TIMP1, a promising target of tumor development diagnosis and treatment for colorectal cancer

doi:10.21203/rs.3.rs-1729514/v1

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TIMP1, a promising target of tumor development diagnosis and treatment for colorectal cancer

https://doi.org/10.21203/rs.3.rs-1729514/v1

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Background: Colorectal cancer is one of the most common malignancies globally, causing a tremendous social burden. Revealing causes and underlying molecular mechanisms and discovering molecular biomarkers for early diagnosis, prevention, and personalized treatment is critical.

Methods: To identify the candidate genes in cancer progression, three microarray datasets were downloaded from the GEO database. GEO2R identified the differentially expressed genes (DEGs), and DAVID performed functional enrichment analyses. The protein-protein interaction network was constructed, and hub gene selection was performed using STRING. GEPIA2 was used to explore the expression level change of 10 hub genes and perform the prognosis corresponding information. RT-qPCR and immunoblotting were performed to verify the expression level of TIMP1 in clinical tissues.

Results: A total of 223 DEGs were identified. The enrichment analysis results showed that the functions of the differentially expressed genes were mainly concentrated in ion transport, protein degradation, mineral absorption, and cancer processes. In the PPI network, ten genes were identified as hub genes. Compared with normal tissues, the rest of the genes were significantly overexpressed apart from CXCL12. Survival analysis showed that TIMP1 was significantly correlated with the poor prognosis of CRC patients. RT-qPCR and immunoblotting showed TIMP1 up-regulated considerably in CRC tissues.

Conclusions: In conclusion, for colorectal cancer, combined with the functional characteristics and the current statistical analysis results, TIMP1 is a very promising target for diagnosis and treatment.

colorectal cancer

TIMP1

target

bioinformatics

Colorectal cancer (CRC) is one of the most common types of cancer worldwide and the second most common cause of cancer-related death, with increasing incidence and mortality rates [1]. In the past decade, the incidence of early-onset colorectal cancer and related mortality rates have gradually increased, and the diagnosis of colorectal cancer has shown a younger trend [2]. According to statistical data, there were about 555,000 new cases of colorectal cancer in China in 2020, accounting for 12.2% of the number of new cancers in that year, and nearly 286,000 deaths, accounting for 9.5% of the number of cancer deaths in China that year [3]. This shows that colorectal cancer has seriously threatened the lives and health of Chinese residents and caused a severe social burden. Most colorectal cancers develop from benign polyps (adenomas and serrated polyps) through genetic and epigenetic changes, which take about 10 to 15 years [4]. This provides a critical time window for early diagnosis and clinical intervention of the disease. The most commonly used screening methods for CRC are fecal immunohistochemical testing (FIT) and colonoscopy [5]. Other emerging colorectal cancer screening techniques such as computed tomography (CT) and colonography, capsule endoscopy, or the latest detection of genetic biomarkers in the stool or blood may play a role in future screening [6]. Still, most screening methods have not yet gained universal application. Therefore, it is crucial to uncover the causes and underlying molecular mechanisms and discover molecular biomarkers for early diagnosis, prevention, and personalized treatment.

A gene chip or gene map is a genetic testing technique that has been used for more than a decade. The integrated bioinformatics knowledge uses gene chips to detect genome-wide expression in the same sample in a single experiment, particularly suitable for screening differentially expressed genes [7]. Microarray technology helps see gene expression changes during cancer development and progression [8–10]. However, due to tissue or sample heterogeneity in independent studies, results are always limited or inconsistent or derived from single cohort studies. Therefore, no reliable biomarkers were found in the CRC. However, an integrated bioinformatics approach combining expression profiling techniques will be innovative and may address shortcomings.

This study aimed to identify genetic changes in CRCs from three original microarray datasets (GSE41657, GSE44861, and GSE74602) in the Gene Expression Omnibus (GEO) database by obtaining differentially expressed genes (DEGs) between CRC and normal tissues. Functional enrichment analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) networks were used to analyze associations between DEGs. 223 DEGs and ten central genes were identified, which may be candidate biomarkers for CRC.

Identification of DEGs

A total of 3520, 424, and 1675 DEGs were identified from GSE41657, GSE44861, and GSE74602 profiles, respectively. Two hundred twenty-three genes were screened out in all three datasets and were selected for further analysis (Fig. 1). There are 184 up-regulated genes (Fig. 1A) and 39 downregulated genes (Fig. 1B) in CRC tissues compared to adjacent tissues.

GO and KEGG Pathway Enrichment Analyses

To explore the internal relations, we uploaded the differently expressed genes to the online website DAVID to identify GO Terms and KEGG pathways and classified them into three functional categories: biological process (BP), cellular component (CC), and molecular function (MF).

In the BP category, the up-regulated gene enrichment GO terms were mainly proteolysis, transport, ion transmembrane transport, response to drug and bicarbonate transport, while the downregulated gene enrichment GO terms were mainly positive regulation of neutrophil chemotaxis, chemokine-mediated signaling pathway, canonical Wnt signaling pathway, response to a drug, positive regulation of protein oligomerization, response to lipopolysaccharide and regulation of cell proliferation (Fig. 2B). Besides, in the CC category, the differently expressed gene enrichment GO terms were mainly extracellular exosome, extracellular space, apical plasma membrane, perinuclear region of cytoplasm, and integral component of the plasma membrane, while the downregulated gene enrichment GO terms were mainly extracellular space and extracellular matrix. Moreover, in the MF category, the differently expressed gene enrichment GO terms were mainly zinc ion binding, calcium ion binding, chloride channel activity, hormone activity, and oxidoreductase activity. In contrast, the downregulated gene enrichment GO terms were mainly calcium ion binding, CXCR chemokine receptor binding, chemokine activity, and metalloendopeptidase activity.

As for the KEGG pathway, the most significant results were eight up-regulated genes (MT2A, MT1M, MT1F, MT1G, MT1H, MT1X, SLC26A3, MT1E) involved in Mineral absorption (Fig. 2C) and five downregulated genes (CXCL8, CCND1, MMP1, MYC, MET) involved in pathways in cancer (Fig. 2D).

Construction of PPI network

The PPI network of the differently expressed genes was constructed with 164 nodes and 371 edges by using the STRING database and Cytoscape 3.9.1 software (Fig. 3). Scores of PPI network nodes were calculated by Cytoscape 3.9.1 built-in plugin cytoHubba. Then, the top ten genes, including MYC, CCND1, MMP7, TIMP1, MMP1, MMP3, CXCL2, CXCL1, CXCL8, and CXCL12, were identified as hub genes (Table 1).

Table 1

Hub genes screened from PPI.

Gene name	MCODE Score	Degree
MYC	7	21.0
CCND1	7	20.0
MMP7	6.377777778	12.0
TIMP1	6.377777778	15.0
MMP1	6.377777778	10.0
MMP3	6.377777778	11.0
CXCL2	5.303030303	12.0
CXCL1	5.303030303	17.0
CXCL8	4.846153846	20.0
CXCL12	4.846153846	17.0

mRNA Expression and Survival Analysis.

GEPIA2 database was used to analyze the expression levels of the ten hub genes. Compared with normal samples, colorectal cancer samples had higher expression of MYC, CCND1, MMP7, TIMP1, MMP1, MMP3, CXCL2, CXCL1, and CXCL8. The expression of CXCL12 was lower in colorectal cancer samples than in normal samples (Fig. 4).

GEPIA2 was used to predict the prognostic value of the ten hub genes, perform survival analysis and plot a Kaplan-Meier curve. Our results showed that high TIMP1 expression was associated with the worse overall survival of colorectal cancer patients (p < 0.05) for all colorectal cancer cases. In addition, there was no significant correlation between the differential expression of other Hub genes and the prognosis of patients (Fig. 5).

Validation of potential biomarkers in CRC Tissues

To further verify the results of the above omics data analysis, we collected tissue samples from colorectal cancer patients in the First Affiliated Hospital of Zhejiang Chinese Medicine University. Protein and nucleic acid samples are obtained after tissue samples are lysed at low temperatures. RT-qPCR and western blot verified the mRNA and protein expression trends of the potential biomarker TIMP1. The results showed that TIMP1 was significantly overexpressed in CRC tissues compared with the control group at DNA transcription and protein expression levels (Fig. 6A, B, and C).

Colorectal cancer is the most common gastrointestinal tumor. With the prevalence of high greasy, meaty eating habits and irregular diets, the incidence of the disease is increasing year by year [11]. It is a threat to human health due to its high degree of malignancy, short survival period, and poor quality of life.

This paper obtained 184 overlapping up-regulated and 39 overlapping down-regulated genes by analyzing GSE74602, GSE41657, and GSE44861 profiles in the GEO database. The enrichment analysis results showed that the functions of the differentially expressed genes were mainly concentrated in ion transport and protein degradation inside and outside the plasma membrane. The most significant results in the KEGG pathway were eight up-regulated genes (MT2A, MT1M, MT1F, MT1G, MT1H, MT1X, SLC26A3, MT1E) involved in mineral absorption and five downregulated genes (CXCL8, CCND1, MMP1, MYC, MET) involved in cancer processes. MYC, CCND1, MMP7, TIMP1, MMP1, MMP3, CXCL2, and CXCL1 were selected from a PPI network consisting of 164 nodes and 371 boundaries constructed by STRING database and Cytoscape 3.8.2. CXCL8 and CXCL12 function as hub genes. GEPIA2 database showed that compared with normal tissues, MYC, CCND1, MMP7, TIMP1, MMP1, MMP3, CXCL2, CXCL1, and CXCL8 were significantly overexpressed in colorectal cancer tissues, and TIMP1 was significantly correlated with the prognosis of colorectal cancer patients. Besides, our RT-qPCR and western blot verification results indicated that TIMP1 significantly up-regulated both nucleic acid transcription level and protein translation level.

TIMP1 (tissue inhibitor of metalloproteinases 1), as a specific inhibitor of collagenase and related metalloproteinases, irreversibly forms inactive complexes with metalloproteinases. Although in terms of its main functional characteristics, it seems to contradict the phenomenon of over-expression of MMP family proteins in most tumors. However, in various cancers, statistical results show that abnormal expression of TIMP1 is significantly associated with poor prognosis. It is speculated that the different functions of TIMP1 in the process of tumor genesis and metastasis are probably because TIMP1 can play the metalloproteinase-independent and metalloproteinase-dependent functions. The promoting function of TIMP1 on the tumor is mainly through promoting the proliferation of tumor cells, inhibiting apoptosis of tumor cells, and promoting angiogenesis (Fig. 6D).

The function mechanism of TIMP1 on tumor cell proliferation is a complicated issue. The entire TIMPs family, including TIMP1, plays an inhibitory or activating role in specific cells and related systems. An experiment with Burkitt’s lymphoma cells may be the best example yet to help make that vision easier to understand. The researchers found that TIMP1 overexpressed in Burkitt’s lymphoma cells has a bimodal effect, which initially promotes tumor growth, then promotes tumor necrosis, leading to tumor regression [12]. And for colorectal cancer, the conclusion that there is a significant correlation between the overexpression of TIMP1 and the poor prognosis of cancer patients is not the first time that it has been reported [13]. However, detailed mechanisms need to be elaborated.

Tumor cells use several means to evade apoptosis, including anchoring independent growth and activating anti-apoptotic factors [12, 14]. TIMP1 can inhibit or promote apoptosis in specific cell types, and these effects have not always been associated with MMP inhibitory activity [15, 16]. TIMP1 overexpression or recombinant protein can inhibit the apoptosis of various normal and tumor cells. Existing studies have shown that this anti-apoptotic effect is usually generated through AKT phosphorylation and activation of PI3K signaling [17, 18]. Besides, CD63 has also been reported to be the binding partner of TIMP1 on the cell surface, promoting the interaction between TIMP1 and integrin β1 and activating the β1 integrin/FAK survival pathway.

TIMP-1 can enhance vessel formation through an MMP-dependent mechanism, and it does so by blocking the production of antiangiogenic fragment tumstatin [19]. It has been reported that angiogenesis and vascular remodeling are essential pathophysiological steps in malignant pleural effusion (MPE) formation [20], which is a devastating complication in patients with cancer, and there is no effective treatment for this lethal complication currently [21]. Therefore, TIMP1 has been proved to be a crucial factor in MPE production.

TIMP1 is a special factor with multifaceted characteristics due to different periods and tissue specificity for tumor cells. With the advancement of tumor development, the function and mode of action of TIMP1 are changing. And this kind of change is according to the different tumor type, which has their own rules. Although there is no systematic explanation for this complex function and mechanism, it has been confirmed in numerous literature reports. Therefore, the current research basis is sufficient to prove that TIMP1 can be further promoted as a diagnostic and therapeutic target for tumor genesis and development.

TIMP1 is a multi-functional cytokine that plays different functions due to different tissue specificity and other conditions at the tumor stage. It has regular changes in specific tumor types and tissues. For colorectal cancer, combined with the functional characteristics and the current statistical analysis results, TIMP1 is a very promising target for tumor development diagnosis and treatment.

Microarray Data

Gene Expression Omnibus (GEO), hosting a substantial proportion of publicly available high throughput gene expression data, is a database repository maintained by the National Biotechnology Information Center (NCBI). This study downloaded three gene expression datasets (GSE41657, GSE44861, and GSE74602) from the GEO database. The GSE41657 dataset contains 12 cases of normal mucosa, 21 cases of low-grade adenoma, 30 cases of high-grade adenoma, and 25 cases of adenocarcinoma. GSE44861 contains 111 colon tissues from tumors and adjacent non-cancerous tissues. GSE74602 contains 30 paired normal and colorectal tumor samples. Subsequently, the gene probes were converted into the corresponding gene symbol based on the annotation information in the platform (GPL6480, GPL3921, and GPL6104 platforms). Further analysis of common differentially expressed genes will be made from the three databases.

Identification of differentially expressed genes

The samples in the three datasets are divided into normal tissue and tumor tissue. Then, the differentially expressed genes (DEGs) between CRC and non-cancerous samples were screened using GEO2R ( http://www.ncbi.nlm.nih.gov/geo/geo2r/) with the cut-off criterion of ∣log fold change (FC)∣ > 1.0 and P < 0.05. These DEGs were identified as important genes that may play an essential role in developing colorectal cancer. A Venn diagram of the DEGs of these datasets was implemented using an online tool, Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny/index.html).

Gene ontology and pathway enrichment analysis of DEGs

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses always be performed for gene function annotation and visualization. GO is a functional classification system for genes and gene products. GO annotations are divided into three categories, Molecular Function (MF), Biological Process (BP), and Cellular Components (CC), which defines and describes the function of a gene in many ways. Pathway enrichment analysis of genes was performed on the KEGG database. In this study, we used the Database Annotation, Visualization, and Integrated Discovery (DAVID, http://david.ncifcrf.gov ) which is a comprehensive set of functional annotation tools to perform gene ontology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. A P value of <0.05 was used to report significant pathways. Common targets were imported to the DAVID database for GO and KEGG pathway enrichment analysis. Taking P<0.05 as the screening criteria, the top five items are filtered according to the order from the P-value from small to the first five for visual analysis.

PPI network construction and modules selection

The protein-protein interaction (PPI) network briefly describes the interaction of proteins and shows the results of the analysis of protein components and their enrichment. Exploring protein interactions can further clarify their role in the regulation of life. Network interaction analysis is of great significance in understanding protein function and relationship. The Search Tool for the Retrieval of Interacting Genes (STRING; http://string-db.org ) database was used to analyze the PPI network of DEGs. The cut-off criterion for the combined score was >0.4. Then, the results were visualized by Cytoscape software. Subsequently, the degrees of genes in the PPI network and the MCODE plugin in Cytoscape were used to identify hub genes. The top 10 genes were identified as hub genes. These ten core genes were selected for further exploration. Furthermore, MCODE was used to screen modules in the PPI network.

Hub gene validation and Survival Analysis Based on the Hub Genes

The online database Gene Expression Profile Interactive Analysis (GEPIA) database (http://gepia.cancer-pku.cn) was used further to confirm the significant correlation of central gene expression levels.

Kaplan-Meier analysis is often used to evaluate the influence of a gene on clinical prognosis. Therefore, we predicted the prognostic value of the hub genes in colorectal cancer patients by GEPIA. Patients were divided into two groups according to the particular gene expression level (high vs. low expression). The OS of the two patient groups was then analyzed based on these categories. The hazard ratios (HRs) with 95% confidence intervals and log-rank P values are shown.

Clinical samples

Clinical tissue samples of 4 patients with colorectal cancer were gathered from the First Affiliated Hospital of Zhejiang Traditional Chinese Medicine University. After surgical removal from the patient, all tissue samples were stored in a -80℃ cryogenic refrigerator and kept cryogenic during the samples transfer and non-experimental disposal period. All biometric tests of tissue samples were performed with the patients' informed consent. The Ethics Committee of the First Affiliated Hospital of Zhejiang Chinese Medical University approved the research (No. 2017-002-01).

Real-time quantitative polymerase chain reaction (RT-qPCR)

Total RNA was extracted from human colorectal cancer tissues and normal tissues according to TRIZOL (Carlsbad, California, USA) reagent specifications. Then, Revert Aid First Strand cDNA Synthesis Kit (Waltham, Massachusetts, USA) was used to obtain cDNA following the instructions, and the iTaqTM Universal SYBR@ Green Master Mix kit was used to detect the level of mRNA. All primers (TIMP1 Forward primer: ATCCTGTTGTTGCTGTGGCTGATAG; Reverse primer: CGCTGGTATAAGGTGGTCTGGTTG) were synthesized by Sangon Bioengineering (Shanghai) Co., Ltd., and the Sequence of all primers are listed in Table 1. Histon 3 (Forward primer: GTCTTACCTGGTGGGGCTGTTTG; Reverse primer: GCTGGATGTCCTTAGGCATGATGG) was used as endogenous control, and all results were calculated and expressed as 2^-ΔΔCt.

Western blot

Clinical tissues were lysed in RIPA lysis buffer for 30 min on ice. The lysates were clarified by centrifugation at 4 ℃ for 15min at 12000g. Total protein concentrations were determined using a BCA Protein Assay Kit (KeyGEN BioTECH, China). Cell lysates were separated with SDS-PAGE gels and transferred to a polyvinylidene difluoride (PVDF) membrane (Millipore, Eschborn, Germany). The blots were blocked in 5% milk for 2h at room temperature. PVDF membranes were incubated with rabbit antibodies against primary antibodies overnight at 4℃. Membranes were washed three times with TBST, incubated with a second antibody for 2h, and evaluated by chemiluminescence.

Statistical analysis

GraphPad Prism 7.0 software was used for mapping. Results were expressed as (x±s) and one-way analysis of variance (ANOVA), (*) p < 0.05 indicates statistically significant, (**) p < 0.01 indicates significant statistical significance.

DEGs: differentially expressed genes; CRC: Colorectal cancer; FIT: fecal immunohistochemical testing; CT: computed tomography; GEO: Gene Expression Omnibus; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; PPI: protein-protein interaction; BP: biological process; CC: cellular component; MF: molecular function; MCODE: Molecular Complex Detection; GEPIA: Gene expression profiling and interactive analyses; STRING: Search Tool for the Retrieval of Interacting Genes

Ethics approval and consent to participate

The Ethics Committee of the First Affiliated Hospital of Zhejiang Chinese Medical University approved the research (No. 2017-002-01).

Consent for publication

Not applicable.

Availability of data and materials

The open-access datasets are available through the following URL: GSE41657(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41657), GSE44861(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE44861), and GSE74602(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE74602). All data generated or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests

Funding

This study was supported by grants from China's National Natural Science Foundation (81874355).

Authors' contributions

HW, ZJF, and YTC designed experiments and wrote the main manuscript text. YC, HL, and MAL organized the database, analyzed the data, and prepared figures. All authors reviewed the manuscript. All authors read and approved the final manuscript.

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No competing interests reported.

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Version 1

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TIMP1, a promising target of tumor development diagnosis and treatment for colorectal cancer

Status:

Version 1

Abstract

Figures

Background

Results

Identification of DEGs

GO and KEGG Pathway Enrichment Analyses

Construction of PPI network

mRNA Expression and Survival Analysis.

Validation of potential biomarkers in CRC Tissues

Discussion

Conclusion

Methods

Microarray Data

Identification of differentially expressed genes

Gene ontology and pathway enrichment analysis of DEGs

PPI network construction and modules selection

Hub gene validation and Survival Analysis Based on the Hub Genes

Clinical samples

Real-time quantitative polymerase chain reaction (RT-qPCR)

Western blot

Statistical analysis

Abbreviations

Declarations

Ethics approval and consent to participate

Consent for publication

Availability of data and materials

Competing interests

Funding

Authors' contributions

References

Additional Declarations

Supplementary Files

Status:

Version 1