Identifying liver metastasis-related hub genes in breast cancer and characterizing SPARCL1 as a potential prognostic biomarker

Identifying liver metastasis-related hub genes in breast cancer and characterizing SPARCL1 as a potential prognostic biomarker | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identifying liver metastasis-related hub genes in breast cancer and characterizing SPARCL1 as a potential prognostic biomarker Mingkuan Chen, Wenfang Zheng, Lin Fang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2183292/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: The liver is currently the third most common metastatic site for advanced breast cancer (BC), and liver metastases predict poor prognoses. However, the characterized biomarkers and mechanisms underlying liver metastasis in BC remain unclear. Methods: The GSE124648 dataset was used to identify differentially expressed genes (DEGs) between BC and liver metastases. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted to annotate these DEGs and understand the biological functions they are involved in. A protein–protein interaction (PPI) network was constructed to identify hub genes. Clinicopathological correlation of hub gene expression in patients with BC was determined. Gene set enrichment analysis (GSEA) was performed to explore DEG-related signaling pathways. SPARCL1 expression in BC tissues and cell lines was verified (RT-qPCR). SPARCL1 knockdown was performed using siRNAs; its biological function in BC cells was then investigated. Results: We identified 332 liver metastasis-related DEGs from GSE124648 and 30 hub genes, including SPARCL1 , from the PPI network. SPARCL1 was related to patient prognosis, and its expression in BC was associated with age, TNM stage, estrogen receptor (ER) status, progesterone receptor (PR) status, histological type, molecular type, and living status of patients. GSEA results suggested that low SPARCL1 expression in BC was related to the cell cycle, DNA replication, oxidative phosphorylation, and homologous recombination. In vitro SPARCL1 inhibition promoted BC cell proliferation and migration. Conclusion: We identified SPARCL1 as a tumor suppressor in BC, which shows potential as a target for BC and liver metastasis therapy and diagnosis. breast cancer liver metastasis SPARCL1 bioinformatics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1 Introduction Breast cancer (BC) in women has now become the most newly diagnosed malignant tumor worldwide, with both the number of new cases and the number of deaths ranking first among all malignant tumors. Generally, patients with BC have a relatively good prognosis, with approximately 44% of patients with early-stage BC having a nearly 100% 5-year survival rate, whereas once organ metastasis occurs, this survival rate drops sharply to 26% (Miller et al. 2019 ). The organs most likely to metastasize from BC are the lung, bone, liver, and brain (Cummings et al. 2014 ). Metastasis of the liver can cause various fatal complications, including liver failure, intractable ascites, portal vein thrombosis, and malnutrition (Diamond et al. 2009 ). Thus, the occurrence of liver metastases indicates a worse prognosis in patients with BC, with a reported median survival time of approximately 3 years (Zhao et al. 2018 ). However, the molecular mechanism of metastasis is unclear. Identifying potential molecular biomarkers of liver metastasis could provide more accurate information to guide clinical decisions and predict prognosis, as well as provide direction and theoretical support for research of the mechanism of metastasis. The Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) database have collected and stored a large amount of tumor sequencing data, which are free for public to access. Re-analyzing these sequencing data using bioinformatics methods and mining for differences in the genetic information among different samples can help provide a scientific explanation of the occurrence and progression of disease (Gauthier et al. 2019 ). Therefore, based on public database and bioinformatics analyses, we screened differentially expressed genes (DEGs) between BC and liver metastasis, identified hub genes, and analyzed their correlation with the clinical characteristics of patients with BC. Finally, BC tissues and cells were collected to detect the expression of the hub genes and explore their biological functions in BC cells. These findings will provide a new understanding of BC and liver metastasis, driving future research and providing potential biomarkers for BC diagnosis and therapy. 2 Materials And Methods 2.1 Discovery and validation datasets In the GEO ( http://www.ncbi.nlm.nih.gov/geo/ ), datasets GSE124648 (Sinn et al. 2019 ) and GSE58708 (McBryan et al. 2015 ) were obtained with the following keywords: breast neoplasms, breast cancer, liver metastasis, expression profiling by array, attribute name tissue, and Homo sapiens . Array data of liver metastases (N = 16) and primary tumors (control, N = 130) from the GPL96 ([HG-U133A] Affymetrix Human Genome U133A Array) platform were selected for differential expression analysis. The array data for GSE58708 consisted of three patients with BC liver metastasis vs. three controls as a validation dataset (Table 1 ). Table 1 Discovery and validation of BC datasets Datasets Platform Contributor(s) Experiment type Number of cases (metastasis/primary) GSE124648 GPL96 Sinn et al. ( 2019 ) Expression profiling by array 16/130 GSE58708 GPL11154 Young et al. (2015) Expression profiling by high-throughput sequencing 3/3 2.2 DEG identification R software (version 4.0.3) was employed for bioinformatics analysis. The (“limma”) package was adopted to identify DEGs between BC and liver metastasis (Ritchie et al. 2015 ), with the following cutoff criteria: |log 2 FC|>2.0 and adj. P .Val < 0.05. Similarly, the R package “TCGAbiolinks” was used to obtain biological data for BC in the TCGA database (Colaprico et al. 2016 ), and hub gene expression was verified by analyzing the differentially expressed mRNAs in BC. “ggplot2” and “heatmap” packages were used for visualizing DEGs. 2.3 Enrichment analysis of DEGs The “clusterProfile” package was employed to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the DEGs (Yu et al. 2012 ), and a gene set enrichment analysis (GSEA) method was used in the KEGG enrichment analysis. p < 0.05 was the criteria for statistical significance. 2.4 Integration of PPI networks and identification of hub genes The STRING ( http://www.string-db.org/ ) database provides comprehensive information on protein–protein interactions (PPI) (Szklarczyk et al. 2021 ). A PPI network of DEGs was integrated using the STRING database. Then, the results were visualized and analyzed using Cytoscape (version 3.7.2). The important modules were extracted via the plug-in MCODE, with the cutoff criteria being an MCODE score > 5 and nodes > 5. The other default parameters were set as Max. Depth = 100, K-Core = 2, Node score cutoff = 0.2, Degree Cutoff = 2. The plug-in cytoHubba was employed to identify the top 30 hub genes in the PPI network, with the cutoff criteria: degree.layout ≥ 25 (MCC algorithm) (Shannon et al. 2003 ). GEPIA ( http://gepia.cancer-pku.cn/ ) was used to obtain the hub gene expression between BC and normal tissues, with the default parameters: p ≤ 0.01 and |Log 2 FC| ≥ 1 (Tang et al. 2017 ). The Kaplan–Meier (K–M) plotter ( https://kmplot.com/analysis/ ) was used to evaluate the correlation between the expression of hub genes and survival (Lánczky et al. 2021). The median expression level divided patients into high and low expression groups. Then, the K–M curves of overall survival (OS) and recurrence-free survival (RFS) were drawn. Finally, hub genes ( SPARCL1 and SERPINA1 ) with prognostic values were identified. p < 0.05 using the log-rank test was considered statistically significant. 2.5 Validation of hub genes and clinicopathological correlation analysis A box plot was plotted to verify SPARCL1 expression in liver metastasis based on the GSE58708 dataset. The correlations between SPARCL1 expression and clinicopathological characteristics of patients with BC were analyzed using TCGA-BRCA data. Then, based on patient survival data and SPARCL1 expression level, the optimal expression threshold was calculated using the “survminer” package. The correlation between gene expression and clinicopathological parameters was tested using Pearson’s chi-squared test. p < 0.05 was considered statistically significant. 2.6 GSEA GSEA can be used to evaluate whether a predefined gene set shows statistically significant differences between two groups with different phenotypes. Expression (.gct) and phenotype (.cls) information files for SPARCL1 were uploaded to the GSEA software (version 4.1.0) to conduct enrichment analysis. The chip platform and gene set database selected were “Human_ENSEMBL_Gene_ID_MSigDB.v7.0” and “c2.cp.kegg.v7.1. symbols.gmt,” respectively. The normalized enrichment score was calculated. Both false discovery rate (FDR q-val) and nominal p -value (NOM p -val) < 0.05 were considered statistically significant. 2.7 Clinical specimens and cell lines Tissue samples were obtained from patients with BC who underwent a radical mastectomy at Shanghai Tenth People’s Hospital (Shanghai, China). After mastectomy, 30 pairs of fresh breast tumor and matched normal adjacent breast tissues were collected and preserved in liquid nitrogen. BC cells (MDA-MB-231, BT549, and MCF-7) and mammary epithelial cells (MCF-10A) were purchased from the Chinese Academy of Sciences' Cell Bank and grown in mammary epithelial basal medium (Cambrex, New Jersey, USA) and Dulbecco’s modified Eagle medium (DMEM, Gibco, Grand Island, USA) supplemented with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin-streptomycin (Enpromise, Shanghai, China), respectively. A 5% CO 2 incubator was used to culture all cells at 37° C. 2.8 Cell transfection, RNA extraction, and RT-qPCR Following the manufacturer’s instructions, the negative control (si-NC) and SPARCL1 -siRNAs ( SPARCL1 si1 sense: 5′-GAUUCUAACCAACAAGAAAGU-3′, anti-sense: 5′-UUUCUUGUUGGUUAGAAUCUG-3′), and SPARCL1 si2 (sense: 5′-GACAAAUGCAAGAUUAUUAUC-3′, anti-sense: 5′-UAAUAAUCUUGCAUUUGUCGG-3′) were transfected into BC cells using Lipofectamine® 3000 (Invitrogen, USA). si-NC and siRNAs targeting SPARCL1 ( SPARCL1 si1 and SPARCL1 si2) were purchased from IBSbio (Shanghai, China). TRIzol reagent (Invitrogen) was applied to extract total RNA from cell lines and tissues. HiScript® III RT SuperMix for qPCR (Vazyme, China) was used to generate the cDNAs, and Hieff® qPCR SYBR Green Master Mix (YEASEN, Shanghai, China) was used to conduct real time quantitative polymerase chain reaction (RT-qPCR) following the manufacturer’s protocol, with β-actin acting as an internal control for normalizing SPARCL1 expression. We used the following primers to conduct RT-qPCR. SPARCL1 (forward: 5′-CCAACTGAAGGTACATTGGACAT-3′, reverse: 5′-CTGTGAAGGAACTAACACCAGG-3′) and β-actin (forward: 5′-CATGTACGTTGCTATCCAGGC-3′, reverse: 5′-CTCCTTAATGTCACGCACGAT-3′). 2.9 Methylthiazolyldiphenyl-tetrazolium, colony formation, wound-healing, and Transwell assay After transfection with si-NC and SPARCL1 siRNAs, in 96-well plates, BC cell lines were cultured at a density of 1,500 cells. Next, 20 µL methylthiazolyldiphenyl-tetrazolium bromide (MTT, YEASEN) was added to each well at 0, 24, 48, 72, and 96 h after inoculation, and the samples were incubated for 4 h at 37 °C in a 5% CO 2 incubator to assess cell viability. Then, 150 µL DMSO was added to each well after removing the supernatant. The absorbance was measured by a microplate spectrophotometer (BioTek, Germany) at 490 nm. Cell proliferation curves were plotted according to the absorbance value. BC cell lines transfected with si-NC and SPARCL1 siRNAs were prepared as single-cell suspensions, inoculated at a density of 750 cells per well until prominent colonies formed. The cells were fixed with 95% ethanol and stained with 0.1% crystal violet (YEASEN) to detect cell colony formation ability. Representative pictures were recorded, and clone colonies were counted. To detect cell mobility, BC cells transfected with si-NC and SPARCL1 siRNAs were cultured and scratched with non-RNA enzyme tips when the cell fusion rate reached 90% or more. In the following step, DMEM supplemented with 2% FBS was used as the culture medium. The healing of the scratches was observed at 0 and 12 h using the same field of view to calculate cell mobility. The transfected BC cells and 500 µL of DMEM containing 10% FBS were added to Transwell's upper and lower chambers (Corning, USA), respectively. After culturing for 18 h, migrated cells were fixed in 4% paraformaldehyde and stained with 0.1% crystal violet to assess the migratory ability. Representative images were captured using an inverted microscope. 2.10 Statistical analysis A Wilcoxon matched-pairs signed-rank test was used to compare the expression level of SPARCL1 between BC and control samples. An unpaired Student's t -test was used to compare the expression of SPARCL1 between MCF-10A and BC cell lines. The results of the MTT assay were analyzed using a two-way analysis of variance (ANOVA). All experiments were repeated three times. The experimental data were analyzed and plotted with GraphPad Prism (v8.3.0, USA). p < 0.05 was considered statistically significant. 3 Results 3.1 DEG identification We identified 332 DEGs comprising 116 upregulated and 216 downregulated genes from the GSE124648 dataset (Fig. 1 a). The heatmap was used to visualize the top 50 DEGs, as the top 50 liver metastasis-related genes in GSE124648 (Fig. 1 b). 3.2 GO and KEGG enrichment analysis of DEGs GO annotation divides gene function into three categories: cellular components (CC), molecular function (MF), and biological process (BP) (Sinn et al. 2019 ). The top eight enriched GO terms for each category are shown in Fig. 2 . CC analysis indicated that these DEGs were particularly related to the extracellular matrix, endoplasmic reticulum lumen, collagen-containing extracellular matrix, collagen trimer, and blood microparticles (Fig. 2 a). MF analysis showed that DEGs were mainly involved in extracellular matrix structural constituents, glycosaminoglycan binding, extracellular matrix structural constituents conferring tensile strength, heparin-binding, and collagen binding (Fig. 2 b). The BP category was mainly enriched in extracellular structure organization, extracellular matrix organization, wound healing, humoral immune response, and complement activation (Fig. 2 c). The KEGG pathway enrichment analysis results showed that the upregulated DEGs were significantly enriched in neutrophil extracellular trap formation, alcoholic liver disease, and neuroactive ligand − receptor interaction, whereas the downregulated DEGs were significantly enriched in malignancy-related pathways, including pathways in cancer, BC, PI3K − Akt signaling pathway, MAPK signaling pathway, and focal adhesion (Fig. 2 d). 3.3 PPI network and SPARCL1 identified as a prognostic-related hub gene The PPI network contained 332 nodes and 2,972 edges (Online Resource Fig. 1 ), and we selected the top three hub modules identified by the MCODE plug-in for display (Fig. 3 a–c), where each node represents one DEG, and the edges between nodes represent interactions, which can reflect the importance of genes and modules in the network. The top 30 hub genes with a high degree of connectivity were identified from the network (Fig. 3 d). The expression levels of these genes and their modules are listed in Table 2 . Among the 30 hub genes, SERPINA1 and VCAN were found to be upregulated in BC, whereas ALB , IGFBP3 , SPARCL1 , and FSTL1 were downregulated and no significant difference was discovered in the expression of other hub genes. Survival analyses showed favorable OS and RFS in patients with BC with upregulated SERPINA1 and SPARCL1 expression (Fig. 4 ). Table 2 Top 30 hub genes associated with BC liver metastasis Gene symbol log 2 FC adj. P .Val Expression Modules FGG 6.149436 5.24E-29 UP Module 1 APOA1 4.69204 7.81E-21 UP Module 1 IGFBP7 -2.9555 5.11E-21 DOWN Module 1 APOB 3.954259 1.62E-25 UP Module 1 FSTL1 -3.02757 2.78E-28 DOWN Module 1 PRSS23 -2.34018 1.94E-10 DOWN Module 1 VCAN -2.54093 5.33E-17 DOWN Module 1 TNC -2.71678 3.74E-10 DOWN Module 1 ORM1 6.685187 5.25E-28 UP Module 2 IGFBP3 -2.10444 1.15E-15 DOWN Module 1 LAMB1 -3.0713 8.26E-22 DOWN Module 1 SERPIND1 3.39324 4.63E-13 UP Module 1 SPP2 2.236803 4.46E-15 UP Module 1 IGFBP5 -2.35175 1.19E-07 DOWN Module 1 FGA 4.820105 1.17E-28 UP Module 1 GC 6.136118 8.01E-31 UP Module 2 FGB 5.56398 3.49E-29 UP Module 2 HRG 4.776554 1.28E-25 UP Module 2 CP 2.150198 2.51E-05 UP Module 1 FBN1 -4.03832 1.62E-25 DOWN Module 1 SERPINA10 2.515328 4.67E-10 UP Module 1 ALB 7.627917 3.85E-34 UP Module 1 APOA2 5.683622 9.91E-27 UP Module 1 AHSG 3.816468 2.15E-19 UP Module 1 SERPINA1 3.350588 1.20E-13 UP Module 1 SPARCL1 -3.75713 8.01E-31 DOWN Module 1 TF 4.63786 4.81E-14 UP Module 1 F5 2.037343 1.08E-11 UP Module 1 SERPINC1 4.646863 8.04E-28 UP Module 1 ITIH2 2.98753 6.57E-26 UP Module 1 3.4 Validation of SPARCL1 and clinicopathological correlation analysis Following differential expression analysis of the GSE58708 dataset, 683 DEGs were obtained, including 410 upregulated and 273 downregulated genes (Online Resource Fig. 2 ). SPARCL1 expression was also significantly downregulated in liver metastasis compared to that in BC tissues (log 2 FC = − 2.617; Online Resource Fig. 3 a). In the TCGA-BRCA dataset, compared to normal mammary gland tissue, SPARCL1 expression in BC tissues was also significantly downregulated (Online Resource Fig. 3 b). The correlation between SPARCL1 level and clinical characteristics of patients with BC was further analyzed and showed that the lower SPARCL1 level was significantly related to age, TNM stage, ER status, PR status, histological type, molecular type, and living status, whereas there was no significant difference in node stage and Her-2 status (Table 3 ). Table 3 Relationship between SPARCL1 expression and clinical characteristics of patients with BC SPARCL1 expression Total (N = 1049) High (N = 432) Low (N = 617) p -Value Age (years) < 55 432 (41.2%) 200 (46.3%) 232 (37.6%) 0.00592 ≥ 55 617 (58.8%) 232 (53.7%) 385 (62.4%) TNM stage I 175 (16.7%) 90 (20.8%) 85 (13.8%) 0.0027 II 599 (57.1%) 221 (51.2%) 378 (61.3%) III 242 (23.1%) 110 (25.5%) 132 (21.4%) IV 20 (1.9%) 5 (1.2%) 15 (2.4%) Unknown 13 (1.2%) 6 (1.4%) 7 (1.1%) Node stage N0–N1 615 (58.6%) 245 (56.7%) 370 (60.0%) 0.139 N2–N3 137 (13.1%) 51 (11.8%) 86 (13.9%) Unknown 297 (28.3%) 136 (31.5%) 161 (26.1%) ER status Negative 170 (16.2%) 31 (7.2%) 139 (22.5%) < 0.001 Positive 571 (54.4%) 265 (61.3%) 306 (49.6%) Unknown 308 (29.4%) 136 (31.5%) 172 (27.9%) PR status Negative 238 (22.7%) 58 (13.4%) 180 (29.2%) < 0.001 Positive 500 (47.7%) 235 (54.4%) 265 (42.9%) Unknown 311 (29.6%) 139 (32.2%) 172 (27.9%) HER2 status Negative 621 (59.2%) 251 (58.1%) 370 (60.0%) 0.145 Positive 107 (10.2%) 37 (8.6%) 70 (11.3%) Unknown 321 (30.6%) 144 (33.3%) 177 (28.7%) Histological type IDC 752 (71.7%) 260 (60.2%) 492 (79.7%) < 0.001 ILC 196 (18.7%) 137 (31.7%) 59 (9.6%) Others 100 (9.5%) 35 (8.1%) 65 (10.5%) Unknown 1 (0.1%) 0 (0%) 1 (0.2%) Molecular type Basal-like 94 (9.0%) 11 (2.5%) 83 (13.5%) < 0.001 Lum A 219 (20.9%) 135 (31.3%) 84 (13.6%) Lum B 120 (11.4%) 21 (4.9%) 99 (16.0%) HER2-enriched 52 (5.0%) 14 (3.2%) 38 (6.2%) Normal-like 7 (0.7%) 5 (1.2%) 2 (0.3%) Unknown 557 (53.1%) 246 (56.9%) 311 (50.4%) Living status Alive 902 (86.0%) 385 (89.1%) 517 (83.8%) 0.0185 Dead 147 (14.0%) 47 (10.9%) 100 (16.2%) 3.5 GSEA From the viewpoint of enrichment of gene sets, finding the effects of subtle changes on biological pathways or functions is easier in theory (Subramanian et al. 2005 ). Figure 5 displays six signaling pathways (DNA replication, cell cycle, oxidative phosphorylation, homologous recombination, spliceosome, and proteasome) that were significantly enriched when SPARCL1 was downregulated in BC, revealing the potential molecular mechanisms by which SPARCL1 participates in BC occurrence and progression. 3.6 SPARCL1 downregulation in BC tissues and cells Total RNA was extracted from tissues and cell lines for RT-qPCR to validate SPARCL1 expression in BC. In comparison with the paired adjacent normal breast tissues, SPARCL1 was significantly downregulated in BC tissues (N = 30; Fig. 6 a). In comparison with the normal breast epithelial cell line, SPARCL1 expression was also decreased in the BC cell lines (Fig. 6 b). These results are consistent with those from our bioinformatics analysis. 3.7 SPARCL1 knockdown-induced proliferation and migration of BC cells in vitro To explore the biological function of SPARCL1 in BC, we used siRNAs to artificially knock down SPARCL1 in BC cells. The efficiency of the siRNA suggested that the expression of SPARCL1 was significantly knocked down by both SPARCL1 si-1 and SPARCL1 si-2 (Fig. 7 a). The results of colony formation and MTT assays suggested that inhibiting SPARCL1 promoted the proliferation of BC cells (Fig. 7 b and c). Cell migration is an essential step in tumor progression and metastasis. In comparison with the control group (si-NC), the healing ability of SPARCL1 -inhibited MDA-MB-231 cells was significantly enhanced, and cell migration was significantly increased (Fig. 7 d and e). Collectively, these results suggested that repressing SPARCL1 enhances the proliferation and migration of BC cells. 4 Discussion The bioinformatics analysis of sequencing data can improve our understanding of gene function, including gene expression levels between different experimental conditions or phenotypes, identification of biological processes related to gene expression levels, and screening of therapeutic targets and prognostic markers (Hu et al. 2015 ; Kaifi et al. 2015 ; Tao et al. 2017 ). In this study, 332 DEGs associated with liver metastasis were identified by differential expression analysis of sequencing data from the GEO database. GO enrichment analysis suggested that these DEGs were mostly located in the extracellular matrix and participated in biological processes, including extracellular matrix organization, wound healing, angiogenesis, and humoral immune response. Extracellular matrix organization is closely correlated with the occurrence and progression of cancer; a neatly arranged matrix can promote the invasion of tumor cells. The tumor suppressor PTEN participates in the regulation of matrix remodeling, which is negatively correlated with the arrangement of collagen in human breast tissue (Jones et al. 2019 ). Tumor angiogenesis can supply nutrients and oxygen essential for tumor growth and metastasis (Weis et al. 2011). Hypoxia-inducible factor (HIF)-dependent angiogenesis is vital for the invasion, progression, and drug resistance of BC and is closely correlated with its poor prognosis (de Heer et al. 2020 ). KEGG pathway analysis showed that downregulated DEGs were particularly enriched in malignant tumor-related pathways, such as MAPK signaling, PI3K/AKT signaling, focal adhesion, Wnt signaling, and Hippo signaling pathways. The MAPK signaling pathway is crucial for BC invasion and metastasis, promoting the occurrence and progression of the disease (Cotrim et al. 2013 ; Jiang et al. 2020 ; Ke et al. 2021 ). In BC, more than 70% of patients have PI3K signaling pathway alterations, and activation of the PI3K pathway is significantly associated with an HR-negative, basal-like phenotype, high histological grade, and cancer-specific death (López-Knowles et al. 2010 ). The PI3K/AKT signaling pathway also participated in liver metastasis of various malignant tumors. Indeed, microRNA-582 can promote gastric cancer liver metastasis via the PI3K/Akt/Snail pathway mediated by FOXO-3 (Xie et al. 2020 ). The c-Met/PI3K/AKT/mTOR axis can activate the liver metastasis-specific cholesterol metabolism pathway in colorectal cancer, providing conditions for tumor cell colonization and growth in the liver (Zhang et al. 2021 ). There are also reports on the influence of Wnt, Hippo, and other signaling pathways on the metastasis of malignant liver tumors (Chai et al. 2019 ; Yuan et al. 2019 ). Based on the prognostic value of hub DEGs in BC, we identified SPARCL1 , a member of the secreted protein acidic and rich in cysteine ( SPARC ) family, which is downregulated in both BC and liver metastasis. SPARCL1 is a newly discovered player in tumors and is mainly associated with physiological processes such as cell migration, adhesion, and cell proliferation regulation (Gagliardi et al. 2017 ). The expression of SPARCL1 is downregulated in colorectal cancer and is related to tumor differentiation, stage, distant metastasis, and OS (Hu et al. 2012 ; Zhang et al. 2022 ). SPARCL1 is also downregulated in prostate cancer, being associated with disease progression, especially in invasive prostate cancer. Moreover, SPARCL1 can inhibit migration, invasion, and metastasis of prostate cancer (Hurley et al. 2012 ; Xiang et al. 2013 ). Similarly, SPARCL1 was found to be a tumor suppressor in gastric cancer (Li et al. 2012 ), osteosarcoma (Zhao et al. 2018 ), pancreatic cancer (Esposito et al. 2007 ), lung cancer (Deng et al. 2021 ), and renal cell carcinoma (Ye et al. 2017 ). SPARCL1 expression in colorectal cancer liver metastasis is downregulated, with SPARCL1 being considered the hub gene of liver metastasis and a biomarker with a significant prognostic value (Zhang et al. 2021 ). Furthermore, the downregulation of SPARCL1 expression enhances liver metastasis of malignant gastrointestinal stromal tumor cells (Shen et al. 2018 ). However, only a few studies have reported the expression and function of SPARCL1 in BC. Consistent with our findings, SPARCL1 has been reported to be downregulated in BC (Cao et al. 2013 ). In addition, our clinicopathological correlation analysis revealed that the low expression of SPARCL1 was related to age, TNM stage, ER status, PR status, histological type, molecular type, and survival status of patients with BC, while younger age, higher TNM stage, HR-negative status, and basal-like type were among the unfavorable phenotypes in patients with BC. GSEA indicated that pathways such as DNA replication, cell cycle, oxidative phosphorylation, and homologous recombination were significantly enriched when SPARCL1 was downregulated, revealing potential molecular mechanisms by which SPARCL1 participates in BC. The most vulnerable cellular process in oncogenic lesions is DNA replication, with oncogene-induced replication stress being a fundamental step and early driver of tumorigenesis (Kotsantis et al. 2018 ). The cell cycle and DNA replication are closely coordinated to ensure correct single-genome replication during cell division, thereby avoiding the occurrence of diseases such as cancer (Dai et al. 2021 ). Homologous recombination repair is an essential pathway of DNA damage repair, and homologous recombination deficiency is considered an important biomarker and risk factor for BC (den Brok et al. 2017 ; Telli et al. 2018 ; Shen et al. 2020 ). In vitro studies suggested that inhibition of SPARCL1 promoted BC cell proliferation, colony formation, and migration. This work indicates the tumor suppressing role for SPARCL1 in BC. However, the specific mechanism of SPARCL1 inhibiting the proliferation and migration of BC cells is still not clear, and more experiments are needed to validate the role of SPARCL1 in BC and liver metastasis. In addition, the data in this study were taken from a public database; to obtain more accurate and credible results, hub genes require further experimental verification using liver metastasis tissue and cell lines. In summary, we screened DEGs and hub genes in BC liver metastasis and identified SPARCL1 as a potential prognostic biomarker for BC and liver metastasis diagnosis and therapy. Declarations Funding This work was supported by the Shanghai Municipal Health Commission, China (Grant number 202040157), and the National Natural Science Foundation of China (Grant number 82073204). Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions The study was designed by Lin Fang and Mingkuan Chen. Experiments, sample collection, and analysis were performed by Mingkuan Chen and Wenfang Zheng. Data collection and bioinformatics analysis were performed by Mingkuan Chen. Valuable comments were provided by Lin Fang. The manuscript was written by Mingkuan Chen and Wenfang Zheng. All authors read and approved the final manuscript. Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Shanghai Tenth People’s Hospital (Date October 14, 2020/No. 2020-KN174-01). Consent to participate Informed consent was obtained from all individual participants included in the study. References CA A Cancer J Clinicians - 2021 - Sung - Global Cancer Statistics 2020 GLOBOCAN Estimates of Incidence and Mortality.pdf>.https://doi.org/10.3322/caac.21660 Cao F, Wang K, Zhu R, Hu Y-W, Fang W-Z & Ding H-Z (2013) Clinicopathological significance of reduced SPARCL1 expression in human breast cancer. Asian Pac J Cancer Prev 14:195-200 Chai W-X, Sun L-G, Dai F-H, Shao H-S, Zheng N-G & Cai H-Y (2019) Inhibition of PRRX2 suppressed colon cancer liver metastasis via inactivation of Wnt/β-catenin signaling pathway. Pathol Res Pract 215:152593.https://doi.org/10.1016/j.prp.2019.152593 Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, Sabedot TS, Malta TM, Pagnotta SM, Castiglioni I, Ceccarelli M, Bontempi G & Noushmehr H (2016) TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res 44:e71.https://doi.org/10.1093/nar/gkv1507 Cotrim CZ, Fabris V, Doria ML, Lindberg K, Gustafsson JÅ, Amado F, Lanari C & Helguero LA (2013) Estrogen receptor beta growth-inhibitory effects are repressed through activation of MAPK and PI3K signalling in mammary epithelial and breast cancer cells. Oncogene 32:2390-2402.https://doi.org/10.1038/onc.2012.261 Cummings MC, Simpson PT, Reid LE, Jayanthan J, Skerman J, Song S, Mccart Reed AE, Kutasovic JR, Morey AL, Marquart L, O'rourke P & Lakhani SR (2014) Metastatic progression of breast cancer: insights from 50 years of autopsies. J Pathol 232:23-31.https://doi.org/10.1002/path.4288 Dai M, Boudreault J, Wang N, Poulet S, Daliah G, Yan G, Moamer A, Burgos SA, Sabri S, Ali S & Lebrun J-J (2021) Differential Regulation of Cancer Progression by CDK4/6 Plays a Central Role in DNA Replication and Repair Pathways. Cancer research 81:1332-1346.https://doi.org/10.1158/0008-5472.CAN-20-2121 De Heer EC, Jalving M & Harris AL (2020) HIFs, angiogenesis, and metabolism: elusive enemies in breast cancer. J Clin Invest 130:5074-5087.https://doi.org/10.1172/JCI137552 Den Brok WD, Schrader KA, Sun S, Tinker AV, Zhao EY, Aparicio S & Gelmon KA (2017) Homologous Recombination Deficiency in Breast Cancer: A Clinical Review. JCO Precis Oncol 1.https://doi.org/10.1200/PO.16.00031 Deng H, Hang Q, Shen D, Zhang Y & Chen M (2021) Low expression of CHRDL1 and SPARCL1 predicts poor prognosis of lung adenocarcinoma based on comprehensive analysis and immunohistochemical validation. Cancer Cell Int 21:259.https://doi.org/10.1186/s12935-021-01933-9 Diamond JR, Finlayson CA & Borges VF (2009) Hepatic complications of breast cancer. The Lancet. Oncology 10:615-621.https://doi.org/10.1016/S1470-2045(09)70029-4 Esposito I, Kayed H, Keleg S, Giese T, Sage EH, Schirmacher P, Friess H & Kleeff J (2007) Tumor-suppressor function of SPARC-like protein 1/Hevin in pancreatic cancer. Neoplasia 9 Gagliardi F, Narayanan A & Mortini P (2017) SPARCL1 a novel player in cancer biology. Crit Rev Oncol Hematol 109:63-68.https://doi.org/10.1016/j.critrevonc.2016.11.013 Gauthier J, Vincent AT, Charette SJ & Derome N (2019) A brief history of bioinformatics. Brief Bioinform 20:1981-1996.https://doi.org/10.1093/bib/bby063 Hu H, Zhang H, Ge W, Liu X, Loera S, Chu P, Chen H, Peng J, Zhou L, Yu S, Yuan Y, Zhang S, Lai L, Yen Y & Zheng S (2012) Secreted protein acidic and rich in cysteines-like 1 suppresses aggressiveness and predicts better survival in colorectal cancers. Clin Cancer Res 18:5438-5448.https://doi.org/10.1158/1078-0432.CCR-12-0124 Hu N, Wang C, Clifford RJ, Yang HH, Su H, Wang L, Wang Y, Xu Y, Tang Z-Z, Ding T, Zhang T, Goldstein AM, Giffen C, Lee MP & Taylor PR (2015) Integrative genomics analysis of genes with biallelic loss and its relation to the expression of mRNA and micro-RNA in esophageal squamous cell carcinoma. BMC Genomics 16:732.https://doi.org/10.1186/s12864-015-1919-0 Hurley PJ, Marchionni L, Simons BW, Ross AE, Peskoe SB, Miller RM, Erho N, Vergara IA, Ghadessi M, Huang Z, Gurel B, Park BH, Davicioni E, Jenkins RB, Platz EA, Berman DM & Schaeffer EM (2012) Secreted protein, acidic and rich in cysteine-like 1 (SPARCL1) is down regulated in aggressive prostate cancers and is prognostic for poor clinical outcome. Proc Natl Acad Sci U S A 109:14977-14982.https://doi.org/10.1073/pnas.1203525109 Jiang W, Wang X, Zhang C, Xue L & Yang L (2020) Expression and clinical significance of MAPK and EGFR in triple-negative breast cancer. Oncology letters 19:1842-1848.https://doi.org/10.3892/ol.2020.11274 Jones CE, Hammer AM, Cho Y, Sizemore GM, Cukierman E, Yee LD, Ghadiali SN, Ostrowski MC & Leight JL (2019) Stromal PTEN Regulates Extracellular Matrix Organization in the Mammary Gland. Neoplasia 21:132-145.https://doi.org/10.1016/j.neo.2018.10.010 Kaifi JT, Kunkel M, Das A, Harouaka RA, Dicker DT, Li G, Zhu J, Clawson GA, Yang Z, Reed MF, Gusani NJ, Kimchi ET, Staveley-O'carroll KF, Zheng S-Y & El-Deiry WS (2015) Circulating tumor cell isolation during resection of colorectal cancer lung and liver metastases: a prospective trial with different detection techniques. Cancer Biol Ther 16:699-708.https://doi.org/10.1080/15384047.2015.1030556 Ke J, Han W, Meng F, Guo F, Wang Y & Wang L (2021) CTI-2 Inhibits Metastasis and Epithelial-Mesenchymal Transition of Breast Cancer Cells by Modulating MAPK Signaling Pathway. Int J Mol Sci 22.https://doi.org/10.3390/ijms222212229 Kotsantis P, Petermann E & Boulton SJ (2018) Mechanisms of Oncogene-Induced Replication Stress: Jigsaw Falling into Place. Cancer Discov 8:537-555.https://doi.org/10.1158/2159-8290.CD-17-1461 Lánczky A & Győrffy B (2021) Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation. J Med Internet Res 23:e27633.https://doi.org/10.2196/27633 Li P, Qian J, Yu G, Chen Y, Liu K, Li J & Wang J (2012) Down-regulated SPARCL1 is associated with clinical significance in human gastric cancer. J Surg Oncol 105:31-37.https://doi.org/10.1002/jso.22025 López-Knowles E, O'toole SA, Mcneil CM, Millar EKA, Qiu MR, Crea P, Daly RJ, Musgrove EA & Sutherland RL (2010) PI3K pathway activation in breast cancer is associated with the basal-like phenotype and cancer-specific mortality. Int J Cancer 126:1121-1131.https://doi.org/10.1002/ijc.24831 Mcbryan J, Fagan A, Mccartan D, Bane FT, Varešlija D, Cocchiglia S, Byrne C, Bolger J, Mcilroy M, Hudson L, Tibbitts P, Gaora PÓ, Hill AD & Young LS (2015) Transcriptomic Profiling of Sequential Tumors from Breast Cancer Patients Provides a Global View of Metastatic Expression Changes Following Endocrine Therapy. Clin Cancer Res 21:5371-5379.https://doi.org/10.1158/1078-0432.CCR-14-2155 Miller KD, Nogueira L, Mariotto AB, Rowland JH, Yabroff KR, Alfano CM, Jemal A, Kramer JL & Siegel RL (2019) Cancer treatment and survivorship statistics, 2019. CA Cancer J Clin 69:363-385.https://doi.org/10.3322/caac.21565 Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W & Smyth GK (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47.https://doi.org/10.1093/nar/gkv007 Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B & Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498-2504 Shen C, Yin Y, Chen H, Wang R, Yin X, Cai Z, Zhang B, Chen Z & Zhou Z (2018) Secreted protein acidic and rich in cysteine-like 1 suppresses metastasis in gastric stromal tumors. BMC Gastroenterol 18:105.https://doi.org/10.1186/s12876-018-0833-8 Shen J, Song R, Chow W-H & Zhao H (2020) Homologous recombination repair capacity in peripheral blood lymphocytes and breast cancer risk. Carcinogenesis 41:1363-1367.https://doi.org/10.1093/carcin/bgaa081 Sinn BV, Fu C, Lau R, Litton J, Tsai T-H, Murthy R, Tam A, Andreopoulou E, Gong Y, Murthy R, Gould R, Zhang Y, King TA, Viale A, Andrade V, Giri D, Salgado R, Laios I, Sotiriou C, Marginean EC, Kwiatkowski DN, Layman RM, Booser D, Hatzis C, Vicente Valero V & Fraser Symmans W (2019) SET: a robust 18-gene predictor for sensitivity to endocrine therapy for metastatic breast cancer. NPJ Breast Cancer 5:16.https://doi.org/10.1038/s41523-019-0111-0 Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES & Mesirov JP (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102:15545-15550 Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, Doncheva NT, Legeay M, Fang T, Bork P, Jensen LJ & Von Mering C (2021) The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 49:D605-D612.https://doi.org/10.1093/nar/gkaa1074 Tang Z, Li C, Kang B, Gao G, Li C & Zhang Z (2017) GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 45.https://doi.org/10.1093/nar/gkx247 Tao Z, Shi A, Li R, Wang Y, Wang X & Zhao J (2017) Microarray bioinformatics in cancer- a review. J BUON 22:838-843 Telli ML, Stover DG, Loi S, Aparicio S, Carey LA, Domchek SM, Newman L, Sledge GW & Winer EP (2018) Homologous recombination deficiency and host anti-tumor immunity in triple-negative breast cancer. Breast cancer research and treatment 171:21-31.https://doi.org/10.1007/s10549-018-4807-x Weis SM & Cheresh DA (2011) Tumor angiogenesis: molecular pathways and therapeutic targets. Nat Med 17:1359-1370.https://doi.org/10.1038/nm.2537 Xiang Y, Qiu Q, Jiang M, Jin R, Lehmann BD, Strand DW, Jovanovic B, Degraff DJ, Zheng Y, Yousif DA, Simmons CQ, Case TC, Yi J, Cates JM, Virostko J, He X, Jin X, Hayward SW, Matusik RJ, George AL & Yi Y (2013) SPARCL1 suppresses metastasis in prostate cancer. Mol Oncol 7:1019-1030.https://doi.org/10.1016/j.molonc.2013.07.008 Xie T, Wu D, Li S, Li X, Wang L, Lu Y, Song Q, Sun X & Wang X (2020) microRNA-582 Potentiates Liver and Lung Metastasis of Gastric Carcinoma Cells Through the FOXO3-Mediated PI3K/Akt/Snail Pathway. Cancer Manag Res 12:5201-5212.https://doi.org/10.2147/CMAR.S245674 Ye H, Wang W-G, Cao J & Hu X-C (2017) SPARCL1 suppresses cell migration and invasion in renal cell carcinoma. Mol Med Rep 16:7784-7790.https://doi.org/10.3892/mmr.2017.7535 Yu G, Wang L-G, Han Y & He Q-Y (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16:284-287.https://doi.org/10.1089/omi.2011.0118 Yuan L, Zhou M, Wasan HS, Zhang K, Li Z, Guo K, Shen F, Shen M & Ruan S (2019) Jiedu Sangen Decoction Inhibits the Invasion and Metastasis of Colorectal Cancer Cells by Regulating EMT through the Hippo Signaling Pathway. Evid Based Complement Alternat Med 2019:1431726.https://doi.org/10.1155/2019/1431726 Zhang H-P, Wu J, Liu Z-F, Gao J-W & Li S-Y (2022) SPARCL1 Is a Novel Prognostic Biomarker and Correlates with Tumor Microenvironment in Colorectal Cancer. Biomed Res Int 2022:1398268.https://doi.org/10.1155/2022/1398268 Zhang K-L, Zhu W-W, Wang S-H, Gao C, Pan J-J, Du Z-G, Lu L, Jia H-L, Dong Q-Z, Chen J-H, Lu M & Qin L-X (2021) Organ-specific cholesterol metabolic aberration fuels liver metastasis of colorectal cancer. Theranostics 11:6560-6572.https://doi.org/10.7150/thno.55609 Zhang T, Yuan K, Wang Y, Xu M, Cai S, Chen C & Ma J (2021) Identification of Candidate Biomarkers and Prognostic Analysis in Colorectal Cancer Liver Metastases. Frontiers in oncology 11:652354.https://doi.org/10.3389/fonc.2021.652354 Zhao H-Y, Gong Y, Ye F-G, Ling H & Hu X (2018) Incidence and prognostic factors of patients with synchronous liver metastases upon initial diagnosis of breast cancer: a population-based study. Cancer Manag Res 10:5937-5950.https://doi.org/10.2147/CMAR.S178395 Zhao SJ, Jiang YQ, Xu NW, Li Q, Zhang Q, Wang SY, Li J, Wang YH, Zhang YL, Jiang SH, Wang YJ, Huang YJ, Zhang XX, Tian GA, Zhang CC, Lv YY, Dai M, Liu F, Zhang R, Zhou D & Zhang ZG (2018) SPARCL1 suppresses osteosarcoma metastasis and recruits macrophages by activation of canonical WNT/β-catenin signaling through stabilization of the WNT-receptor complex. Oncogene 37:1049-1061.https://doi.org/10.1038/onc.2017.403 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2183292","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":145668894,"identity":"3497fce1-b04a-45cb-a5c0-bf7fab9b3506","order_by":0,"name":"Mingkuan Chen","email":"","orcid":"","institution":"Department of Breast and Thyroid Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mingkuan","middleName":"","lastName":"Chen","suffix":""},{"id":145668895,"identity":"b4730743-21e4-4028-998f-104e67a657ea","order_by":1,"name":"Wenfang Zheng","email":"","orcid":"","institution":"Department of Breast and Thyroid Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenfang","middleName":"","lastName":"Zheng","suffix":""},{"id":145668896,"identity":"bd322ee8-e921-4086-9984-91e48f522e90","order_by":2,"name":"Lin Fang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIie3QsQrCMBCA4SuBdAlkTRef4aBQHARf5Vw6VXHspiC0g4izb9FHKAac6u7gUBdxcKijIGJ0c2kzCuYbjgz3BxIAx/lB/tyMGqHHmbzVlA66E1GaQQih9CHEuoptE4DRZg1RcMq2Fom/1xeaHr1CQ5wSL0HmS2pPxCTuE54ZatgdSBxBVfuiNRlCEiGh5qi97EDqDKjG7YmQ108iUDM+NQeLRCVhbTZVsOAciKySa2Q+WaNkgikqY9H5FiGTsGkeepbJyrvdn4OezFfticHV1x1d62+ssdlyHMf5Yy83VkhvoQwKCAAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Breast and Thyroid Surgery, Shanghai Tenth People's Hospital, Tongji University School of Medicine","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Fang","suffix":""}],"badges":[],"createdAt":"2022-10-19 13:59:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2183292/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2183292/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":28184516,"identity":"20b3a1e3-9538-4771-ba76-82cbb7d5d68c","added_by":"auto","created_at":"2022-10-24 15:54:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":447760,"visible":true,"origin":"","legend":"\u003cp\u003eDEGs of liver metastasis of BC identified from GSE124648. (a) A volcano plot of the 332 DEGs, where blue indicates downregulated DEGs and red indicates upregulated DEGs. The cutoff criteria were adj.\u003cem\u003eP\u003c/em\u003e.Val \u0026lt; 0.05 and |log\u003csub\u003e2\u003c/sub\u003eFC|\u0026gt;2.0. (b) A heatmap of the systematic cluster analysis of the top 50 DEGs. DEGs: differentially expressed genes. BC: breast cancer\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2183292/v1/2b649659f6b6830562bd276a.png"},{"id":28183497,"identity":"9b211778-2651-48f1-861f-d030809b25a4","added_by":"auto","created_at":"2022-10-24 15:49:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":492846,"visible":true,"origin":"","legend":"\u003cp\u003eGO annotation and KEGG pathway enrichment analyses of DEGs. (a–c) GO annotation enrichment analyses of the 332 DEGs. (d) KEGG pathway enrichment analyses of the DEGs, where blue indicates downregulated DEGs and red indicates upregulated DEGs\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-2183292/v1/d311faecb57f2aa272ca8f1c.png"},{"id":28183351,"identity":"e21c468d-3a7f-447b-ae9d-dea6fcd2ad26","added_by":"auto","created_at":"2022-10-24 15:44:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1710337,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of hub modules and hub genes from the PPI network. (a–c) The top three modules with high scores identified from the PPI network. Blue indicates downregulated DEGs, and red indicates upregulated DEGs. (d) The top 30 hub genes identified from the PPI network. Ordered from high (red) to low (yellow) connectivity degrees\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-2183292/v1/1f8498c6b1508890da970d25.png"},{"id":28183353,"identity":"ca67ec66-5e32-420b-83c9-232e6562e926","added_by":"auto","created_at":"2022-10-24 15:44:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1001200,"visible":true,"origin":"","legend":"\u003cp\u003eThe mRNA expression levels and prognostic values of \u003cem\u003eSERPINA1\u003c/em\u003e and \u003cem\u003eSPARCL1\u003c/em\u003e. (a) \u003cem\u003eSPARCL1\u003c/em\u003e and (b) \u003cem\u003eSERPINA1\u003c/em\u003e. The red line represents a patient with high gene expression, while the black line represents a patient with low gene expression. OS: overall survival, RFS: recurrence-free survival, HR: hazard ratio (* \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05)\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-2183292/v1/ee20df1f8831205902c5eb84.png"},{"id":28183499,"identity":"45904200-5c83-48de-a9a8-5c4cbe4c2d76","added_by":"auto","created_at":"2022-10-24 15:49:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1640527,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-gene GSEA of low \u003cem\u003eSPARCL1\u003c/em\u003eexpression in BC. NES: normalized enrichment score, FDR: false discovery rate, NOM p-val: nominal \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-2183292/v1/ad43eda98dc5b760f1cd2b78.png"},{"id":28184517,"identity":"abcce9c5-f2ac-4b62-a099-c188c503f439","added_by":"auto","created_at":"2022-10-24 15:54:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":288165,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSPARCL1\u003c/em\u003e expression level in tissues and cell lines detected by RT-qPCR. (a) \u003cem\u003eSPARCL1 \u003c/em\u003eexpression in BC tissues and matched normal adjacent breast tissues (Wilcoxon matched-pairs signed-rank test). (b) The expression level of \u003cem\u003eSPARCL1\u003c/em\u003e in MCF-10A and breast cancer cell lines (Student's \u003cem\u003et\u003c/em\u003e-test). (*\u003cem\u003e p \u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003e p \u003c/em\u003e\u0026lt; 0.01, *** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001, **** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.0001)\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-2183292/v1/9dc46b177589413249c08f08.png"},{"id":28183355,"identity":"80e312ca-1ef9-4957-ad00-9bfb1cb93c7c","added_by":"auto","created_at":"2022-10-24 15:44:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1158557,"visible":true,"origin":"","legend":"\u003cp\u003eBiological function experiments in BC cells with \u003cem\u003eSPARCL1\u003c/em\u003e knockdown. (a) RT-qPCR assessing the knockdown efficiency of \u003cem\u003eSPARCL1\u003c/em\u003e siRNA on BC cells. (b) Colony formation assay measuring the proliferation ability of BC cells when \u003cem\u003eSPARCL1 \u003c/em\u003ewas knocked down. Cell colonies were photographed, and the number of cell colonies was counted. (c) MTT assay was performed to detect the proliferation in BC cell lines when \u003cem\u003eSPARCL1\u003c/em\u003e was knocked down. (d) A wound-healing assay was performed to assess the migration of MDA-MB-231 cells. Cells were photographed at 0 h and 12 h after scratching, and wound closures were compared. (e) The migration ability of MDA-MB-231 was evaluated with a Transwell assay using images taken 20 h after culture, and the number of cells was counted. (* \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, ** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01, *** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.001, **** \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.0001)\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-2183292/v1/490f7cfe9d94c7f6b9930d04.png"},{"id":28184750,"identity":"0f7230af-2337-460a-acc7-727b8021a114","added_by":"auto","created_at":"2022-10-24 15:54:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3690920,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2183292/v1/162f3e81-1017-495b-833d-24ae009bd81f.pdf"},{"id":28183501,"identity":"d6135963-28db-4e59-b04c-b70f4f77e36a","added_by":"auto","created_at":"2022-10-24 15:49:44","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2735813,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-2183292/v1/28ace982c96f8f0415634607.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identifying liver metastasis-related hub genes in breast cancer and characterizing SPARCL1 as a potential prognostic biomarker","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eBreast cancer (BC) in women has now become the most newly diagnosed malignant tumor worldwide, with both the number of new cases and the number of deaths ranking first among all malignant tumors. Generally, patients with BC have a relatively good prognosis, with approximately 44% of patients with early-stage BC having a nearly 100% 5-year survival rate, whereas once organ metastasis occurs, this survival rate drops sharply to 26% (Miller et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The organs most likely to metastasize from BC are the lung, bone, liver, and brain (Cummings et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Metastasis of the liver can cause various fatal complications, including liver failure, intractable ascites, portal vein thrombosis, and malnutrition (Diamond et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Thus, the occurrence of liver metastases indicates a worse prognosis in patients with BC, with a reported median survival time of approximately 3 years (Zhao et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, the molecular mechanism of metastasis is unclear. Identifying potential molecular biomarkers of liver metastasis could provide more accurate information to guide clinical decisions and predict prognosis, as well as provide direction and theoretical support for research of the mechanism of metastasis. The Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) database have collected and stored a large amount of tumor sequencing data, which are free for public to access. Re-analyzing these sequencing data using bioinformatics methods and mining for differences in the genetic information among different samples can help provide a scientific explanation of the occurrence and progression of disease (Gauthier et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, based on public database and bioinformatics analyses, we screened differentially expressed genes (DEGs) between BC and liver metastasis, identified hub genes, and analyzed their correlation with the clinical characteristics of patients with BC. Finally, BC tissues and cells were collected to detect the expression of the hub genes and explore their biological functions in BC cells. These findings will provide a new understanding of BC and liver metastasis, driving future research and providing potential biomarkers for BC diagnosis and therapy.\u003c/p\u003e"},{"header":"2 Materials And Methods","content":"\u003ch2\u003e2.1 Discovery and validation datasets\u003c/h2\u003e\n\u003cp\u003eIn the GEO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003c/span\u003e), datasets GSE124648 (Sinn et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) and GSE58708 (McBryan et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) were obtained with the following keywords: breast neoplasms, breast cancer, liver metastasis, expression profiling by array, attribute name tissue, and \u003cem\u003eHomo sapiens\u003c/em\u003e. Array data of liver metastases (N\u0026thinsp;=\u0026thinsp;16) and primary tumors (control, N\u0026thinsp;=\u0026thinsp;130) from the GPL96 ([HG-U133A] Affymetrix Human Genome U133A Array) platform were selected for differential expression analysis. The array data for GSE58708 consisted of three patients with BC liver metastasis vs. three controls as a validation dataset (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDiscovery and validation of BC datasets\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDatasets\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePlatform\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eContributor(s)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExperiment type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of cases (metastasis/primary)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE124648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGPL96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSinn et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpression profiling by array\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16/130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGSE58708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGPL11154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYoung et al. (2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExpression profiling by high-throughput sequencing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3/3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch2\u003e2.2 DEG identification\u003c/h2\u003e\n\u003cp\u003eR software (version 4.0.3) was employed for bioinformatics analysis. The (\u0026ldquo;limma\u0026rdquo;) package was adopted to identify DEGs between BC and liver metastasis (Ritchie et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), with the following cutoff criteria: |log\u003csub\u003e2\u003c/sub\u003eFC|\u0026gt;2.0 and adj.\u003cem\u003eP\u003c/em\u003e.Val\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Similarly, the R package \u0026ldquo;TCGAbiolinks\u0026rdquo; was used to obtain biological data for BC in the TCGA database (Colaprico et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), and hub gene expression was verified by analyzing the differentially expressed mRNAs in BC. \u0026ldquo;ggplot2\u0026rdquo; and \u0026ldquo;heatmap\u0026rdquo; packages were used for visualizing DEGs.\u003c/p\u003e\n\u003ch2\u003e2.3 Enrichment analysis of DEGs\u003c/h2\u003e\n\u003cp\u003eThe \u0026ldquo;clusterProfile\u0026rdquo; package was employed to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the DEGs (Yu et al. \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e), and a gene set enrichment analysis (GSEA) method was used in the KEGG enrichment analysis. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was the criteria for statistical significance.\u003c/p\u003e\n\u003ch2\u003e2.4 Integration of PPI networks and identification of hub genes\u003c/h2\u003e\n\u003cp\u003eThe STRING (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.string-db.org/\u003c/span\u003e\u003c/span\u003e) database provides comprehensive information on protein\u0026ndash;protein interactions (PPI) (Szklarczyk et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). A PPI network of DEGs was integrated using the STRING database. Then, the results were visualized and analyzed using Cytoscape (version 3.7.2). The important modules were extracted via the plug-in MCODE, with the cutoff criteria being an MCODE score\u0026thinsp;\u0026gt;\u0026thinsp;5 and nodes\u0026thinsp;\u0026gt;\u0026thinsp;5. The other default parameters were set as Max. Depth\u0026thinsp;=\u0026thinsp;100, K-Core\u0026thinsp;=\u0026thinsp;2, Node score cutoff\u0026thinsp;=\u0026thinsp;0.2, Degree Cutoff\u0026thinsp;=\u0026thinsp;2. The plug-in cytoHubba was employed to identify the top 30 hub genes in the PPI network, with the cutoff criteria: degree.layout\u0026thinsp;\u0026ge;\u0026thinsp;25 (MCC algorithm) (Shannon et al. \u003cspan class=\"CitationRef\"\u003e2003\u003c/span\u003e). GEPIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia.cancer-pku.cn/\u003c/span\u003e\u003c/span\u003e) was used to obtain the hub gene expression between BC and normal tissues, with the default parameters: \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u003cspan class=\"Underline\" name=\"Emphasis\" type=\"Underline\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;0.01 and |Log\u003csub\u003e2\u003c/sub\u003eFC| \u0026ge; 1 (Tang et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). The Kaplan\u0026ndash;Meier (K\u0026ndash;M) plotter (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://kmplot.com/analysis/\u003c/span\u003e\u003c/span\u003e) was used to evaluate the correlation between the expression of hub genes and survival (L\u0026aacute;nczky et al. 2021). The median expression level divided patients into high and low expression groups. Then, the K\u0026ndash;M curves of overall survival (OS) and recurrence-free survival (RFS) were drawn. Finally, hub genes (\u003cem\u003eSPARCL1\u003c/em\u003e and \u003cem\u003eSERPINA1\u003c/em\u003e) with prognostic values were identified. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 using the log-rank test was considered statistically significant.\u003c/p\u003e\n\u003ch2\u003e2.5 Validation of hub genes and clinicopathological correlation analysis\u003c/h2\u003e\n\u003cp\u003eA box plot was plotted to verify \u003cem\u003eSPARCL1\u003c/em\u003e expression in liver metastasis based on the GSE58708 dataset. The correlations between \u003cem\u003eSPARCL1\u003c/em\u003e expression and clinicopathological characteristics of patients with BC were analyzed using TCGA-BRCA data. Then, based on patient survival data and \u003cem\u003eSPARCL1\u003c/em\u003e expression level, the optimal expression threshold was calculated using the \u0026ldquo;survminer\u0026rdquo; package. The correlation between gene expression and clinicopathological parameters was tested using Pearson\u0026rsquo;s chi-squared test. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n\u003ch2\u003e2.6 GSEA\u003c/h2\u003e\n\u003cp\u003eGSEA can be used to evaluate whether a predefined gene set shows statistically significant differences between two groups with different phenotypes. Expression (.gct) and phenotype (.cls) information files for \u003cem\u003eSPARCL1\u003c/em\u003e were uploaded to the GSEA software (version 4.1.0) to conduct enrichment analysis. The chip platform and gene set database selected were \u0026ldquo;Human_ENSEMBL_Gene_ID_MSigDB.v7.0\u0026rdquo; and \u0026ldquo;c2.cp.kegg.v7.1. symbols.gmt,\u0026rdquo; respectively. The normalized enrichment score was calculated. Both false discovery rate (FDR q-val) and nominal \u003cem\u003ep\u003c/em\u003e-value (NOM \u003cem\u003ep\u003c/em\u003e-val)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e\n\u003ch2\u003e2.7 Clinical specimens and cell lines\u003c/h2\u003e\n\u003cp\u003eTissue samples were obtained from patients with BC who underwent a radical mastectomy at Shanghai Tenth People\u0026rsquo;s Hospital (Shanghai, China). After mastectomy, 30 pairs of fresh breast tumor and matched normal adjacent breast tissues were collected and preserved in liquid nitrogen. BC cells (MDA-MB-231, BT549, and MCF-7) and mammary epithelial cells (MCF-10A) were purchased from the Chinese Academy of Sciences\u0026apos; Cell Bank and grown in mammary epithelial basal medium (Cambrex, New Jersey, USA) and Dulbecco\u0026rsquo;s modified Eagle medium (DMEM, Gibco, Grand Island, USA) supplemented with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin-streptomycin (Enpromise, Shanghai, China), respectively. A 5% CO\u003csub\u003e2\u003c/sub\u003e incubator was used to culture all cells at 37\u0026deg; C.\u003c/p\u003e\n\u003ch2\u003e2.8 Cell transfection, RNA extraction, and RT-qPCR\u003c/h2\u003e\n\u003cp\u003eFollowing the manufacturer\u0026rsquo;s instructions, the negative control (si-NC) and \u003cem\u003eSPARCL1\u003c/em\u003e-siRNAs (\u003cem\u003eSPARCL1\u003c/em\u003e si1 sense: 5\u0026prime;-GAUUCUAACCAACAAGAAAGU-3\u0026prime;, anti-sense: 5\u0026prime;-UUUCUUGUUGGUUAGAAUCUG-3\u0026prime;), and \u003cem\u003eSPARCL1\u003c/em\u003e si2 (sense: 5\u0026prime;-GACAAAUGCAAGAUUAUUAUC-3\u0026prime;, anti-sense: 5\u0026prime;-UAAUAAUCUUGCAUUUGUCGG-3\u0026prime;) were transfected into BC cells using Lipofectamine\u0026reg; 3000 (Invitrogen, USA). si-NC and siRNAs targeting SPARCL1 (\u003cem\u003eSPARCL1\u003c/em\u003e si1 and \u003cem\u003eSPARCL1\u003c/em\u003e si2) were purchased from IBSbio (Shanghai, China). TRIzol reagent (Invitrogen) was applied to extract total RNA from cell lines and tissues. HiScript\u0026reg; III RT SuperMix for qPCR (Vazyme, China) was used to generate the cDNAs, and Hieff\u0026reg; qPCR SYBR Green Master Mix (YEASEN, Shanghai, China) was used to conduct real time quantitative polymerase chain reaction (RT-qPCR) following the manufacturer\u0026rsquo;s protocol, with \u0026beta;-actin acting as an internal control for normalizing \u003cem\u003eSPARCL1\u003c/em\u003e expression. We used the following primers to conduct RT-qPCR. \u003cem\u003eSPARCL1\u003c/em\u003e (forward: 5\u0026prime;-CCAACTGAAGGTACATTGGACAT-3\u0026prime;, reverse: 5\u0026prime;-CTGTGAAGGAACTAACACCAGG-3\u0026prime;) and \u0026beta;-actin (forward: 5\u0026prime;-CATGTACGTTGCTATCCAGGC-3\u0026prime;, reverse: 5\u0026prime;-CTCCTTAATGTCACGCACGAT-3\u0026prime;).\u003c/p\u003e\n\u003ch2\u003e2.9 Methylthiazolyldiphenyl-tetrazolium, colony formation, wound-healing, and Transwell assay\u003c/h2\u003e\n\u003cp\u003eAfter transfection with si-NC and \u003cem\u003eSPARCL1\u003c/em\u003e siRNAs, in 96-well plates, BC cell lines were cultured at a density of 1,500 cells. Next, 20 \u0026micro;L methylthiazolyldiphenyl-tetrazolium bromide (MTT, YEASEN) was added to each well at 0, 24, 48, 72, and 96 h after inoculation, and the samples were incubated for 4 h at 37 \u0026deg;C in a 5% CO\u003csub\u003e2\u003c/sub\u003e incubator to assess cell viability. Then, 150 \u0026micro;L DMSO was added to each well after removing the supernatant. The absorbance was measured by a microplate spectrophotometer (BioTek, Germany) at 490 nm. Cell proliferation curves were plotted according to the absorbance value.\u003c/p\u003e\n\u003cp\u003eBC cell lines transfected with si-NC and \u003cem\u003eSPARCL1\u003c/em\u003e siRNAs were prepared as single-cell suspensions, inoculated at a density of 750 cells per well until prominent colonies formed. The cells were fixed with 95% ethanol and stained with 0.1% crystal violet (YEASEN) to detect cell colony formation ability. Representative pictures were recorded, and clone colonies were counted.\u003c/p\u003e\n\u003cp\u003eTo detect cell mobility, BC cells transfected with si-NC and \u003cem\u003eSPARCL1\u003c/em\u003e siRNAs were cultured and scratched with non-RNA enzyme tips when the cell fusion rate reached 90% or more. In the following step, DMEM supplemented with 2% FBS was used as the culture medium. The healing of the scratches was observed at 0 and 12 h using the same field of view to calculate cell mobility.\u003c/p\u003e\n\u003cp\u003eThe transfected BC cells and 500 \u0026micro;L of DMEM containing 10% FBS were added to Transwell\u0026apos;s upper and lower chambers (Corning, USA), respectively. After culturing for 18 h, migrated cells were fixed in 4% paraformaldehyde and stained with 0.1% crystal violet to assess the migratory ability. Representative images were captured using an inverted microscope.\u003c/p\u003e\n\u003ch2\u003e2.10 Statistical analysis\u003c/h2\u003e\n\u003cp\u003eA Wilcoxon matched-pairs signed-rank test was used to compare the expression level of \u003cem\u003eSPARCL1\u003c/em\u003e between BC and control samples. An unpaired Student\u0026apos;s \u003cem\u003et\u003c/em\u003e-test was used to compare the expression of \u003cem\u003eSPARCL1\u003c/em\u003e between MCF-10A and BC cell lines. The results of the MTT assay were analyzed using a two-way analysis of variance (ANOVA). All experiments were repeated three times. The experimental data were analyzed and plotted with GraphPad Prism (v8.3.0, USA). \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"3 Results","content":"\u003ch2\u003e3.1 DEG identification\u003c/h2\u003e\n\u003cp\u003eWe identified 332 DEGs comprising 116 upregulated and 216 downregulated genes from the GSE124648 dataset (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea). The heatmap was used to visualize the top 50 DEGs, as the top 50 liver metastasis-related genes in GSE124648 (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e\n\u003ch2\u003e3.2 GO and KEGG enrichment analysis of DEGs\u003c/h2\u003e\n\u003cp\u003eGO annotation divides gene function into three categories: cellular components (CC), molecular function (MF), and biological process (BP) (Sinn et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The top eight enriched GO terms for each category are shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. CC analysis indicated that these DEGs were particularly related to the extracellular matrix, endoplasmic reticulum lumen, collagen-containing extracellular matrix, collagen trimer, and blood microparticles (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). MF analysis showed that DEGs were mainly involved in extracellular matrix structural constituents, glycosaminoglycan binding, extracellular matrix structural constituents conferring tensile strength, heparin-binding, and collagen binding (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb). The BP category was mainly enriched in extracellular structure organization, extracellular matrix organization, wound healing, humoral immune response, and complement activation (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec). The KEGG pathway enrichment analysis results showed that the upregulated DEGs were significantly enriched in neutrophil extracellular trap formation, alcoholic liver disease, and neuroactive ligand\u0026thinsp;\u0026minus;\u0026thinsp;receptor interaction, whereas the downregulated DEGs were significantly enriched in malignancy-related pathways, including pathways in cancer, BC, PI3K\u0026thinsp;\u0026minus;\u0026thinsp;Akt signaling pathway, MAPK signaling pathway, and focal adhesion (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e\n\u003ch2\u003e3.3 PPI network and \u003cem\u003eSPARCL1\u003c/em\u003e identified as a prognostic-related hub gene\u003c/h2\u003e\n\u003cp\u003eThe PPI network contained 332 nodes and 2,972 edges (Online Resource Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), and we selected the top three hub modules identified by the MCODE plug-in for display (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea\u0026ndash;c), where each node represents one DEG, and the edges between nodes represent interactions, which can reflect the importance of genes and modules in the network. The top 30 hub genes with a high degree of connectivity were identified from the network (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ed). The expression levels of these genes and their modules are listed in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Among the 30 hub genes, \u003cem\u003eSERPINA1\u003c/em\u003e and \u003cem\u003eVCAN\u003c/em\u003e were found to be upregulated in BC, whereas \u003cem\u003eALB\u003c/em\u003e, \u003cem\u003eIGFBP3\u003c/em\u003e, \u003cem\u003eSPARCL1\u003c/em\u003e, and \u003cem\u003eFSTL1\u003c/em\u003e were downregulated and no significant difference was discovered in the expression of other hub genes. Survival analyses showed favorable OS and RFS in patients with BC with upregulated \u003cem\u003eSERPINA1\u003c/em\u003e and \u003cem\u003eSPARCL1\u003c/em\u003e expression (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTop 30 hub genes associated with BC liver metastasis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGene symbol\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elog\u003csub\u003e2\u003c/sub\u003eFC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eadj.\u003cem\u003eP\u003c/em\u003e.Val\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExpression\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModules\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFGG\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.149436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.24E-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOA1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.69204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.81E-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIGFBP7\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.9555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.11E-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.954259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.62E-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFSTL1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.02757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.78E-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePRSS23\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.34018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.94E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eVCAN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.54093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.33E-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTNC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.71678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.74E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eORM1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.685187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.25E-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIGFBP3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.10444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLAMB1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.0713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.26E-22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSERPIND1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.39324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.63E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSPP2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.236803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.46E-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIGFBP5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.35175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.19E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFGA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.820105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17E-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eGC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.136118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.01E-31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFGB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.56398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.49E-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHRG\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.776554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.28E-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.150198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.51E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eFBN1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.03832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.62E-25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSERPINA10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.515328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.67E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eALB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.627917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.85E-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAPOA2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.683622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.91E-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAHSG\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.816468\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.15E-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSERPINA1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.350588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20E-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSPARCL1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.75713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.01E-31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDOWN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eTF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.63786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.81E-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eF5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.037343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08E-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSERPINC1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.646863\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.04E-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eITIH2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.98753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.57E-26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModule 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ch2\u003e3.4 Validation of \u003cem\u003eSPARCL1\u003c/em\u003e and clinicopathological correlation analysis\u003c/h2\u003e\n\u003cp\u003eFollowing differential expression analysis of the GSE58708 dataset, 683 DEGs were obtained, including 410 upregulated and 273 downregulated genes (Online Resource Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). \u003cem\u003eSPARCL1\u003c/em\u003e expression was also significantly downregulated in liver metastasis compared to that in BC tissues (log\u003csub\u003e2\u003c/sub\u003eFC\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.617; Online Resource Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea). In the TCGA-BRCA dataset, compared to normal mammary gland tissue, \u003cem\u003eSPARCL1\u003c/em\u003e expression in BC tissues was also significantly downregulated (Online Resource Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb). The correlation between \u003cem\u003eSPARCL1\u003c/em\u003e level and clinical characteristics of patients with BC was further analyzed and showed that the lower \u003cem\u003eSPARCL1\u003c/em\u003e level was significantly related to age, TNM stage, ER status, PR status, histological type, molecular type, and living status, whereas there was no significant difference in node stage and Her-2 status (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRelationship between \u003cem\u003eSPARCL1\u003c/em\u003e expression and clinical characteristics of patients with BC\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eSPARCL1\u003c/em\u003e expression\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N\u0026thinsp;=\u0026thinsp;1049)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N\u0026thinsp;=\u0026thinsp;432)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(N\u0026thinsp;=\u0026thinsp;617)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan class=\"BoldItalic\" name=\"Emphasis\" type=\"BoldItalic\"\u003ep\u003c/span\u003e\u003cstrong\u003e-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e432 (41.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200 (46.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e232 (37.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.00592\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e617 (58.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e232 (53.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e385 (62.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTNM stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90 (20.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85 (13.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e599 (57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e221 (51.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e378 (61.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIII\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e242 (23.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110 (25.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132 (21.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (2.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNode stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN0\u0026ndash;N1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e615 (58.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e245 (56.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e370 (60.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN2\u0026ndash;N3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137 (13.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 (11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86 (13.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e297 (28.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161 (26.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eER status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e170 (16.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (7.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139 (22.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e571 (54.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e265 (61.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e306 (49.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e308 (29.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172 (27.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePR status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e238 (22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58 (13.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180 (29.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500 (47.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e235 (54.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e265 (42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e311 (29.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139 (32.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172 (27.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHER2 status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e621 (59.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e251 (58.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e370 (60.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107 (10.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70 (11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e321 (30.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e177 (28.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistological type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e752 (71.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e260 (60.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e492 (79.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e196 (18.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137 (31.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 (9.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 (9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (8.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasal-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94 (9.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83 (13.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLum A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e219 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135 (31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84 (13.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLum B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99 (16.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHER2-enriched\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (1.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e557 (53.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e246 (56.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e311 (50.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiving status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e902 (86.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e385 (89.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e517 (83.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0185\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147 (14.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47 (10.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100 (16.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ch2\u003e3.5 GSEA\u003c/h2\u003e\n\u003cp\u003eFrom the viewpoint of enrichment of gene sets, finding the effects of subtle changes on biological pathways or functions is easier in theory (Subramanian et al. \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). Figure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e displays six signaling pathways (DNA replication, cell cycle, oxidative phosphorylation, homologous recombination, spliceosome, and proteasome) that were significantly enriched when \u003cem\u003eSPARCL1\u003c/em\u003e was downregulated in BC, revealing the potential molecular mechanisms by which \u003cem\u003eSPARCL1\u003c/em\u003e participates in BC occurrence and progression.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e3.6 SPARCL1\u003c/em\u003e downregulation in BC tissues and cells\u003c/h2\u003e\n\u003cp\u003eTotal RNA was extracted from tissues and cell lines for RT-qPCR to validate \u003cem\u003eSPARCL1\u003c/em\u003e expression in BC. In comparison with the paired adjacent normal breast tissues, \u003cem\u003eSPARCL1\u003c/em\u003e was significantly downregulated in BC tissues (N\u0026thinsp;=\u0026thinsp;30; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea). In comparison with the normal breast epithelial cell line, \u003cem\u003eSPARCL1\u003c/em\u003e expression was also decreased in the BC cell lines (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb). These results are consistent with those from our bioinformatics analysis.\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003e3.7 SPARCL1\u003c/em\u003e knockdown-induced proliferation and migration of BC cells \u003cem\u003ein vitro\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eTo explore the biological function of \u003cem\u003eSPARCL1\u003c/em\u003e in BC, we used siRNAs to artificially knock down \u003cem\u003eSPARCL1\u003c/em\u003e in BC cells. The efficiency of the siRNA suggested that the expression of \u003cem\u003eSPARCL1\u003c/em\u003e was significantly knocked down by both \u003cem\u003eSPARCL1\u003c/em\u003e si-1 and \u003cem\u003eSPARCL1\u003c/em\u003e si-2 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ea). The results of colony formation and MTT assays suggested that inhibiting \u003cem\u003eSPARCL1\u003c/em\u003e promoted the proliferation of BC cells (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eb and c). Cell migration is an essential step in tumor progression and metastasis. In comparison with the control group (si-NC), the healing ability of \u003cem\u003eSPARCL1\u003c/em\u003e-inhibited MDA-MB-231 cells was significantly enhanced, and cell migration was significantly increased (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003ed and e). Collectively, these results suggested that repressing \u003cem\u003eSPARCL1\u003c/em\u003e enhances the proliferation and migration of BC cells.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe bioinformatics analysis of sequencing data can improve our understanding of gene function, including gene expression levels between different experimental conditions or phenotypes, identification of biological processes related to gene expression levels, and screening of therapeutic targets and prognostic markers (Hu et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kaifi et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tao et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In this study, 332 DEGs associated with liver metastasis were identified by differential expression analysis of sequencing data from the GEO database. GO enrichment analysis suggested that these DEGs were mostly located in the extracellular matrix and participated in biological processes, including extracellular matrix organization, wound healing, angiogenesis, and humoral immune response. Extracellular matrix organization is closely correlated with the occurrence and progression of cancer; a neatly arranged matrix can promote the invasion of tumor cells. The tumor suppressor \u003cem\u003ePTEN\u003c/em\u003e participates in the regulation of matrix remodeling, which is negatively correlated with the arrangement of collagen in human breast tissue (Jones et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Tumor angiogenesis can supply nutrients and oxygen essential for tumor growth and metastasis (Weis et al. 2011). Hypoxia-inducible factor (HIF)-dependent angiogenesis is vital for the invasion, progression, and drug resistance of BC and is closely correlated with its poor prognosis (de Heer et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). KEGG pathway analysis showed that downregulated DEGs were particularly enriched in malignant tumor-related pathways, such as MAPK signaling, PI3K/AKT signaling, focal adhesion, Wnt signaling, and Hippo signaling pathways. The MAPK signaling pathway is crucial for BC invasion and metastasis, promoting the occurrence and progression of the disease (Cotrim et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Jiang et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ke et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In BC, more than 70% of patients have PI3K signaling pathway alterations, and activation of the PI3K pathway is significantly associated with an HR-negative, basal-like phenotype, high histological grade, and cancer-specific death (L\u0026oacute;pez-Knowles et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The PI3K/AKT signaling pathway also participated in liver metastasis of various malignant tumors. Indeed, microRNA-582 can promote gastric cancer liver metastasis via the PI3K/Akt/Snail pathway mediated by FOXO-3 (Xie et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The c-Met/PI3K/AKT/mTOR axis can activate the liver metastasis-specific cholesterol metabolism pathway in colorectal cancer, providing conditions for tumor cell colonization and growth in the liver (Zhang et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). There are also reports on the influence of Wnt, Hippo, and other signaling pathways on the metastasis of malignant liver tumors (Chai et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yuan et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the prognostic value of hub DEGs in BC, we identified \u003cem\u003eSPARCL1\u003c/em\u003e, a member of the secreted protein acidic and rich in cysteine (\u003cem\u003eSPARC\u003c/em\u003e) family, which is downregulated in both BC and liver metastasis. \u003cem\u003eSPARCL1\u003c/em\u003e is a newly discovered player in tumors and is mainly associated with physiological processes such as cell migration, adhesion, and cell proliferation regulation (Gagliardi et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The expression of \u003cem\u003eSPARCL1\u003c/em\u003e is downregulated in colorectal cancer and is related to tumor differentiation, stage, distant metastasis, and OS (Hu et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cem\u003eSPARCL1\u003c/em\u003e is also downregulated in prostate cancer, being associated with disease progression, especially in invasive prostate cancer. Moreover, \u003cem\u003eSPARCL1\u003c/em\u003e can inhibit migration, invasion, and metastasis of prostate cancer (Hurley et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Xiang et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Similarly, SPARCL1 was found to be a tumor suppressor in gastric cancer (Li et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), osteosarcoma (Zhao et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), pancreatic cancer (Esposito et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), lung cancer (Deng et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and renal cell carcinoma (Ye et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). \u003cem\u003eSPARCL1\u003c/em\u003e expression in colorectal cancer liver metastasis is downregulated, with \u003cem\u003eSPARCL1\u003c/em\u003e being considered the hub gene of liver metastasis and a biomarker with a significant prognostic value (Zhang et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, the downregulation of \u003cem\u003eSPARCL1\u003c/em\u003e expression enhances liver metastasis of malignant gastrointestinal stromal tumor cells (Shen et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, only a few studies have reported the expression and function of \u003cem\u003eSPARCL1\u003c/em\u003e in BC. Consistent with our findings, \u003cem\u003eSPARCL1\u003c/em\u003e has been reported to be downregulated in BC (Cao et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In addition, our clinicopathological correlation analysis revealed that the low expression of \u003cem\u003eSPARCL1\u003c/em\u003e was related to age, TNM stage, ER status, PR status, histological type, molecular type, and survival status of patients with BC, while younger age, higher TNM stage, HR-negative status, and basal-like type were among the unfavorable phenotypes in patients with BC.\u003c/p\u003e \u003cp\u003eGSEA indicated that pathways such as DNA replication, cell cycle, oxidative phosphorylation, and homologous recombination were significantly enriched when \u003cem\u003eSPARCL1\u003c/em\u003e was downregulated, revealing potential molecular mechanisms by which \u003cem\u003eSPARCL1\u003c/em\u003e participates in BC. The most vulnerable cellular process in oncogenic lesions is DNA replication, with oncogene-induced replication stress being a fundamental step and early driver of tumorigenesis (Kotsantis et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The cell cycle and DNA replication are closely coordinated to ensure correct single-genome replication during cell division, thereby avoiding the occurrence of diseases such as cancer (Dai et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Homologous recombination repair is an essential pathway of DNA damage repair, and homologous recombination deficiency is considered an important biomarker and risk factor for BC (den Brok et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Telli et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Shen et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u003cem\u003eIn vitro\u003c/em\u003e studies suggested that inhibition of \u003cem\u003eSPARCL1\u003c/em\u003e promoted BC cell proliferation, colony formation, and migration. This work indicates the tumor suppressing role for \u003cem\u003eSPARCL1\u003c/em\u003e in BC. However, the specific mechanism of \u003cem\u003eSPARCL1\u003c/em\u003e inhibiting the proliferation and migration of BC cells is still not clear, and more experiments are needed to validate the role of \u003cem\u003eSPARCL1\u003c/em\u003e in BC and liver metastasis. In addition, the data in this study were taken from a public database; to obtain more accurate and credible results, hub genes require further experimental verification using liver metastasis tissue and cell lines.\u003c/p\u003e \u003cp\u003eIn summary, we screened DEGs and hub genes in BC liver metastasis and identified \u003cem\u003eSPARCL1\u003c/em\u003e as a potential prognostic biomarker for BC and liver metastasis diagnosis and therapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by\u0026nbsp;the Shanghai Municipal Health Commission, China (Grant number 202040157), and the National Natural Science Foundation of China (Grant number 82073204).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was designed by Lin Fang and Mingkuan Chen. Experiments, sample collection,\u0026nbsp;and\u0026nbsp;analysis were performed by Mingkuan Chen and Wenfang Zheng. Data collection and bioinformatics analysis were performed by Mingkuan Chen. Valuable comments were provided by Lin Fang. The manuscript was written by Mingkuan Chen and Wenfang Zheng. All\u0026nbsp;authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Shanghai Tenth People\u0026rsquo;s Hospital\u0026nbsp;(Date October 14, 2020/No.\u0026nbsp;2020-KN174-01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eCA A Cancer J Clinicians - 2021 - Sung - Global Cancer Statistics 2020 GLOBOCAN Estimates of Incidence and Mortality.pdf\u0026gt;.https://doi.org/10.3322/caac.21660\u003c/li\u003e\n \u003cli\u003eCao F, Wang K, Zhu R, Hu Y-W, Fang W-Z \u0026amp; Ding H-Z (2013) Clinicopathological significance of reduced SPARCL1 expression in human breast cancer. Asian Pac J Cancer Prev 14:195-200\u003c/li\u003e\n \u003cli\u003eChai W-X, Sun L-G, Dai F-H, Shao H-S, Zheng N-G \u0026amp; Cai H-Y (2019) Inhibition of PRRX2 suppressed colon cancer liver metastasis via inactivation of Wnt/\u0026beta;-catenin signaling pathway. Pathol Res Pract 215:152593.https://doi.org/10.1016/j.prp.2019.152593\u003c/li\u003e\n \u003cli\u003eColaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, Sabedot TS, Malta TM, Pagnotta SM, Castiglioni I, Ceccarelli M, Bontempi G \u0026amp; Noushmehr H (2016) TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res 44:e71.https://doi.org/10.1093/nar/gkv1507\u003c/li\u003e\n \u003cli\u003eCotrim CZ, Fabris V, Doria ML, Lindberg K, Gustafsson J\u0026Aring;, Amado F, Lanari C \u0026amp; Helguero LA (2013) Estrogen receptor beta growth-inhibitory effects are repressed through activation of MAPK and PI3K signalling in mammary epithelial and breast cancer cells. Oncogene 32:2390-2402.https://doi.org/10.1038/onc.2012.261\u003c/li\u003e\n \u003cli\u003eCummings MC, Simpson PT, Reid LE, Jayanthan J, Skerman J, Song S, Mccart Reed AE, Kutasovic JR, Morey AL, Marquart L, O\u0026apos;rourke P \u0026amp; Lakhani SR (2014) Metastatic progression of breast cancer: insights from 50 years of autopsies. J Pathol 232:23-31.https://doi.org/10.1002/path.4288\u003c/li\u003e\n \u003cli\u003eDai M, Boudreault J, Wang N, Poulet S, Daliah G, Yan G, Moamer A, Burgos SA, Sabri S, Ali S \u0026amp; Lebrun J-J (2021) Differential Regulation of Cancer Progression by CDK4/6 Plays a Central Role in DNA Replication and Repair Pathways. Cancer research 81:1332-1346.https://doi.org/10.1158/0008-5472.CAN-20-2121\u003c/li\u003e\n \u003cli\u003eDe Heer EC, Jalving M \u0026amp; Harris AL (2020) HIFs, angiogenesis, and metabolism: elusive enemies in breast cancer. J Clin Invest 130:5074-5087.https://doi.org/10.1172/JCI137552\u003c/li\u003e\n \u003cli\u003eDen Brok WD, Schrader KA, Sun S, Tinker AV, Zhao EY, Aparicio S \u0026amp; Gelmon KA (2017) Homologous Recombination Deficiency in Breast Cancer: A Clinical Review. JCO Precis Oncol 1.https://doi.org/10.1200/PO.16.00031\u003c/li\u003e\n \u003cli\u003eDeng H, Hang Q, Shen D, Zhang Y \u0026amp; Chen M (2021) Low expression of CHRDL1 and SPARCL1 predicts poor prognosis of lung adenocarcinoma based on comprehensive analysis and immunohistochemical validation. Cancer Cell Int 21:259.https://doi.org/10.1186/s12935-021-01933-9\u003c/li\u003e\n \u003cli\u003eDiamond JR, Finlayson CA \u0026amp; Borges VF (2009) Hepatic complications of breast cancer. The Lancet. Oncology 10:615-621.https://doi.org/10.1016/S1470-2045(09)70029-4\u003c/li\u003e\n \u003cli\u003eEsposito I, Kayed H, Keleg S, Giese T, Sage EH, Schirmacher P, Friess H \u0026amp; Kleeff J (2007) Tumor-suppressor function of SPARC-like protein 1/Hevin in pancreatic cancer. Neoplasia 9\u003c/li\u003e\n \u003cli\u003eGagliardi F, Narayanan A \u0026amp; Mortini P (2017) SPARCL1 a novel player in cancer biology. Crit Rev Oncol Hematol 109:63-68.https://doi.org/10.1016/j.critrevonc.2016.11.013\u003c/li\u003e\n \u003cli\u003eGauthier J, Vincent AT, Charette SJ \u0026amp; Derome N (2019) A brief history of bioinformatics. Brief Bioinform 20:1981-1996.https://doi.org/10.1093/bib/bby063\u003c/li\u003e\n \u003cli\u003eHu H, Zhang H, Ge W, Liu X, Loera S, Chu P, Chen H, Peng J, Zhou L, Yu S, Yuan Y, Zhang S, Lai L, Yen Y \u0026amp; Zheng S (2012) Secreted protein acidic and rich in cysteines-like 1 suppresses aggressiveness and predicts better survival in colorectal cancers. Clin Cancer Res 18:5438-5448.https://doi.org/10.1158/1078-0432.CCR-12-0124\u003c/li\u003e\n \u003cli\u003eHu N, Wang C, Clifford RJ, Yang HH, Su H, Wang L, Wang Y, Xu Y, Tang Z-Z, Ding T, Zhang T, Goldstein AM, Giffen C, Lee MP \u0026amp; Taylor PR (2015) Integrative genomics analysis of genes with biallelic loss and its relation to the expression of mRNA and micro-RNA in esophageal squamous cell carcinoma. BMC Genomics 16:732.https://doi.org/10.1186/s12864-015-1919-0\u003c/li\u003e\n \u003cli\u003eHurley PJ, Marchionni L, Simons BW, Ross AE, Peskoe SB, Miller RM, Erho N, Vergara IA, Ghadessi M, Huang Z, Gurel B, Park BH, Davicioni E, Jenkins RB, Platz EA, Berman DM \u0026amp; Schaeffer EM (2012) Secreted protein, acidic and rich in cysteine-like 1 (SPARCL1) is down regulated in aggressive prostate cancers and is prognostic for poor clinical outcome. Proc Natl Acad Sci U S A 109:14977-14982.https://doi.org/10.1073/pnas.1203525109\u003c/li\u003e\n \u003cli\u003eJiang W, Wang X, Zhang C, Xue L \u0026amp; Yang L (2020) Expression and clinical significance of MAPK and EGFR in triple-negative breast cancer. Oncology letters 19:1842-1848.https://doi.org/10.3892/ol.2020.11274\u003c/li\u003e\n \u003cli\u003eJones CE, Hammer AM, Cho Y, Sizemore GM, Cukierman E, Yee LD, Ghadiali SN, Ostrowski MC \u0026amp; Leight JL (2019) Stromal PTEN Regulates Extracellular Matrix Organization in the Mammary Gland. Neoplasia 21:132-145.https://doi.org/10.1016/j.neo.2018.10.010\u003c/li\u003e\n \u003cli\u003eKaifi JT, Kunkel M, Das A, Harouaka RA, Dicker DT, Li G, Zhu J, Clawson GA, Yang Z, Reed MF, Gusani NJ, Kimchi ET, Staveley-O\u0026apos;carroll KF, Zheng S-Y \u0026amp; El-Deiry WS (2015) Circulating tumor cell isolation during resection of colorectal cancer lung and liver metastases: a prospective trial with different detection techniques. Cancer Biol Ther 16:699-708.https://doi.org/10.1080/15384047.2015.1030556\u003c/li\u003e\n \u003cli\u003eKe J, Han W, Meng F, Guo F, Wang Y \u0026amp; Wang L (2021) CTI-2 Inhibits Metastasis and Epithelial-Mesenchymal Transition of Breast Cancer Cells by Modulating MAPK Signaling Pathway. Int J Mol Sci 22.https://doi.org/10.3390/ijms222212229\u003c/li\u003e\n \u003cli\u003eKotsantis P, Petermann E \u0026amp; Boulton SJ (2018) Mechanisms of Oncogene-Induced Replication Stress: Jigsaw Falling into Place. Cancer Discov 8:537-555.https://doi.org/10.1158/2159-8290.CD-17-1461\u003c/li\u003e\n \u003cli\u003eL\u0026aacute;nczky A \u0026amp; Győrffy B (2021) Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation. J Med Internet Res 23:e27633.https://doi.org/10.2196/27633\u003c/li\u003e\n \u003cli\u003eLi P, Qian J, Yu G, Chen Y, Liu K, Li J \u0026amp; Wang J (2012) Down-regulated SPARCL1 is associated with clinical significance in human gastric cancer. J Surg Oncol 105:31-37.https://doi.org/10.1002/jso.22025\u003c/li\u003e\n \u003cli\u003eL\u0026oacute;pez-Knowles E, O\u0026apos;toole SA, Mcneil CM, Millar EKA, Qiu MR, Crea P, Daly RJ, Musgrove EA \u0026amp; Sutherland RL (2010) PI3K pathway activation in breast cancer is associated with the basal-like phenotype and cancer-specific mortality. Int J Cancer 126:1121-1131.https://doi.org/10.1002/ijc.24831\u003c/li\u003e\n \u003cli\u003eMcbryan J, Fagan A, Mccartan D, Bane FT, Vare\u0026scaron;lija D, Cocchiglia S, Byrne C, Bolger J, Mcilroy M, Hudson L, Tibbitts P, Gaora P\u0026Oacute;, Hill AD \u0026amp; Young LS (2015) Transcriptomic Profiling of Sequential Tumors from Breast Cancer Patients Provides a Global View of Metastatic Expression Changes Following Endocrine Therapy. Clin Cancer Res 21:5371-5379.https://doi.org/10.1158/1078-0432.CCR-14-2155\u003c/li\u003e\n \u003cli\u003eMiller KD, Nogueira L, Mariotto AB, Rowland JH, Yabroff KR, Alfano CM, Jemal A, Kramer JL \u0026amp; Siegel RL (2019) Cancer treatment and survivorship statistics, 2019. CA Cancer J Clin 69:363-385.https://doi.org/10.3322/caac.21565\u003c/li\u003e\n \u003cli\u003eRitchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W \u0026amp; Smyth GK (2015) limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 43:e47.https://doi.org/10.1093/nar/gkv007\u003c/li\u003e\n \u003cli\u003eShannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B \u0026amp; Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498-2504\u003c/li\u003e\n \u003cli\u003eShen C, Yin Y, Chen H, Wang R, Yin X, Cai Z, Zhang B, Chen Z \u0026amp; Zhou Z (2018) Secreted protein acidic and rich in cysteine-like 1 suppresses metastasis in gastric stromal tumors. BMC Gastroenterol 18:105.https://doi.org/10.1186/s12876-018-0833-8\u003c/li\u003e\n \u003cli\u003eShen J, Song R, Chow W-H \u0026amp; Zhao H (2020) Homologous recombination repair capacity in peripheral blood lymphocytes and breast cancer risk. Carcinogenesis 41:1363-1367.https://doi.org/10.1093/carcin/bgaa081\u003c/li\u003e\n \u003cli\u003eSinn BV, Fu C, Lau R, Litton J, Tsai T-H, Murthy R, Tam A, Andreopoulou E, Gong Y, Murthy R, Gould R, Zhang Y, King TA, Viale A, Andrade V, Giri D, Salgado R, Laios I, Sotiriou C, Marginean EC, Kwiatkowski DN, Layman RM, Booser D, Hatzis C, Vicente Valero V \u0026amp; Fraser Symmans W (2019) SET: a robust 18-gene predictor for sensitivity to endocrine therapy for metastatic breast cancer. NPJ Breast Cancer 5:16.https://doi.org/10.1038/s41523-019-0111-0\u003c/li\u003e\n \u003cli\u003eSubramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES \u0026amp; Mesirov JP (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102:15545-15550\u003c/li\u003e\n \u003cli\u003eSzklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, Doncheva NT, Legeay M, Fang T, Bork P, Jensen LJ \u0026amp; Von Mering C (2021) The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res 49:D605-D612.https://doi.org/10.1093/nar/gkaa1074\u003c/li\u003e\n \u003cli\u003eTang Z, Li C, Kang B, Gao G, Li C \u0026amp; Zhang Z (2017) GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 45.https://doi.org/10.1093/nar/gkx247\u003c/li\u003e\n \u003cli\u003eTao Z, Shi A, Li R, Wang Y, Wang X \u0026amp; Zhao J (2017) Microarray bioinformatics in cancer- a review. J BUON 22:838-843\u003c/li\u003e\n \u003cli\u003eTelli ML, Stover DG, Loi S, Aparicio S, Carey LA, Domchek SM, Newman L, Sledge GW \u0026amp; Winer EP (2018) Homologous recombination deficiency and host anti-tumor immunity in triple-negative breast cancer. Breast cancer research and treatment 171:21-31.https://doi.org/10.1007/s10549-018-4807-x\u003c/li\u003e\n \u003cli\u003eWeis SM \u0026amp; Cheresh DA (2011) Tumor angiogenesis: molecular pathways and therapeutic targets. Nat Med 17:1359-1370.https://doi.org/10.1038/nm.2537\u003c/li\u003e\n \u003cli\u003eXiang Y, Qiu Q, Jiang M, Jin R, Lehmann BD, Strand DW, Jovanovic B, Degraff DJ, Zheng Y, Yousif DA, Simmons CQ, Case TC, Yi J, Cates JM, Virostko J, He X, Jin X, Hayward SW, Matusik RJ, George AL \u0026amp; Yi Y (2013) SPARCL1 suppresses metastasis in prostate cancer. Mol Oncol 7:1019-1030.https://doi.org/10.1016/j.molonc.2013.07.008\u003c/li\u003e\n \u003cli\u003eXie T, Wu D, Li S, Li X, Wang L, Lu Y, Song Q, Sun X \u0026amp; Wang X (2020) microRNA-582 Potentiates Liver and Lung Metastasis of Gastric Carcinoma Cells Through the FOXO3-Mediated PI3K/Akt/Snail Pathway. Cancer Manag Res 12:5201-5212.https://doi.org/10.2147/CMAR.S245674\u003c/li\u003e\n \u003cli\u003eYe H, Wang W-G, Cao J \u0026amp; Hu X-C (2017) SPARCL1 suppresses cell migration and invasion in renal cell carcinoma. Mol Med Rep 16:7784-7790.https://doi.org/10.3892/mmr.2017.7535\u003c/li\u003e\n \u003cli\u003eYu G, Wang L-G, Han Y \u0026amp; He Q-Y (2012) clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16:284-287.https://doi.org/10.1089/omi.2011.0118\u003c/li\u003e\n \u003cli\u003eYuan L, Zhou M, Wasan HS, Zhang K, Li Z, Guo K, Shen F, Shen M \u0026amp; Ruan S (2019) Jiedu Sangen Decoction Inhibits the Invasion and Metastasis of Colorectal Cancer Cells by Regulating EMT through the Hippo Signaling Pathway. Evid Based Complement Alternat Med 2019:1431726.https://doi.org/10.1155/2019/1431726\u003c/li\u003e\n \u003cli\u003eZhang H-P, Wu J, Liu Z-F, Gao J-W \u0026amp; Li S-Y (2022) SPARCL1 Is a Novel Prognostic Biomarker and Correlates with Tumor Microenvironment in Colorectal Cancer. Biomed Res Int 2022:1398268.https://doi.org/10.1155/2022/1398268\u003c/li\u003e\n \u003cli\u003eZhang K-L, Zhu W-W, Wang S-H, Gao C, Pan J-J, Du Z-G, Lu L, Jia H-L, Dong Q-Z, Chen J-H, Lu M \u0026amp; Qin L-X (2021) Organ-specific cholesterol metabolic aberration fuels liver metastasis of colorectal cancer. Theranostics 11:6560-6572.https://doi.org/10.7150/thno.55609\u003c/li\u003e\n \u003cli\u003eZhang T, Yuan K, Wang Y, Xu M, Cai S, Chen C \u0026amp; Ma J (2021) Identification of Candidate Biomarkers and Prognostic Analysis in Colorectal Cancer Liver Metastases. Frontiers in oncology 11:652354.https://doi.org/10.3389/fonc.2021.652354\u003c/li\u003e\n \u003cli\u003eZhao H-Y, Gong Y, Ye F-G, Ling H \u0026amp; Hu X (2018) Incidence and prognostic factors of patients with synchronous liver metastases upon initial diagnosis of breast cancer: a population-based study. Cancer Manag Res 10:5937-5950.https://doi.org/10.2147/CMAR.S178395\u003c/li\u003e\n \u003cli\u003eZhao SJ, Jiang YQ, Xu NW, Li Q, Zhang Q, Wang SY, Li J, Wang YH, Zhang YL, Jiang SH, Wang YJ, Huang YJ, Zhang XX, Tian GA, Zhang CC, Lv YY, Dai M, Liu F, Zhang R, Zhou D \u0026amp; Zhang ZG (2018) SPARCL1 suppresses osteosarcoma metastasis and recruits macrophages by activation of canonical WNT/\u0026beta;-catenin signaling through stabilization of the WNT-receptor complex. Oncogene 37:1049-1061.https://doi.org/10.1038/onc.2017.403\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"breast cancer, liver metastasis, SPARCL1, bioinformatics ","lastPublishedDoi":"10.21203/rs.3.rs-2183292/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2183292/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e The liver is currently the third most common metastatic site for advanced breast cancer (BC), and liver metastases predict poor prognoses. However, the characterized biomarkers and mechanisms underlying liver metastasis in BC remain unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The GSE124648 dataset was used to identify differentially expressed genes (DEGs) between BC and liver metastases. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted to annotate these DEGs and understand the biological functions they are involved in. A protein–protein interaction (PPI) network was constructed to identify hub genes. Clinicopathological correlation of hub gene expression in patients with BC was determined. Gene set enrichment analysis (GSEA) was performed to explore DEG-related signaling pathways. \u003cem\u003eSPARCL1\u003c/em\u003eexpression in BC tissues and cell lines was verified (RT-qPCR). \u003cem\u003eSPARCL1 \u003c/em\u003eknockdown was performed using siRNAs; its biological function in BC cells was then investigated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e We identified 332 liver metastasis-related DEGs from GSE124648 and 30 hub genes, including \u003cem\u003eSPARCL1\u003c/em\u003e, from the PPI network. \u003cem\u003eSPARCL1\u003c/em\u003ewas related to patient prognosis, and its expression in BC was associated with age, TNM stage, estrogen receptor (ER) status, progesterone receptor (PR) status, histological type, molecular type, and living status of patients. GSEA results suggested that low \u003cem\u003eSPARCL1\u003c/em\u003e expression in BC was related to the cell cycle, DNA replication, oxidative phosphorylation, and homologous recombination. \u003cem\u003eIn vitro\u003c/em\u003e \u003cem\u003eSPARCL1 \u003c/em\u003einhibition promoted BC cell proliferation and migration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e We identified \u003cem\u003eSPARCL1\u003c/em\u003e as a tumor suppressor in BC, which shows potential as a target for BC and liver metastasis therapy and diagnosis.\u003c/p\u003e","manuscriptTitle":"Identifying liver metastasis-related hub genes in breast cancer and characterizing SPARCL1 as a potential prognostic biomarker","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-10-24 15:44:41","doi":"10.21203/rs.3.rs-2183292/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"74c43c1a-0110-45fd-ab92-e96d12a419f5","owner":[],"postedDate":"October 24th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-10-24T15:44:43+00:00","versionOfRecord":[],"versionCreatedAt":"2022-10-24 15:44:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2183292","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2183292","identity":"rs-2183292","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}