Integrated bioinformatics analysis reveals ISG15, IFIH1, OAS2, MX1, and CXCL10 as predictive biomarkers of neoadjuvant chemotherapy response in triple-negative breast cancer

Integrated bioinformatics analysis reveals ISG15, IFIH1, OAS2, MX1, and CXCL10 as predictive biomarkers of neoadjuvant chemotherapy response in triple-negative breast cancer | 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 Integrated bioinformatics analysis reveals ISG15, IFIH1, OAS2, MX1, and CXCL10 as predictive biomarkers of neoadjuvant chemotherapy response in triple-negative breast cancer Danial Ahdi, Naser Elmi, Sima Mansoori Derakhshan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8937161/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 Background: The prognosis for triple-negative breast cancer (TNBC), an aggressive subtype of breast cancer, is challenging, and there are few available treatments. Despite neoadjuvant chemotherapy (NAC) being the prevalent treatment, it has a wide range of side effects. Hence, identifying predictive indicators of NAC response could enhance treatment selection and outcomes. Methods: We employed a standard bioinformatics technique to analyze the RNA-seq data from GSE260989 on a Linux system. After trimming and quality assessment, the reads were aligned to the GRCh38 reference genome, yielding gene-level counts. We employed DESeq2 to analyze expression differences and utilized WGCNA to identify co-expression modules associated with the NAC response. Functional enrichment analyses (KEGG, Reactome) and protein–protein interaction studies were performed to identify key pathways. Hub genes were ranked based on their topological scores within the PPI network. Results: The pre- and post-NAC TNBC samples exhibited 1023 genes that were either up- or down-regulated (padj < 0.05). The strongest association between treatment response and the turquoise module was observed. Five hub genes—ISG15, IFIH1, OAS2, MX1, and CXCL10—linked to interferon signaling, immune modulation, and chemotherapy resistance, were identified through combined network and enrichment analyses. After NAC treatment, all five genes showed consistent downregulation, suggesting increased chemosensitivity and a shift toward a less aggressive tumor phenotype. Conclusion: ISG15 , IFIH1 , OAS2 , MX1 , and CXCL10 are identified as putative predictive biomarkers of NAC response in TNBC by this integrative bioinformatics research. Better treatment sensitivity may be reflected in their coordinated downregulation, warranting additional verification in larger clinical cohorts. RNA-Seq Next Generation Sequencing Breast Cancer WGCA Neoadjuvant Chemotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Breast cancer is the most common type of cancer in the world and refers to a variety of malignancies that develop in the mammary glands ( 1 ). Every year, 55,000 (15%) new cases of cancer are caused by breast cancer in the UK {ONS. Breast Cancer: Incidence, #105}. Women are disproportionately impacted, and the incidence rises with age; over 80% of breast cancer diagnoses occur in women over 50. In 2020, breast cancer accounted for 685,000 deaths worldwide, making it the top cause of cancer-related deaths ( 1 , 2 ). Breast cancer is a heterogeneous disease with multiple subtypes, each exhibiting distinct epidemiological features ( 3 ). In 2013, a new way to classify molecular subtypes of breast cancer was revealed. It included several groups, such as luminal A, luminal B, HER2, HER2 overexpression, basal-like, Triple Negative Breast Cancer (TNBC), and other specific subtypes ( 4 ). Triple-negative breast cancer (TNBC) is defined by the lack of estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptor-2 (HER2) ( 5 ). Epidemiological studies show that TNBC mainly affects women under 40 who are not yet pregnant. This group makes up about 15–20% of all breast cancer incidences. Patients with triple-negative breast cancer (TNBC) had a 40% chance of dying within the first five years of diagnosis and lived less time than patients with other forms of breast cancer. And about 46% of TNBC patients will experience distant metastases, and the disease is extremely aggressive ( 6 – 8 ). Neoadjuvant chemotherapy (NAC) is a type of treatment that is administered before surgery, as opposed to conventional treatments, and is of high clinical value in locally advanced and inoperable breast cancer ( 9 , 10 ). Neoadjuvant therapy has three primary objectives: enhancing surgical options, achieving operability in primary tumors that cannot be operated on, and enhancing surgical options in primary cancers that can be operated on; improving outcome by achieving pathological complete response (pCR); and gathering response data at mid-course, which may aid in further modifying therapy ( 11 ). For BC patients, NAC is a crucial treatment approach that aims to increase pCR by downstaging the tumor and tracking treatment response for prognostic purposes ( 12 ). For TNBC patients, endocrine or HER2-targeted drugs are ineffective because there are no relevant receptor markers. When given standard chemotherapy therapies such as anthracyclines or taxanes, TNBC is the subtype that responds the best. Compared to non-TNBC subtypes, TNBC still has higher death and recurrence rates, and fewer than 30% of patients fully recover. For people with TNBC, chemotherapy is the most popular treatment. Despite the more aggressive clinical presentation of TNBC, with neoadjuvant chemotherapy, around 30 to 40 percent of patients achieve a pCR with no histological evidence of illness at the time of surgery. Additionally, the survival rate for these patients is significantly higher ( 13 – 15 ). Based on epidemiological, therapeutic, pathophysiological, or other scientific evidence, biomarkers are objectively measured indicators of pathogenic processes, natural biological processes, or drug responses to a therapeutic intervention. They serve a variety of purposes and are intended to replace a clinical endpoint that predicts benefit or harm. Targeted drugs have drastically changed how cancer is managed and reduced costs, which has increased the need to evaluate predictive biomarkers to monitor treatment effectiveness ( 11 , 16 ). In many recent studies, researchers have faced a significant hurdle in identifying molecular biomarkers that can predict chemosensitivity and stratify individuals likely to benefit from NAC in clinical practice. Furthermore, molecular biomarkers can help assess the attainment of pCR in patients with breast cancer who do not respond to NAC, and they may be essential in preventing needless treatments and related toxicities ( 17 ). Tumors with identical histologies may have distinct prognoses and reactions to treatment. Following NAC, some molecular subtypes of breast cancer may experience high rates of pCR, but other subtypes might not benefit as much from the same treatment. Predictive biomarkers are therefore crucial for identifying patients who are unlikely to benefit from NAC, which makes it easier to create innovative treatment plans for them ( 18 ). In the process of finding new biomarkers, bioinformatics is essential because it bridges the gap between the first stages of discovery, like designing experiments and conducting clinical studies, and bioanalytics, which includes high-throughput profiling, sample preparation, separation, and independent validation of candidate biomarkers that have been found ( 19 ). In this investigation, we analyzed RNA-seq data from people with TNBC who underwent neoadjuvant therapy. We aimed to identify potential biomarkers for predicting therapy response in TNBC patients by integrating differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein–protein interaction (PPI) network construction, ultimately leading to hub gene identification. The purpose of this study was to use integrative bioinformatics methods to identify potential biomarkers associated with NAC response in TNBC. Materials and Methods 2.1 Data Collection and Preprocessing The Gene Expression Omnibus (GEO) ( 20 ) The database provided the RNA-seq dataset GSE260989 ( 21 ). Patients with TNBC, the availability of paired pre- and post-neoadjuvant therapy samples, neoadjuvant therapy treatment, and female sex were the criteria used to select the samples. This dataset includes 14 samples, with an equal number of pre- and post-neoadjuvant therapy samples. 2.2 Processing Raw Data in a Linux Environment To perform effective and repeatable RNA-seq data analysis, all preparation procedures were conducted in a Linux Ubuntu (version 24.04) environment. The SRA Toolkit ( 22 ) It was utilized to convert SRA files into FASTQ format for further analysis and to retrieve RNA-seq data from the GEO database. FASTQC (Version 0.12.0) ( 23 ) It was used to evaluate sequencing quality before alignment and to perform quality control on FASTQ files. Trimmomatic ( 24 ) Was employed to improve read quality by removing adaptor sequences and trimming low-quality bases. Additionally, the files are re-examined with FASTQC to determine whether Trimmomatic has resolved the issues. SAMtools ( 25 ) It was applied to manage alignments, including sorting, indexing, and converting SAM to BAM, to prepare data for read quantification. Finally, HTSeq-count ( 26 ) It was used to generate input data for differential expression analysis by measuring the number of reads mapped to each gene and creating a count matrix. It is worth mentioning that we used the human reference genome GRCh38 build for our analysis. 2.3. Differential Expression Analysis RStudio (R version 4.4.2) was used to import the count files created in the Linux environment. The DESeq2 package ( 27 ), which models count data using the negative binomial distribution, was used for differential expression analysis. The org.. Hs.eg.db ( 28 ) and AnnotationDbi ( 29 ) Packages were used to connect gene identifiers to official gene symbols and to annotate genes functionally with biological annotations. The ggplot2 ( 30 ) It was used to create boxplots and principal component analysis (PCA) plots to evaluate normalization and sample distribution. Only genes exhibiting an adjusted p-value 1 were deemed statistically significant and physiologically pertinent, and were included for subsequent investigation. 2.4 Weighted Gene Co-expression Network Analysis (WGCNA) The WGCNA ( 31 ). The package was used in RStudio to reconstruct co-expression networks and identify modules. Once a scale-free topology was achieved by choosing a suitable soft-thresholding power (β = 6), an adjacency matrix was created and converted into a topological overlap matrix (TOM). Gene modules were found using the dynamicTreeCut package and hierarchical clustering. To identify biologically relevant modules, module eigengenes were computed, and their relationships with clinical characteristics were evaluated. 2.5 Protein-Protein Interaction (PPI) Network Construction The protein–protein interaction (PPI) network was constructed by importing the commonly differentially expressed genes (DEGs) into the STRING database ( 32 ), considering only interactions supported by evidence from databases and experiments. Cytoscape software (version 3.10.3) ( 33 ) It was used to visualize and analyze the generated PPI network, identifying hub genes and significant clusters by analyzing network topological properties. Additionally, the CytoHubba plugin (version 0.1) ( 33 ) It was employed to identify hub genes by applying various ranking algorithms, such as Degree, MCC, and MNC, with hub candidates being genes that consistently achieved high scores across multiple techniques. 2.6 Functional Enrichment Analysis Using the Enrichr web service, we performed KEGG pathway enrichment analysis to identify biological processes associated with differentially expressed genes. Enriched pathways were those with adjusted p-values below the significance threshold. Additionally, Reactome pathway enrichment analysis was performed using Enrichr to examine the functional roles of the identified genes in cellular signaling and biological processes. 2.7 Fisher’s Exact Test We used Fisher's exact test, available in the stats package in R, to determine whether the overlap between genes in enriched KEGG pathways and those in enriched Reactome pathways was statistically significant. This research evaluated whether the number of shared genes between the two pathway enrichment results was greater than expected by chance. Results 3.1 Data Acquisition, Quality Control, and Read Processing We obtained raw RNA-seq data for breast cancer patients (GSE260989) from GEO using the SRA Toolkit and converted it to FASTQ format. FastQC's quality check indicated that the overall quality was high, with Q30 scores exceeding 90%. We used Trimmomatic to trim low-quality bases and remove adapter sequences. HIMAT2 was employed to align the cleaned reads to the human reference genome (GRCh38), and SAMtools was used to create and manage BAM files. Using HTSeq-count and Ensembl GTF annotations, we quantified gene expression at the gene level. All the count matrices from the samples were combined and imported into R for further analysis. 3.3 Differential Expression Analysis (DEGs) We used the DESeq2 package in R to identify differentially expressed genes (DEGs) between TNBC samples before and after treatment. If the adjusted p-value (padj) of a gene was less than 0.05, it was considered significant. Three hundred nineteen genes were upregulated (logFC > 1), and 704 genes were downregulated (logFC< -1). We used PCA and boxplots to assess data quality and ensure that normalization was consistent across groups and that samples were grouped consistently (Fig. 1 ). A volcano map was created to show how DEGs were distributed across samples collected before and after treatment. It showed which genes were strongly elevated and downregulated (Fig. 2 ). 3.4 Weighted Gene Co-expression Network Analysis (WGCNA) Fourteen co-expression modules were identified in the study, each represented by a different color (Fig. 3 ). Topology was achieved by choosing a suitable soft-thresholding power (β = 6). Among them, the turquoise module showed the strongest association with the clinical characteristic (TNBC status before and after treatment) (correlation coefficient = 0.97994008, p-value = 9.010013e-10). As a result, this module was chosen as the main one for further analysis. To obtain 1206 overlapping genes, considered promising candidates for biomarker discovery, the genes from this key module were intersected with the previously identified DEGs. Figure 4 displays a Venn diagram illustrating this overlap. 3.5 Identification of Common Genes An intersection analysis between the main WGCNA module (turquoise) and differentially expressed genes (DEGs) was performed to identify candidate genes linked to the trait of interest. Ensembl ID, Gene Symbol, log2FoldChange, adjusted p-value (padj), and module membership (kME) were all included in the combined dataset. Duplicate genes were removed, as were those without annotation or marked as "Not found." To select genes associated with our trait and showing significantly altered expression levels, we used a combined criterion. We retained genes with absolute(logFC) > 1 and padj 0.8. As a result, 503 genes that were deemed both statistically significant and highly connected within the main module were identified through this filtering. These selected genes will be used in subsequent analyses to pinpoint key genes. 3.6 Functional Enrichment Analysis and Overlap Significance KEGG and Reactome Enrichment : With an emphasis on the KEGG and Reactome pathways (Fig. 5 ), the filtered intersection genes were examined for pathway enrichment using the Enrichr website. Pathways were considered significant only if their adjusted p-value (padj) was less than 0.05. The list of genes contributing to each pathway was documented for each pathway database. Overlap Between KEGG and Reactome Genes : Genes that contribute to significant KEGG and Reactome pathways have been identified to overlap. This overlapping group represents genes that are consistently implicated in biologically significant pathways across both databases. Statistical Significance of Overlap : Fisher's exact test in R showed that the overlap between important genes in KEGG and Reactome studies (adjusted p < 0.05) was highly significant (p < 2.2 × 10⁻¹⁶; OR = 1104.6; 95% CI = 445.4–2915.3), suggesting substantial biological relevance rather than random chance. 3.7 Protein–Protein Interaction (PPI) Network and Hub Gene Analysis PPI Network Construction : To create a protein–protein interaction network, the filtered genes from the previous step were uploaded to STRING and subsequently imported into Cytoscape. Initial Hub Analysis Using Cytoscape Analyze Topology Tools (Degree) : First, Degree was used to evaluate the PPI network using Cytoscape's evaluate Topology Tools. The hub genes found by Degree in this stage are shown in Fig. 3.6 and are compiled in Table 3.1. This analysis gave an early overview of the network's highly linked nodes. The top 10 Genes by Degree are presented in Table 1 . Table 1 The ten genes with the highest Degree, encompassing factors of stress and neighborhood connectedness, were analyzed, highlighting their significance beyond Degree. Gene name Degree Neighborhood Connectivity Stress ISG15 22 17.22 148 IFIH1 22 17.54 142 OAS2 21 17.95 102 MX1 21 17.95 102 CXCL10 21 17.71 144 IFI16 20 17.75 96 RSAD2 20 18.6 68 OAS1 20 18.6 68 IFIT1 20 18.6 68 The network employed four algorithms from the CytoHubba plugin—MCC (Maximal Clique Centrality), Bottleneck, Degree, and Closeness—to systematically analyze the data and identify hub genes. The results were then cross-validated using the original Degree analysis in Cytoscape. All necessary tables and figures are included in the supplementary material. 3.8 Final Candidate Hub Genes After extensive research, including DEG identification, WGCNA, filtering for significantly enriched biological pathways, and network architecture analysis, five genes emerged as strong candidates: ISG15, IFIH1, OAS2, MX1, and CXCL10. These genes consistently met all criteria, including high log2 fold changes, prominent roles in the main WGCNA turquoise module, involvement in biologically significant pathways, and frequent ranking among the top 10 genes across several network analysis metrics. Table 2 presents the key traits and supporting data used to select these genes as potential biomarker candidates. Table 2 lists the key characteristics of the five potential hub genes identified in this study. While OAS2, MX1, and CXCL10 were not among the top 10 genes based on the Bottleneck algorithm, they were included in the final five genes due to their significant roles in other centrality measures, strong membership in the turquoise WGCNA module, and notable log2 fold change values. Rank Gene name Degree Rank Bottle neck MCC closeness Log Fold Change 1 ISG15 1 2 1 1 -4.933117622 2 IFIH1 1 2 2 1 -3.507490843 3 OAS2 3 Null 3 3 -3.802113706 4 MX1 3 Null 3 3 -4.661680706 5 CXCL10 3 Null 8 3 -8.673982894 3.9 DEG plots To enable a more precise observation of expression changes that may be less noticeable in raw data tables, plots were made to graphically illustrate the differential expression of the genes CXCL10 , IFIH1, ISG15, MX1 , and OAS2 before (pre-treatment) and after (post-treatment) therapy. These illustrations effectively highlight variations in gene expression patterns. Mean Gene Expression Before and After Treatment The mean expression levels of the genes CXCL10, IFIH1, ISG15, MX1 , and OAS2 are shown in this bar plot before Fig. 3 –11 (pre-treatment, green) and after (post-treatment, orange) treatment. Expression values are shown as Log2(CPM). To better illustrate average expression changes and their variability, a plot was created that highlighted differences between the pre- and post-treatment groups. Discussion Breast cancer is a significant public health issue worldwide, being one of the most common and deadly. Despite advances in treatment, it causes significant morbidity and mortality ( 39 – 41 ). Clinically, TNBCs are aggressive. More common in younger women, these tumors account for 15–20% of breast cancer incidences. 46% of TNBC patients develop distant metastases, making it an aggressive breast cancer subtype ( 8 , 42 , 43 ). NAC is essential for BC patients to improve pCR by lowering staging and monitoring therapy response for prognosis. In locally advanced breast cancer, neoadjuvant chemotherapy is beneficial ( 9 , 12 ). Regardless of therapy, a prognostic biomarker predicts cancer progression. If a biomarker is predictive and a treatment is effective, patients benefit and may save time and finances while making treatment selection smoother ( 44 ). Bioinformatics techniques, such as those used to analyze gene sequence data for various diseases to detect DEGs, advanced genome sequencing methods, more reliable databases, and improved online tools, have made understanding the complex mechanisms of known diseases easier ( 45 ). In this context, we performed an integrative bioinformatics analysis to identify potential biomarkers associated with NAC response in TNBC. By controlling interferon signaling or binding viral proteins to inhibit replication, the ubiquitin-like protein ISG15 functions as an antiviral agent in the immune system ( 46 , 47 ). ISG15 uses an enzymatic cascade that involves many enzymes to conjugate with target proteins and control protein homeostasis ( 48 ). ISGylation is crucial for blocking protein translation, either by blocking eIF2α or by blocking dsRNA-dependent protein kinase ( 49 ). While ISG15 and ISGylation are most known for their ability to combat viruses, it has been shown that ISG15 contributes significantly to the tumor environment by increasing the cytokines of T cells and B cells ( 48 , 50 , 51 ).Melanoma and lung, breast, prostate, and hepatic cancers are among the many cancer forms that have been shown to have elevated levels of ISG15 ( 48 , 52 ). Compared to normal tissue, breast cancer tissue shows an upregulation of ISG15 , which promotes breast cell deformation, enhances breast cancer cell motility, and stabilizes important cellular proteins involved in cell migration and metastasis ( 52 – 54 ). By investigating the role of this gene in breast cancer, Feiran Wang and colleagues found that ISG15 plays a significant role in TNBC. They demonstrated that this gene was more highly expressed in patients with metastatic disease compared to those without metastases ( 55 ). The function of ISG15 in chemotherapy has been the subject of numerous investigations. One study found that CPT sensitivity was linked to ISG15 and its components' expression in multiple groups of breast cancer cell lines ( 56 ). According to a different study on breast cancer, a set of genes, including ISG15 , is linked to treatment resistance, and their lower expression makes TNBC cells more sensitive to chemotherapy and radiation therapy. This emphasizes the significance of this gene as a prospective biomarker in cancer, and its exploration holds promise for enhancing therapy efficacy ( 57 , 58 ). In contrast to prior studies, reduced ISG15 expression decreases the responsiveness of breast cancer cells to camptothecin. In contrast, it is higher in irinotecan-sensitive gastric cancer tumors than in those resistant to irinotecan. ( 59 , 60 ). According to another study, ISG15 generally plays two roles in TNBC. Increased expression has been connected to tumor growth and metastasis ( 61 , 62 ).Our in-silico study showed that post-treatment samples had lower ISG15 expression compared to pre-treatment samples. The decrease in ISG15 might be related to the NAC response in TNBC; however, further functional validation is needed to confirm this relationship, as previous studies have suggested that reduced ISG15 levels may be associated with increased susceptibility to chemotherapy; this hypothesis remains in its early stages ( 57 , 58 ). When taken together, these results imply that ISG15 may serve as a context-dependent regulator in TNBC, and its expression pattern could have predictive value; nevertheless, its exact role requires additional mechanistic studies. Studies have yielded conflicting results; nonetheless, this suggests that larger populations and more thorough research are needed to improve the reliability of the findings. The IFIH1 (also known as MDA5) gene encodes a cytoplasmic receptor that detects viral double-stranded RNA to initiate type I interferon signaling. This can affect cellular immune responses ( 63 ).Beyond its essential immunological role in defending against infections, the IFIH1 gene also plays an important part in cancer development. It is markedly overexpressed in various cancers, including breast and testicular tumors, with research on testicular cancer showing differential expression. IFIH1's dual role in cancer biology and its potential as a therapeutic target are emphasized by its strong connection to apoptosis and tumor-related immune processes. ( 64 , 65 ). The role of IFIH1 in breast cancer is complex. According to Lan et al., it has two functions: it mediates type I interferon signaling, which may boost anticancer immunity or, depending on the context, promote chronic inflammation and tumor growth. The results suggest that IFIH1 may significantly influence breast cancer biology, however the precise function needs more clarification (66). IFIH1 is constitutively active in TNBC, leading to type I interferon production and the expression of related genes, including ISG15. This process makes cancer cells more resistant to DNA damage caused by PARP inhibitors or chemotherapy. (67). Furthermore, IFIH1 is expressed at significantly higher levels in TNBC cells. IFIH1 promotes apoptosis and increases PD-L1 expression, suggesting it could be a promising therapeutic target in TNBC and a predictive biomarker of treatment response ( 65 ).Elevated IFIH1 expression has also been observed in drug-resistant ovarian cancer, linking it to chemotherapy resistance via interferon pathway activation (68, 69). In TNBC, IFIH1 is expressed in lymphocytes, fibroblasts, and non-pCR cancer cells, unlike in cells from pCR patients, indicating that reduced pathway activation may contribute to chemotherapy sensitivity (70, 71). Our findings support previous reports that IFIH1 promotes inflammatory signaling and resistance to PARP inhibitors and DNA-damaging drugs, as shown by decreased IFIH1 expression in TNBC. By disrupting these pathways, targeting IFIH1 increases cancer cell sensitivity to olaparib and carboplatin, highlighting its key role in treatment resistance (67). A study by Hu et al. found that the IFIH1 gene influences TNBC treatment response. Additionally, blocking eIF4A triggers a strong interferon response that, when combined with chemotherapy, diminishes TNBC's effects. Interestingly, IFIH1 is crucial for this reaction, promoting proteogenomic processes that give TNBC cells resistance to chemotherapy (72). Our bioinformatic analysis revealed reduced IFIH1 expression following therapy, possibly associated with NAC response; however, more experimental validation is required. Members of the 2–5'-oligoadenylate synthetase ( OAS ) family are antiviral enzymes activated by interferon (IFN). The OAS family is essential for host defense against viral infections (73, 74). One of the interferon-stimulated antiviral genes is OAS2 (75). Numerous illnesses and disorders, including inflammation, are linked to OAS2 , such as cancerous disorders and autoimmune disorders (76, 77). Research indicates that the OAS family has been associated with cancers, especially breast cancer. In 1986, Liu and colleagues identified a potential connection between OAS activity and tumor development in human breast tumors (78). This indicates that there has been a longstanding discussion about this gene family's role in breast cancer. Unlike OAS1 and OAS3, which are linked to poorer outcomes, OAS2 has been associated with a better prognosis in breast cancer among the OAS family members, according to a study by Yujie Zhang and his colleagues. (79). OAS2 expression decreased with NAC, potentially attributable to alterations in therapy. However, we cannot definitively assert that this is connected to the reaction until we undertake research. Unlike previous research, another study found higher OAS2 levels in breast cancer patients. Although patients didn't show mutations in the OAS family, OAS2 was mutated in 1% of samples, indicating a more significant role for this gene (80). Biological databases confirm increased expression of this gene, with OAS2 significantly elevated in various cancers, especially breast cancer, based on TCGA and UALCAN data. (81).Additionally, the OAS2 gene can be used as a predictor of immunotherapy and chemotherapy resistance (81) A study on trastuzumab-resistant gastric cancer highlighted the importance of the OAS gene family, including OAS2, through network analysis, emphasizing their key role in treatment resistance. (82). Another study on pan-cancer showed a positive correlation between itraconazole drug sensitivity and OAS2 and OASL expression. (83). Additionally, other studies have also highlighted the role of the OAS2 gene and its influence on neoadjuvant therapy (84–86). Prior studies have indicated a correlation between this gene and response to neoadjuvant therapy; however, conclusive data remain lacking. OAS2 is highly expressed in inflammatory TNBC subpopulations and linked to chemoresistance and poor relapse-free survival, indicating a role in post-treatment resistance, according to Mauricio Jacobo and colleagues. (87). OAS2 may indicate the effectiveness of neoadjuvant immunochemotherapy in ESCC. A study showed that patients with pCR had higher OAS2 levels. It highlights the significance of OAS2 in treatment (88).These findings suggest that OAS2 has a complex role in breast cancer. We observed a decrease in OAS2 after NAC in TNBC, even though research shows an increase of OAS2 in other cancers linked to immune regulation and resistance. Its downregulation may indicate heightened chemosensitivity, highlighting OAS2's potential as a biomarker and treatment target in TNBC. The MX1 gene, a key host restriction factor in mammals, is activated by type I and III interferons during viral infections (89, 90). MX1 is overexpressed and linked to various cancers. It helps suppress invasiveness and motility in some malignancies, such as melanoma and prostate cancer (91, 92). relating to breast cancer, according to a study, MX1 could promote tumor growth and metastasis (93). Another study found that EGFR and E-cadherin loss, which can lead to enhanced migration and invasion, was substantially correlated with high MX1 protein expression (94). A Further investigation showed that higher levels of the MX1 protein were associated with more aggressive features and shorter breast cancer-specific survival, emphasizing the importance of the MX1 gene as a predictor in invasive breast cancer (95). In a single study, miR-204 and miR-211 were used to investigate the anticancer role of the MX1 gene. These two microRNAs promote breast cancer cell growth by inhibiting MX1 expression. MX1 was identified as an anticancer gene in this study (96).In some breast cancer subtypes, MX1 overexpression links to immune activation, more tumor-infiltrating lymphocytes (TILs), and improved response to anthracycline chemotherapy (97, 98). We observed reduced MX1 expression following NAC; however, the direct relationship between this reduction and treatment response requires additional validation. In PDX models of ER-negative breast cancer, MX1 was significantly increased after a single chemotherapy cycle, but not in resistant models (99). In a study of 109 TNBC patients, short disease-free survival after chemotherapy was associated with high MX1 expression in tumor cells; this association was reduced when MX1 expression was inhibited. MX1 may be a resistance factor in NAC TNBC, as shown in the study (100). Another study found high MX1 levels linked to worse prognosis in women with breast cancer not receiving chemotherapy. Its predictive effect was attenuated in patients who did, indicating that more research is needed to validate MX1 as a biomarker (95).MX1 regulates TNBC depending on context. Although high MX1 is associated with aggressive tumors, poor prognosis, and resistance, our results suggest that decreased MX1 expression after treatment may reflect therapy-related changes; however, its effectiveness as a biomarker requires further validation. More research is needed to clarify its role, as conflicting findings have emerged. Many cell types express CXCL10 , a significant signaling protein that has a role in immune response and host defense against bacterial and viral infections (101, 102). Many human disorders are characterized by elevated CXCL10 expression. It has been demonstrated to be implicated in the pathogenic processes of three significant human illnesses (103). CXCL10 is a chemokine with anti-tumor properties that inhibits angiogenesis. However, advanced human malignancies have also been associated with increased CXCL10 expression (104). The CXCL10 gene significantly influences the development of breast cancer (105). A. Jafarzadeh and colleagues showed that high CXCL10 in BC patients confirms its role in tumor growth, emphasizing CXCL10's importance in breast cancer (106). Ahmed A. Ejaeidi and colleagues concluded that CXCL10 is crucial in breast cancer growth. The study also indicates that HR-negative patients, including those with TNBC, have higher CXCL10 levels, suggesting that CXCL10 may promote TNBC invasion and metastasis. (107). A study found the CXCL10 gene is crucial for TNBC breast cancer, using the GEO database and network analysis. It identified CXCL10 as one of two key biomarkers for TNBC, similar to our study (108). The CXCL10 gene may have two roles in TNBC: it promotes motility and infiltration leading to lung metastasis, and serves as a target for immunotherapy by increasing immune infiltration (109). CXCL10 contributes to breast cancer treatment resistance, especially in chemotherapy escape and tamoxifen resistance in TNBC. In tamoxifen-resistant cells, CXCL10 promotes growth via the AKT pathway. Inhibiting it restores therapy sensitivity, making it a potential target and marker (110). According to a separate study examining the relationship between CXCL10 in TNBC and its treatment, CXCL10 in TNBC converts "cold" tumors into "hot" ones by activating the type I IFN pathway, and the combination of carboplatin and HDACi can improve TNBC's response to NAC by increasing CXCL10 (111). Milim Kim's research demonstrates that CXCL10 can facilitate progression from DCIS to invasive disease by enhancing TNBC aggressiveness and predicting an immunogenic NAC response. It shows that CXCL10 plays two roles: one in tumor growth and the other in immunotherapy (112). CXCL10 has two roles in TNBC: it can make cancers resistant to chemotherapy and tamoxifen by activating the AKT pathway, and it can also enhance the immune system's ability to fight tumors by signaling through type I interferon. This process transforms "cold" tumors into "hot," immune-active tumors. After NAC, CXCL10 expression decreases dramatically (logFC = -8.6), presumably indicating increased treatment sensitivity and suggesting that CXCL10 may serve as a biomarker for predicting NAC response in TNBC; however, further clinical studies are needed to confirm this. Our bioinformatics research identified five key genes—ISG15, IFIH1, OAS2, MX1, and CXCL10—as potential indicators for the NAC response in TNBC. These genes play roles in regulating the immune system, signaling through interferon, and resistance. This highlights the importance of immunological pathways for treatment outcomes. Their downregulation after NAC may indicate a better response and increased sensitivity to chemotherapy. While these findings offer new insights into NAC mechanisms and potential prognostic markers, further validation is necessary. Limitations This study has several limitations that need attention. The analysis relies on a single RNA-seq dataset (GSE260989) with only 14 paired pre- and post-treatment samples. This may cause selection bias and limit how well the identified hub genes (ISG15, IFIH1, OAS2, MX1, and CXCL10) can serve as predictive biomarkers across different TNBC populations, which may vary by ethnicity, tumor stage, or treatment protocols. The findings are solely based on in silico bioinformatics methods and lack experimental validation through laboratory techniques such as quantitative real-time PCR (qRT-PCR), Western blotting, immunohistochemistry, or functional assays in cell lines, patient-derived xenografts, or animal models to explore their mechanistic roles in NAC response. Intrinsic batch effects within the dataset could also affect gene expression patterns and co-expression networks. Future research should include multi-omics integration, large multi-center cohorts, and prospective clinical trials to validate these biomarkers and overcome these limitations. Conclusion In conclusion, five genes, ISG15, IFIH1, OAS2, MX1, and CXCL10, were identified as candidate markers that may be associated with NAC response in TNBC through this integrated bioinformatics study. After treatment, all five genes consistently showed decreased expression, suggesting that downregulation may be related to treatment-associated biological changes; however, further validation is needed to support this interpretation. And a shift toward a less aggressive tumor microenvironment. More experiments are necessary to confirm their clinical relevance and mechanistic roles. However, the study's main limitation was the relatively small sample size. Therefore, further research with larger samples and experimental validation is essential to confirm these findings and better understand the molecular functions of these genes. Declarations Author Contribution Danial Ahdi : Script writer and data analystNaser Elmi : Review of data analysis and script writingSima Mansoori Derakhshan : Supervision of the text and writing of the discussion 66. Acknowledgement of Reviewers of Canadian Journal of Public Health articles CJPHFds—. 67. Yamashita N, Fushimi A, Morimoto Y, Bhattacharya A, Long M, Liu S, et al. Abstract P1-04-09: Essential role for MUC1-C in chronic activation of cytosolic nucleotide sensing and the type I interferon pathway in triple-negative breast cancer. Cancer Research. 2022;82(4_Supplement):P1-04-9-P1–9. 68. Nowacka M G-MB, Świerczewska M, Nowicki M, Zabel M, Sterzyńska K, Januchowski R. The significance of HERC5, IFIH1, SAMD4, SEMA3A and MCTP1 genes expression in resistance to cytotoxic drugs in ovarian cancer cell lines. Medical Journal of Cell Biology. 2021;9(3):138 − 47. References 2022) WHOBchwwin-rf-sdb-caJ. 2022) CRUBcshwcoh-pc-ss-b-c-tb-caJ. Wu J FD, Shao Z, Xu B, Ren G, Jiang Z, Wang Y, Jin F, Zhang J, Zhang Q, Ma F, Ma J, Wang Z, Wang S, Wang X, Wang S, Wang H, Wang T, Wang X, Wang J, Wang J, Wang B, Fu L, Li H, Shi Y, Gan L, Liu Y, Liu J, Liu Z, Liu Q, Sun Q, Cheng W, Yu K, Tong Z, Wu X, Song C, Zhang J, Zhang J, Li J, Li B, Li M, Li H, Yang W, Yang H, Yang B, Bu H, Shen J, Shen Z, Chen Y, Chen C, Pang D, Fan Z, Zheng Y, Yu X, Liu G, Hu X, Ling Y, Tang J, Yin Y, Geng C, Yuan P, Gu Y, Chang C, Cao X, Sheng Y, Huang Y, Huang J, Peng W, Zeng X, Xie Y, Liao N; Committee of Breast Cancer Society, Chinese Anti-Cancer Association. CACA Guidelines for Holistic Integrative Management of Breast Cancer. Holist Integr Oncol. 2022;1(1):7. doi: 10.1007/s44178-022-00007-8. Epub 2022 Jul 5. PMID: 37520336; PMCID: PMC9255514. Goldhirsch A WE, Coates AS, Gelber RD, Piccart-Gebhart M, Thürlimann B, Senn HJ; Panel members. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol. 2013 Sep;24(9):2206-23. doi: 10.1093/annonc/mdt303. Epub 2013 Aug 4. PMID: 23917950; PMCID: PMC3755334. Wolff AC HM, Hicks DG, Dowsett M, McShane LM, Allison KH, Allred DC, Bartlett JM, Bilous M, Fitzgibbons P, Hanna W, Jenkins RB, Mangu PB, Paik S, Perez EA, Press MF, Spears PA, Vance GH, Viale G, Hayes DF; American Society of Clinical Oncology; College of American Pathologists. Recommendations for human epidermal growth factor receptor two testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update. J Clin Oncol. 2013 Nov 1;31(31):3997-4013. doi: 10.1200/JCO.2013.50.9984. Epub 2013 Oct 7. PMID: 24101045. Morris GJ NS, Topham AK, Guiles F, Xu Y, McCue P, Schwartz GF, Park PK, Rosenberg AL, Brill K, Mitchell EP. Differences in breast carcinoma characteristics in newly diagnosed African-American and Caucasian patients: a single-institution compilation compared with the National Cancer Institute's Surveillance, Epidemiology, and End Results database. Cancer. 2007 Aug 15;110(4):876-84. doi: 10.1002/cncr.22836. PMID: 17620276. Dent R TM, Pritchard KI, Hanna WM, Kahn HK, Sawka CA, Lickley LA, Rawlinson E, Sun P, Narod SA. Triple-negative breast cancer: clinical features and patterns of recurrence. Clin Cancer Res. 2007 Aug 1;13(15 Pt 1):4429-34. doi: 10.1158/1078-0432.CCR-06-3045. PMID: 17671126. Lin NU CE, Sohl J, Razzak AR, Arnaout A, Winer EP. Sites of distant recurrence and clinical outcomes in patients with metastatic triple-negative breast cancer: high incidence of central nervous system metastases. Cancer. 2008 Nov 15;113(10):2638-45. doi: 10.1002/cncr.23930. PMID: 18833576; PMCID: PMC2835546. Schegerin M TA, Kaufman PA, Paulsen KD, Pogue BW. Prognostic imaging in neoadjuvant chemotherapy of locally-advanced breast cancer should be cost-effective. Breast Cancer Res Treat. 2009 Apr;114(3):537-47. doi: 10.1007/s10549-008-0025-2. Epub 2008 Apr 25. PMID: 18437559; PMCID: PMC2807135. McElnay P LEAoncfNJTDMSSS-dji-. Denkert C SB, Issa Y, Mueller BM, Maisch A, Untch M, Von Minckwitz G, Loibl S. Prediction of response to neoadjuvant chemotherapy: new biomarker approaches and concepts. Breast Care. 2011 Aug 29;6(4):265-72. Asselain B BW, Bartlett J, Bergh J, Bergsten-Nordström E, Bliss J, Boccardo F, Boddington C, Bogaerts J, Bonadonna G, Bradley R. Long-term outcomes for neoadjuvant versus adjuvant chemotherapy in early breast cancer: meta-analysis of individual patient data from ten randomised trials. The Lancet Oncology. 2018 Jan 1;19(1):27-39. Glück S RJ, Royce M, McKenna EF Jr, Perou CM, Avisar E, Wu L. TP53 genomics predict higher clinical and pathologic tumor response in operable early-stage breast cancer treated with docetaxel-capecitabine ± trastuzumab. Breast Cancer Res Treat. 2012 Apr;132(3):781-91. doi: 10.1007/s10549-011-1412-7. Epub 2011 Mar 4. PMID: 21373875. Liedtke C MC, Hess KR, André F, Tordai A, Mejia JA, Symmans WF, Gonzalez-Angulo AM, Hennessy B, Green M, Cristofanilli M, Hortobagyi GN, Pusztai L. Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. J Clin Oncol. 2008 Mar 10;26(8):1275-81. doi: 10.1200/JCO.2007.14.4147. Epub 2008 Feb 4. PMID: 18250347. Garrido-Castro AC LN, Polyak K. Insights into Molecular Classifications of Triple-Negative Breast Cancer: Improving Patient Selection for Treatment. Cancer Discov. 2019 Feb;9(2):176-198. doi: 10.1158/2159-8290.CD-18-1177. Epub 2019 Jan 24. PMID: 30679171; PMCID: PMC6387871. Biomarkers Definitions Working Group AJA, Colburn WA, DeGruttola VG, DeMets DL, Downing GJ, Hoth DF, Oates JA, Peck CC, Schooley RT, Spilker BA. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clinical pharmacology & therapeutics. 2001 Mar;69(3):89-95. Katayama A MI, Shiino S, Toss MS, Eldib K, Kurozumi S, Quinn CM, Badr N, Murray C, Provenzano E, Callagy G. Predictors of pathological complete response to neoadjuvant treatment and changes to post-neoadjuvant HER2 status in HER2-positive invasive breast cancer. Modern Pathology. 2021 Jul 1;34(7):1271-81. Freitas AJ CR, Varuzza MB, Hidalgo Filho CM, Silva VD, Souza CD, Marques MM. Molecular biomarkers predict pathological complete response of neoadjuvant chemotherapy in breast cancer patients. Cancers. 2021 Oct 31;13(21):5477. Baumgartner C OM, Netzer M, Baumgartner D. Bioinformatic-driven search for metabolic biomarkers in disease. Journal of clinical bioinformatics. 2011 Jan 20;1(1):2. Edgar R DM, Lash AE., repository GEONgeahad, 1;30(1):207-10 NARJ. Gandhi S SR, Janes C, Fitzpatrick V, Miller J, Attwood K, Ioannou G, Ozbey S, De Souza I, Roudko V, Kumar P, Kalathil S, Kokolus KM, Wang J, Cortes Gomez E, Takabe K, Edge S, Young J, Cappuccino H, Opyrchal M, O'Connor T, Levine EG, Gnjatic S, Kalinski P. Systemic chemokine-modulatory regimen combined with neoadjuvant chemotherapy in patients with triple-negative breast cancer. J Immunother Cancer. 2024 Nov 14;12(11):e010058. doi: 10.1136/jitc-2024-010058. PMID: 39542655; PMCID: PMC11575314. https://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?view=software. [ from:https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ASFaqctfhtsdIA. Bolger AM LM, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014 Aug 1;30(15):2114-20. doi: 10.1093/bioinformatics/btu170. Epub 2014 Apr 1. PMID: 24695404; PMCID: PMC4103590. Li H HB, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R; 1000 Genome Project Data Processing Subgroup. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009 Aug 15;25(16):2078-9. doi: 10.1093/bioinformatics/btp352. Epub 2009 Jun 8. PMID: 19505943; PMCID: PMC2723002. Anders S PP, Huber W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015 Jan 15;31(2):166-9. doi: 10.1093/bioinformatics/btu638. Epub 2014 Sep 25. PMID: 25260700; PMCID: PMC4287950. Love MI HW, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. doi: 10.1186/s13059-014-0550-8. PMID: 25516281; PMCID: PMC4302049. Carlson M FS, Pages H, Li N. org. Hs. eg. db: Genome wide annotation for Human. R package version. 2019;3(2):3. Pagès H CM, Falcon S, Li N. AnnotationDbi: Manipulation of SQLite-based annotations in Bioconductor. R package version. 2021 Apr;1(1). author = {Hadley Wickham}, title = {ggplot2: Elegant Graphics for Data Analysis}, publisher = {Springer-Verlag New York}, year = {2016}, isbn = {978-3-319-24277-4}, url = {https://ggplot2.tidyverse.org}. Langfelder P HSWaRpfwcnaBBDd---P. Szklarczyk D KR, Koutrouli M, Nastou K, Mehryary F, Hachilif R, Gable AL, Fang T, Doncheva NT, Pyysalo S, Bork P, Jensen LJ, von Mering C. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023 Jan 6;51(D1):D638-D646. doi: 10.1093/nar/gkac1000. PMID: 36370105; PMCID: PMC9825434. Shannon P MA, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003 Nov;13(11):2498-504. doi: 10.1101/gr.1239303. PMID: 14597658; PMCID: PMC403769. Chen EY TC, Kou Y, Duan Q, Wang Z, Meirelles GV, Clark NR, Ma'ayan A. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013 Apr 15;14:128. doi: 10.1186/1471-2105-14-128. PMID: 23586463; PMCID: PMC3637064. @Book{, author = {Hadley Wickham}, title = {ggplot2: Elegant Graphics for Data Analysis}, publisher = {Springer-Verlag New York}, year = {2016}, isbn = {978-3-319-24277-4}, et al. @Manual{, title = {tidyr: Tidy Messy Data}, author = {Hadley Wickham and Davis Vaughan and Maximilian Girlich}, year = {2025}, note = {R package version 1.3.1}, url = {https://tidyr.tidyverse.org}, et al. @Manual{, title = {dplyr: A Grammar of Data Manipulation}, author = {Hadley Wickham and Romain François and Lionel Henry and Kirill Müller and Davis Vaughan}, year = {2025}, note = {R package version 1.1.4}, url = {https://dplyr.tidyverse.org}, et al. @Article{, author = {Yunshun Chen and Lizhong Chen and Aaron T L Lun and Pedro Baldoni and Gordon K Smyth}, title = {{edgeR} v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets}, year = {2025}, journal = {Nucleic Acids Research}, volume = {53}, et al. Loibl S PP, Morrow M, Denkert C, Curigliano G. Breast cancer. Lancet. 2021 May 8;397(10286):1750-1769. doi: 10.1016/S0140-6736(20)32381-3. Epub 2021 Apr 1. Erratum in: Lancet. 2021 May 8;397(10286):1710. doi: 10.1016/S0140-6736(21)00838-2. PMID: 33812473. https://www.cancer.org/. [ Xu S MS, Han Y, Wan F, Toriola AT. Breast Cancer Incidence Among US Women Aged 20 to 49 Years by Race, Stage, and Hormone Receptor Status. JAMA Netw Open. 2024 Jan 2;7(1):e2353331. doi: 10.1001/jamanetworkopen.2023.53331. PMID: 38277147; PMCID: PMC10818222. Foulkes WD SI, Reis-Filho JS. Triple-negative breast cancer. N Engl J Med. 2010 Nov 11;363(20):1938-48. doi: 10.1056/NEJMra1001389. PMID: 21067385. O'Brien KM CS, Tse CK, Perou CM, Carey LA, Foulkes WD, Dressler LG, Geradts J, Millikan RC. Intrinsic breast tumor subtypes, race, and long-term survival in the Carolina Breast Cancer Study. Clin Cancer Res. 2010 Dec 15;16(24):6100-10. doi: 10.1158/1078-0432.CCR-10-1533. PMID: 21169259; PMCID: PMC3029098. Iwamoto T, Kajiwara Y, Zhu Y, Iha S. Biomarkers of neoadjuvant/adjuvant chemotherapy for breast cancer. Chinese Clinical Oncology. 2020;9(3):27. Stearman RS BQ, Speyer G, Handen A, Cornelius AR, Graham BB, Kim S, Mickler EA, Tuder RM, Chan SY, Geraci MW. Systems Analysis of the Human Pulmonary Arterial Hypertension Lung Transcriptome. Am J Respir Cell Mol Biol. 2019 Jun;60(6):637-649. doi: 10.1165/rcmb.2018-0368OC. PMID: 30562042; PMCID: PMC6543748. Perng YC LDIiaiabNRMJ-ds---P. Xu T ZC, Chen J, Song F, Ren X, Wang S, Yi X, Zhang Y, Zhang W, Hu Q, Qin H, Liu Y, Zhang S, Tan Z, Pan Z, Huang P, Ge M. ISG15 and ISGylation modulates cancer stem cell-like characteristics in promoting tumor growth of anaplastic thyroid carcinoma. J Exp Clin Cancer Res. 2023 Jul 27;42(1):182. doi: 10.1186/s13046-023-02751-9. Erratum in: J Exp Clin Cancer Res. 2024 Nov 13;43(1):301. doi: 10.1186/s13046-024-03226-1. PMID: 37501099; PMCID: PMC10373324. Han HG MH, Jeon YJ. ISG15 in cancer: Beyond ubiquitin-like protein. Cancer Lett. 2018 Dec 1;438:52-62. doi: 10.1016/j.canlet.2018.09.007. Epub 2018 Sep 11. PMID: 30213559. Okumura F OA, Uematsu K, Hatakeyama S, Zhang DE, Kamura T. Activation of double-stranded RNA-activated protein kinase (PKR) by interferon-stimulated gene 15 (ISG15) modification down-regulates protein translation. J Biol Chem. 2013 Jan 25;288(4):2839-47. doi: 10.1074/jbc.M112.401851. Epub 2012 Dec 10. PMID: 23229543; PMCID: PMC3554948. Xu D ZT, Xiao J, Zhu K, Wei R, Wu Z, Meng H, Li Y, Yuan J. Modification of BECN1 by ISG15 plays a crucial role in autophagy regulation by type I IFN/interferon. Autophagy. 2015 Apr 3;11(4):617-28. doi: 10.1080/15548627.2015.1023982. PMID: 25906440; PMCID: PMC4502663. D'Cunha J KEJ, Haas AL, Truitt RL, Borden EC. Immunoregulatory properties of ISG15, an interferon-induced cytokine. Proc Natl Acad Sci U S A. 1996 Jan 9;93(1):211-5. doi: 10.1073/pnas.93.1.211. PMID: 8552607; PMCID: PMC40208. Desai SD RR, Burks J, Wood LM, Pullikuth AK, Haas AL, Liu LF, Breslin JW, Meiners S, Sankar S. ISG15 disrupts cytoskeletal architecture and promotes motility in human breast cancer cells. Exp Biol Med (Maywood). 2012 Jan;237(1):38-49. doi: 10.1258/ebm.2011.011236. Epub 2011 Dec 20. PMID: 22185919. Burks J RR, Desai SD. ISGylation governs the oncogenic function of Ki-Ras in breast cancer. Oncogene. 2014 Feb 6;33(6):794-803. doi: 10.1038/onc.2012.633. Epub 2013 Jan 14. PMID: 23318454. Angeles C. Tecalco-Cruz EC-R, Protein ISGylation and free ISG15 levels are increased by interferon gamma in breast cancer cells, Biochemical and Biophysical Research Communications, Volume 499 I, 2018, Pages 973-978, et al. Feiran Wang NZ, Ruishu Niu, Yunpeng Lu, Wei Zhang, Zhixian He,, Identification of biomimetic nanoplatform-mediated delivery of si-ISG15 for treatment of triple-negative breast cancer, Cellular Signalling, Volume 118, 2024, 111117, et al. Desai SD, Wood LM, Tsai Y-C, Hsieh T-S, Marks JR, Scott GL, et al. ISG15 as a novel tumor biomarker for drug sensitivity. Molecular Cancer Therapeutics. 2008;7(6):1430-9. Boelens MC WT, Nabet BY, Xu B, Qiu Y, Yoon T, Azzam DJ, Twyman-Saint Victor C, Wiemann BZ, Ishwaran H, Ter Brugge PJ. Exosome transfer from stromal to breast cancer cells regulates therapy resistance pathways. Cell. 2014 Oct 23;159(3):499-513. Kang JA, Kim YJ, Jeon YJ. The diverse repertoire of ISG15: more intricate than initially thought. Experimental & Molecular Medicine. 2022;54(11):1779-92. Shen J WJ, Wang H, Yue G, Yu L, Yang Y, Xie L, Zou Z, Qian X, Ding Y, Guan W. A three-gene signature as potential predictive biomarker for irinotecan sensitivity in gastric cancer. Journal of translational medicine. 2013 Mar 22;11(1):73. Chun JH KH, Kim E, Kim IH, Kim JH, Chang HJ, Choi IJ, Lim HS, Kim IJ, Kang HC, Park JH. Increased expression of metallothionein is associated with irinotecan resistance in gastric cancer. Cancer research. 2004 Jul 15;64(14):4703-6. Fan JB M-IS, Arimoto K, Liu D, Yan M, Liu CW, Győrffy B, Zhang DE. Type I IFN induces protein ISGylation to enhance cytokine expression and augments colonic inflammation. Proc Natl Acad Sci U S A. 2015 Nov 17;112(46):14313-8. doi: 10.1073/pnas.1505690112. Epub 2015 Oct 29. PMID: 26515094; PMCID: PMC4655505. Lo PK YY, Lee JS, Zhang Y, Huang W, Kane MA, Zhou Q. LIPG signaling promotes tumor initiation and metastasis of human basal-like triple-negative breast cancer. Elife. 2018 Jan 19;7:e31334. doi: 10.7554/eLife.31334. PMID: 29350614; PMCID: PMC5809145. Lamborn IT JH, Zhang Y, Drutman SB, Abbott JK, Munir S, Bade S, Murdock HM, Santos CP, Brock LG, Masutani E, Fordjour EY, McElwee JJ, Hughes JD, Nichols DP, Belkadi A, Oler AJ, Happel CS, Matthews HF, Abel L, Collins PL, Subbarao K, Gelfand EW, Ciancanelli MJ, Casanova JL, Su HC. Recurrent rhinovirus infections in a child with inherited MDA5 deficiency. J Exp Med. 2017 Jul 3;214(7):1949-1972. doi: 10.1084/jem.20161759. Epub 2017 Jun 12. PMID: 28606988; PMCID: PMC5502429. Yao L CR, Ji C, Zhou X, Luan J, Meng X, Song N. RNA-Binding Proteins Play an Important Role in the Prognosis of Patients With Testicular Germ Cell Tumor. Front Genet. 2021 Mar 11;12:610291. doi: 10.3389/fgene.2021.610291. PMID: 33777092; PMCID: PMC7990889. Shi C WX, Li J, Wu S, Liu Z, Ren X, Zhang X, Liu Y. IFIH1 promotes apoptosis through the TBK1/IRF3 pathway in triple-negative breast cancer. Neoplasma. 2024 Dec;71(6):533-543. doi: 10.4149/neo_2024_240614N255. PMID: 39832201. Acknowledgement of Reviewers of Canadian Journal of Public Health articles CJPHFds---. Yamashita N, Fushimi A, Morimoto Y, Bhattacharya A, Long M, Liu S, et al. Abstract P1-04-09: Essential role for MUC1-C in chronic activation of cytosolic nucleotide sensing and the type I interferon pathway in triple-negative breast cancer. Cancer Research. 2022;82(4_Supplement):P1-04-9-P1--9. Nowacka M G-MB, Świerczewska M, Nowicki M, Zabel M, Sterzyńska K, Januchowski R. The significance of HERC5, IFIH1, SAMD4, SEMA3A and MCTP1 genes expression in resistance to cytotoxic drugs in ovarian cancer cell lines. Medical Journal of Cell Biology. 2021;9(3):138-47. Ther. sgic-rpcMC, DOI:10.1158/1535-7163.MCT-11-0289. -. Khazaei G SF, Yamchi A, Golalipour M, Jhingan GD, Shahbazi M. Proteomics evaluation of MDA-MB-231 breast cancer cells in response to RNAi-induced silencing of hPTTG. Life Sci. 2019 Dec 15;239:116873. doi: 10.1016/j.lfs.2019.116873. Epub 2019 Sep 12. PMID: 31521689. Bauer M, Vetter M, Maia A, Vlachavas E, Michels B, Berdiel-Acer M, et al. Abstract P1-08-15: Communication between tumor cells and fibroblasts as a prognostic factor of NACT in TNBC. Cancer Research. 2022;82(4_Supplement):P1-08-15-P1-08-15. Zhao N, Kabotyanski EB, Saltzman AB, Malovannaya A, Yuan X, Reineke LC, et al. Targeting eIF4A triggers an interferon response to synergize with chemotherapy and suppress triple-negative breast cancer. The Journal of Clinical Investigation. 2023;133(24). Kakuta S SS, Iwakura Y. Genomic structure of the mouse 2',5'-oligoadenylate synthetase gene family. J Interferon Cytokine Res. 2002 Sep;22(9):981-93. doi: 10.1089/10799900260286696. PMID: 12396720. Lin RJ YH, Chang BL, Tang WC, Liao CL, Lin YL. Distinct antiviral roles for human 2',5'-oligoadenylate synthetase family members against dengue virus infection. J Immunol. 2009 Dec 15;183(12):8035-43. doi: 10.4049/jimmunol.0902728. PMID: 19923450. Liao X XH, Li S, Ye H, Li S, Ren K, Li Y, Xu M, Lin W, Duan X, Yang C. 2′, 5′-oligoadenylate synthetase 2 (OAS2) inhibits Zika virus replication through activation of type Ι IFN signaling pathway. Viruses. 2020 Apr 8;12(4):418. Manning G TA, Sirák I, Badie C. Radiotherapy-Associated Long-term Modification of Expression of the Inflammatory Biomarker Genes ARG1, BCL2L1, and MYC. Front Immunol. 2017 Apr 10;8:412. doi: 10.3389/fimmu.2017.00412. PMID: 28443095; PMCID: PMC5385838. Fite BZ WJ, Kare AJ, Ilovitsh A, Chavez M, Ilovitsh T, Zhang N, Chen W, Robinson E, Zhang H, Kheirolomoom A, Silvestrini MT, Ingham ES, Mahakian LM, Tam SM, Davis RR, Tepper CG, Borowsky AD, Ferrara KW. Immune modulation resulting from MR-guided high intensity focused ultrasound in a model of murine breast cancer. Sci Rep. 2021 Jan 13;11(1):927. doi: 10.1038/s41598-020-80135-1. PMID: 33441763; PMCID: PMC7806949. Liu DK OG, Feil PD. 2',5'-oligoadenylate synthetase activity in human mammary tumors and its potential correlation with tumor growth or hormonal responsiveness. Cancer Res. 1986 Dec;46(12 Pt 1):6207-10. PMID: 3779641. Zhang Y YCPcoOOOOibcBcJ. Lu J YL, Yang X, Chen B, Liu Z. Investigating the clinical significance of OAS family genes in breast cancer: an in vitro and in silico study. Hereditas. 2024 Dec 5;161(1):50. Jia H LX, Wang Z, Zhang W, Chen X. A Pan-Cancer Analysis Reveals OAS2 as a Biomarker for Cancer Prognosis and Immunotherapy. Yu C XP, Zhang L, Pan R, Cai Z, He Z, Sun J, Zheng M. Prediction of key genes and pathways involved in trastuzumab-resistant gastric cancer. World Journal of Surgical Oncology. 2018 Aug 22;16(1):174. Wang X CY, Tian Y, Song Z, He Z, Shen P, Wang H, Luo L, Cui R. Prognosis and Immune Cell Infiltration Analysis of OAS Family Genes in Pan-Cancer. Zhang Y XX, Mu X, Wang J, Zhang J, Xiang G, Li J, Zheng C, Wang H, Lu Q. Effect of immune infiltration intensity on the efficacy of neoadjuvant immunotherapy for esophageal cancer. Front Immunol. 2025 Jun 12;16:1543283. doi: 10.3389/fimmu.2025.1543283. PMID: 40574841; PMCID: PMC12198219. Kim JC HY, Tak KH, Roh SA, Kwon YH, Kim CW, et al. (2018) Opposite functions of GSN and OAS2 on colorectal cancer metastasis, mediating perineural and lymphovascular invasion, respectively. PLoS ONE 13(8): e0202856. https://doi.org/10.1371/journal.pone.0202856. Ho W-HJ, Law AMK, Masle-Farquhar E, Castillo LE, Mawson A, O’Bryan MK, et al. Activation of the viral sensor oligoadenylate synthetase 2 (Oas2) prevents pregnancy-driven mammary cancer metastases. Breast Cancer Research. 2022;24(1):31. Jacobo Jacobo M, Donnella HJ, Sobti S, Kaushik S, Goga A, Bandyopadhyay S. An inflamed tumor cell subpopulation promotes chemotherapy resistance in triple negative breast cancer. Scientific Reports. 2024;14(1):3694. Jiang N, Jiang M, Zhu X, Ren B, Zhang J, Guo Z, et al. SCALE-1: Safety and efficacy of short course neoadjuvant chemo-radiotherapy plus toripalimab for locally advanced resectable squamous cell carcinoma of esophagus. Journal of Clinical Oncology. 2022;40(16_suppl):4063-. Haller O AH, Pavlovic J, Staeheli P. The Discovery of the Antiviral Resistance Gene Mx: A Story of Great Ideas, Great Failures, and Some Success. Annu Rev Virol. 2018 Sep 29;5(1):33-51. doi: 10.1146/annurev-virology-092917-043525. Epub 2018 Jun 29. PMID: 29958082. Bergmann S BL, Schughart K. Differential lung gene expression changes in C57BL/6 and DBA/2 mice carrying an identical functional Mx1 gene reveals crucial differences in the host response. BMC Genom Data. 2024 Feb 15;25(1):19. doi: 10.1186/s12863-024-01203-3. PMID: 38360537; PMCID: PMC10870463. Ernest C. Borden, 53 - Interferons, Editor(s): John Mendelsohn JWG, Peter M. Howley, Mark A. Israel, Craig B. Thompson,, The Molecular Basis of Cancer (Fourth Edition), W.B. Saunders, 2015, et al. Calmon MF RR, Kaneto CM, Moura RP, Silva SD, Mota LD, Pinheiro DG, Torres C, De Carvalho AF, Cury PM, Nunes FD. Epigenetic silencing of CRABP2 and MX1 in head and neck tumors. Neoplasia. 2009 Dec 1;11(12):1329-IN9. Johansson HJ SB, Forshed J, Stål O, Fohlin H, Lewensohn R, Hall P, Bergh J, Lehtiö J, Linderholm BK. Proteomics profiling identify CAPS as a potential predictive marker of tamoxifen resistance in estrogen receptor positive breast cancer. Clinical Proteomics. 2015 Dec;12(1):8. Masuda H ZD, Bartholomeusz C, Doihara H, Hortobagyi GN, Ueno NT. Role of epidermal growth factor receptor in breast cancer. Breast Cancer Res Treat. 2012 Nov;136(2):331-45. doi: 10.1007/s10549-012-2289-9. Epub 2012 Oct 17. PMID: 23073759; PMCID: PMC3832208. Aljohani AI JC, Kurozumi S, Mohammed OJ, Miligy IM, Green AR, Rakha EA. Myxovirus resistance 1 (MX1) is an independent predictor of poor outcome in invasive breast cancer. Breast cancer research and treatment. 2020 Jun;181(3):541-51. Lee H LS, Bae H, Kang HS, Kim SJ. Genome-wide identification of target genes for miR-204 and miR-211 identifies their proliferation stimulatory role in breast cancer cells. Sci Rep. 2016 Apr 28;6:25287. doi: 10.1038/srep25287. PMID: 27121770; PMCID: PMC4848534. Sistigu A YT, Vacchelli E, Chaba K, Enot DP, Adam J, Vitale I, Goubar A, Baracco EE, Remédios C, Fend L. Cancer cell–autonomous contribution of type I interferon signaling to the efficacy of chemotherapy. Nature medicine. 2014 Nov;20(11):1301-9. Lee SJ HC, Kim YK, Lee HJ, Ahn SJ, Shin N, Lee JH, Shin DH, Choi KU, Park DY, Lee CH. Expression of myxovirus resistance A (MxA) is associated with tumor-infiltrating lymphocytes in human epidermal growth factor receptor 2 (HER2)-positive breast cancers. Cancer Res Treat. 2017 Apr 1;49(2):313-21. Legrier M-E, Bièche I, Gaston J, Beurdeley A, Yvonnet V, Déas O, et al. Activation of IFN/STAT1 signalling predicts response to chemotherapy in oestrogen receptor-negative breast cancer. British Journal of Cancer. 2016;114(2):177-87. Broad RV, Jones SJ, Teske MC, Wastall LM, Hanby AM, Thorne JL, et al. Inhibition of interferon-signalling halts cancer-associated fibroblast-dependent protection of breast cancer cells from chemotherapy. British Journal of Cancer. 2021;124(6):1110-20. Qin Y WC, Wu H. CXCL10-based gene cluster model serves as a potential diagnostic biomarker for premature ovarian failure. PeerJ. 2023 Dec 13;11:e16659. doi: 10.7717/peerj.16659. PMID: 38107572; PMCID: PMC10725173. Karin N RHCbc-aCaisricaaCS-djc. Kanda N ST, Tada Y, Watanabe S. IL‐18 enhances IFN‐γ‐induced production of CXCL9, CXCL10, and CXCL11 in human keratinocytes. European journal of immunology. 2007 Feb;37(2):338-50. Liu M, Guo, S., Stiles, J. K."The emerging role of CXCL10 in cancer (Review)". Oncology Letters 2, no. 4 (2011): 583-589. https://doi.org/10.3892/ol.2011.300. Goldberg-Bittman L NE, Sagi-Assif O, et al. The expression of the chemokine receptor CXCR3 and its ligand, CXCL10, in human breast adenocarcinoma cell lines. Immunol Lett 2004; 92: 171–8. Jafarzadeh A FH, Nemati M, Assadollahi Z, Sheikhi A, Ghaderi A. Higher circulating levels of chemokine CXCL10 in patients with breast cancer: Evaluation of the influences of tumor stage and chemokine gene polymorphism . Cancer Biomarkers. 2016;16(4):545-554. doi:10.3233/CBM-160596, . Ahmed A. Ejaeidi BSC, Louis V. Puneky, Robert E. Lewis, Julius M. Cruse,, Hormone receptor-independent CXCL10 production is associated with the regulation of cellular factors linked to breast cancer progression and metastasis, Experimental and Molecular Pathology, Volume 99 I, 2015, Pages 163-172, et al. Chuan T LT, Yi C. Identification of CXCR4 and CXCL10 as Potential Predictive Biomarkers in Triple Negative Breast Cancer (TNBC). Med Sci Monit. 2020 Jan 11;26:e918281. doi: 10.12659/MSM.918281. PMID: 31924747; PMCID: PMC6977636. Madkhali OA MS, Almoshari Y, Sabei FY, Safhi AY. Dual role of CXCL10 in cancer progression: implications for immunotherapy and targeted treatment‎. Cancer Biol Ther. 2025 Dec;26(1):2538962. doi: 10.1080/15384047.2025.2538962. Epub 2025 Aug 4. PMID: 40760734; PMCID: PMC12326575. Xiuming Wu AS, Weifeng Yu, Chengye Hong, Zhonghua Liu,, CXCL10 mediates breast cancer tamoxifen resistance and promotes estrogen-dependent and independent proliferation, Molecular and Cellular Endocrinology, Volume 512, 2020, 110866, et al. Kalfeist L, Petit S, Galland L, Poirrier C, Aucagne R, Ghiringhelli F, et al. Abstract 1188: Identification of CXCL10-inducing chemotherapy/targeted therapy combinations for PD-1 blockade sensitization in “cold” triple negative breast cancer. Cancer Research. 2024;84(6_Supplement):1188-. Kim M CH, Woo JW, Chung YR, Park SY. Role of CXCL10 in the progression of in situ to invasive carcinoma of the breast. Scientific reports. 2021 Sep 9;11(1):18007. 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-8937161","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595296622,"identity":"eed82f65-5ad3-4630-9f13-87c5cf9f21ab","order_by":0,"name":"Danial Ahdi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYDACCQaDAzwMDDwM7MwHH3wACrCxE62FmS3ZcAZICzMRWoDqgYCZx0wawiCgg39288YDb2ruyPCDtNj82ibPx8zA+OFjDh5L7hwrODjn2DMeyWa2YuvcvtuGbcwMzJIzt+Gx5kaOwWEetsM8BoeZN97O7bnNCNTCxsyLR4s8WMu/wzz2hxkMpC17btsT1GIA0sLbBrSFmcVImuHH7USCWgxvpBUcnNt3mEfiMDCQextuJ7cxMzbj9YvcjeTNH958O2zP39588MGPP7dt5wMZHz7i8z4KYGwDkw3EqgeBP6QoHgWjYBSMgpECAFqZUg9w1aFtAAAAAElFTkSuQmCC","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Danial","middleName":"","lastName":"Ahdi","suffix":""},{"id":595296623,"identity":"868afb90-3fc1-42eb-b973-ceb8ffc2e873","order_by":1,"name":"Naser Elmi","email":"","orcid":"","institution":"University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Naser","middleName":"","lastName":"Elmi","suffix":""},{"id":595296624,"identity":"2d6e16c6-3aa0-457b-8b95-5ca9e15e75f6","order_by":2,"name":"Sima Mansoori Derakhshan","email":"","orcid":"","institution":"Tabriz University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Sima","middleName":"Mansoori","lastName":"Derakhshan","suffix":""}],"badges":[],"createdAt":"2026-02-22 06:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8937161/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8937161/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103718691,"identity":"f66fcfa5-0fde-4f37-90e9-a258277d1281","added_by":"auto","created_at":"2026-03-02 06:41:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":77361,"visible":true,"origin":"","legend":"\u003cp\u003eA) PCA plot of vst-normalized gene expression (DESeq2) for 14 samples (7 pre-treatment and seven post-treatment), demonstrating clear separation between the two groups. (B) Box plot of vst-normalized gene expression (DESeq2) for the same 14 samples, confirming proper data normalization across all samples\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8937161/v1/44ab9f6c47708bfbc26b1622.png"},{"id":103718735,"identity":"34467780-ef53-46bc-8a90-af7dcfb162c1","added_by":"auto","created_at":"2026-03-02 06:41:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":164751,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano plot of DEGs, with log₂FoldChange on the x-axis and -log₁₀(p-value) on the y-axis, highlighting significant genes (padj \u0026lt; 0.05 and |log₂FoldChange| \u0026gt; 1) in red and non-significant in blue.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8937161/v1/fff31941a46f6fc1acae8d25.png"},{"id":103718678,"identity":"67541cdc-babd-4494-b6ed-1e07eab4d8a4","added_by":"auto","created_at":"2026-03-02 06:40:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":278599,"visible":true,"origin":"","legend":"\u003cp\u003eModule-Trait Relationships Heatmap of WGCNA module correlations with NAC response (Post_vs_Pre) in TNBC. Turquoise shows the strongest positive correlation (r = 0.98, p = 9e-10), while blue shows a strong negative correlation (r = -0.95, p = 1.2e-07), indicating key roles in treatment response.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8937161/v1/b7a55097313db535cceb510c.png"},{"id":103718597,"identity":"fdc414bb-1d9c-40a7-b2d6-73768c29b09d","added_by":"auto","created_at":"2026-03-02 06:40:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":179189,"visible":true,"origin":"","legend":"\u003cp\u003eVenn diagram illustrating the overlap between differentially expressed genes (DEGs) and the turquoise module (1206 genes), highlighting the number of shared genes. Generated with a clean white background and distinct color coding to emphasize the intersection, providing a clear visual of gene commonality\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8937161/v1/2296cd0bacd1d2a65f9b310c.png"},{"id":103718551,"identity":"7a2c7621-7ba1-45a5-ae48-dfa92ece54ce","added_by":"auto","created_at":"2026-03-02 06:40:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":84447,"visible":true,"origin":"","legend":"\u003cp\u003eThe R tool generated dot plots of KEGG and Reactome, emphasizing the significance of pathways related to immunity.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8937161/v1/9e40ef4616cdb8255481e508.png"},{"id":103718557,"identity":"3a6db3aa-8cd8-4564-a7d7-e601f28ab425","added_by":"auto","created_at":"2026-03-02 06:40:18","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":398895,"visible":true,"origin":"","legend":"\u003cp\u003eGene interactions derived from Stringdb were categorized in Cytoscape by component degree, with color variations reflecting their significance within each component.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8937161/v1/a0bb13f1498f13554fbb6b34.png"},{"id":103718746,"identity":"69639cb5-d7e4-4ce9-ab91-7fed11cb0922","added_by":"auto","created_at":"2026-03-02 06:41:28","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":227313,"visible":true,"origin":"","legend":"\u003cp\u003eThe mean expression levels of the genes CXCL10, IFIH1, ISG15, MX1, and OAS2are shown in this bar plot, both before (pre-treatment, green) and after (post-treatment, orange) treatment. The stars (****) signify a highly significant difference between the groups and statistical significance (p-value or adjusted p-value \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8937161/v1/249210fa3cfa722802470fe7.png"},{"id":104780217,"identity":"04e25da1-354c-44d4-ae64-c528980ed45e","added_by":"auto","created_at":"2026-03-17 07:51:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2485342,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8937161/v1/a02c631d-33be-4ea7-a597-2e4bab4cb358.pdf"},{"id":103718685,"identity":"6e55658e-e5c3-4581-939c-0f1788060eab","added_by":"auto","created_at":"2026-03-02 06:40:57","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":184172,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8937161/v1/e7660fc230bcfa8e001a555f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated bioinformatics analysis reveals ISG15, IFIH1, OAS2, MX1, and CXCL10 as predictive biomarkers of neoadjuvant chemotherapy response in triple-negative breast cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is the most common type of cancer in the world and refers to a variety of malignancies that develop in the mammary glands (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Every year, 55,000 (15%) new cases of cancer are caused by breast cancer in the UK {ONS. Breast Cancer: Incidence, #105}. Women are disproportionately impacted, and the incidence rises with age; over 80% of breast cancer diagnoses occur in women over 50. In 2020, breast cancer accounted for 685,000 deaths worldwide, making it the top cause of cancer-related deaths (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Breast cancer is a heterogeneous disease with multiple subtypes, each exhibiting distinct epidemiological features (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In 2013, a new way to classify molecular subtypes of breast cancer was revealed. It included several groups, such as luminal A, luminal B, HER2, HER2 overexpression, basal-like, Triple Negative Breast Cancer (TNBC), and other specific subtypes (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Triple-negative breast cancer (TNBC) is defined by the lack of estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptor-2 (HER2) (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Epidemiological studies show that TNBC mainly affects women under 40 who are not yet pregnant. This group makes up about 15\u0026ndash;20% of all breast cancer incidences. Patients with triple-negative breast cancer (TNBC) had a 40% chance of dying within the first five years of diagnosis and lived less time than patients with other forms of breast cancer. And about 46% of TNBC patients will experience distant metastases, and the disease is extremely aggressive (\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNeoadjuvant chemotherapy (NAC) is a type of treatment that is administered before surgery, as opposed to conventional treatments, and is of high clinical value in locally advanced and inoperable breast cancer (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Neoadjuvant therapy has three primary objectives: enhancing surgical options, achieving operability in primary tumors that cannot be operated on, and enhancing surgical options in primary cancers that can be operated on; improving outcome by achieving pathological complete response (pCR); and gathering response data at mid-course, which may aid in further modifying therapy (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor BC patients, NAC is a crucial treatment approach that aims to increase pCR by downstaging the tumor and tracking treatment response for prognostic purposes (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). For TNBC patients, endocrine or HER2-targeted drugs are ineffective because there are no relevant receptor markers. When given standard chemotherapy therapies such as anthracyclines or taxanes, TNBC is the subtype that responds the best. Compared to non-TNBC subtypes, TNBC still has higher death and recurrence rates, and fewer than 30% of patients fully recover. For people with TNBC, chemotherapy is the most popular treatment. Despite the more aggressive clinical presentation of TNBC, with neoadjuvant chemotherapy, around 30 to 40 percent of patients achieve a pCR with no histological evidence of illness at the time of surgery. Additionally, the survival rate for these patients is significantly higher (\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on epidemiological, therapeutic, pathophysiological, or other scientific evidence, biomarkers are objectively measured indicators of pathogenic processes, natural biological processes, or drug responses to a therapeutic intervention. They serve a variety of purposes and are intended to replace a clinical endpoint that predicts benefit or harm. Targeted drugs have drastically changed how cancer is managed and reduced costs, which has increased the need to evaluate predictive biomarkers to monitor treatment effectiveness (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). In many recent studies, researchers have faced a significant hurdle in identifying molecular biomarkers that can predict chemosensitivity and stratify individuals likely to benefit from NAC in clinical practice. Furthermore, molecular biomarkers can help assess the attainment of pCR in patients with breast cancer who do not respond to NAC, and they may be essential in preventing needless treatments and related toxicities (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Tumors with identical histologies may have distinct prognoses and reactions to treatment. Following NAC, some molecular subtypes of breast cancer may experience high rates of pCR, but other subtypes might not benefit as much from the same treatment. Predictive biomarkers are therefore crucial for identifying patients who are unlikely to benefit from NAC, which makes it easier to create innovative treatment plans for them (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the process of finding new biomarkers, bioinformatics is essential because it bridges the gap between the first stages of discovery, like designing experiments and conducting clinical studies, and bioanalytics, which includes high-throughput profiling, sample preparation, separation, and independent validation of candidate biomarkers that have been found (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this investigation, we analyzed RNA-seq data from people with TNBC who underwent neoadjuvant therapy. We aimed to identify potential biomarkers for predicting therapy response in TNBC patients by integrating differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein\u0026ndash;protein interaction (PPI) network construction, ultimately leading to hub gene identification.\u003c/p\u003e \u003cp\u003eThe purpose of this study was to use integrative bioinformatics methods to identify potential biomarkers associated with NAC response in TNBC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Collection and Preprocessing\u003c/h2\u003e \u003cp\u003eThe Gene Expression Omnibus (GEO) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) The database provided the RNA-seq dataset GSE260989 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Patients with TNBC, the availability of paired pre- and post-neoadjuvant therapy samples, neoadjuvant therapy treatment, and female sex were the criteria used to select the samples. This dataset includes 14 samples, with an equal number of pre- and post-neoadjuvant therapy samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Processing Raw Data in a Linux Environment\u003c/h2\u003e \u003cp\u003eTo perform effective and repeatable RNA-seq data analysis, all preparation procedures were conducted in a Linux Ubuntu (version 24.04) environment. The SRA Toolkit (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) It was utilized to convert SRA files into FASTQ format for further analysis and to retrieve RNA-seq data from the GEO database. FASTQC (Version 0.12.0) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) It was used to evaluate sequencing quality before alignment and to perform quality control on FASTQ files. Trimmomatic (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) Was employed to improve read quality by removing adaptor sequences and trimming low-quality bases. Additionally, the files are re-examined with FASTQC to determine whether Trimmomatic has resolved the issues. SAMtools (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) It was applied to manage alignments, including sorting, indexing, and converting SAM to BAM, to prepare data for read quantification. Finally, HTSeq-count (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) It was used to generate input data for differential expression analysis by measuring the number of reads mapped to each gene and creating a count matrix. It is worth mentioning that we used the human reference genome GRCh38 build for our analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Differential Expression Analysis\u003c/h2\u003e \u003cp\u003eRStudio (R version 4.4.2) was used to import the count files created in the Linux environment. The DESeq2 package (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), which models count data using the negative binomial distribution, was used for differential expression analysis. The org.. Hs.eg.db (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) and AnnotationDbi (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) Packages were used to connect gene identifiers to official gene symbols and to annotate genes functionally with biological annotations. The ggplot2 (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) It was used to create boxplots and principal component analysis (PCA) plots to evaluate normalization and sample distribution. Only genes exhibiting an adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and an absolute log₂ fold change (|log₂FC|)\u0026thinsp;\u0026gt;\u0026thinsp;1 were deemed statistically significant and physiologically pertinent, and were included for subsequent investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Weighted Gene Co-expression Network Analysis (WGCNA)\u003c/h2\u003e \u003cp\u003eThe WGCNA (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The package was used in RStudio to reconstruct co-expression networks and identify modules. Once a scale-free topology was achieved by choosing a suitable soft-thresholding power (β\u0026thinsp;=\u0026thinsp;6), an adjacency matrix was created and converted into a topological overlap matrix (TOM). Gene modules were found using the dynamicTreeCut package and hierarchical clustering. To identify biologically relevant modules, module eigengenes were computed, and their relationships with clinical characteristics were evaluated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Protein-Protein Interaction (PPI) Network Construction\u003c/h2\u003e \u003cp\u003eThe protein\u0026ndash;protein interaction (PPI) network was constructed by importing the commonly differentially expressed genes (DEGs) into the STRING database (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), considering only interactions supported by evidence from databases and experiments. Cytoscape software (version 3.10.3) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) It was used to visualize and analyze the generated PPI network, identifying hub genes and significant clusters by analyzing network topological properties. Additionally, the CytoHubba plugin (version 0.1) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) It was employed to identify hub genes by applying various ranking algorithms, such as Degree, MCC, and MNC, with hub candidates being genes that consistently achieved high scores across multiple techniques.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Functional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eUsing the Enrichr web service, we performed KEGG pathway enrichment analysis to identify biological processes associated with differentially expressed genes. Enriched pathways were those with adjusted p-values below the significance threshold. Additionally, Reactome pathway enrichment analysis was performed using Enrichr to examine the functional roles of the identified genes in cellular signaling and biological processes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Fisher\u0026rsquo;s Exact Test\u003c/h2\u003e \u003cp\u003eWe used Fisher's exact test, available in the stats package in R, to determine whether the overlap between genes in enriched KEGG pathways and those in enriched Reactome pathways was statistically significant. This research evaluated whether the number of shared genes between the two pathway enrichment results was greater than expected by chance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data Acquisition, Quality Control, and Read Processing\u003c/h2\u003e \u003cp\u003eWe obtained raw RNA-seq data for breast cancer patients (GSE260989) from GEO using the SRA Toolkit and converted it to FASTQ format. FastQC's quality check indicated that the overall quality was high, with Q30 scores exceeding 90%. We used Trimmomatic to trim low-quality bases and remove adapter sequences. HIMAT2 was employed to align the cleaned reads to the human reference genome (GRCh38), and SAMtools was used to create and manage BAM files. Using HTSeq-count and Ensembl GTF annotations, we quantified gene expression at the gene level. All the count matrices from the samples were combined and imported into R for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Differential Expression Analysis (DEGs)\u003c/h2\u003e \u003cp\u003eWe used the DESeq2 package in R to identify differentially expressed genes (DEGs) between TNBC samples before and after treatment. If the adjusted p-value (padj) of a gene was less than 0.05, it was considered significant. Three hundred nineteen genes were upregulated (logFC\u0026thinsp;\u0026gt;\u0026thinsp;1), and 704 genes were downregulated (logFC\u0026lt; -1). We used PCA and boxplots to assess data quality and ensure that normalization was consistent across groups and that samples were grouped consistently (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A volcano map was created to show how DEGs were distributed across samples collected before and after treatment. It showed which genes were strongly elevated and downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Weighted Gene Co-expression Network Analysis (WGCNA)\u003c/h2\u003e \u003cp\u003eFourteen co-expression modules were identified in the study, each represented by a different color (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Topology was achieved by choosing a suitable soft-thresholding power (β\u0026thinsp;=\u0026thinsp;6). Among them, the turquoise module showed the strongest association with the clinical characteristic (TNBC status before and after treatment) (correlation coefficient\u0026thinsp;=\u0026thinsp;0.97994008, p-value\u0026thinsp;=\u0026thinsp;9.010013e-10). As a result, this module was chosen as the main one for further analysis. To obtain 1206 overlapping genes, considered promising candidates for biomarker discovery, the genes from this key module were intersected with the previously identified DEGs. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays a Venn diagram illustrating this overlap.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Identification of Common Genes\u003c/h2\u003e \u003cp\u003eAn intersection analysis between the main WGCNA module (turquoise) and differentially expressed genes (DEGs) was performed to identify candidate genes linked to the trait of interest. Ensembl ID, Gene Symbol, log2FoldChange, adjusted p-value (padj), and module membership (kME) were all included in the combined dataset. Duplicate genes were removed, as were those without annotation or marked as \"Not found.\"\u003c/p\u003e \u003cp\u003eTo select genes associated with our trait and showing significantly altered expression levels, we used a combined criterion. We retained genes with absolute(logFC)\u0026thinsp;\u0026gt;\u0026thinsp;1 and padj\u0026thinsp;\u0026lt;\u0026thinsp;0.05, as well as those with a membership (kME) in the turquoise module\u0026thinsp;\u0026gt;\u0026thinsp;0.8. As a result, 503 genes that were deemed both statistically significant and highly connected within the main module were identified through this filtering. These selected genes will be used in subsequent analyses to pinpoint key genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Functional Enrichment Analysis and Overlap Significance\u003c/h2\u003e \u003cp\u003e \u003cb\u003eKEGG and Reactome Enrichment\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eWith an emphasis on the KEGG and Reactome pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the filtered intersection genes were examined for pathway enrichment using the Enrichr website. Pathways were considered significant only if their adjusted p-value (padj) was less than 0.05. The list of genes contributing to each pathway was documented for each pathway database.\u003c/p\u003e \u003cp\u003e \u003cb\u003eOverlap Between KEGG and Reactome Genes\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eGenes that contribute to significant KEGG and Reactome pathways have been identified to overlap. This overlapping group represents genes that are consistently implicated in biologically significant pathways across both databases.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eStatistical Significance of Overlap\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eFisher's exact test in R showed that the overlap between important genes in KEGG and Reactome studies (adjusted p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;2.2 \u0026times; 10⁻\u0026sup1;⁶; OR\u0026thinsp;=\u0026thinsp;1104.6; 95% CI\u0026thinsp;=\u0026thinsp;445.4\u0026ndash;2915.3), suggesting substantial biological relevance rather than random chance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Protein\u0026ndash;Protein Interaction (PPI) Network and Hub Gene Analysis\u003c/h2\u003e \u003cp\u003e \u003cb\u003ePPI Network Construction\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eTo create a protein\u0026ndash;protein interaction network, the filtered genes from the previous step were uploaded to STRING and subsequently imported into Cytoscape.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInitial Hub Analysis Using Cytoscape Analyze Topology Tools (Degree)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eFirst, Degree was used to evaluate the PPI network using Cytoscape's evaluate Topology Tools.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe hub genes found by Degree in this stage are shown in Fig.\u0026nbsp;3.6 and are compiled in Table\u0026nbsp;3.1.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThis analysis gave an early overview of the network's highly linked nodes.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe top 10 Genes by Degree are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe ten genes with the highest Degree, encompassing factors of stress and neighborhood connectedness, were analyzed, highlighting their significance beyond Degree.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeighborhood Connectivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eISG15\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIFIH1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOAS2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMX1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCXCL10\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIFI16\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRSAD2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOAS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIFIT1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe network employed four algorithms from the CytoHubba plugin\u0026mdash;MCC (Maximal Clique Centrality), Bottleneck, Degree, and Closeness\u0026mdash;to systematically analyze the data and identify hub genes. The results were then cross-validated using the original Degree analysis in Cytoscape. All necessary tables and figures are included in the supplementary material.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Final Candidate Hub Genes\u003c/h2\u003e \u003cp\u003eAfter extensive research, including DEG identification, WGCNA, filtering for significantly enriched biological pathways, and network architecture analysis, five genes emerged as strong candidates: ISG15, IFIH1, OAS2, MX1, and CXCL10. These genes consistently met all criteria, including high log2 fold changes, prominent roles in the main WGCNA turquoise module, involvement in biologically significant pathways, and frequent ranking among the top 10 genes across several network analysis metrics. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the key traits and supporting data used to select these genes as potential biomarker candidates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003elists the key characteristics of the five potential hub genes identified in this study. While OAS2, MX1, and CXCL10 were not among the top 10 genes based on the Bottleneck algorithm, they were included in the final five genes due to their significant roles in other centrality measures, strong membership in the turquoise WGCNA module, and notable log2 fold change values.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDegree Rank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBottle neck\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMCC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ecloseness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLog Fold Change\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eISG15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.933117622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIFIH1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.507490843\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eOAS2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-3.802113706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMX1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-4.661680706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCXCL10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNull\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-8.673982894\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.9 DEG plots\u003c/h2\u003e \u003cp\u003eTo enable a more precise observation of expression changes that may be less noticeable in raw data tables, plots were made to graphically illustrate the differential expression of the genes \u003cem\u003eCXCL10\u003c/em\u003e, \u003cem\u003eIFIH1, ISG15, MX1\u003c/em\u003e, and \u003cem\u003eOAS2\u003c/em\u003e before (pre-treatment) and after (post-treatment) therapy. These illustrations effectively highlight variations in gene expression patterns.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMean Gene Expression Before and After Treatment\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe mean expression levels of the genes \u003cem\u003eCXCL10, IFIH1, ISG15, MX1\u003c/em\u003e, and \u003cem\u003eOAS2\u003c/em\u003e are shown in this bar plot before Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;11 (pre-treatment, green) and after (post-treatment, orange) treatment. Expression values are shown as Log2(CPM). To better illustrate average expression changes and their variability, a plot was created that highlighted differences between the pre- and post-treatment groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBreast cancer is a significant public health issue worldwide, being one of the most common and deadly. Despite advances in treatment, it causes significant morbidity and mortality (\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Clinically, TNBCs are aggressive. More common in younger women, these tumors account for 15\u0026ndash;20% of breast cancer incidences. 46% of TNBC patients develop distant metastases, making it an aggressive breast cancer subtype (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNAC is essential for BC patients to improve pCR by lowering staging and monitoring therapy response for prognosis. In locally advanced breast cancer, neoadjuvant chemotherapy is beneficial (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Regardless of therapy, a prognostic biomarker predicts cancer progression. If a biomarker is predictive and a treatment is effective, patients benefit and may save time and finances while making treatment selection smoother (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBioinformatics techniques, such as those used to analyze gene sequence data for various diseases to detect DEGs, advanced genome sequencing methods, more reliable databases, and improved online tools, have made understanding the complex mechanisms of known diseases easier (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, we performed an integrative bioinformatics analysis to identify potential biomarkers associated with NAC response in TNBC.\u003c/p\u003e \u003cp\u003eBy controlling interferon signaling or binding viral proteins to inhibit replication, the ubiquitin-like protein ISG15 functions as an antiviral agent in the immune system (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). \u003cem\u003eISG15\u003c/em\u003e uses an enzymatic cascade that involves many enzymes to conjugate with target proteins and control protein homeostasis (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). ISGylation is crucial for blocking protein translation, either by blocking eIF2α or by blocking dsRNA-dependent protein kinase (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). While \u003cem\u003eISG15\u003c/em\u003e and ISGylation are most known for their ability to combat viruses, it has been shown that ISG15 contributes significantly to the tumor environment by increasing the cytokines of T cells and B cells (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e).Melanoma and lung, breast, prostate, and hepatic cancers are among the many cancer forms that have been shown to have elevated levels of \u003cem\u003eISG15\u003c/em\u003e (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Compared to normal tissue, breast cancer tissue shows an upregulation of \u003cem\u003eISG15\u003c/em\u003e, which promotes breast cell deformation, enhances breast cancer cell motility, and stabilizes important cellular proteins involved in cell migration and metastasis (\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). By investigating the role of this gene in breast cancer, Feiran Wang and colleagues found that ISG15 plays a significant role in TNBC. They demonstrated that this gene was more highly expressed in patients with metastatic disease compared to those without metastases (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe function of \u003cem\u003eISG15\u003c/em\u003e in chemotherapy has been the subject of numerous investigations. One study found that CPT sensitivity was linked to \u003cem\u003eISG15\u003c/em\u003e and its components' expression in multiple groups of breast cancer cell lines (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). According to a different study on breast cancer, a set of genes, including \u003cem\u003eISG15\u003c/em\u003e, is linked to treatment resistance, and their lower expression makes TNBC cells more sensitive to chemotherapy and radiation therapy. This emphasizes the significance of this gene as a prospective biomarker in cancer, and its exploration holds promise for enhancing therapy efficacy (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). In contrast to prior studies, reduced \u003cem\u003eISG15\u003c/em\u003e expression decreases the responsiveness of breast cancer cells to camptothecin. In contrast, it is higher in irinotecan-sensitive gastric cancer tumors than in those resistant to irinotecan. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). According to another study, ISG15 generally plays two roles in TNBC. Increased expression has been connected to tumor growth and metastasis (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e).Our in-silico study showed that post-treatment samples had lower ISG15 expression compared to pre-treatment samples. The decrease in ISG15 might be related to the NAC response in TNBC; however, further functional validation is needed to confirm this relationship, as previous studies have suggested that reduced ISG15 levels may be associated with increased susceptibility to chemotherapy; this hypothesis remains in its early stages (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). When taken together, these results imply that ISG15 may serve as a context-dependent regulator in TNBC, and its expression pattern could have predictive value; nevertheless, its exact role requires additional mechanistic studies. Studies have yielded conflicting results; nonetheless, this suggests that larger populations and more thorough research are needed to improve the reliability of the findings.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eIFIH1\u003c/em\u003e (also known as MDA5) gene encodes a cytoplasmic receptor that detects viral double-stranded RNA to initiate type I interferon signaling. This can affect cellular immune responses (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e).Beyond its essential immunological role in defending against infections, the IFIH1 gene also plays an important part in cancer development. It is markedly overexpressed in various cancers, including breast and testicular tumors, with research on testicular cancer showing differential expression. IFIH1's dual role in cancer biology and its potential as a therapeutic target are emphasized by its strong connection to apoptosis and tumor-related immune processes. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). The role of IFIH1 in breast cancer is complex. According to Lan et al., it has two functions: it mediates type I interferon signaling, which may boost anticancer immunity or, depending on the context, promote chronic inflammation and tumor growth. The results suggest that IFIH1 may significantly influence breast cancer biology, however the precise function needs more clarification (66). IFIH1 is constitutively active in TNBC, leading to type I interferon production and the expression of related genes, including ISG15. This process makes cancer cells more resistant to DNA damage caused by PARP inhibitors or chemotherapy. (67). Furthermore, IFIH1 is expressed at significantly higher levels in TNBC cells. IFIH1 promotes apoptosis and increases PD-L1 expression, suggesting it could be a promising therapeutic target in TNBC and a predictive biomarker of treatment response (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e).Elevated \u003cem\u003eIFIH1\u003c/em\u003e expression has also been observed in drug-resistant ovarian cancer, linking it to chemotherapy resistance via interferon pathway activation (68, 69). In TNBC, IFIH1 is expressed in lymphocytes, fibroblasts, and non-pCR cancer cells, unlike in cells from pCR patients, indicating that reduced pathway activation may contribute to chemotherapy sensitivity (70, 71). Our findings support previous reports that IFIH1 promotes inflammatory signaling and resistance to PARP inhibitors and DNA-damaging drugs, as shown by decreased IFIH1 expression in TNBC. By disrupting these pathways, targeting IFIH1 increases cancer cell sensitivity to olaparib and carboplatin, highlighting its key role in treatment resistance (67). A study by Hu et al. found that the IFIH1 gene influences TNBC treatment response. Additionally, blocking eIF4A triggers a strong interferon response that, when combined with chemotherapy, diminishes TNBC's effects. Interestingly, IFIH1 is crucial for this reaction, promoting proteogenomic processes that give TNBC cells resistance to chemotherapy (72).\u003c/p\u003e \u003cp\u003eOur bioinformatic analysis revealed reduced IFIH1 expression following therapy, possibly associated with NAC response; however, more experimental validation is required.\u003c/p\u003e \u003cp\u003eMembers of the 2\u0026ndash;5'-oligoadenylate synthetase (\u003cem\u003eOAS\u003c/em\u003e) family are antiviral enzymes activated by interferon (IFN). The \u003cem\u003eOAS\u003c/em\u003e family is essential for host defense against viral infections (73, 74). One of the interferon-stimulated antiviral genes is \u003cem\u003eOAS2\u003c/em\u003e (75). Numerous illnesses and disorders, including inflammation, are linked to \u003cem\u003eOAS2\u003c/em\u003e, such as cancerous disorders and autoimmune disorders (76, 77). Research indicates that the OAS family has been associated with cancers, especially breast cancer. In 1986, Liu and colleagues identified a potential connection between OAS activity and tumor development in human breast tumors (78). This indicates that there has been a longstanding discussion about this gene family's role in breast cancer. Unlike OAS1 and OAS3, which are linked to poorer outcomes, OAS2 has been associated with a better prognosis in breast cancer among the OAS family members, according to a study by Yujie Zhang and his colleagues. (79). OAS2 expression decreased with NAC, potentially attributable to alterations in therapy. However, we cannot definitively assert that this is connected to the reaction until we undertake research. Unlike previous research, another study found higher OAS2 levels in breast cancer patients. Although patients didn't show mutations in the OAS family, OAS2 was mutated in 1% of samples, indicating a more significant role for this gene (80). Biological databases confirm increased expression of this gene, with OAS2 significantly elevated in various cancers, especially breast cancer, based on TCGA and UALCAN data. (81).Additionally, the \u003cem\u003eOAS2\u003c/em\u003e gene can be used as a predictor of immunotherapy and chemotherapy resistance (81) A study on trastuzumab-resistant gastric cancer highlighted the importance of the OAS gene family, including OAS2, through network analysis, emphasizing their key role in treatment resistance. (82). Another study on pan-cancer showed a positive correlation between itraconazole drug sensitivity and OAS2 and OASL expression. (83). Additionally, other studies have also highlighted the role of the OAS2 gene and its influence on neoadjuvant therapy (84\u0026ndash;86). Prior studies have indicated a correlation between this gene and response to neoadjuvant therapy; however, conclusive data remain lacking. OAS2 is highly expressed in inflammatory TNBC subpopulations and linked to chemoresistance and poor relapse-free survival, indicating a role in post-treatment resistance, according to Mauricio Jacobo and colleagues. (87). OAS2 may indicate the effectiveness of neoadjuvant immunochemotherapy in ESCC. A study showed that patients with pCR had higher OAS2 levels. It highlights the significance of OAS2 in treatment (88).These findings suggest that OAS2 has a complex role in breast cancer. We observed a decrease in OAS2 after NAC in TNBC, even though research shows an increase of OAS2 in other cancers linked to immune regulation and resistance. Its downregulation may indicate heightened chemosensitivity, highlighting OAS2's potential as a biomarker and treatment target in TNBC.\u003c/p\u003e \u003cp\u003eThe MX1 gene, a key host restriction factor in mammals, is activated by type I and III interferons during viral infections (89, 90). MX1 is overexpressed and linked to various cancers. It helps suppress invasiveness and motility in some malignancies, such as melanoma and prostate cancer (91, 92). relating to breast cancer, according to a study, \u003cem\u003eMX1\u003c/em\u003e could promote tumor growth and metastasis (93). Another study found that EGFR and E-cadherin loss, which can lead to enhanced migration and invasion, was substantially correlated with high \u003cem\u003eMX1\u003c/em\u003e protein expression (94). A Further investigation showed that higher levels of the MX1 protein were associated with more aggressive features and shorter breast cancer-specific survival, emphasizing the importance of the MX1 gene as a predictor in invasive breast cancer (95). In a single study, miR-204 and miR-211 were used to investigate the anticancer role of the MX1 gene. These two microRNAs promote breast cancer cell growth by inhibiting MX1 expression. MX1 was identified as an anticancer gene in this study (96).In some breast cancer subtypes, MX1 overexpression links to immune activation, more tumor-infiltrating lymphocytes (TILs), and improved response to anthracycline chemotherapy (97, 98). We observed reduced MX1 expression following NAC; however, the direct relationship between this reduction and treatment response requires additional validation. In PDX models of ER-negative breast cancer, MX1 was significantly increased after a single chemotherapy cycle, but not in resistant models (99). In a study of 109 TNBC patients, short disease-free survival after chemotherapy was associated with high MX1 expression in tumor cells; this association was reduced when MX1 expression was inhibited. MX1 may be a resistance factor in NAC TNBC, as shown in the study (100). Another study found high MX1 levels linked to worse prognosis in women with breast cancer not receiving chemotherapy. Its predictive effect was attenuated in patients who did, indicating that more research is needed to validate MX1 as a biomarker (95).MX1 regulates TNBC depending on context. Although high MX1 is associated with aggressive tumors, poor prognosis, and resistance, our results suggest that decreased MX1 expression after treatment may reflect therapy-related changes; however, its effectiveness as a biomarker requires further validation. More research is needed to clarify its role, as conflicting findings have emerged.\u003c/p\u003e \u003cp\u003eMany cell types express \u003cem\u003eCXCL10\u003c/em\u003e, a significant signaling protein that has a role in immune response and host defense against bacterial and viral infections (101, 102). Many human disorders are characterized by elevated \u003cem\u003eCXCL10\u003c/em\u003e expression. It has been demonstrated to be implicated in the pathogenic processes of three significant human illnesses (103). CXCL10 is a chemokine with anti-tumor properties that inhibits angiogenesis. However, advanced human malignancies have also been associated with increased CXCL10 expression (104). The CXCL10 gene significantly influences the development of breast cancer (105). A. Jafarzadeh and colleagues showed that high CXCL10 in BC patients confirms its role in tumor growth, emphasizing CXCL10's importance in breast cancer (106). Ahmed A. Ejaeidi and colleagues concluded that CXCL10 is crucial in breast cancer growth. The study also indicates that HR-negative patients, including those with TNBC, have higher CXCL10 levels, suggesting that CXCL10 may promote TNBC invasion and metastasis. (107). A study found the CXCL10 gene is crucial for TNBC breast cancer, using the GEO database and network analysis. It identified CXCL10 as one of two key biomarkers for TNBC, similar to our study (108). The CXCL10 gene may have two roles in TNBC: it promotes motility and infiltration leading to lung metastasis, and serves as a target for immunotherapy by increasing immune infiltration (109).\u003c/p\u003e \u003cp\u003eCXCL10 contributes to breast cancer treatment resistance, especially in chemotherapy escape and tamoxifen resistance in TNBC. In tamoxifen-resistant cells, CXCL10 promotes growth via the AKT pathway. Inhibiting it restores therapy sensitivity, making it a potential target and marker (110). According to a separate study examining the relationship between CXCL10 in TNBC and its treatment, CXCL10 in TNBC converts \"cold\" tumors into \"hot\" ones by activating the type I IFN pathway, and the combination of carboplatin and HDACi can improve TNBC's response to NAC by increasing CXCL10 (111). Milim Kim's research demonstrates that CXCL10 can facilitate progression from DCIS to invasive disease by enhancing TNBC aggressiveness and predicting an immunogenic NAC response. It shows that CXCL10 plays two roles: one in tumor growth and the other in immunotherapy (112). CXCL10 has two roles in TNBC: it can make cancers resistant to chemotherapy and tamoxifen by activating the AKT pathway, and it can also enhance the immune system's ability to fight tumors by signaling through type I interferon. This process transforms \"cold\" tumors into \"hot,\" immune-active tumors. After NAC, CXCL10 expression decreases dramatically (logFC = -8.6), presumably indicating increased treatment sensitivity and suggesting that CXCL10 may serve as a biomarker for predicting NAC response in TNBC; however, further clinical studies are needed to confirm this.\u003c/p\u003e \u003cp\u003eOur bioinformatics research identified five key genes\u0026mdash;ISG15, IFIH1, OAS2, MX1, and CXCL10\u0026mdash;as potential indicators for the NAC response in TNBC. These genes play roles in regulating the immune system, signaling through interferon, and resistance. This highlights the importance of immunological pathways for treatment outcomes. Their downregulation after NAC may indicate a better response and increased sensitivity to chemotherapy. While these findings offer new insights into NAC mechanisms and potential prognostic markers, further validation is necessary.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eThis study has several limitations that need attention. The analysis relies on a single RNA-seq dataset (GSE260989) with only 14 paired pre- and post-treatment samples. This may cause selection bias and limit how well the identified hub genes (ISG15, IFIH1, OAS2, MX1, and CXCL10) can serve as predictive biomarkers across different TNBC populations, which may vary by ethnicity, tumor stage, or treatment protocols. The findings are solely based on in silico bioinformatics methods and lack experimental validation through laboratory techniques such as quantitative real-time PCR (qRT-PCR), Western blotting, immunohistochemistry, or functional assays in cell lines, patient-derived xenografts, or animal models to explore their mechanistic roles in NAC response. Intrinsic batch effects within the dataset could also affect gene expression patterns and co-expression networks. Future research should include multi-omics integration, large multi-center cohorts, and prospective clinical trials to validate these biomarkers and overcome these limitations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, five genes, ISG15, IFIH1, OAS2, MX1, and CXCL10, were identified as candidate markers that may be associated with NAC response in TNBC through this integrated bioinformatics study. After treatment, all five genes consistently showed decreased expression, suggesting that downregulation may be related to treatment-associated biological changes; however, further validation is needed to support this interpretation. And a shift toward a less aggressive tumor microenvironment. More experiments are necessary to confirm their clinical relevance and mechanistic roles. However, the study's main limitation was the relatively small sample size. Therefore, further research with larger samples and experimental validation is essential to confirm these findings and better understand the molecular functions of these genes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDanial Ahdi : Script writer and data analystNaser Elmi : Review of data analysis and script writingSima Mansoori Derakhshan : Supervision of the text and writing of the discussion\u003c/p\u003e\u003ch2\u003e66. Acknowledgement\u003c/h2\u003e \u003cp\u003eof Reviewers of Canadian Journal of Public Health articles CJPHFds\u0026mdash;.\u003c/p\u003e \u003cp\u003e67. Yamashita N, Fushimi A, Morimoto Y, Bhattacharya A, Long M, Liu S, et al. Abstract P1-04-09: Essential role for MUC1-C in chronic activation of cytosolic nucleotide sensing and the type I interferon pathway in triple-negative breast cancer. Cancer Research. 2022;82(4_Supplement):P1-04-9-P1\u0026ndash;9.\u003c/p\u003e \u003cp\u003e68. Nowacka M G-MB, Świerczewska M, Nowicki M, Zabel M, Sterzyńska K, Januchowski R. The significance of HERC5, IFIH1, SAMD4, SEMA3A and MCTP1 genes expression in resistance to cytotoxic drugs in ovarian cancer cell lines. Medical Journal of Cell Biology. 2021;9(3):138\u0026thinsp;\u0026minus;\u0026thinsp;47.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e2022) WHOBchwwin-rf-sdb-caJ.\u003c/li\u003e\n\u003cli\u003e2022) CRUBcshwcoh-pc-ss-b-c-tb-caJ.\u003c/li\u003e\n\u003cli\u003eWu J FD, Shao Z, Xu B, Ren G, Jiang Z, Wang Y, Jin F, Zhang J, Zhang Q, Ma F, Ma J, Wang Z, Wang S, Wang X, Wang S, Wang H, Wang T, Wang X, Wang J, Wang J, Wang B, Fu L, Li H, Shi Y, Gan L, Liu Y, Liu J, Liu Z, Liu Q, Sun Q, Cheng W, Yu K, Tong Z, Wu X, Song C, Zhang J, Zhang J, Li J, Li B, Li M, Li H, Yang W, Yang H, Yang B, Bu H, Shen J, Shen Z, Chen Y, Chen C, Pang D, Fan Z, Zheng Y, Yu X, Liu G, Hu X, Ling Y, Tang J, Yin Y, Geng C, Yuan P, Gu Y, Chang C, Cao X, Sheng Y, Huang Y, Huang J, Peng W, Zeng X, Xie Y, Liao N; Committee of Breast Cancer Society, Chinese Anti-Cancer Association. CACA Guidelines for Holistic Integrative Management of Breast Cancer. Holist Integr Oncol. 2022;1(1):7. doi: 10.1007/s44178-022-00007-8. Epub 2022 Jul 5. PMID: 37520336; PMCID: PMC9255514.\u003c/li\u003e\n\u003cli\u003eGoldhirsch A WE, Coates AS, Gelber RD, Piccart-Gebhart M, Th\u0026uuml;rlimann B, Senn HJ; Panel members. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol. 2013 Sep;24(9):2206-23. doi: 10.1093/annonc/mdt303. Epub 2013 Aug 4. PMID: 23917950; PMCID: PMC3755334.\u003c/li\u003e\n\u003cli\u003eWolff AC HM, Hicks DG, Dowsett M, McShane LM, Allison KH, Allred DC, Bartlett JM, Bilous M, Fitzgibbons P, Hanna W, Jenkins RB, Mangu PB, Paik S, Perez EA, Press MF, Spears PA, Vance GH, Viale G, Hayes DF; American Society of Clinical Oncology; College of American Pathologists. Recommendations for human epidermal growth factor receptor two testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update. J Clin Oncol. 2013 Nov 1;31(31):3997-4013. doi: 10.1200/JCO.2013.50.9984. Epub 2013 Oct 7. PMID: 24101045.\u003c/li\u003e\n\u003cli\u003eMorris GJ NS, Topham AK, Guiles F, Xu Y, McCue P, Schwartz GF, Park PK, Rosenberg AL, Brill K, Mitchell EP. Differences in breast carcinoma characteristics in newly diagnosed African-American and Caucasian patients: a single-institution compilation compared with the National Cancer Institute\u0026apos;s Surveillance, Epidemiology, and End Results database. Cancer. 2007 Aug 15;110(4):876-84. doi: 10.1002/cncr.22836. PMID: 17620276.\u003c/li\u003e\n\u003cli\u003eDent R TM, Pritchard KI, Hanna WM, Kahn HK, Sawka CA, Lickley LA, Rawlinson E, Sun P, Narod SA. Triple-negative breast cancer: clinical features and patterns of recurrence. Clin Cancer Res. 2007 Aug 1;13(15 Pt 1):4429-34. doi: 10.1158/1078-0432.CCR-06-3045. PMID: 17671126.\u003c/li\u003e\n\u003cli\u003eLin NU CE, Sohl J, Razzak AR, Arnaout A, Winer EP. Sites of distant recurrence and clinical outcomes in patients with metastatic triple-negative breast cancer: high incidence of central nervous system metastases. Cancer. 2008 Nov 15;113(10):2638-45. doi: 10.1002/cncr.23930. PMID: 18833576; PMCID: PMC2835546.\u003c/li\u003e\n\u003cli\u003eSchegerin M TA, Kaufman PA, Paulsen KD, Pogue BW. Prognostic imaging in neoadjuvant chemotherapy of locally-advanced breast cancer should be cost-effective. Breast Cancer Res Treat. 2009 Apr;114(3):537-47. doi: 10.1007/s10549-008-0025-2. Epub 2008 Apr 25. PMID: 18437559; PMCID: PMC2807135.\u003c/li\u003e\n\u003cli\u003eMcElnay P LEAoncfNJTDMSSS-dji-.\u003c/li\u003e\n\u003cli\u003eDenkert C SB, Issa Y, Mueller BM, Maisch A, Untch M, Von Minckwitz G, Loibl S. Prediction of response to neoadjuvant chemotherapy: new biomarker approaches and concepts. Breast Care. 2011 Aug 29;6(4):265-72.\u003c/li\u003e\n\u003cli\u003eAsselain B BW, Bartlett J, Bergh J, Bergsten-Nordstr\u0026ouml;m E, Bliss J, Boccardo F, Boddington C, Bogaerts J, Bonadonna G, Bradley R. Long-term outcomes for neoadjuvant versus adjuvant chemotherapy in early breast cancer: meta-analysis of individual patient data from ten randomised trials. The Lancet Oncology. 2018 Jan 1;19(1):27-39.\u003c/li\u003e\n\u003cli\u003eGl\u0026uuml;ck S RJ, Royce M, McKenna EF Jr, Perou CM, Avisar E, Wu L. TP53 genomics predict higher clinical and pathologic tumor response in operable early-stage breast cancer treated with docetaxel-capecitabine \u0026plusmn; trastuzumab. Breast Cancer Res Treat. 2012 Apr;132(3):781-91. doi: 10.1007/s10549-011-1412-7. Epub 2011 Mar 4. PMID: 21373875.\u003c/li\u003e\n\u003cli\u003eLiedtke C MC, Hess KR, Andr\u0026eacute; F, Tordai A, Mejia JA, Symmans WF, Gonzalez-Angulo AM, Hennessy B, Green M, Cristofanilli M, Hortobagyi GN, Pusztai L. Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. J Clin Oncol. 2008 Mar 10;26(8):1275-81. doi: 10.1200/JCO.2007.14.4147. Epub 2008 Feb 4. PMID: 18250347.\u003c/li\u003e\n\u003cli\u003eGarrido-Castro AC LN, Polyak K. Insights into Molecular Classifications of Triple-Negative Breast Cancer: Improving Patient Selection for Treatment. Cancer Discov. 2019 Feb;9(2):176-198. doi: 10.1158/2159-8290.CD-18-1177. Epub 2019 Jan 24. PMID: 30679171; PMCID: PMC6387871.\u003c/li\u003e\n\u003cli\u003eBiomarkers Definitions Working Group AJA, Colburn WA, DeGruttola VG, DeMets DL, Downing GJ, Hoth DF, Oates JA, Peck CC, Schooley RT, Spilker BA. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clinical pharmacology \u0026amp; therapeutics. 2001 Mar;69(3):89-95.\u003c/li\u003e\n\u003cli\u003eKatayama A MI, Shiino S, Toss MS, Eldib K, Kurozumi S, Quinn CM, Badr N, Murray C, Provenzano E, Callagy G. Predictors of pathological complete response to neoadjuvant treatment and changes to post-neoadjuvant HER2 status in HER2-positive invasive breast cancer. Modern Pathology. 2021 Jul 1;34(7):1271-81.\u003c/li\u003e\n\u003cli\u003eFreitas AJ CR, Varuzza MB, Hidalgo Filho CM, Silva VD, Souza CD, Marques MM. Molecular biomarkers predict pathological complete response of neoadjuvant chemotherapy in breast cancer patients. Cancers. 2021 Oct 31;13(21):5477.\u003c/li\u003e\n\u003cli\u003eBaumgartner C OM, Netzer M, Baumgartner D. Bioinformatic-driven search for metabolic biomarkers in disease. Journal of clinical bioinformatics. 2011 Jan 20;1(1):2.\u003c/li\u003e\n\u003cli\u003eEdgar R DM, Lash AE., repository GEONgeahad, 1;30(1):207-10 NARJ.\u003c/li\u003e\n\u003cli\u003eGandhi S SR, Janes C, Fitzpatrick V, Miller J, Attwood K, Ioannou G, Ozbey S, De Souza I, Roudko V, Kumar P, Kalathil S, Kokolus KM, Wang J, Cortes Gomez E, Takabe K, Edge S, Young J, Cappuccino H, Opyrchal M, O\u0026apos;Connor T, Levine EG, Gnjatic S, Kalinski P. Systemic chemokine-modulatory regimen combined with neoadjuvant chemotherapy in patients with triple-negative breast cancer. J Immunother Cancer. 2024 Nov 14;12(11):e010058. doi: 10.1136/jitc-2024-010058. PMID: 39542655; PMCID: PMC11575314.\u003c/li\u003e\n\u003cli\u003ehttps://trace.ncbi.nlm.nih.gov/Traces/sra/sra.cgi?view=software. [\u003c/li\u003e\n\u003cli\u003efrom:https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ASFaqctfhtsdIA.\u003c/li\u003e\n\u003cli\u003eBolger AM LM, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014 Aug 1;30(15):2114-20. doi: 10.1093/bioinformatics/btu170. Epub 2014 Apr 1. PMID: 24695404; PMCID: PMC4103590.\u003c/li\u003e\n\u003cli\u003eLi H HB, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R; 1000 Genome Project Data Processing Subgroup. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009 Aug 15;25(16):2078-9. doi: 10.1093/bioinformatics/btp352. Epub 2009 Jun 8. PMID: 19505943; PMCID: PMC2723002.\u003c/li\u003e\n\u003cli\u003eAnders S PP, Huber W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015 Jan 15;31(2):166-9. doi: 10.1093/bioinformatics/btu638. Epub 2014 Sep 25. PMID: 25260700; PMCID: PMC4287950.\u003c/li\u003e\n\u003cli\u003eLove MI HW, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. doi: 10.1186/s13059-014-0550-8. PMID: 25516281; PMCID: PMC4302049.\u003c/li\u003e\n\u003cli\u003eCarlson M FS, Pages H, Li N. org. Hs. eg. db: Genome wide annotation for Human. R package version. 2019;3(2):3.\u003c/li\u003e\n\u003cli\u003ePag\u0026egrave;s H CM, Falcon S, Li N. AnnotationDbi: Manipulation of SQLite-based annotations in Bioconductor. R package version. 2021 Apr;1(1).\u003c/li\u003e\n\u003cli\u003eauthor = {Hadley Wickham}, title = {ggplot2: Elegant Graphics for Data Analysis}, publisher = {Springer-Verlag New York}, year = {2016}, isbn = {978-3-319-24277-4}, url = {https://ggplot2.tidyverse.org}.\u003c/li\u003e\n\u003cli\u003eLangfelder P HSWaRpfwcnaBBDd---P.\u003c/li\u003e\n\u003cli\u003eSzklarczyk D KR, Koutrouli M, Nastou K, Mehryary F, Hachilif R, Gable AL, Fang T, Doncheva NT, Pyysalo S, Bork P, Jensen LJ, von Mering C. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023 Jan 6;51(D1):D638-D646. doi: 10.1093/nar/gkac1000. PMID: 36370105; PMCID: PMC9825434.\u003c/li\u003e\n\u003cli\u003eShannon P MA, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003 Nov;13(11):2498-504. doi: 10.1101/gr.1239303. PMID: 14597658; PMCID: PMC403769.\u003c/li\u003e\n\u003cli\u003eChen EY TC, Kou Y, Duan Q, Wang Z, Meirelles GV, Clark NR, Ma\u0026apos;ayan A. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013 Apr 15;14:128. doi: 10.1186/1471-2105-14-128. PMID: 23586463; PMCID: PMC3637064.\u003c/li\u003e\n\u003cli\u003e@Book{, author = {Hadley Wickham}, title = {ggplot2: Elegant Graphics for Data Analysis}, publisher = {Springer-Verlag New York}, year = {2016}, isbn = {978-3-319-24277-4}, et al.\u003c/li\u003e\n\u003cli\u003e@Manual{, title = {tidyr: Tidy Messy Data}, author = {Hadley Wickham and Davis Vaughan and Maximilian Girlich}, year = {2025}, note = {R package version 1.3.1}, url = {https://tidyr.tidyverse.org}, et al.\u003c/li\u003e\n\u003cli\u003e@Manual{, title = {dplyr: A Grammar of Data Manipulation}, author = {Hadley Wickham and Romain Fran\u0026ccedil;ois and Lionel Henry and Kirill M\u0026uuml;ller and Davis Vaughan}, year = {2025}, note = {R package version 1.1.4}, url = {https://dplyr.tidyverse.org}, et al.\u003c/li\u003e\n\u003cli\u003e@Article{, author = {Yunshun Chen and Lizhong Chen and Aaron T L Lun and Pedro Baldoni and Gordon K Smyth}, title = {{edgeR} v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets}, year = {2025}, journal = {Nucleic Acids Research}, volume = {53}, et al.\u003c/li\u003e\u003cli\u003eLoibl S PP, Morrow M, Denkert C, Curigliano G. Breast cancer. Lancet. 2021 May 8;397(10286):1750-1769. doi: 10.1016/S0140-6736(20)32381-3. Epub 2021 Apr 1. Erratum in: Lancet. 2021 May 8;397(10286):1710. doi: 10.1016/S0140-6736(21)00838-2. PMID: 33812473.\u003c/li\u003e\u003cli\u003ehttps://www.cancer.org/. [\u003c/li\u003e\u003cli\u003eXu S MS, Han Y, Wan F, Toriola AT. Breast Cancer Incidence Among US Women Aged 20 to 49 Years by Race, Stage, and Hormone Receptor Status. JAMA Netw Open. 2024 Jan 2;7(1):e2353331. doi: 10.1001/jamanetworkopen.2023.53331. PMID: 38277147; PMCID: PMC10818222.\u003c/li\u003e\u003cli\u003eFoulkes WD SI, Reis-Filho JS. Triple-negative breast cancer. N Engl J Med. 2010 Nov 11;363(20):1938-48. doi: 10.1056/NEJMra1001389. PMID: 21067385.\u003c/li\u003e\u003cli\u003eO\u0026apos;Brien KM CS, Tse CK, Perou CM, Carey LA, Foulkes WD, Dressler LG, Geradts J, Millikan RC. Intrinsic breast tumor subtypes, race, and long-term survival in the Carolina Breast Cancer Study. Clin Cancer Res. 2010 Dec 15;16(24):6100-10. doi: 10.1158/1078-0432.CCR-10-1533. PMID: 21169259; PMCID: PMC3029098.\u003c/li\u003e\u003cli\u003eIwamoto T, Kajiwara Y, Zhu Y, Iha S. Biomarkers of neoadjuvant/adjuvant chemotherapy for breast cancer. Chinese Clinical Oncology. 2020;9(3):27.\u003c/li\u003e\u003cli\u003eStearman RS BQ, Speyer G, Handen A, Cornelius AR, Graham BB, Kim S, Mickler EA, Tuder RM, Chan SY, Geraci MW. Systems Analysis of the Human Pulmonary Arterial Hypertension Lung Transcriptome. Am J Respir Cell Mol Biol. 2019 Jun;60(6):637-649. doi: 10.1165/rcmb.2018-0368OC. PMID: 30562042; PMCID: PMC6543748.\u003c/li\u003e\u003cli\u003ePerng YC LDIiaiabNRMJ-ds---P.\u003c/li\u003e\u003cli\u003eXu T ZC, Chen J, Song F, Ren X, Wang S, Yi X, Zhang Y, Zhang W, Hu Q, Qin H, Liu Y, Zhang S, Tan Z, Pan Z, Huang P, Ge M. ISG15 and ISGylation modulates cancer stem cell-like characteristics in promoting tumor growth of anaplastic thyroid carcinoma. J Exp Clin Cancer Res. 2023 Jul 27;42(1):182. doi: 10.1186/s13046-023-02751-9. Erratum in: J Exp Clin Cancer Res. 2024 Nov 13;43(1):301. doi: 10.1186/s13046-024-03226-1. PMID: 37501099; PMCID: PMC10373324.\u003c/li\u003e\u003cli\u003eHan HG MH, Jeon YJ. ISG15 in cancer: Beyond ubiquitin-like protein. Cancer Lett. 2018 Dec 1;438:52-62. doi: 10.1016/j.canlet.2018.09.007. Epub 2018 Sep 11. PMID: 30213559.\u003c/li\u003e\u003cli\u003eOkumura F OA, Uematsu K, Hatakeyama S, Zhang DE, Kamura T. Activation of double-stranded RNA-activated protein kinase (PKR) by interferon-stimulated gene 15 (ISG15) modification down-regulates protein translation. J Biol Chem. 2013 Jan 25;288(4):2839-47. doi: 10.1074/jbc.M112.401851. Epub 2012 Dec 10. PMID: 23229543; PMCID: PMC3554948.\u003c/li\u003e\u003cli\u003eXu D ZT, Xiao J, Zhu K, Wei R, Wu Z, Meng H, Li Y, Yuan J. Modification of BECN1 by ISG15 plays a crucial role in autophagy regulation by type I IFN/interferon. Autophagy. 2015 Apr 3;11(4):617-28. doi: 10.1080/15548627.2015.1023982. PMID: 25906440; PMCID: PMC4502663.\u003c/li\u003e\u003cli\u003eD\u0026apos;Cunha J KEJ, Haas AL, Truitt RL, Borden EC. Immunoregulatory properties of ISG15, an interferon-induced cytokine. Proc Natl Acad Sci U S A. 1996 Jan 9;93(1):211-5. doi: 10.1073/pnas.93.1.211. PMID: 8552607; PMCID: PMC40208.\u003c/li\u003e\u003cli\u003eDesai SD RR, Burks J, Wood LM, Pullikuth AK, Haas AL, Liu LF, Breslin JW, Meiners S, Sankar S. ISG15 disrupts cytoskeletal architecture and promotes motility in human breast cancer cells. Exp Biol Med (Maywood). 2012 Jan;237(1):38-49. doi: 10.1258/ebm.2011.011236. Epub 2011 Dec 20. PMID: 22185919.\u003c/li\u003e\u003cli\u003eBurks J RR, Desai SD. ISGylation governs the oncogenic function of Ki-Ras in breast cancer. Oncogene. 2014 Feb 6;33(6):794-803. doi: 10.1038/onc.2012.633. Epub 2013 Jan 14. PMID: 23318454.\u003c/li\u003e\u003cli\u003eAngeles C. Tecalco-Cruz EC-R, Protein ISGylation and free ISG15 levels are increased by interferon gamma in breast cancer cells, Biochemical and Biophysical Research Communications, Volume 499 I, 2018, Pages 973-978, et al.\u003c/li\u003e\u003cli\u003eFeiran Wang NZ, Ruishu Niu, Yunpeng Lu, Wei Zhang, Zhixian He,, Identification of biomimetic nanoplatform-mediated delivery of si-ISG15 for treatment of triple-negative breast cancer, Cellular Signalling, Volume 118, 2024, 111117, et al.\u003c/li\u003e\u003cli\u003eDesai SD, Wood LM, Tsai Y-C, Hsieh T-S, Marks JR, Scott GL, et al. ISG15 as a novel tumor biomarker for drug sensitivity. Molecular Cancer Therapeutics. 2008;7(6):1430-9.\u003c/li\u003e\u003cli\u003eBoelens MC WT, Nabet BY, Xu B, Qiu Y, Yoon T, Azzam DJ, Twyman-Saint Victor C, Wiemann BZ, Ishwaran H, Ter Brugge PJ. Exosome transfer from stromal to breast cancer cells regulates therapy resistance pathways. Cell. 2014 Oct 23;159(3):499-513.\u003c/li\u003e\u003cli\u003eKang JA, Kim YJ, Jeon YJ. The diverse repertoire of ISG15: more intricate than initially thought. Experimental \u0026amp; Molecular Medicine. 2022;54(11):1779-92.\u003c/li\u003e\u003cli\u003eShen J WJ, Wang H, Yue G, Yu L, Yang Y, Xie L, Zou Z, Qian X, Ding Y, Guan W. A three-gene signature as potential predictive biomarker for irinotecan sensitivity in gastric cancer. Journal of translational medicine. 2013 Mar 22;11(1):73.\u003c/li\u003e\u003cli\u003eChun JH KH, Kim E, Kim IH, Kim JH, Chang HJ, Choi IJ, Lim HS, Kim IJ, Kang HC, Park JH. Increased expression of metallothionein is associated with irinotecan resistance in gastric cancer. Cancer research. 2004 Jul 15;64(14):4703-6.\u003c/li\u003e\u003cli\u003eFan JB M-IS, Arimoto K, Liu D, Yan M, Liu CW, Győrffy B, Zhang DE. Type I IFN induces protein ISGylation to enhance cytokine expression and augments colonic inflammation. Proc Natl Acad Sci U S A. 2015 Nov 17;112(46):14313-8. doi: 10.1073/pnas.1505690112. Epub 2015 Oct 29. PMID: 26515094; PMCID: PMC4655505.\u003c/li\u003e\u003cli\u003eLo PK YY, Lee JS, Zhang Y, Huang W, Kane MA, Zhou Q. LIPG signaling promotes tumor initiation and metastasis of human basal-like triple-negative breast cancer. Elife. 2018 Jan 19;7:e31334. doi: 10.7554/eLife.31334. PMID: 29350614; PMCID: PMC5809145.\u003c/li\u003e\u003cli\u003eLamborn IT JH, Zhang Y, Drutman SB, Abbott JK, Munir S, Bade S, Murdock HM, Santos CP, Brock LG, Masutani E, Fordjour EY, McElwee JJ, Hughes JD, Nichols DP, Belkadi A, Oler AJ, Happel CS, Matthews HF, Abel L, Collins PL, Subbarao K, Gelfand EW, Ciancanelli MJ, Casanova JL, Su HC. Recurrent rhinovirus infections in a child with inherited MDA5 deficiency. J Exp Med. 2017 Jul 3;214(7):1949-1972. doi: 10.1084/jem.20161759. Epub 2017 Jun 12. PMID: 28606988; PMCID: PMC5502429.\u003c/li\u003e\u003cli\u003eYao L CR, Ji C, Zhou X, Luan J, Meng X, Song N. RNA-Binding Proteins Play an Important Role in the Prognosis of Patients With Testicular Germ Cell Tumor. Front Genet. 2021 Mar 11;12:610291. doi: 10.3389/fgene.2021.610291. PMID: 33777092; PMCID: PMC7990889.\u003c/li\u003e\u003cli\u003eShi C WX, Li J, Wu S, Liu Z, Ren X, Zhang X, Liu Y. IFIH1 promotes apoptosis through the TBK1/IRF3 pathway in triple-negative breast cancer. Neoplasma. 2024 Dec;71(6):533-543. doi: 10.4149/neo_2024_240614N255. PMID: 39832201.\u003c/li\u003e\u003cli\u003eAcknowledgement of Reviewers of Canadian Journal of Public Health articles CJPHFds---.\u003c/li\u003e\u003cli\u003eYamashita N, Fushimi A, Morimoto Y, Bhattacharya A, Long M, Liu S, et al. Abstract P1-04-09: Essential role for MUC1-C in chronic activation of cytosolic nucleotide sensing and the type I interferon pathway in triple-negative breast cancer. Cancer Research. 2022;82(4_Supplement):P1-04-9-P1--9.\u003c/li\u003e\u003cli\u003eNowacka M G-MB, Świerczewska M, Nowicki M, Zabel M, Sterzyńska K, Januchowski R. The significance of HERC5, IFIH1, SAMD4, SEMA3A and MCTP1 genes expression in resistance to cytotoxic drugs in ovarian cancer cell lines. Medical Journal of Cell Biology. 2021;9(3):138-47.\u003c/li\u003e\u003cli\u003eTher. sgic-rpcMC, DOI:10.1158/1535-7163.MCT-11-0289. -.\u003c/li\u003e\u003cli\u003eKhazaei G SF, Yamchi A, Golalipour M, Jhingan GD, Shahbazi M. Proteomics evaluation of MDA-MB-231 breast cancer cells in response to RNAi-induced silencing of hPTTG. Life Sci. 2019 Dec 15;239:116873. doi: 10.1016/j.lfs.2019.116873. Epub 2019 Sep 12. PMID: 31521689.\u003c/li\u003e\u003cli\u003eBauer M, Vetter M, Maia A, Vlachavas E, Michels B, Berdiel-Acer M, et al. Abstract P1-08-15: Communication between tumor cells and fibroblasts as a prognostic factor of NACT in TNBC. Cancer Research. 2022;82(4_Supplement):P1-08-15-P1-08-15.\u003c/li\u003e\u003cli\u003eZhao N, Kabotyanski EB, Saltzman AB, Malovannaya A, Yuan X, Reineke LC, et al. Targeting eIF4A triggers an interferon response to synergize with chemotherapy and suppress triple-negative breast cancer. The Journal of Clinical Investigation. 2023;133(24).\u003c/li\u003e\u003cli\u003eKakuta S SS, Iwakura Y. Genomic structure of the mouse 2\u0026apos;,5\u0026apos;-oligoadenylate synthetase gene family. J Interferon Cytokine Res. 2002 Sep;22(9):981-93. doi: 10.1089/10799900260286696. PMID: 12396720.\u003c/li\u003e\u003cli\u003eLin RJ YH, Chang BL, Tang WC, Liao CL, Lin YL. Distinct antiviral roles for human 2\u0026apos;,5\u0026apos;-oligoadenylate synthetase family members against dengue virus infection. J Immunol. 2009 Dec 15;183(12):8035-43. doi: 10.4049/jimmunol.0902728. PMID: 19923450.\u003c/li\u003e\u003cli\u003eLiao X XH, Li S, Ye H, Li S, Ren K, Li Y, Xu M, Lin W, Duan X, Yang C. 2\u0026prime;, 5\u0026prime;-oligoadenylate synthetase 2 (OAS2) inhibits Zika virus replication through activation of type \u0026Iota; IFN signaling pathway. Viruses. 2020 Apr 8;12(4):418.\u003c/li\u003e\u003cli\u003eManning G TA, Sir\u0026aacute;k I, Badie C. Radiotherapy-Associated Long-term Modification of Expression of the Inflammatory Biomarker Genes ARG1, BCL2L1, and MYC. Front Immunol. 2017 Apr 10;8:412. doi: 10.3389/fimmu.2017.00412. PMID: 28443095; PMCID: PMC5385838.\u003c/li\u003e\u003cli\u003eFite BZ WJ, Kare AJ, Ilovitsh A, Chavez M, Ilovitsh T, Zhang N, Chen W, Robinson E, Zhang H, Kheirolomoom A, Silvestrini MT, Ingham ES, Mahakian LM, Tam SM, Davis RR, Tepper CG, Borowsky AD, Ferrara KW. Immune modulation resulting from MR-guided high intensity focused ultrasound in a model of murine breast cancer. Sci Rep. 2021 Jan 13;11(1):927. doi: 10.1038/s41598-020-80135-1. PMID: 33441763; PMCID: PMC7806949.\u003c/li\u003e\u003cli\u003eLiu DK OG, Feil PD. 2\u0026apos;,5\u0026apos;-oligoadenylate synthetase activity in human mammary tumors and its potential correlation with tumor growth or hormonal responsiveness. Cancer Res. 1986 Dec;46(12 Pt 1):6207-10. PMID: 3779641.\u003c/li\u003e\u003cli\u003eZhang Y YCPcoOOOOibcBcJ.\u003c/li\u003e\u003cli\u003eLu J YL, Yang X, Chen B, Liu Z. Investigating the clinical significance of OAS family genes in breast cancer: an in vitro and in silico study. Hereditas. 2024 Dec 5;161(1):50.\u003c/li\u003e\u003cli\u003eJia H LX, Wang Z, Zhang W, Chen X. A Pan-Cancer Analysis Reveals OAS2 as a Biomarker for Cancer Prognosis and Immunotherapy.\u003c/li\u003e\u003cli\u003eYu C XP, Zhang L, Pan R, Cai Z, He Z, Sun J, Zheng M. Prediction of key genes and pathways involved in trastuzumab-resistant gastric cancer. World Journal of Surgical Oncology. 2018 Aug 22;16(1):174.\u003c/li\u003e\u003cli\u003eWang X CY, Tian Y, Song Z, He Z, Shen P, Wang H, Luo L, Cui R. Prognosis and Immune Cell Infiltration Analysis of OAS Family Genes in Pan-Cancer.\u003c/li\u003e\u003cli\u003eZhang Y XX, Mu X, Wang J, Zhang J, Xiang G, Li J, Zheng C, Wang H, Lu Q. Effect of immune infiltration intensity on the efficacy of neoadjuvant immunotherapy for esophageal cancer. Front Immunol. 2025 Jun 12;16:1543283. doi: 10.3389/fimmu.2025.1543283. PMID: 40574841; PMCID: PMC12198219.\u003c/li\u003e\u003cli\u003eKim JC HY, Tak KH, Roh SA, Kwon YH, Kim CW, et al. (2018) Opposite functions of GSN and OAS2 on colorectal cancer metastasis, mediating perineural and lymphovascular invasion, respectively. PLoS ONE 13(8): e0202856. https://doi.org/10.1371/journal.pone.0202856.\u003c/li\u003e\u003cli\u003eHo W-HJ, Law AMK, Masle-Farquhar E, Castillo LE, Mawson A, O\u0026rsquo;Bryan MK, et al. Activation of the viral sensor oligoadenylate synthetase 2 (Oas2) prevents pregnancy-driven mammary cancer metastases. Breast Cancer Research. 2022;24(1):31.\u003c/li\u003e\u003cli\u003eJacobo Jacobo M, Donnella HJ, Sobti S, Kaushik S, Goga A, Bandyopadhyay S. An inflamed tumor cell subpopulation promotes chemotherapy resistance in triple negative breast cancer. Scientific Reports. 2024;14(1):3694.\u003c/li\u003e\u003cli\u003eJiang N, Jiang M, Zhu X, Ren B, Zhang J, Guo Z, et al. SCALE-1: Safety and efficacy of short course neoadjuvant chemo-radiotherapy plus toripalimab for locally advanced resectable squamous cell carcinoma of esophagus. Journal of Clinical Oncology. 2022;40(16_suppl):4063-.\u003c/li\u003e\u003cli\u003eHaller O AH, Pavlovic J, Staeheli P. The Discovery of the Antiviral Resistance Gene Mx: A Story of Great Ideas, Great Failures, and Some Success. Annu Rev Virol. 2018 Sep 29;5(1):33-51. doi: 10.1146/annurev-virology-092917-043525. Epub 2018 Jun 29. PMID: 29958082.\u003c/li\u003e\u003cli\u003eBergmann S BL, Schughart K. Differential lung gene expression changes in C57BL/6 and DBA/2 mice carrying an identical functional Mx1 gene reveals crucial differences in the host response. BMC Genom Data. 2024 Feb 15;25(1):19. doi: 10.1186/s12863-024-01203-3. PMID: 38360537; PMCID: PMC10870463.\u003c/li\u003e\u003cli\u003eErnest C. Borden, 53 - Interferons, Editor(s): John Mendelsohn JWG, Peter M. Howley, Mark A. Israel, Craig B. Thompson,, The Molecular Basis of Cancer (Fourth Edition), W.B. Saunders, 2015, et al.\u003c/li\u003e\u003cli\u003eCalmon MF RR, Kaneto CM, Moura RP, Silva SD, Mota LD, Pinheiro DG, Torres C, De Carvalho AF, Cury PM, Nunes FD. Epigenetic silencing of CRABP2 and MX1 in head and neck tumors. Neoplasia. 2009 Dec 1;11(12):1329-IN9.\u003c/li\u003e\u003cli\u003eJohansson HJ SB, Forshed J, St\u0026aring;l O, Fohlin H, Lewensohn R, Hall P, Bergh J, Lehti\u0026ouml; J, Linderholm BK. Proteomics profiling identify CAPS as a potential predictive marker of tamoxifen resistance in estrogen receptor positive breast cancer. Clinical Proteomics. 2015 Dec;12(1):8.\u003c/li\u003e\u003cli\u003eMasuda H ZD, Bartholomeusz C, Doihara H, Hortobagyi GN, Ueno NT. Role of epidermal growth factor receptor in breast cancer. Breast Cancer Res Treat. 2012 Nov;136(2):331-45. doi: 10.1007/s10549-012-2289-9. Epub 2012 Oct 17. PMID: 23073759; PMCID: PMC3832208.\u003c/li\u003e\u003cli\u003eAljohani AI JC, Kurozumi S, Mohammed OJ, Miligy IM, Green AR, Rakha EA. Myxovirus resistance 1 (MX1) is an independent predictor of poor outcome in invasive breast cancer. Breast cancer research and treatment. 2020 Jun;181(3):541-51.\u003c/li\u003e\u003cli\u003eLee H LS, Bae H, Kang HS, Kim SJ. Genome-wide identification of target genes for miR-204 and miR-211 identifies their proliferation stimulatory role in breast cancer cells. Sci Rep. 2016 Apr 28;6:25287. doi: 10.1038/srep25287. PMID: 27121770; PMCID: PMC4848534.\u003c/li\u003e\u003cli\u003eSistigu A YT, Vacchelli E, Chaba K, Enot DP, Adam J, Vitale I, Goubar A, Baracco EE, Rem\u0026eacute;dios C, Fend L. Cancer cell\u0026ndash;autonomous contribution of type I interferon signaling to the efficacy of chemotherapy. Nature medicine. 2014 Nov;20(11):1301-9.\u003c/li\u003e\u003cli\u003eLee SJ HC, Kim YK, Lee HJ, Ahn SJ, Shin N, Lee JH, Shin DH, Choi KU, Park DY, Lee CH. Expression of myxovirus resistance A (MxA) is associated with tumor-infiltrating lymphocytes in human epidermal growth factor receptor 2 (HER2)-positive breast cancers. Cancer Res Treat. 2017 Apr 1;49(2):313-21.\u003c/li\u003e\u003cli\u003eLegrier M-E, Bi\u0026egrave;che I, Gaston J, Beurdeley A, Yvonnet V, D\u0026eacute;as O, et al. Activation of IFN/STAT1 signalling predicts response to chemotherapy in oestrogen receptor-negative breast cancer. British Journal of Cancer. 2016;114(2):177-87.\u003c/li\u003e\u003cli\u003eBroad RV, Jones SJ, Teske MC, Wastall LM, Hanby AM, Thorne JL, et al. Inhibition of interferon-signalling halts cancer-associated fibroblast-dependent protection of breast cancer cells from chemotherapy. British Journal of Cancer. 2021;124(6):1110-20.\u003c/li\u003e\u003cli\u003eQin Y WC, Wu H. CXCL10-based gene cluster model serves as a potential diagnostic biomarker for premature ovarian failure. PeerJ. 2023 Dec 13;11:e16659. doi: 10.7717/peerj.16659. PMID: 38107572; PMCID: PMC10725173.\u003c/li\u003e\u003cli\u003eKarin N RHCbc-aCaisricaaCS-djc.\u003c/li\u003e\u003cli\u003eKanda N ST, Tada Y, Watanabe S. IL‐18 enhances IFN‐\u0026gamma;‐induced production of CXCL9, CXCL10, and CXCL11 in human keratinocytes. European journal of immunology. 2007 Feb;37(2):338-50.\u003c/li\u003e\u003cli\u003eLiu M, Guo, S., Stiles, J. K.\u0026quot;The emerging role of CXCL10 in cancer (Review)\u0026quot;. Oncology Letters 2, no. 4 (2011): 583-589. https://doi.org/10.3892/ol.2011.300.\u003c/li\u003e\u003cli\u003eGoldberg-Bittman L NE, Sagi-Assif O, et al. The expression of the chemokine receptor CXCR3 and its ligand, CXCL10, in human breast adenocarcinoma cell lines. Immunol Lett 2004; 92: 171\u0026ndash;8.\u003c/li\u003e\u003cli\u003eJafarzadeh A FH, Nemati M, Assadollahi Z, Sheikhi A, Ghaderi A. Higher circulating levels of chemokine CXCL10 in patients with breast cancer: Evaluation of the influences of tumor stage and chemokine gene polymorphism . Cancer Biomarkers. 2016;16(4):545-554. doi:10.3233/CBM-160596, .\u003c/li\u003e\u003cli\u003eAhmed A. Ejaeidi BSC, Louis V. Puneky, Robert E. Lewis, Julius M. Cruse,, Hormone receptor-independent CXCL10 production is associated with the regulation of cellular factors linked to breast cancer progression and metastasis, Experimental and Molecular Pathology, Volume 99 I, 2015, Pages 163-172, et al.\u003c/li\u003e\u003cli\u003eChuan T LT, Yi C. Identification of CXCR4 and CXCL10 as Potential Predictive Biomarkers in Triple Negative Breast Cancer (TNBC). Med Sci Monit. 2020 Jan 11;26:e918281. doi: 10.12659/MSM.918281. PMID: 31924747; PMCID: PMC6977636.\u003c/li\u003e\u003cli\u003eMadkhali OA MS, Almoshari Y, Sabei FY, Safhi AY. Dual role of CXCL10 in cancer progression: implications for immunotherapy and targeted treatment\u0026lrm;. Cancer Biol Ther. 2025 Dec;26(1):2538962. doi: 10.1080/15384047.2025.2538962. Epub 2025 Aug 4. PMID: 40760734; PMCID: PMC12326575.\u003c/li\u003e\u003cli\u003eXiuming Wu AS, Weifeng Yu, Chengye Hong, Zhonghua Liu,, CXCL10 mediates breast cancer tamoxifen resistance and promotes estrogen-dependent and independent proliferation, Molecular and Cellular Endocrinology, Volume 512, 2020, 110866, et al.\u003c/li\u003e\u003cli\u003eKalfeist L, Petit S, Galland L, Poirrier C, Aucagne R, Ghiringhelli F, et al. Abstract 1188: Identification of CXCL10-inducing chemotherapy/targeted therapy combinations for PD-1 blockade sensitization in \u0026ldquo;cold\u0026rdquo; triple negative breast cancer. Cancer Research. 2024;84(6_Supplement):1188-.\u003c/li\u003e\u003cli\u003eKim M CH, Woo JW, Chung YR, Park SY. Role of CXCL10 in the progression of in situ to invasive carcinoma of the breast. Scientific reports. 2021 Sep 9;11(1):18007.\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"RNA-Seq, Next Generation Sequencing, Breast Cancer, WGCA, Neoadjuvant Chemotherapy","lastPublishedDoi":"10.21203/rs.3.rs-8937161/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8937161/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eThe prognosis for triple-negative breast cancer (TNBC), an aggressive subtype of breast cancer, is challenging, and there are few available treatments. Despite neoadjuvant chemotherapy (NAC) being the prevalent treatment, it has a wide range of side effects. Hence, identifying predictive indicators of NAC response could enhance treatment selection and outcomes.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe employed a standard bioinformatics technique to analyze the RNA-seq data from GSE260989 on a Linux system. After trimming and quality assessment, the reads were aligned to the GRCh38 reference genome, yielding gene-level counts. We employed DESeq2 to analyze expression differences and utilized WGCNA to identify co-expression modules associated with the NAC response. Functional enrichment analyses (KEGG, Reactome) and protein\u0026ndash;protein interaction studies were performed to identify key pathways. Hub genes were ranked based on their topological scores within the PPI network.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe pre- and post-NAC TNBC samples exhibited 1023 genes that were either up- or down-regulated (padj\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The strongest association between treatment response and the turquoise module was observed. Five hub genes\u0026mdash;ISG15, IFIH1, OAS2, MX1, and CXCL10\u0026mdash;linked to interferon signaling, immune modulation, and chemotherapy resistance, were identified through combined network and enrichment analyses. After NAC treatment, all five genes showed consistent downregulation, suggesting increased chemosensitivity and a shift toward a less aggressive tumor phenotype.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003e \u003cem\u003eISG15\u003c/em\u003e, \u003cem\u003eIFIH1\u003c/em\u003e, \u003cem\u003eOAS2\u003c/em\u003e, \u003cem\u003eMX1\u003c/em\u003e, and \u003cem\u003eCXCL10\u003c/em\u003e are identified as putative predictive biomarkers of NAC response in TNBC by this integrative bioinformatics research. Better treatment sensitivity may be reflected in their coordinated downregulation, warranting additional verification in larger clinical cohorts.\u003c/p\u003e","manuscriptTitle":"Integrated bioinformatics analysis reveals ISG15, IFIH1, OAS2, MX1, and CXCL10 as predictive biomarkers of neoadjuvant chemotherapy response in triple-negative breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-02 06:38:13","doi":"10.21203/rs.3.rs-8937161/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":"a756bb8d-4073-479d-9155-f792f89896b3","owner":[],"postedDate":"March 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-11T18:24:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-02 06:38:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8937161","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8937161","identity":"rs-8937161","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}