GeneMANIA. This shows various relationships, including interactions, colocalization, and common pathways (Fig. 3A). The genes co-expressed with TBC1D8 in TCGA-COAD were examined using R "limma" (Fig. 3B). In all, 521 differentially expressed genes (DEGs) were used for the protein–protein interaction network (PPI) assembly. In the network, SOX2 has the highest score as a hub gene based on cytoHubba (Fig. 3C). Enrichment analysis based on SOX2 as the core sub-network showed that the network may involve "regulation of peptidase activity", "cell–cell adhesion via plasma-membrane adhesion molecules", "humoral immune response", "maintenance of gastrointestinal epithelium", "digestive system process", and “regulation of transmembrane receptor protein serine/threonine kinase signaling pathway” (Fig. 3D,E). GSEA for the whole PPI showed that TBC1D8 may be associated with hypoxia, angiogenesis, and matrix degradation (Fig. 3F,G). These findings offered evidence regarding the relationship between TBC1D8 and cancer-related signaling pathway. This led us to perform a more in-depth molecular mechanism study.
Protein–protein interaction (PPI) network and enrichment analysis. (A) TBC1D8 with neighboring genes showing physical interactions, co-expression, co-localization, predicted common pathways, genetic interactions, and common protein domains based on GENEMANIA database. Each node represents a gene. The node size represents the strength of interactions, and the line color represents the types of interactions. (B) Volcano plot of Differentially expressed genes (DEGs) induced by alterations in TBC1D8 levels. Yellow dots, upregulation; blue dots, downregulation; abscissa, expression differences (log2 fold change); ordinate, significance of differences (− log10 padj). (C) Cytoscape was conducted to visualized the network of TBC1D8 and significantly correlated genes. Darker color and larger size indicate higher degree. (D) The hub module with the highest scores analyzed by Molecular Complex Detection (MCODE). Darker color and larger size indicate higher degree. (E) The “clusterProfiler” R package was used for the Gene ontology (GO) _biological_process (BP) enrichment analysis based on the module. (F,G) Gene Set Enrichment Analysis (GSEA) for TBC1D8. (F) The enriched gene sets in KEGG and Reactome collection by the high TBC1D8 expression sample. (G) The enriched gene sets in GO collection by the high TBC1D8 expression sample. Only gene sets with Normal P < 0.05 and false discovery rate (FDR) < 0.1 were considered significant and displayed in the plot (The x axis represents the distribution of log-fold change (logFC) corresponding to the core molecules in each gene set).
Gene Set Variation Analysis (GSVA) based on TBC1D8 expression
The above results suggested that TBC1D8 may be closely associated with the hypoxia phenotype, and here we performed a hallmark geneset-based GSVA to further analyze the possible involvement of TBC1D8 in cancer-related pathways. The distribution of GSVA scores for the CRC samples in TCGA and GSE37182 is shown in Fig. 4A,B. The results of the differential score analysis based on the median TBC1D8 levels showed that “HYPOXIA” was active in the high-TBC1D8 group, in both cohorts (P < 0.05) (Fig. 4C,D). The relationships between TBC1D8 and hypoxia-associated genes were also examined (Fig. 4E,F). This further implied that TBC1D8 play an important role in the adaptation to hypoxia.
The differentially enriched genesets correlated with TBC1D8 expression were studied through the gene set variation analysis (GSVA) algorithm based on TCGA and Gene Expression Omnibus (GEO) datasets. (A,B) Heatmap displaying the hierarchical clustering of enrichment scores obtained through Gene set variation analysis (GSVA) based on the enrichment degree profile of the datasets. (A) TCGA; (B) GSE37182. (C,D) Volcano plot of the differentially enriched genesets in CRC patients with different expression of TBC1D8 in (TCGA) and (GSE37182) dataset was analyzed by GSVA by “limma” R package (C TCGA, n = 480, D GSE37182, n = 84). Blue nodes indicate downregulation, red indicates upregulation. (E,F) Relationship between TBC1D8 and hypoxia-related genes based on TCGA (n = 480) (E) and GSE37182 (n = 84) (F). Yellow indicates a positive and blue indicates a negative relationship; darker color shows stronger correlation. (*P < 0.05, **P < 0.01).
Single-cell level analysis of TBC1D8
Herein, we sought to further search for an association between TBC1D8 and hypoxia through single-cell datasets. The CancerSEA database found that TBC1D8 levels were positively correlated with hypoxia (R = 0.31, P < 0.05) (Fig. 5A). The tumour microenvironment (TME) consists of cancer-associated fibroblasts (caf), myofibroblasts, immune cells and other components. to further examine the relationship between TBC1D8 expression distribution and hypoxia, we explored the CRC single-cell datasets GSE139555 and GSE146771. Figure 5B,G show the cellular components of the two datasets, respectively. We observed that cells expressing TBC1D8 were enriched for hypoxia-associated genes (Fig. 5C,D,H,I) and found that TBC1D8 was expressed in both immune and stromal cell single cell subpopulations (Fig. 5E,F,J,K).
Single-cell analysis for TBC1D8 based on Tumor Immune Single-cell Hub (TISCH) database. (A) Functional relevance of TBC1D8 in patients with CRC. (B–K) (B,G) Cellular components based on (B) GSE139555 and (G) GSE146771. (C,H) Uniform manifold approximation and projection (UMAP) plots illustrating the expression of TBC1D8 clusters based on (C) GSE139555 and (H) GSE146771. (D,I) Enrichment scores of genes from the Hallmark hypoxia geneset in individual cells, from gene set variation analysis based on (D) GSE139555 and (I) GSE146771. (E,J) UMAP plots showing the CRC cell landscape. Different cell types after quality control, dimensionality reduction, and clustering based on (E) GSE139555 and (H) GSE146771. (F,K) Violin plots for CRC cell cluster marker genes and TBC1D8 in different cell types based on (F) GSE139555 and (K) GSE146771.
TBC1D8 can be induced by hypoxic conditions, thereby contributing to tumor proliferation and tumor stemness
The enrichment, GSVA, and single-cell analysis results suggested that TBC1D8 may be associated with hypoxia. Given that its Hub Gene is an indicator of SOX2, which is involved in the development and maintenance of stem-like properties in cancer cells, we considered that TBC1D8 may also be associated with tumor cell stemness. A possible mechanism is proposed in Fig. 6A. Based on the above results we performed a preliminary experimental validation. To simulate neoplastic hypoxic microenvironmental conditions, we used CoCl2 to induce a hypoxic environment. It was found that TBC1D8 expression gradually increased with prolongation of the CoCl2 treatment (Fig. 6B,C). Western blot analysis was used to determine levels of hypoxia-inducible factor (HIF)-1α to confirm the induction of hypoxia (Figure S2). High levels of TBC1D8 were seen in HCT116 and HT-29 cells, so these were used for further study. Transfection efficiencies were confirmed by GFP intensity and western blotting (Fig. 6D,E). Silencing of TBC1D8 reduced clone formation (Fig. 6F). The schematic diagram of subcutaneous tumor models is presented in Fig. 6G. Moreover, TBC1D8 knockdown suppressed xenograft tumor growth in vivo (Fig. 6H,I). IHC staining of xenograft tumors revealed a significant decrease in cytosolic Ki-67 expression accompanied by knockdown of TBC1D8 (Fig. 6J). Tumor sphere formation is documented to be linked with the stemness of cancer cells. Quantification of spheres showed that TBC1D8 knockdown significantly reduced both the numbers (Fig. 6K) and sizes (Fig. 6L) of CRC cell tumor spheres. The expression levels of SOX2 were significantly increased following exposure to hypoxia compared with those under normoxic conditions (Fig. 6M). The expression levels of SOX2 were markedly downregulated following TBC1D8 knockdown (Fig. 6N). This part of the results suggested that TBC1D8 plays an important role in linking hypoxia and stem cell characteristics of CRC cells.
TBC1D8 can be induced by hypoxia conditions, thereby contributing to tumor proliferation and tumor stemness. (A) Schematic model of the possible role of TBC1D8 in CRC. (B,C) Effects of CoCl2-induced hypoxia on TBCD18 expression. (D,E) Transfection efficiencies (%) shown by Green fluorescent protein (GFP) expression and western blotting (Magnification, × 400, scale bars = 20 μm). (F) Clone formation capability of CRC cells transfected with NC and sh- TBC1D8 constructs, shown by colony formation determination (Magnification, × 1, scale bars = 1000 μm). (G) Schematic diagram of subcutaneous tumor models. (H) Xenograft mouse tumors (n = 6 mice per group). (I) Volumes of xenograft tumors measured twice a week and weights of xenograft tumors at completion of the study. (J) IHC staining of Ki-67 proteins in mouse xenograft tumor tissues (Magnification, × 100, scale bars = 100 μm; Magnification × 200, scale bars = 20 μm). All the IHC scores were repeated three times using a double-blind method.Statistical analysis All experiments were repeated at least three times, independently. (K) Sphere-forming assay of cells (magnification, × 100, scale bars = 100 μm). (L) The number of cell spheres and the sphere size were measured. All experiments were repeated at least three times, independently. (M) The relative expression level of SOX2 under hypoxic condition was detected using Western blot analysis (n = 3 replicates). (N) The relative expression level of SOX2 levels in CRC cells transfected with NC and sh-TBCID8, were examined using western blot analysis (n = 3 replicates). Data are means ± SEM *p < 0.05; **p < 0.01; ***p < 0.001. One-way ANOVA was conducted. All experiments were repeated at least three times, independently.
Immune microenvironment analysis of TBC1D8
In this section, we explored the relationship between immune infiltration and CRC pathogenesis. Relationships between TBC1D8 and infiltration were examined with the EPIC algorithm. This showed that macrophages, neutrophils, CD8 T cells, and T helper cells were present at higher levels when TBC1D8 was strongly expressed (Fig. 7A). TIMER was also used to explore potential correlations between TBC1D8 levels and immune cell infiltration (Fig. 7B). There were positive correlations between TBC1D8 and CD8 + T cells (R = 0.345, P = 9.13e−13), CD4 + T cells (R = 0.215, P = 1.32e−05), macrophages (R = 0.233, P = 2.24e−06), neutrophils (R = 0.394, P = 2.21e−16), dendritic cells (R = 0.353, P = 2.94e−13), and B cells (R = 0.153, P = 2.04e−03) levels.
Immune analysis of TBC1D8. (A) Effects of high and low TBC1D8 expression on immune cells. (B) Relationship between TBC1D8 level and degree of immune infiltration, from TIMER. (C,D) Relationship between TBC1D8 and levels of (C) M2 macrophage markers and (D) M1 macrophage markers. (E) Tumor-initiating cell proportions in CRC samples. from GSE10950 and GSE37182 data based on CIBERSORT. (F) Relationship between TBC1D8 level and macrophage abundance based on CIBERSORT, from GSE10950 and GSE37182 data. (G) Diagram of co-culture system. (H) Double-immunofluorescence staining of M2 macrophage markers CD206 (red) and CD163 (green); nuclei are stained with DAPI (blue) (Magnification, × 400, scale bars = 20 μm) (n = 3 replicates). (I) Intensity of immunofluorescence (mean ± SEM) (n = 3 replicates). Data are means ± SEM *p < 0.05; **p < 0.01; ***p < 0.001.
We further analyzed the relationship between TBC1D8 and macrophage polarization based on TCGA-COAD. TBC1D8 was positively correlated with MS4A4A (R = 0.210, P < 0.001), VSIG4 (R = 0.210, P < 0.001), MRC1 (R = 0.270, P < 0.001), CD163 (R = 0.320, P < 0.001), MSR1 (R = 0.220, P < 0.001), IRF5 (R = 0.120, P = 0.010), and PTGS2 (R = 0.250, P < 0.001) but not with NOS2 (P = 0.580) (Fig. 7C,D). Finally, we further found that M2 macrophages had a higher degree of infiltration in CRC patients in the TBC1D8 high expression group using GSE10950 and GSE37182 (Fig. 7E,F). This series of results suggested the presence of a positive association between TBC1D8 expression and M2 macrophage infiltration. To further investigate the influence of TBC1D8 expression on M2 macrophage abundance in CRC, we established a tumor–macrophage co-culture model using a transwell non-contact co-culture unit (Fig. 7G). We observed that TBC1D8 knockdown significantly down -regulated the surface markers of M2 tumor-associated macrophages (TAMs) (CD206 and CD163) in THP-1 macrophages (Fig. 7H,I). Our results suggested that TBC1D8 can exert immunosuppressive functions by promoting the proliferation of M2 macrophages.
Genetic alteration analysis and DNA methylation of TBC1D8
Here, we attempted to investigate the potential mechanism of TBC1D8 in CRC in terms of mutation and DNA methylation. We first detected the mutation frequency in 8 groups of CRC cases through the cBioPortal database (Fig. 8A). Volcano map showing DEGs after a change in TBC1D8 (mutated and wild) (Fig. 8B). Mutation site profile of the TBC1D8 shown in Fig. 8C. After identifying the DEGs, we then performed GSEA to research the functional enrichment of these DEGs. The DEGs were enriched in “REGULATION OF MACROPHAGE ACTIVATION”, “MACROPHAGE ACTIVATION INVOLVED IN IMMUNE RESPONSE”, “MACROPHAGE CHEMOTAXIS”, “POSITIVE REGULATION OF MACROPHAGE MIGRATION”, “RESPONSE TO MACROPHAGE COLONY STIMULATING FACTOR” (Fig. 8D), which all point to macrophages. Subsequently, M1 and M2 macrophage markers were analyzed under TBC1D8 (mutated and wild) status (Fig. 8E,F). Differential methylation status of TBC1D8 between CRC tissues and normal tissues were analyzed. The statistically significant CG sites are shown in the Fig. 8G. Based on methylation data from TCGA-COAD, we established that the methylation values obtained from 9 methylation probes were significantly correlated with TBC1D8 expression levels (Fig. 8H). The intersection of CG sites in the above two results (Fig. 8I) were then used to perform a survival analysis and cg20893936 was identified to be related to CRC patients’ survival (Fig. 8J). These results suggested that TBC1D8 gene DNA alternation may affect colorectal tumorigenesis.
The genetic alteration of TBC1D8 in CRC. (A) Frequencies of TBC1D8 mutations and copy number alterations (CNA) in the 8 datasets shown on the right side. (B) Volcano map of genes showing differential expression after a change in TBC1D8 (mutated and wild). Red dots, upregulated genes; blue dots, downregulated genes; abscissa, differences in gene expression (log2 fold change); and ordinate, significance of these differences (− log10 padj). (C) The mutation site profile of the TBC1D8 gene is shown. (D) GSEA was used to determine the functions of differential gene sets between the mutated and wild groups based on GO. Only gene sets with Normal P < 0.05 and FDR < 0.1 were considered significant and displayed in the plot. The x axis represents the distribution of log-fold change (logFC) corresponding to the core molecules in each gene set. (E–F) Differential Analysis of TBC1D8 (mutated and wild) with M1 (E) and M2 (F) macrophage markers. Wilcoxon-Mann–Whitney test was performed based on TIMER database. (G) The Differential Analysis of the TBC1D8 probe methylation were indicated. (H) Waterfall plot of the methylation levels in the TBC1D8 gene. The correlations between TBC1D8 methylation or expression levels were also analyzed. (I) Venn diagram showing the intersection of (G,H). (J) Survival analysis based on the intersection methylation probes; P < 0.05 was considered statistically significant.
Discussion
CRC is a heterogeneous disease, which occurs and progresses in a complex microenvironment41. Hypoxia is an important feature of the colorectal cancer microenvironment, and at the same time, hypoxia makes tumor signaling pathways more intricate42,43. In this complex molecular network, some genes are upregulated under hypoxic conditions and can contribute to the malignant biological behavior of tumor cells through multiple signaling cascades44,45. This also means that there are more opportunities to find effective targets for action. With the support of computational disciplines such as bioinformatics and systems biology, our group has used several publicly available online databases to initially identify an oncogene that has not been previously mentioned in colorectal cancer. This provides a potentially interesting direction for the development of therapeutic, targeted drugs for colorectal cancer.
Several studies have stated that a dysregulated TBC family expression is involved in various human diseases such as cancer, obesity and X-linked early-onset nephrotic syndrome and so on46,47,48,49. There are few reports on TBC1 domain family members in tumor, but the available studies suggest that it mostly functions as an oncogene in tumors. TBC1D3 overexpression inhibits the ubiquitination and degradation of Epidermal growth factor receptor (EGFR), thereby enhancing the EGFR signaling pathway and promoting cell proliferation50. TBC1D15 has been identified as a powerful oncogene in hepatocellular carcinoma, and the mechanisms involved include promotion of Tumor-initiating-stem-like cells self-renewal and p53 deletion51. In addition, TBC1D23 has been suggested to be a powerful promoter of tumor cell proliferation, migration, and invasion in non-small cell lung cancer52. In conclusion, most of the available studies reveal a possible fact that TBC1 domain family members can participate in malignant cell transformation or metastasis by regulating Rab proteins. However, to date, few studies have been reported on its involvement in colorectal cancer.
In this study, we first analyze the LASSO regression based on TCGA-COAD, and showed that TBC1D8 and TBC1D17 were potentially valuable research targets. While it has been reported that TBC1D8 can regulated metabolic reprogramming to drive ovarian cancer progression, little research has been reported on TBC1D17 in cancer. TBC1D17 is currently identified as a mitochondria-localized RAB7A GTPase-activating protein (GAP) that helps maintain mitochondrial homeostasis by regulating RAB7A activity and limiting mitophagy. And a work by Xi Sheng Rao et al. demonstrated that TBC1D17 can act as a molecular bridge linking AMPK and Rab5 to play the role of an GTPase activating protein to regulate glucose homeostasis. Further Cox analysis incorporating TNM staging suggested that TBC1D8 may be an independent prognostic factor for CRC, and therefore we selected TBC1D8 as our study target. The results of three independent CRC datasets showed significantly elevated transcription of TBC1D8 in CRC tissues and, the HPA database and our samples further validated the overexpression of TBC1D8 in colorectal cancer at the protein level.
In order to explore the possible biological function of TBC1D8, we identified 578 significantly related genes based on TCGA-COAD and performed a functional enrichment analysis. The results showed that TBC1D8 and its related genes are significantly associated with many related to the tumor microenvironment, including “ANGIOGENESIS”, “DEGRADATION OF THE EXTRACELLULAR MATRIX”, “ACTIN BINDING”, and “INTEGRIN BINDING”. All these processes are important components of the tumor microenvironment, and they can interact to further promote tumor growth and metastasis. It is worth noting that we have observed that TBC1D8 may also be related to the hypoxic phenotype, and by analyzing the TBC1D8-based PPI, we found that SOX2, a hypoxia-related indicator, was the highest-scoring hub gene. It has been well documented that SOX2 can promote tumor proliferation and invasion and can regulate tumor cell stemness in a hypoxic microenvironment through a variety of pathways53. Cell sphere formation capacity have been used as read-out for tumor stemness54.Previous studies have reported a less differentiated phenotype and/or an increase in stemness induced by hypoxia in Various tumors55,56,57. In the present study, it was shown that TBC1D8 knockdown potentiated sphere-forming capacity in CRC cells. However, the correlation between SOX2 and TBC1D8 was poor. This implies that TBC1D8 may be performing a similar biological function to SOX2 although through different mechanisms. On the one hand, hypoxia leads to a transcriptional program promoting basement membrane degradation, while increasing the ab initio synthesis of protofibrillar collagen as a physical pathway of tumor invasion58,59; on the other hand, hypoxia is a potent promoter of tumor-associated angiogenesis60, which was significantly activated in the high TBC1D8 expression group in the GSVA. Single-cell level analysis also suggested that cells expressing TBC1D8 were enriched for hypoxia-associated genes. We calculated the correlation between TBC1D8 and hypoxia-related genes, however, no consistent results were observed in two independent datasets, which may be due to heterogeneity between samples. We then used a cellular model of hypoxia to verify the upregulation of TBC1D8 under hypoxic conditions. Based on these results, we subsequently verified the biological functions of TBC1D8 in CRC cells. This showed that blocking TBC1D8 expression inhibited CRC cell proliferation, decreased clonogenic formation, invasion, and stemness in vitro. In addition, growth factor secretion induced by the hypoxic tumor environment also promotes macrophage aggregation, and macrophages are sensitive to hypoxia and alter their gene expression accordingly. Recent studies have found that macrophages in hypoxic regions induce fibrosis by growth factor production, thereby attracting both additional macrophages and mesenchymal cells61,62. Based on this observation, we examined the link between TBC1D8 and macrophage infiltration, finding that CRC patients with high TBC1D8 expression tended to have higher levels of M2 macrophage infiltration. Subsequent results also supported this conclusion. Because hypoxia broadly affects molecular events involved in cancer progression, aggressiveness, and treatment resistance, targeting hypoxia is an attractive approach for solid cancer treatment63. However, in practice, it is difficult to quantify hypoxia specifically, while targeting specific hypoxia-related targets is a feasible option. On the one hand, hypoxia can help tumor cells maintain stem cell-like characteristics to enhance their aggressiveness64, while on the other hand, immunosuppressive cells, including M2 macrophages65,66, accumulate in hypoxic regions of the tumor where they promote tumor progression and activate immune tolerance mechanisms that enable cancer cells to evade host immune surveillance67,68. Thus, targeting the hypoxia-related molecular signaling cascade network not only helps to improve tumor resistance to conventional chemotherapy, but also increases the probability that tumor cells will be recognized and killed by the body's immune system.
Altered genomic status is strongly associated with CRC carcinogenesis69. We observed that that TBC1D8 has a reasonable chance of being mutated in CRC. Functional enrichment suggested that TBC1D8 mutations may be associated with macrophage activation, and further calculations also showed that MSR1 and MS4A4A tend to be more strongly expressed in patients with mutated TBC1D8 in CRC. MSR1 and MS4A4A proteins have been shown to be associated with the activation of both tumor-associated and M2 macrophages70. Accordingly, we hypothesize that mutant TBC1D8 may be more able to activate immunosuppressive macrophages. Considering that DNA methylation is the most important method of epigenetic modification, we also observed that the degree of methylation of cg20893936 may correlate with the prognosis of CRC patients and can serve as a diagnostic biomarker. Further investigation into the question of how methylation of different sites in TBC1D8 affects the outcomes of CRC patients is warranted.
In conclusion, in this study, we found that TBC1D8 is a potential marker for both CRC diagnosis and prognosis. TBC1D8 may function as an oncogene triggered by hypoxia, a result that could lay the foundation for subsequent studies and experimental verification.
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Funding
This research was supported by funds from the China National Natural Scientific Fund (81904110) and the Foundation Research Project of Jiangsu Province (BK20191086); Advantageous Disciplines Program of Nanjing University of Chinese Medicine (ZYX03KF022 and ZYX03KF019), Science and Technology Project of Affiliated Hospital of Nanjing University of Chinese Medicine (Y2020CX62), State Administration of Chinese Medicine Project (20085-9-3), Jiangsu Provincial Science and Technology Department Project (BE2019771), Jiangsu Province Postgraduate Research Innovation Program Project (KYCX21-1677), Affiliated Hospital of Nanjing University of Chinese Medicine Peak Academic Professionals Program (y2021rc19 and y2021rc46).
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S.L. and X.Z. developed the experimental plan and provided the research funding. Y.L. and J.L. performed all experiments. H.L., S.Z. and Q.Y. analyzed the data. Y.C., M.H. and J.Z. support and providing equipment/instrumentation. Y.L. and J.L. wrote the manuscript. All authors have read and approved the final manuscript.
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Liu, Yj., Li, Jp., Li, Hr. et al. An analysis of the significance of the Tre2/Bub2/CDC 16 (TBC) domain protein family 8 in colorectal cancer. Sci Rep 12, 13245 (2022). https://doi.org/10.1038/s41598-022-15629-1
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DOI: https://doi.org/10.1038/s41598-022-15629-1
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