Transcriptomic and proteomic analysis of the mechanisms underlying the role of the CCT3 gene in breast cancer

doi:10.21203/rs.3.rs-2603275/v1

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Transcriptomic and proteomic analysis of the mechanisms underlying the role of the CCT3 gene in breast cancer

https://doi.org/10.21203/rs.3.rs-2603275/v1

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Background Breast cancer has surpassed lung cancer as the most common cancer in the world. Our previous research showed that silencing the expression of the CCT3 gene significantly decreased the proliferation and metastasis of breast cancer cells, but the mechanism is still unclear. In this study, we investigated CCT3 gene and protein expression by transcriptomics and proteomics to explore the mechanism underlying its function.

Methods MDA-MB-231 cells were transfected with CCT3-siRNA to knock down the expression of the CCT3 gene. qRT‒PCR was used to measure the efficiency of the suppression. We used RNA-seq and tandem mass tag proteomics analysis to identify differentially expressed genes and proteins, respectively. The possible biological consequences were determined by bioinformatics analysis.

Results The mRNA expression of CCT3 decreased by 87.6% in MDA-MB-231 cells transfected with shCCT3 lentivirus. RNA-seq identified 449 differentially expressed genes after transfection with shCCT3, including 240 upregulated genes and 209 downregulated genes, and 61 differentially expressed proteins (33 upregulated and 28 downregulated) were identified by TMT proteomics analysis. GO and KEGG enrichment revealed the biological and molecular functions in CCT3-knockdown cells. Conjoint analysis revealed the expression of several genes and proteins, such as RPUSD4, LUC7L3, HELLS, ARL1, and SPINT2.

Conclusions We explored the molecular mechanism by which the proliferation and metastasis of breast cancer cells are promoted after the expression of CCT3 is inhibited through transcriptomics and proteomics, and we initially identified several key genes and proteins. The functional verification of these genes requires further research.

Breast Cancer

CCT3

Transcriptomic

proteomics

Breast cancer surpassed lung cancer as the most common cancer in 2020, with an estimated 2.3 million newly diagnosed cases, accounting for 11.7% of all cancer cases, and it is the fifth leading cause of cancer related mortality, with 685,000 deaths. In China, the crude incidence of breast cancer increased from 14.14/100,000 to 52.81/100,000, and the crude mortality increased from 7.22/100,000 to 13.4/100,000 during 1990–2019 [1]. At present, breast cancer treatment mainly includes neoadjuvant therapy, surgery, chemotherapy, radiotherapy, targeted therapy and endocrine therapy [2]. The application of comprehensive treatment has improved the prognosis of breast cancer and prolonged the survival time of patients, but the overall treatment effect is still unsatisfactory, especially for patients with stage IV disease, for whom the median overall survival is only 2–3 years [3]. Therefore, it is an urgent problem to find an effective biomarker for the prognosis of breast cancer.

Chaperonin containing TCP-1 (CCT) is an important chaperone protein in eukaryotic cells that facilitates the folding of approximately 10% of proteins, including most cytoskeleton proteins and cell cycle-regulating proteins; CCT plays an important role in protein stability [4, 5]. CCT also plays an irreplaceable role in the occurrence and development of carcinomas [6]. As an important subunit of the chaperone CCT, CCT3 has been shown to be highly expressed in many tumors, and its high expression has an important relationship with tumor development and prognosis [7]. Current reports show that CCT3 is closely related to the prognosis of hepatocellular carcinoma [8], gastric cancer [9], papillary thyroid carcinoma [10], cervical cancer [11], and other tumors.

Our previous study showed the abnormal expression of the CCT3 gene in breast cancer patients through analysis of The Cancer Genome Atlas (TCGA). After silencing the expression of the CCT3 gene, the proliferation and metastasis of breast cancer cells significantly decreased [12], but the mechanism is still unclear. In this study, we investigated protein and gene expression of CCT3 by proteomics and transcriptomics to explore the mechanism underlying the function of the CCT3 gene.

Cell culture

The MDA-MB-231 cell line was cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum. The cells were maintained at 37 ℃ in a 5% CO₂ humidified incubator.

Lentivirus Vector Construction And Infection

For the construction of shRNA expression plasmids, CCT3-siRNA was designed based on the targeted sequence 5’- CAAGTCCATGATCGAAATT-3’, as described in a previous study [12]. In brief, the ligated products were transfected into Escherichia coli cells. The positive recombinants were identified for sequencing verification and plasmid extraction. Lentiviral vector DNA and packaging vectors were transfected into 293T cells, and the empty GV115 lentiviral vector was used as the shRNA control (shCtrl). After 48 h of culture, supernatants containing lentiviruses were harvested, and the lentiviruses were purified. Lentivirus infection was performed on cells at 80% confluence. Seventy-two hours after infection, the cells were used for downstream assays or transplantation.

Qrt-pcr

Total RNA was isolated by TRIzol method. The cDNA was reverse-transcribed and amplified using the following primer sets: CCT3: forward,5’- TCA GTC GGT GGT CAT CTT TGG-3’; reverse, 5’- CCT CCA GGT ATC TTT TCC ACT CT-3’; GAPDH: forward, 5’- TGA CTT CAA CAG CGA CAC CCA − 3’; reverse, 5’- CAC CCT GTT GCT GTA GCC AAA-3’. Real-time PCR using the SYBR Green PCR Master Mix kit was performed with an ABI Prisma 7000 Sequence Detection System (Applied Biosystems, Foster City, CA, USA). The data were normalized to the respective GAPDH expression values. The value of cells infected with shCtrl was considered 100 in each run.

Rna-sequencing

Total RNA was extracted from MDA-MB-231 cells transfected with or without CCT3-siRNA by a TRIzol Reagent Kit (Invitrogen, Carlsbad, CA, USA). RNA purity was evaluated using the NanoPhotometer® spectrophotometer (IMPLEN, CA, USA), and RNA integrity was assessed using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA, USA). A total amount of 1 µg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using the NEBNext® UltraTM RNA Library Prep Kit for Illumina® (NEB, USA) following the manufacturer’s recommendations and index codes were added to attribute sequences to each sample. Differentially expressed RNAs were selected according to fold change > 2 or fold change < 0.5 and p value < 0.05 by R package edgeR or DESeq2. The differentially expressed genes (DEGs) were subjected to Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis.

Tandem Mass Tag Proteomics (Tmt) Analysis

Sample Preparation

SDT buffer (4% SDS, 100mM Tris-HCl, pH 7.6) was added to the samples. The lysates were sonicated (this step can be skipped for protein solution) and then boiled for 15 min. After centrifugation at 14000 × g for 15 min, the protein concentrations of the supernatants were measured with the BCA Protein Assay Kit (P0012, Beyotime, Shanghai, China). The samples were stored at -80°C.

Filter-aided Sample Preparation (Fasp Digestion)

A total of 150–200 µg of protein from each sample was reduced with 100 mM DTT for 5 min at 100°C. Then the detergent, DTT and other low-molecular-weight components were removed using UA buffer (8 M urea, 150 mM Tris-HCl pH 8.5) and repeated ultrafiltration (Sartorius, 30 kD). Then 100 µl iodoacetamide (100 mM IAA in UA buffer) was added to block reduced cysteine residues and the samples were incubated for 30 min in the dark. The filters were washed with 100 µl UA buffer three times and then 100 µl 0.1 M TEAB buffer twice. Finally, the protein suspensions were digested with 4 µg trypsin (Promega) in 40 µl 0.1 M TEAB buffer overnight at 37°C, and the resulting peptides were collected as a filtrate. The peptide content was estimated by UV light spectral density at 280 nm using an extinction coefficient of 1.1 of 0.1% (g/l) solution, which was calculated on the basis of the frequency of tryptophan and tyrosine in vertebrate proteins.

Tmt Labeling

A 100 µg peptide mixture of each sample was labeled using TMT reagent according to the manufacturer’s instructions (Thermo Fisher Scientific).

Peptide Fractionation With Reversed-phase (Rp) Chromatography

TMT labeled peptides were fractionated by RP chromatography using Agilent 1260 infinity II HPLC. The peptide mixture was diluted with buffer A (10 mM HCOONH4, 5% ACN, pH 10.0) and loaded onto an XBridge Peptide BEH C18 Column (130 Å, 5 µm, 4.6 mm X 100 mm). The peptides were eluted at a flow rate of 1 ml/min with a gradient of 0% buffer B (10 mM HCOONH4, 85% ACN, pH 10.0) from 0–25 min, 0–7% buffer B from 25–30 min., 7–40% buffer B from 30–58 min, 40–100% buffer B from 58–70 min, 100% buffer B from 70–80 min. 100-0% buffer B from 80–85 min. 0-100% buffer B from 85–90 min. The elution was monitored at 214 nm based on the UV light trace, and fractions were collected every 1 min from 31–65 min. The collected fractions were combined into 34 fractions. The samples were freeze-dried, reconstituted with 0.1% FA, and combined into 10 parts.

Mass Spectrometry Analysis

Easy nLC

Each fraction was injected for nano LC-MS/MS analysis. The peptide mixture was loaded onto a C18-reversed phase analytical column (Thermo Fisher Scientific, Acclaim PepMap RSLC 50 um X 15 cm, nano viper,P/N164943) in buffer A (0.1% formic acid) and separated with a linear gradient of buffer B (80% acetonitrile and 0.1% formic acid) at a flow rate of 300 nl/min. The linear gradient was set as follows: 6% buffer B for 5 min, 6–28% buffer B for 40 min, 28–38% buffer B for 5min, 38–100% buffer B for 5 min, hold in 100% buffer B for 5 min.

Lc-ms/ms Analysis

LC-MS/MS analysis was performed on a Q Exactive mass spectrometer (Thermo Fisher Scientific) that was coupled to Easy nLC (Thermo Fisher Scientific) for 60 min. The mass spectrometer was operated in positive ion mode. MS data were acquired from the survey scan (350–1800 m/z) for HCD fragmentation. Survey scans were acquired at a resolution of 70000 at m/z 200 with an AGC target of 3e6 and a maxIT of 50 ms. MS2 scans were acquired at a resolution of 17500 or 35000 for HCD spectra at m/z 200 with an AGC target of 2e5 and a maxIT of 45 ms, and the isolation width was 2.0 m/z. Only ions with a charge state between 2–7 and a minimum intensity of 2e3 were selected for fragmentation. Dynamic exclusion for selected ions was 30 s. The normalized collision energy was 30 eV.

Data Analysis

MS/MS raw files were processed using the MASCOT engine (Matrix Science, London, UK; version 2.6) embedded into Proteome Discoverer 2.2, and searched against the database. The search parameters included trypsin as the enzyme used to generate peptides with a maximum of 2 missed cleavages permitted. A precursor mass tolerance of 10 ppm was specified and 0.05 Da tolerance for MS2 fragments. Except for TMT labels, carbamidomethyl (C) was set as a fixed modification. Variable modifications were Oxidation(M) and Acetyl (Protein N-term). A peptide and protein false discovery rate of 1% was enforced using a reverse database search strategy. Proteins with a fold change > 1.2 and p value (Student’s t test) < 0.05 were considered to be differentially expressed proteins.

Bioinformatics Analysis

First, all the protein sequences were aligned to the database downloaded from NCBI (ncbi-blast-2.2.28+-win32.exe), and only the top 10 sequences with and E-value < = 1e-3 were retained. Second, the GO term (database version: go_201504.obo) of the sequence with the top bit score by Blast2GO was selected. Then, the annotation from GO terms to proteins was completed by Blast2GO Command Line. After the elementary annotation, InterProScan was used to search the EBI database by motif and then add the functional information of the motif to proteins to improve annotation. Pathway analysis was performed using the KEGG database. Fisher's exact test was used to identify the significant Go terms and enriched pathways. For protein-protein interactions (PPIs), target proteins were searched in String(https://www.string-db.org/), and the interaction network with the target protein was directly or indirectly searched in the database. Echart software was used to generate the interaction network analysis results.

Conjoint Analysis

The DEGs/DEPs that were identified in transcriptomic and proteomic analyses were analysis, and the genes with the same up- or downregulated trends in both the transcriptome and proteome were identified.

Statistical analysis

The data are expressed as the mean ± standard error (S.E) and were analyzed using the Statistical Program for Social Sciences (SPSS) 10.0 software (IBM, Armonk, NY, USA). Differences between groups were analyzed using analysis of variance (ANOVA) or Student’s t test.

Knockdown of CCT3 expression by transfection with shCCT3.

The breast cancer cell line MDA-MB-231 was transduced with lentivirus carrying short hairpin RNA targeting CCT3 mRNA (shCCT3). As shown in Fig. 1A, the cell transduction rate was greater than 80%. The silencing effect of CCT3 in MDA-MB-231 cells was measured by qRT-PCR. The mRNA expression of CCT3 was decreased by 87.6% in MDA-MB-231 cells transduced with shCCT3 lentivirus compared with the shCtrl group (Fig. 1B).

Rna-seq Analysis Of Mda-mb-231 Cells Transfected With Shcct3

All genes identified in this study was listed in supplementary 1, and using the |log₂FoldChange|>=1.0 and p value < 0.05 as thresholds, 449 differentially expressed DEGs were identified by RNA-seq after transduction with shCCT3(supplementary 2), including 240 upregulated genes and 209 downregulated genes (Fig. 2).

Go And Kegg Pathway Enrichment Analyses Of Degs

The expression of DEGs in MDA-MB-231 cells transduced with shCCT3 (knockdown, KD group) or shCtrl (normal control, NC group) was explored by clustering the genes according to the similarity of gene expression profiles. All the DEGs between the KD group and NC group are displayed in a heatmap, in which the abscissa is the sample and the ordinate is the normalized value of the differential gene FPKM. Different gene expression levels are indicated by different colors. For instance, the redder the color, the higher the expression, and the greener the color, the lower the expression. The heatmap also indicates the chromosome to which each gene belongs, the gene length and the biological type of the gene (Fig. 3A).

The functions of the DEGs were elucidated using GO and KEGG pathway enrichment analyses. The GO enrichment analysis revealed a strong association of the DEGs in the KD group with intrinsic components of the Golgi membrane and intrinsic components of the organelle membrane (Fig. 3B). The KEGG pathway enrichment analysis revealed a close association of the DEGs in the KD group with melanogenesis, Cushing syndrome and protein digestion and absorption (Fig. 3C). The DEGs were categorized based on cellular components (CC), biological processes (BP), and molecular functions (MF). Cell component analysis revealed significant enrichment of DEGs in the intrinsic component of the Golgi membrane, integral component of organelle membrane and integral component of the Golgi membrane. In the biological process group, the DEGs were mainly enriched in hair follicle development, molting cycle process, hair cycle process and skin epidermis development. For molecular function, the DEGs were enriched in growth factor binding, copper ion binding and monosaccharide binding (Fig. 3D).

Proteomics Analysis Of Differentially Expressed Proteins (Deps)

Proteomics analysis was evaluated via TMT and LC-MS/MS analysis. After quality validation, 273,078 (87,660 matched) spectra were obtained, of which 53,803 peptides (51,421 unique peptides) and 6,567 were identified proteins were detected (supplementary 3). In total, 61 DEPS (33 DEPs up-regulated and 28 DEPs down-regulated) were identified (fold change > = 1.2 or < = 0.8333 and p value < 0.05)(supplementary 4).

Go And Kegg Pathway Enrichment Analyses Of Deps

The hierarchical clustering heatmap of the DEPs is shown in Fig. 4A. The functions of the DEPs were categorized based on cellular components (CC), biological processes (BP), and molecular functions (MF) through GO enrichment analysis. GO cell component analysis revealed significant enrichment of DEPs in the binding, catalytic activity, and structural molecule activity. In the biological process category, the DEPs were mainly enriched in cellular process, biological regulation, and cellular component organization or biogenesis. For molecular function, the DEPs were enriched in the cell part, organelle part, and organelle (Fig. 4B).

The DEPs were mapped to KEGG pathways and the results showed that proteins in the CCT3-KD group were largely associated with Staphylococcus aureus infection, nicotine addiction, transcriptional misregulation in cancer, and the estrogen signaling pathway (Fig. 4C). Structural domains of the DEPS were analyzed, and the top 3 categories with the most significant enrichment were intermediated filament protein (conserved site), intermediate filament (rod domain) and intermediate filament protein (Fig. 4D).

Ppi Network Analysis

We screened the proteins with p < 0.05 and the most intense change in expression (50 upregulated and 50 downregulated) as the target protein for direct interaction network analysis (Fig. 5A). As shown in Fig. 5B, 100 target proteins were included in the indirect interaction network with all the proteins identified in this study.

Conjoint Analysis

In addition to the expression of the CCT3 gene being significantly downregulated, genes and proteins that were significantly up- or downregulated were subjected to joint analysis, and one upregulated genes and four downregulated genes that had similar expression patterns in both the transcriptomics and proteomics analyses were identified (Table 1).

Table 1

Joint analysis significantly up- or down-regulated gene and protein expression.
Gene ID	Gene Name	Description	Transcriptome expression (FPKM)		Proteome expression (abundance value)
Gene ID	Gene Name	Description	NC	KD	NC	KD
ENSG00000165526	RPUSD4	RNA pseudouridylate synthase domain containing 4	303.4	892.7	89.1	110.9
ENSG00000108848	LUC7L3	LUC7 like 3 pre-mRNA splicing factor	2309.6	721.4	109.9	90.1
ENSG00000119969	HELLS	helicase, lymphoid specific	1286.6	436.5	111.2	88.9
ENSG00000120805	ARL1	ADP ribosylation factor like GTPase 1	1830.6	327.5	119.2	80.8
ENSG00000167642	SPINT2	serine peptidase inhibitor, Kunitz type 2	2549.9	750.7	123.9	76.1

Our previous research has proven the role of CCT3 in breast cancer. Silencing the expression of CCT3 can increase the proliferation and metastasis of breast cancer cells and increase breast cancer cell apoptosis, and preliminary mechanistic studies and functional recovery experiments were carried out. The results show that there are multiple signal transduction pathways involved in the effect of CCT3, but the specific mechanism is unclear [12]. To further elucidate the specific mechanism underlying the effect, we carried out this study.

In this study, we first constructed a breast cancer cell line in which the expression of CCT3 was silenced by siRNA according to a previous report and the expression of CCT3 was significantly suppressed, which was consistent with the previous study, thus, this cell line could be used for subsequent experiments. In the transcriptomics study, we observed 449 gene expression changes in the CCT3 downregulated group, including 240 upregulated and 209 downregulated genes. GO analysis showed that mutant genes were mainly concentrated in cell components, including the Golgi membrane and organelle membrane. The function of the Golgi body is to process, sort and transport proteins synthesized by the endoplasmic reticulum. The literature report shows that CCT3 participates in the folding of multiple proteins, which is consistent with the literature report [5]. KEGG signal transduction pathway analysis showed that CCT3 is related to melanogenesis, Cushing syndrome and protein digestion and absorption. Recent studies have shown that melanogenesis can be inhibited through the MAPK signal transduction pathway [13]. According to its melanin production, its biological function is mainly concentrated in hair and skin. Melanogenesis also plays an important role in breast cancer metastasis [14].

In terms of TMT proteomics, 61 differentially expressed proteins were identified, of which 33 were upregulated and 28 were downregulated. GO analysis showed the main cell processes, biological regulation and cell components in biological function, and the cell component analysis showed the main performance in binding, catalytic activity and structural molecular activity. In terms of molecular function, cell components, organelles and their components are involved. KEGG related signal transduction pathway analysis shows the signal transduction pathway related to Staphylococcus aureus infection. Recent reports show that multidrug-resistant Staphylococcus aureus infection can promote the metastasis of breast cancer through neutrophil trap net [15]. Our previous research showed that inhibiting CCT3 can promote the metastasis of breast cancer is consistent. There are many signaling pathways involved in nicotine addiction. At present, there are data showing that nicotine can reduce the effect of chemotherapy on breast cancer and promote the generation of breast cancer stem cells [16]. Nicotine also plays an important role in the initiation, development, angiogenesis, invasion, metastasis and resistance to therapy of breast cancer [17]. The loss of tumor transcription regulation plays an important role in the biological function of the normal body, while taberrant regulation often leads to the occurrence and development of tumors, and finally it is the estrogen pathway. The estrogen pathway plays a very important role in breast cancer, which has been supported by a large amount of data [18–20].

In terms of protein-protein interactions, we identified the interactions between the differentially expressed proteins, as well as a very complex protein-protein interaction network related to other proteins. However, the specific signal transduction pathway that participates in the invasion and metastasis of breast cancer still needs further verification. After comprehensive analysis of the transcriptomics and proteomics results, in addition to the expression of CCT3 being downregulated, we observed changes in the expression of five genes, including RPUSD4, LUC7L3, HELLS, ARL1 and SPINT2. RPUSD4 was upregulated, and the other four were downregulated. RPUSD4 was previously identified as a core essential gene in CRISPR/Cas9 screens [21]. Targeting of individual enzymes produced specific mitochondrial protein synthesis and oxidative physiological assembly defects without affecting mitochondrial mRNA levels [22]. Whether this phenomenon is related to breast cancer is requires further research. LUC7L3 has been reported in myeloid neoplasms and renal cell carcinoma [23, 24]. Its abnormal expression is related to the clinicopathological features of the disease [25]. HELLS is an important component of the chrome remolding complex that helps repair DNA through various mechanisms such as DNA methylation, histone posttranslational modification, and nuclear remolding [26]. Its expression is positively correlated with Th2 and Tcm cells in most cancers [27]. ARL1 is mainly related to oxidative damage. In a case report, it was found that patients with non-small cell lung cancer with ARL1-MET fusion mutations responded very well to treatment with crizotinib [28]. SPINT2 plays an important role in the generation and development of a variety of tumors, including breast cancer, and its expression is upregulated in breast cancer, and is related to HER-2 expression and lymph node positivity. High expression of SPINT2 is related to the prognosis of breast cancer [29]. Further bioinformatics displays are involved in cell attachment and metastasis. Therefore, the increased metastasis caused by CCT3 may be related to the downregulation of SPINT2 expression, and this needs further verification in the later stage.

In conclusion, we explored the molecular mechanism by which the proliferation and metastasis of breast cancer cells are promoted after the expression of CCT3 is inhibited through transcriptomics and proteomics, and we initially identified several key genes and proteins. The functional verification of these genes requires further research.

Acknowledgements

Not applicable.

Author contributions

GX and QW conceived the idea and designed the research. YM Land GX performed invitro experiments. QW and HW analyzed the data. GX and HL Lwrote the manuscript. HG revised the manuscript. All authors read and approved the final manuscript.

Funding

This study was supported by the National Cancer Center，No.NCC201916B01.

Availability of data and materials

All data generated or analysed during this study are included in this article and its supplementary information files.

Ethics approval and consent to participate

Not applicable.

Competing interests

The authors declare that there are no conflicts of interest.

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Transcriptomic and proteomic analysis of the mechanisms underlying the role of the CCT3 gene in breast cancer

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Abstract

Figures

Introduction

Methods

Cell culture

Lentivirus Vector Construction And Infection

Qrt-pcr

Rna-sequencing

Tandem Mass Tag Proteomics (Tmt) Analysis

Sample Preparation

Filter-aided Sample Preparation (Fasp Digestion)

Tmt Labeling

Peptide Fractionation With Reversed-phase (Rp) Chromatography

Mass Spectrometry Analysis

Easy nLC

Lc-ms/ms Analysis

Data Analysis

Bioinformatics Analysis

Conjoint Analysis

Statistical analysis

Results

Knockdown of CCT3 expression by transfection with shCCT3.

Rna-seq Analysis Of Mda-mb-231 Cells Transfected With Shcct3

Go And Kegg Pathway Enrichment Analyses Of Degs

Proteomics Analysis Of Differentially Expressed Proteins (Deps)

Go And Kegg Pathway Enrichment Analyses Of Deps

Ppi Network Analysis

Conjoint Analysis

Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

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