Colorectal cancer (CRC) is an important contributor to new cancer cases each year (1). In China, the mortality of CRC is 7.8 per 100,000 in 2011 and is forecasted to reach 8.6 per 100,000 in 2020 (2). CRC also causes the second highest deaths in the United States in 2020 (3). Hence, an effective therapy for CRC is of prime importance. With the development of the biotechnology, epigenetic alterations are providing potential biomarkers for diagnosis, therapeutic plans and prognosis of the CRC (4). On the basis of microarray technology and bio-informatics analysis, gene expression analysis is drawing more attention to the molecular field and helps us discover differentially expressed genes (DEGs) in CRC (5,6). However, transcription analysis presents hundreds of DEGs, raising the level of challenges and complexity to search the target gene. Moreover, co-expression clustering is a conventional method to extract genetic interactions and highlight common features of DEGs in CRC (7,8). Weighted gene co-expression network analysis (WGCNA) is regarded as a systematic biological strategy to screen out modules with higher genetic correlations (9). By identifying gene modules and relative phenotypes, we can study gene modules with significant synergistic action and find hub genes in interesting modules.
In this study, the gene expression profiles of CRC tissues and adjacent normal tissues by microarray analysis were downloaded from Gene Expression Omnibus (GEO) and screened out DEGs (10,11). Then WGCNA was applied to divide DEGs into multiple co-expression modules and screened out tumor-specific modules. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed and provide insight into metabolic processes and functions of gene products (12). Finally, a gene network was set up to identify hub genes upon inter-modular connectivity (13,14). Ultimately, the differentially expressed level and the biological tumor-specific function of the hub gene were verified. The function and clinical role were then performed. Taken as a whole, the hub gene may play a critical role in the occurrence and development of the CRC and provide a potential target for CRC treatment strategy. We present the following article in accordance with the MDAR reporting checklist (available at http://dx.doi.org/10.21037/tcr-20-3322).
Our work was approved by the ethics committee of the Army Medical University. All experiments were completed in specific pathogen-free conditions. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Committee of the Army Medical University (ChiCTR2000033078) and individual consent for this retrospective analysis was waived.
Microarray data source and analysis
The gene expression dataset (GSE44076) was downloaded from GEO. Gene expression profiles of paired normal adjacent mucosa and tumor samples from 98 individuals and 50 healthy colon mucosae were obtained through Affymetrix Human Genome U219 Arrays. DEGs were obtained through R language software 3.6.1 using ‘limma’ packages. Gene symbols showed non-correspondence with probes or multiple probes corresponding to the same gene were moved out or taken the average. We recommended that |log(fold change)| (|logFC|) >1, P value <0.05 is statistically significant.
The DEGs were filtered out and performed WGCNA analysis through R “WGCNA” packages. To reduce the computational burden without losing important information, |logFC| >0.585 and P<0.05 were taken as the standard for mRNA-choosing. In order to stabilize the result, we picked up a soft threshold power to construct the co-expression modules. The average value of connective degree was calculated. A hierarchical clustering tree was built after the matrix conversion in line with the soft threshold and genetic expression data. The colorful cluster dendrograms of tumor and normal tissues were constructed and we took the minModuleSize as 30 in each dendrogram. The associations between the tumor set-specific module and the tumor-normal consensus module were defined using R “WGCNA” packages. A deeper color stands for a closer correlation. Tumor-specific modules with a significant contrast with normal modules were chosen for further analysis.
Gene Ontology (GO) and KEGG analysis
David (http://david.ncifcrg.gov) (version 6.8), the abbreviation of “the Database for Annotation, Visualization and Integrated Discovery”, provides series tools for functional annotation and biological meaning of genes. In our research, we used ‘David’ database to analyze biological processes (BPs), cellular components (CCs), molecular functions (MFs) and enrichment of signaling pathways of genes in hub modules. P value <0.05 was to be statistically significant.
Hub gene identification
Total 3,864 genes of the tumor-specific module were detected and the internal connections among those genes were calculated. We chose top 30 genes to be visualized by Cytoscape3.7.2. The top 5 genes with highest connectivity within the module were defined as hub genes.
GEPIA, the abbreviation of ‘Gene Expression Profiling Interactive Analysis’, is an online database providing RNAseq data of TCGA and GTEx database. We verified the differential expression of hub genes based on the GEPIA and showed in boxplots. The expression data of TTI1, Ki-67 and PCNA were from GEO database. The survival data of CRC patients was downloaded from TCGA database and analyzed by the R ‘survival’ package and ‘survminer’ package. TTI1 expression data of CRC patients with clinical traits were downloaded from USCS Xena (https://xena.ucsc.edu). The PPI network and function of the hub gene were obtained from String (https://www.string-db.org/).
Colorectal tissues collection
Our work was approved by the Ethics Committee of the Army Medical University and the experimental protocols were accorded with the Declaration of Helsinki (clinical trial register no. ChiCTR2000033078). The clinical tissues were used for the scientific investigation with the permissions of all the patients. Patients were selected according to the admission time (from 2017 to 2019) in the Second Affiliated Hospital of the Army Medical University. Patients were diagnosed pathologically and 53 pairs of colorectal tissues and adjacent normal tissues were removed after operation and rapidly stored at –80 °C.
Cell culture and transfection
Human CRC cancer cell lines (CaCO2, HT29) were purchased from the Type Culture Collection of Chinese Academy of Sciences (Shanghai, China). CaCO2 cells were maintained in complete MEM medium with 10% bovine serum albumin, 1% penicillin-streptomycin solution and 1% nonessential amino acid solution. HT29 cells were maintained in McCoy’s 5a complete medium with 10% bovine serum albumin and 1% penicillin-streptomycin solution. Cells were incubated at 37 °C with 5% CO2 and 20% O2. To reduce TTI1 expression, small interfering RNA (siRNA) was used coupled with LipofectamineTM 2000 Transfection Reagent (Invitrogen, USA). siRNA duplexes targeting the TTI1 mRNA were synthesized by Ribobio (Guangzhou, China) as following: GGAAGCAAGTGTGGTGACT. The density of cells reached 30–50% and then cells were transfected with the siRNA targeting TTI1 and negative control siRNA (siCon) through the use of LipofectamineTM 2000 Transfection Reagent (Invitrogen, USA). After a 6-hour transfection, cells were purified for proliferation assay.
Real-time quantitative reverse transcription (qRT-PCR) detecting system
Total RNA was isolated from clinical tissues collected in the Second Affiliated Hospital of the Army Medical University using TRIzol (Invitrogen, Carlsbad, CA, USA) in line with the manufacturers’ instructions. The concentration determination of RNA was based on NanoDrop 2000 (Thermo Fisher Scientific, MA, USA). A SuperScript First-Strand Synthesis System RT-PCR kit (TaKaRa Bio Inc., Japan) was used to reverse-transcribe RNA into complementary DNA (cDNA). GoTAq qPCR Master Mix (Promega, USA) was participated into the PCR process based on the Rotor-Gene Q PCR detection system (Qiagen, Germany). Sequences of primers are as following: TTI1 forward, 5'-CTGGCGACAAGTTGACTATCCT-3'; TTI1 reverse, 5'-CCAGTTCCAGTCTCACCTTCCA-3'; GAPDH forward: 5'-AGCCACATCGC TCAGACAC-3'; GAPDH reverse: 5'-GCCCAATACGACCAAATCC-3'. The PCR process was performed in a 10 µL system with 95 °C for 10 min, 40 cycles of 95 °C for 5 s and 60 °C for 30 s and 72 °C for 30 s. The expression data was calculated by 2–ΔΔT.
Proteins of clinical samples from patients were extracted upon RIPA and PMSF (BOSTER, USA). After polyacrylamide gel transfer electrophoresis, samples were transferred to a PVDF (polyvinylidene difluoride) membrane. The membrane was blocked and incubated with the primary antibody against TTI1 (SANTA CRUZ, sc-365119, 1:100) overnight. Then the membrane was incubated with secondary antibody for 1 hour. Results were detected using Western Blot Imaging System (4000R, Care Tream, USA).
Four percent paraformaldehyde (Boster Biological Technology) was used to fix samples and the primary antibody against TTI1 (SANTA CRUZ, sc-365119, 1:50) was incubated with samples at 4 °C overnight. Then the secondary antibody (Abcam, ab150113, 1:500) and DAPI (Beyotime Biotechnology) was used separately to stain samples. A TCS-SP5 confocal microscope (Leica, Germany) was used for visualization.
Cell proliferation assay
Cell counting kit-8 (CCK-8; Dojindo, Shanghai, China) was used to demonstrate the proliferative ability of CaCO2 and HT29 cells. Cells were cultured in 96-well plates and each well contained 3×103/cells. Twenty-four hrs after transfection, 200 µL CCK-8 solution was added into wells and the optical density was qualified after 2 h cell-incubating at 37 °C. The absorbance was measured by the Multiskan spectrum (Berthod, Germany) at 450 nm. Similarly, the optical density was measured in the following 7 days.
Data was expressed as the mean ± SD. Statistic analysis was done using Graphpad Prism 7.0 (San Diego, CA, USA). Pearson’s correlation coefficients and the two-tailed Student t-test determined the statistical significance. P<0.05 is statistically significant.
DEGs and constructing weighed co-expression network
Ninety-eight CRC samples and 50 normal tissue samples were included in the gene expression profile (GSE44076) in this study. Correction data was assigned to DEG analysis and 1,727 DEGs (808 up-regulated and 929 down-regulated) were screened out according to the condition of |logFC| >1, P<0.05 (Figure 1A). GO and KEGG analysis were then performed using ‘David’. Up-regulated genes were enriched in cell proliferation, DNA-templated and proteinaceous extracellular matrix (ECM), however, down-regulated genes were enriched in oxidation-reduction process, inflammatory response and cell surface in GO analysis (Figure S1A,B). KEGG analysis showed that up-regulated genes were enriched in cell cycles, cytokine-cytokine receptor interaction, ribosome biogenesis and down-regulated genes were enriched in cGMP-PKG signaling pathway, retinol metabolism and PPAR signaling pathway (Figure 1B,C). To provide a further insight into the correlations among DEGs, the WGCNA analysis was performed using R ‘WGCNA’ packages. Four normal samples (GSM1077708, GSM1077694, GSM1077670 and GSM1077712) and one tumor sample (GSM1077800) were discarded after sample clustering (Figure S1C,D). The soft threshold power was separately identified as 6 and 5 (R2>0.9) of normal and tumor samples (Figure S1E,F). The hierarchical cluster analysis was conducted and gene modules were obviously showed (Figure 1D,E).
Identification of the hub module and function enrichment analysis
To investigate the gene expression difference between tumor and normal tissues, we compared gene modules of tumor with those of normal tissues. The results showed the correlation between tumor set-specific modules and tumor-normal consensus modules (Figure 2A). Modules of tumor samples with the lowest correlation and the biggest difference against modules of normal tissues were identified as tumor-specific modules. Results demonstrated that the yellow module was the most tumor-specific module. Therefore, the yellow module was identified as the hub module and suggested to play a critical role in the CRC. To classify the biological function of genes in the hub module, the GO process and the KEGG analysis were performed. The KEGG analysis demonstrated that genes of the hub module were enriched in ECM-receptor interaction, TGF-beta signaling pathway and pyrimidine metabolism (Figure 2B). Results of GO analysis discovered that genes were focused on the oxidation-reduction, cell proliferation and cell division on BP (Figure 2C). Moreover, genes were enriched in cytoplasm, extracellular exosome and cytosol on CC (Figure 2D). In MF analysis, signal transducer activity, transporter activity and cytokine activity were significant for genetic enrichment (Figure 2E).
Identification of hub genes and verifying expression levels
To identify hub genes in the yellow module, genes were ranked according to the inter-modular connectivity. The top 30 genes with the highest inter-modular connectivity were chosen for visualization using Cytoscape3.7.2 (Figure 3A). The bigger size of nodes stood for a higher inter-modular connectivity and the color of nodes stood for the FC. Finally, the top five genes with the highest inter-connectivity (STAU1, KIAA0406, DPM1, CTNNBL1, C20orf11/GID8) were identified as hub genes. Then expression levels of hub genes were identified in the GEPIA database. All hub genes except DPM1, showed a significant difference of the expression level (Figure 3B, Figure S2A). However, KIAA0604 (also named TTI1) showed the most significant differentially expressed level and the second highest connectivity among five hub genes. Therefore, we chose TTI1 for a further analysis. Protein-protein network (PPI) of TTI1 was performed and visualized (Figure 3C). Function analysis using String database was also proved that TTI1 was associated with PI3K/AKT/mTOR pathway (Figure 3D).
TTI1 is up-regulated in CRC
The above part showed the TTI1 as a hub gene with differential expression in CRC tissues rather than normal tissues. We then demonstrated the expression level of clinical CRC tissues and paired adjacent tissues collected from patients in the Second Affiliated Hospital of the Army Medical University. qRT-PCR and Western Blot verified that TTI1 was significantly up-regulated in CRC tissues than adjacent tissues (Figure 4A,B). Immunofluorescence also demonstrated that TTI1 was highly expressed in colorectal tissues compared with adjacent normal tissues (Figure 4C). The result screened out that TTI1 showed higher expression in tumor tissues. However, clinical factors [age, gender, differentiated degree, stage, body mass index (BMI) and occurrence site of cancer] showed no significance between groups with lower and higher expression of TTI1 (Table 1). Moreover, expression data of GSE32323 and GSE21510 revealed that TTI1 was significantly up-regulated in CRC tissues compared with normal samples (Figure 4D).
TTI1 is involved in the proliferation of CRC
The survival curves with different TTI1 expression were performed and there was no significant correlation between survival time and the expression of TTI1 (Figure S2B,C). Moreover, analysis using USCS Xena database demonstrated that a higher level of TTI1 showed no significant correlation with the lymphatic invasion and the venous invasion (Figure S2D,E). Unregulated cell proliferation is closely to tumor progression in the early stage of CRC. Correlation analysis in GSE32323 and GSE21510 identified that there was a positive correlation between the expression level of TTI1 with expression levels of proliferation-related markers Ki-67 and PCNA (Figure 5A,B,C,D). We then probed into the function of TTI1 in CRC cell lines. The HT29 cells and CaCO2 cells were transfected with TTI1 siRNA. Moreover, the proliferative ability of transfected cells was decreased significantly (Figure 5E,F). Therefore, TTI1 played a positive role in promoting cell proliferation of CRC.
CRC as a modern disease, is increasingly common while the mode of life is shifting to the Western style. Risk factors (smoking, obesity and alcoholism) make the disease get out of control. The incidence of young people is increasing (15). Therefore, it is necessary to improve an effective method of diagnosis and treatment. In the past decades, screening CRC using colonoscopy enhanced detection and reduced incidence of the cancer, but the survival fate is still poor (16,17). Genes related to prognosis are increasingly discovered in recent years (18). Nonetheless, targeted therapies for CRC patients is still limited (19). New drugs for immunological therapy show less obvious effects (20). Therefore, a new biomarker as a target of diagnosis or therapies for CRC may be a meaningful direction for scientific researches.
In this study, we compared tissues of the primary colorectal adenocarcinoma and matched distant colon mucosae using RNA-seq. The expression data files and matched clinical data were downloaded from GEO and DEGs were screened out using R ‘limma’ packages. Eight hundred and eight up-regulated genes were mainly enriched in cell proliferation, however, 929 down-regulated genes were enriched in oxidation-reduction process and inflammatory response on basis of ‘David’ database. Then a deeper research was done to explore biological expectations of co-expression genes using R ‘WGCNA’ packages. Then a tumor-specific module (the yellow module) was screened out with the most significant difference. Genes in the yellow module were focused on biological pathways of cell proliferation according to ‘David’ online database. To narrow the range and find hub genes in the module, ‘Cytoscape’ was used to build a WGCNA network based on the inter-connectivity and the FC of genes in this module. Genes with the top five highest inter-connectivity were screened out as hub genes (STAU1, KIAA0406, DPM1, CTNNBL1, C20Orf11). KIAA0406 is also called TTI1 and C20orf11 has another name called GID8. Expression levels were verified from GEPIA database (21). Except for DPM1, other four hub genes showed a significant difference. Moreover, TTI1, with the second highest connectivity and the highest differential expressing level, was highly specific in 53 CRC samples collected in the Second Affiliated Hospital of the Army Medical University and in GEO datasets (GSE32323 and GSE21510).
TTI1 participates in constituting and stabilizing proteins of PIKK family including mTOR, ATR, DNA-PKcs and ATM as a co-chaperon (22,23). TTI1 also participated in maturating PIKKs members acting with Hsp90 (24). PIKKs play a vital regulator in biological pathways of cell growth, proliferation, translation and autograph (25,26). PIKK family members (ATM, ATR, DNA-PKcs, mTOR) played core roles in regulating cell proliferation in cancers (27,28). However, TTI1 as a key regulator in many biologic processes, few studies were done correlated with TTI1. TTI1 is involved in multiple metabolic pathways and mTORC1 signaling activation which promotes cell growth (29). In Kristen’s study, TTI1 participated in DNA damage response and was required to establish the intra-S and G2/M checkpoints (30). Previous study showed TTI1 participated in promoting survival in multiple myeloma through mTORC1 pathway and was significantly altered in number of bladder cancer (31,32). Rao et al. demonstrated that TTI1 participated in binding DNA-PKcs and ATM, which activated phosphorylation of S-15 and p-53 pathway, activated the cell death program of cancer cells (33,34).
Nevertheless, the role of TTI1 in the carcinogenesis and development of the CRC is still uncleared. In this study, we screened out a higher expression level of TTI1 using bio-informatics methods and proved that TTI1 was significantly up-regulated in CRC samples collected in the Second Affiliated Hospital of the Army Medical University. However, CRC patients with higher expression levels of TTI1 were not proved to have a worse prognosis including survival time and lymphatic/venous invasion. Our function analysis showed TTI1 may participate in cell proliferation. Correlation analysis identified that the TTI1 showed a positive correlation with proliferative markers (Ki-67 and PCNA) significantly (35,36). Cell proliferation assay showed down-regulated TTI1 significantly suppressed proliferation of CRC cells. It’s universally accepted that uninhibited cell proliferation is the most fundamental trait and malignant hallmark of cancers (37). Hence, silence of TTI1 may significantly inhibit the early process of the CRC development. Nevertheless, targeting proliferation of cancer cells may only aim at cells with rapid proliferation. Inhibitory effect on cancer stem cells with slower proliferation requires for a further analysis (38,39).
In conclusion, TTI1 expressed specially in tumor tissues than adjacent normal tissues of CRC patients and was a key factor in the proliferative process of CRC. TTI1 may play a vital bio-marker in anti-tumor researches and treatment of CRC.
Funding: This work was supported by the National Natural Science Foundation of China (Grant No. NSFC 81873551, NSFC 81873933).
Reporting Checklist: The authors have completed the MDAR reporting checklist. Available at http://dx.doi.org/10.21037/tcr-20-3322
Peer Review File: Available at http://dx.doi.org/10.21037/tcr-20-3322
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/tcr-20-3322). The authors have no conflicts of interest to declare.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Committee of the Army Medical University (ChiCTR2000033078) and individual consent for this retrospective analysis was waived.
Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
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