GABRP is a potential chemoresistance regulator in colorectal cancer

doi:10.21203/rs.3.rs-1892069/v1

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GABRP is a potential chemoresistance regulator in colorectal cancer

https://doi.org/10.21203/rs.3.rs-1892069/v1

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published 25 Jan, 2023

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Background: Colorectal cancer (CRC) is one of the cancers with high morbidity and mortality worldwide. Chemotherapy is commonly used for metastatic or more advanced CRC. The mechanism of CRC chemoresistance is still under active investigation. Therefore, we identify and validate differentially expressed genes (DEGs) between oxaliplatin/5-FU resistant and sensitive cells.

Methods and Results: Three datasets of colorectal cancer patients (GSE28691, GSE81006, and GSE77932) from Gene Expression Omnibus (GEO) database were analyzed and volcano plots for DEGs were generated using GEO2R tool. The intersection of three GEO datasets showed that GABRP was significantly upregulated in chemo-resistant CRC cells or patients with an adjusted p-value less than 0.01. The STRING website analyzed the potential protein-protein interaction (PPI) network with GABRP. The PPI network predicted ANKRD66, CLINT1, HAP1, PLCL1, GABARPAP, GABARAPL1, NSF, GABARAPL2, TRAK2, and CLIC3 had a high likelihood to interact with GABRP. Especially, GABARAP, GABARAPL1, ANKRD66, CLINT1, and CLIC3 were enriched as the most possible associated protein with GABRP among the networks. GABRP was significantly highly expressed in both oxaliplatin/5-FU resistant CRC cells than in those counterparts sensitive CRC cells using quantitative PCR (qPCR) analysis. Consistently, TCGA and oncomine database confirmed that high expression of GABRP in various cancer patients including CRC patients tends to have relatively lower survival and disease-free survival rate.

Conclusion: We identify GABRP as a promising drug target to mediate oxaliplatin or 5-FU resistance in CRC. It provided the theoretical basis and potential clinical value for CRC patients.

GABRP

colorectal cancer

drug resistance

oxaliplatin

5-FU

GEO

Colorectal cancer (CRC) is a common disease among the lethal cancer types worldwide with the second leading cause of cancer death among all cancers[1, 2]. Furthermore, the age-standardized incidence of CRC has increased by nearly 10% over the past two decades[3]. Although the prompt screening and related treatments have led to clinical improvement in CRC, the prognosis was relatively poor and 30% of patients developed tumor metastasis[4]. Clinically, chemotherapy generally includes oxaliplatin and 5-fluorouracil (5-FU), which were commonly used as the first- or second-line therapy for metastatic colorectal cancer patients[5, 6].

Oxaliplatin, as one of the derivatives of cisplatin, additionally inhibits both DNA and protein synthesis and has relatively higher efficacy of killing cancer cells proliferation in chemotherapy[7, 8]. Curative resection of CRC is often followed by adjuvant chemotherapy mainly based on oxaliplatin, for example, CAPOX (oxaliplatin and capecitabine)[9]. Immune checkpoint inhibitors, recommended as the pioneering treatment, have shown better therapeutic activity in microsatellite instability-high (MSI-H) or deficient mismatch repair (dMMR) CRC patients[10]. Recent studies have indicated that oxaliplatin can improve PD-1 therapy by combination with Trifluridine/Tipiracil in CRC[11]. Long-term oxaliplatin treatment may accumulate acquired chemoresistance, which the mechanism is still unknown.

Since the 1990s, fluorouracil had been the mainstay of colorectal cancer chemotherapy and 5-FU remained one of the main constituents of current combinational chemotherapy in CRC[12]. 5-FU is widely used as a common chemotherapeutic drug in the treatment of different malignant tumors. As an analog of uracil, 5-FU has a fluorine atom instead of hydrogen at the C5 position and inhibited tumor growth[13, 14]. Likewise, acquired drug resistance remained the bottleneck of CRC treatments. Our study generated both oxaliplatin and 5-FU resistant CRC cells using elevated doses of both drugs to stimulate the cancer cells over a long period of time. We are devoted to identifying potential differentially expressed genes (DEGs) between sensitive and resistant CRC cell lines.

HCT-8/5-FU and SW480/LOPH were two CRC-resistant cell lines. We screened DEGs from GEO datamining by comparing our cell line models of chemo-sensitive and corresponding chemo-resistant cells. Gamma-Aminobutyric Acid Type A Receptor Subunit Pi (GABRP) ranked as the top candidate. GABRP is the π subunit of the GABA_A receptor. γ-aminobutyric acid (GABA) is one of the most significant inhibitory neurotransmitters in the adult mammalian brain and has two categories of receptors: ionotropic (GABA_A or GABA_C) and metabotropic (GABA_B) receptors[15]. The GABA_A receptor has 19 subunits (α1–6, β1–3, γ1–3, δ, ε, θ, π, and ρ1–3) and GABRP has an oncogenic function in many cancers including gastroenteric tumors, such as gastric cancer and pancreatic cancer. In gastric cancer, high expression of GABRP leads to abnormal activation of the extracellular signal-regulated kinase (ERK) 1/2 signaling pathway while GABRP-mediated upregulation of the p38 pathway enhanced tumor metastasis[16]. In pancreatic cancer, GABRP activated the MAPK/ERK pathway by increasing Ca²⁺ levels to promote cell proliferation[17]. However, the functional role of GABRP played or whether it mediated drug resistance in CRC remains a mystery.

In summary, this study intends to illustrate the effect of GABRP on CRC drug resistance and assess its potential clinical application for treating advanced CRC patients. GABRP expression was validated at the transcriptional level by the quantitative real-time polymerase chain reaction (qRT-PCR). The result showed that GABRP was upregulated in both chemo-resistant cells (HCT-8/5-FU and SW480/LOPH) than their counterpart chemo-sensitive cells. TCGA database also confirmed that patients with high GABRP expression detected lower survival rates, which was consistent with our results. It suggested that GABRP can be considered as the diagnostic marker and may serve as a promising drug target mediating chemoresistance in CRC.

Identification of DEGs:

We downloaded three datasets including both drug-resistant and drug-sensitive samples from the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) database with accession numbers of GSE28691, GSE77932, and GSE81006. GSE28691 characterized patients’ derived oxaliplatin sensitive tumors in contrast to parental human tumors in the NOD/SCID preclinical model of colorectal cancer. GSE77932 dataset compared DLD1 sensitive and its counterpart oxaliplatin-resistant cell lines, which contained six biological samples. GSE81006 compared the methylation status between HCT-8 wild-type cells (HCT8/WT) and its 5-FU resistant cells (HCT8/5-FU) for the time point at 72 hours. Adjusted p-value < 0.01 or the absolute value of log₂Fold Change (FC) >0.5 was set as the cut-off criteria to identify DEGs in colorectal sensitive and resistant cell lines. Then, DEGs had been analyzed and depicted utilizing GEO2R online tool and the volcano plots, which black dots represented none differentiated genes, red dots mean up-regulated genes while blue dots showed down-regulated genes. Overlapping DEGs were generated using the Venn Diagram (http://bioinformatics.psb.ugent.be/webtools/Venn/).

GO annotation and pathway signatures：

Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.genome.jp/) is a knowledge database to identify DEGs and signaling pathways in relation to molecular interactive networks including cellular or metabolic processes, genetic or environmental effects, human disease, etc. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) online tool (https://david.ncifcrf.gov/) was applied to analyze the GO annotation and the KEGG pathway enrichments. In brief, we imported overlapping genes from three datasets (GSE28691, GSE81006, and GSE77932) into the DAVID online tool and identified the most frequently defined pathways. P < 0.05 is considered the statistical significance for the KEGG pathway.

Cell cultures：

Human SW480 and HCT8 CRC cells were purchased from the Chinese Academy of Science (www.cellbank.org.cn) and authenticated by STR sequencing. The cells were cultured either in Dulbecco’s Modified Eagle’s Medium (DMEM) (Sigma, USA) or Roswell Park Memorial Institute (RPMI-1640) (Sigma, USA) containing 10% FBS (fetal bovine serum) (NSERA, south Africa) supplemented with 1% penicillin/streptomycin antibiotics (Biosharp, China). The CRC cells were incubated in the environment at 37°C consisting of 5% CO₂. 0.25% Trypsin-EDTA solution (Biosharp, China) was used to digest the cells and subcultured every 2–3 days.

Generation of the chemoresistant cell lines：

SW480 and HCT8 cells in the logarithmic growth phase were inoculated and measured by cell viability assay to calculate the IC50 value of oxaliplatin and 5-Fu (preliminary data). SW480-Oxaliplatin (30uM) and HCT-8-5-Fu (20ug/ml) resistant cells were established using an increasing concentration of these two drugs treatment starting from slightly less than the IC₅₀ value of each drug for more than half a year. Cells were stimulated with incremental doses of different drugs over multiple cycles of weeks. The chemoresistance of each cell line has been validated by the cell viability assay[18].

qRT-PCR analysis：

SW480, HCT8, and their counterpart chemoresistant cell lysates were harvested and homogenated. Then, the total RNA was harvested by TRIzol (Invitrogen, Carlsbad, CA) and extracted using chloroform, isopropanol precipitated RNA from the water phase followed by ethanol and centrifugation. The concentration of the total RNA was measured by Nano-one (Hangyan, Hangzhou, China) and displayed as the unit of ng/ul under the ratio of absorbance 260/280. 1.5-2ug of total RNA reversed transcribed into 20ul system using HiFiScript cDNA synthesis kit (CWBIO, China). The real-time quantitative polymerase chain reaction (qRT-PCR) was conducted using SYBR® Green PCR Master Mix (Takara, Japan). The PCR reaction was monitored with Bio-Rad CFX 96 Touch Real-Time PCR Detection System (Bio-Rad, China). The thermocycling conditions for PCR program were 50°C for 2 min, 95°C for 10 min, 40 cycles at 95°C for 45s, and the melting temperature was 55-65°C for 30 s. All the experiments were independently performed at least in triplicates. The cyclic threshold (CT) of GABRP normalized to β-actin using 2−ΔΔCt calculation methods. Primers listed below: β-actin forward primer: 5′-GACCAATCCTGTCACCTC-3′, reversed primer: 5′-GATCTCCGTTCCCATTAAGAG-3′; GABRP forward primer: 5′- GCCCTAACAGAGCCTCAACA-3′, reverse primer: 5′-TTGTCACTTCTGCCGACCTC- 3′.

TCGA analysis:

Kaplan Meier's plot was generated to compare the survival rate among normal, CRC cancer patients and various cancer types of patients. P < 0.05 as the standard is significantly different for oncomine and TCGA analysis.

Protein-protein interaction：

The STRING database (https://string-db.org/) was applied to compose the protein-protein interactive (PPI) PPI network. “GABRP” molecule was imported as the protein name in the rectangular box, the organism was selected as Homo sapiens, and the possible network of protein interaction was generated. The middle circle displayed the hub gene GABRP connecting with other potential interactive proteins by the connecting lines. For example, every single gene was denoted by an individual circle, and the secondary structure of the protein was presented inside the circle. Furthermore, line numbers represented the strength of possible interactive molecules.

Statistical analysis:

Student t-test and ANOVA analysis were utilized for all our data analysis. All the experiments were repeated at least three times and performed independently; each experiment contained triplicates. *, p < 0.05; **p< 0.01; ***p<0.001.

Screening GABRP from human CRC chemoresistant and sensitive patients' samples

In order to screen potential DEGs in chemo-resistant and chemo-sensitive colorectal patients, GSE28691, GSE77932, and GSE81006 datasets had been intersected. Firstly, GABRP stood out among 3981 overlapping genes from both GSE28691 and GSE81006 datasets with the selection condition of the adjusted p-value<0.01 presented by the Venn diagram (Fig 1a). Secondly, GABA family molecules had been enriched when crossing the datasets of GSE28691 and GSE77932, with the threshold setting as |log2FC|>0.5 (Fig 1b), we found GABARAPL1, GABRB1, GABRA3, GABRQ becoming the overlap. Collectively, GABRP involved in the GABA family seems a promising differentiated gene between colorectal sensitive and resistant cells, therefore, we further decided to validate GABRP in two colorectal chemo-resistant and chemo-sensitive cancer cells.

KEGG pathway analysis and GO annotation

We imported 3981 genes into the DAVID database for GO and KEGG enrichment analysis. The results showed that the DEGs mainly participated in the biological process based on FDR ranking: such as signal transduction, positive regulation of transcription from RNA polymerase II promoter, inflammatory response, response to drugs, positive regulation of cell migration, cell-cell signaling, intracellular signal transduction, axon guidance, chemical synaptic transmission, ion transport (Fig 2a). Our candidate gene GABRP was enriched in three biological processes: signal transduction, chemical synaptic transmission, and ion transport. Regarding cellular components, the DEGs were mainly concentrated in extracellular exosome, plasma membrane, extracellular region, integral component of plasma membrane, cell surface, membrane, cytosol, extracellular space, cytoplasm, and neuron projection, etc (Fig 2b). The gene of our interest GABRP was enriched in plasma membrane, integral component of plasma membrane, membrane, and neuron projection. KEGG pathway analysis enriched in phagosome, rheumatoid arthritis, Th17 cell differentiation, relaxin signaling pathway, calcium signaling pathway, human papillomavirus infection, axon guidance, PI3K-Akt signaling pathway, GABAergic synapse, and neuroactive ligand-receptor interaction (Fig 2d). Of note, the last two pathways listed in our representative bar graph contained candidate gene GABRP.

Validation of GABRP from the transcriptional level between chemoresistant and chemosenstive CRC cells

GABRP mRNA expression increased more than two folds in oxaliplatin-resistant SW480 cells compared to its corresponding oxaliplatin-sensitive cells (Fig 3a). Interestingly, 5-Fu resistant HCT-8 colorectal cells also detected a relatively higher level, nearly one and a half fold of GABRP at the transcriptional perspective. β-actin served as the reference gene (Fig 3b). These results demonstrated that GABRP was positively associated with resistant colorectal cancer cells.

Clinical relevance of GABRP in CRC patients

In order to further validate our findings in cancer patients in the clinical setting, we firstly compared GABRP expression among several cancer types with different cancer patients, such as breast cancer, colon cancer, gastric cancer, and pancreatic cancer. It was obvious that GABRP was overexpressed in colon cancer compared to the other three cancer types (Fig 4a). Therefore, we switched gear to colorectal cancer patients. We found most colon and rectal cancer patients detected high GABRP expression (Fig 4b). Moreover, GABRP expression was upregulated at least two folds in colorectal adenocarcinoma than in normal colorectal tissues (Fig 4c). High expression of GABRP had a relatively lower survival rate or disease-free survival rate than those GABRP low expression group in colorectal cancer patients. Collectively, consistent with our findings in cell line models, clinical data supported the oncogenic role of GABRP in colorectal cancer patients.

Prediction of protein-protein interaction

In order to find interactive proteins with our candidate gene GABRP, we created its PPI network using the STRING database (Fig 5a,5b). GABRP was radiated as the hub gene in a plain PPI network with 10 circles and 17 lines connecting to it (Fig 5a). There are ten proteins predicted to interact with GABRP: Ankyrin Repeat Domain 66（ANKRD66）, Clathrin Interactor 1（CLINT1）, Huntingtin Associated Protein 1（HAP1）, Phospholipase C Like 1 (Inactive)（PLCL1）, GABA Type A Receptor-Associated Protein（GABARAP）, GABA Type A Receptor Associated Protein Like 1（GABARAPL1）, N-Ethylmaleimide Sensitive Factor, Vesicle Fusing ATPase（NSF）, GABA Type A Receptor Associated Protein Like 2（GABARAPL2）, Trafficking Kinesin Protein 2（TRAK2）, and Chloride Intracellular Channel 3（CLIC3）(Fig 5a). In this network, the average node degree was 3.09, the average local clustering coefficient was 0.896, and the PPI enrichment P-value was 0.0389. Moreover, we found that GABRP has a relatively stronger interaction with GABARAP, GABARAPL1, ANKRD66, CLINT1, and CLIC3 than the above-mentioned molecules due to the indication of more line numbers (Fig 5b). Therefore, we enriched highly interactive proteins in Fig 5b. The average node degree was 2, the average local clustering coefficient was 0.85.

Colorectal cancer (CRC) is one common and fatal cancer [19]. Chemoresistance and metastatic factors contributed to the high mortality rate in colorectal cancer which its increment especially found in men or people under 50 years old[20]. The mechanism of chemoresistance is considered an intractable question. Our study successfully developed chemo-resistant cancer cells with elevated doses of oxaliplatin and 5-Fu, named SW480-LOPH, and HCT-8-5-Fu (Fig. 3). We found that the mRNA level of GABRP was significantly increased in oxaliplatin and 5-Fu chemo-resistant cells than in those chemo-sensitive ones. Consistent with our results, JaeJin An et al. recently found that ligand GABA was critically decreased while its B subunit receptor GABABR expression was compensational upregulated in 5-FU-resistant-HT29 colorectal cancer cells[21]. The expression of GABABR1 was positively correlated with Galectin-3, which was related to the 5 – Fu sensitivity in colon cancer cells[22].

Interestingly, we found that GABARAP, GABARAPL1, and GABARAPL2 had relatively strong interactions with GABRP using the STRING database (Fig. 5). These molecules may also be associated with colorectal cancer. Recent research showed that increased expression of GABARAP in colorectal cancer was significantly associated with the low differentiation and relative shorter overall survival rate[23]. Selenium affected autophagy by up-regulating GABARAPL1, which played a key role in the survival process of colorectal cancer cells. Depletion of GABARAPL1 promoted cell apoptosis in colorectal cancer[24]. Moreover, in colorectal cancer tissues, GABARAPL2 was the highest expressed gene among the subgroup of the autophagy-related genes which play a critical role in autophagosome elongation, and function in the regulation of the receptor transport, cell proliferation, and autophagy[25]. Most predicted interactive molecules with GABRP by the STRING database exerted oncogenic characteristics in various cancer types. For example, GABARAP can participate in the autophagy of hepatocarcinoma (HCC) cells and endow cancer cells with radio-resistance[26]. High GABARAP expression was related to a poor prognosis of breast cancer[27]. While GABARAPL1 displayed the opposite tumor-suppressive role in breast cancer[28].

Given that GABRP and its family proteins were all abnormally expressed in multi cancer types such as gastric cancer, breast cancer, pancreatic cancer, ovarian cancer etc.[16], the mechanism of GABRP-mediated chemo-resistance in colorectal cancer is still unknown. The loss-of-function study displayed that silence or pharmacological inhibition of GABRP downregulated Erk1/2 expression which resulted in reducing cell proliferation, migration, invasion, and metastatic potential[16]. These findings suggested that GABRP may play an indispensable role in chemo-resistance in many cancers. Our study revealed that GABRP was highly expressed in oxaliplatin/5-Fu-resistant colorectal cancer cells, which developing novel targets are in urgent need to improve the therapeutic outcomes of colorectal cancer patients. The expression of GABRP in triple-negative breast cancer (TNBC) cells was upregulated and it promoted tumor growth both in vitro and in vivo. It can also be used as a new target for breast cancer therapy in the future[29]. Anti-GABRP-ADC may have therapeutic effects on colorectal cancer, triple-negative breast cancer, and other cancers[29]. GABRP was associated with macrophage infiltration in pancreatic ductal adenocarcinoma (PDAC) and its expression in cancer cells was higher than that in normal cells. The silence of GABRP inhibited the tumor growth which suggested that GABRP may provide a novel therapeutic target to treat the tumor growth in pancreatic cancer patients[17]. In summary, GABRP may be identified as a promising target for reversing chemo-resistance in a variety of cancer treatments, especially in colorectal cancer.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Funding

This work was supported by PPZY2015B146 (to co-corresponding author Jianghua Chen) Top-notch Academic Programs Project of Jiangsu Higher Education Institutions, BK20190593 (to co-corresponding author Chen Chen) Natural Science Foundation of Jiangsu Province of China，and 2020M681492 (to co-corresponding author Chen Chen) Chinese Post-Doc Science Funds.

Competing Interests

Financial interests: All authors declare they have no financial interests.

Author Contributions

Zhaohui Huang, Chen Chen and Jinghua Chen contributed to the study conception and design. Material preparation, data collection and analysis were performed by Tengyu Wang, Qinghao Zhen, Tong Wu, Lan Jin, Surui Yao, Yuyang Feng. The first draft of the manuscript was written by Tengyu Wang, Qinghao Zhen and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Tengyu Wang and Qinghao Zhen contributed equally to this paper.

Acknowledgments: We appreciate Xinyuan Lao, the CEO of Helixlife (Shanghai) Co. Ltd (https://www.helixlife.cn), and Zhujun Tan, the co-founder of this company for providing bioinformatics online courses to inspire us in the analysis of GEO datamining, KEGG pathways, and TCGA database, etc. We also thank PPZY2015B146 (to Jinghua Chen) Top-notch Academic Programs Project of Jiangsu Higher Education Institutions, BK20190593 (to Chen Chen) Natural Science Foundation of Jiangsu Province of China， and 2020M681492 (to Chen Chen) Chinese Post-Doc Science Funds.

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GABRP is a potential chemoresistance regulator in colorectal cancer

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