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. 2025 Nov 24;16:2158. doi: 10.1007/s12672-025-03972-4

Keratin 80 serves as a potential biomarker in metastatic breast cancer

Feng Sun 1, Dan Wang 2, Mengmeng Chen 1, Xiaona Lin 1,✉
PMCID: PMC12644367  PMID: 41284218

Abstract

Background

Breast cancer (BC) is the most common type of female cancer with alarmingly high morbidity and mortality, especially metastatic BC (mBC).

Objective

Our object is to explore a potential prognostic biomarker for mBC. The RNA-seq data of BC were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases.

Methods

The identification of candidate genes was achieved through the WGCNA, differentially expressed gene analysis and protein-protein interaction network. The prognostic predictive performance of the hub gene, Keratin 80 (KRT80), was evaluated through the Kaplan-Meier and multivariate Cox regression analysis. Validation of the expression of KRT80 in BC cell lines was carried out using qPCR and western blot experiments. The functional role of KRT80 in BC was further examined through loss- and gain-of-function assays in vitro.

Results

The results from the public database indicated a significant increase in KRT80 expression within the mBC group when compared to the primary BC group, and also heightened KRT80 levels were observed in BC tissues relative to the control group. Meanwhile, ROC curve analysis demonstrated the potential diagnostic potential of KRT80 for mBC. Notably, elevated KRT80 levels were found to be an independent predictor of unfavorable prognosis for BC patients. GSEA analysis demonstrated the involvement of KRT80 in NF-kappa B signaling pathway. Furthermore, the results of immune cell infiltration analysis revealed a marked negative correlation between KRT80 expression and CD8 T cells, activated NK cells. Drug sensitivity analysis showed that patients with high expression of KRT80 may exhibit increased sensitivity to Lapatinib. Functionally, overexpression of KRT80 facilitated the viability, migration, invasion and epithelial–mesenchymal transition (EMT) in BT549 cells, a metastatic BC cell line.

Conclusion

Collectively, KRT80 may be linked with metastasis of BC, and it may serve as a potential therapeutic target for mBC treatment.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-03972-4.

Keywords: Breast cancer, Metastatic, KRT80, Prognosis, Immune cell infiltration

Introduction

Breast cancer (BC) is the most common malignant tumor among women and poses a great health threat to the world. In 2023, the incidence of BC accounted for 31% of cancers in the United States [1]. In China, it stands as one of the most prevalent cancers affecting women [2], with an incidence rate of 29.05% among female cancer patients, and the mortality rate ranks among the top five among all cancer patients [3]. The clinical management of BC is guided by its molecular subtypes, primarily luminal A, luminal B, HER2-positive, and triple negative (basal-like), as these subtypes exhibit distinct prognostic outcomes and therapeutic responses [4, 5]. Hormone receptor-positive tumors (luminal A and B) are commonly treated with endocrine therapy, while HER2-positive cases generally respond to anti-HER2 monoclonal antibodies or tyrosine kinase inhibitors [6, 7]. In contrast, triple-negative breast cancer (TNBC), which lacks established molecular targets, relies primarily on chemotherapy or radiotherapy [8]. While advances in the systemic treatment of BC have significantly improved patient outcomes, leading to a 90% five-year survival rate overall, the prognosis remains poor for patients diagnosed with metastasis, whose five-year survival rate is only 23% [9].

Despite medical technology has greatly improved the diagnosis, treatment, and prevention of BC, tumor metastasis has limited the clinical application of chemotherapy drugs [10, 11]. For instance, platinum drugs have proven effective against BC and are widely utilized in neoadjuvant and metastatic therapy, however, numerous patients fail to respond to platinum treatment regimens and experience rapid relapse [12]. The varying response rates to systemic chemotherapy are a critical factor contributing to the failure of targeted therapies. Notably, only 50% of metastatic BC patients (mBC) respond to immunotherapy, whereas 90% of primary BC (pBC) patients do so [13]. In recent years, bioinformatics analysis has been extensively employed to identify prognostic biomarkers or construct predictive models for BC patients [14–16]. For instance, Li et al. have discovered that high EIF4G1 expression modulates the tumor microenvironment (TME) and influences BC metastasis, which is associated with a poor prognosis [17]. Xie et al. have developed a prognostic signature based on 12 genes that can effectively predict the survival rate of BC patients [18]. Additionally, Tan et al. have demonstrated that high expression of CD226 and KLRC4-KLRK1 is linked to better overall survival (OS) in specific stages or subtypes of BC [19]. However, these studies are limited in scope. Furthermore, there is a dearth of research on the mechanisms underlying mBC. Therefore, there is an urgent need for rigorous analyses on large sample sizes to identify novel and reliable biomarkers associated with mBC.

In this study, our goal is to delve into a novel biomarker linked to the prognosis of mBC. By harnessing comprehensive bioinformatics tools, we aspire to unveil the functional pathways of the biomarker in the pathogenesis of mBC, and explore the immune landscape in different patients.

Materials and methods

Subjects

The mRNA expression profile data, clinical information of 1,217 BC (1,104 BC samples and 113 control samples) were downloaded from The Cancer Genome Atlas (TCGA, https://tcga-data.nci.nih.gov/tcga/) database. After excluding patients with incomplete survival information, 1050 patients had complete survival information. Additionally, the MAF file for the TCGA-BC cohort was downloaded for subsequent analysis. The expression profile data can be directly used for analysis after converting the Entrez ID to GeneSymbol.

Furthermore, the datasets GSE102484 (101 mBC and 582 pBC), GSE20685 (83 mBC and 244 pBC), GSE29431 (13 mBC and 18 pBC), GSE42568 (104 BC samples and 17 control samples), and GSE31448 (263 BC subtypes and 31 control samples) were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). Following data download, the probe IDs in the expression matrix were converted to GeneSymbol based on the corresponding platform annotation file, after which the data were ready for analysis.

In addition, the IMvigor210 cohort was used for conducting immunotherapy response analysis, encompassing 298 BLCA samples with immunotherapy responses that had undergone immunotherapy treatment.

Weighted gene co-expression network analysis

The weighted gene co-expression network analysis (WGCNA) was performed to identify mBC-related modules using the R package “WGCNA” (version 1.72-1) to identify genes that exhibited a significant association with the mBC phenotype [20]. Genes exhibiting similar expression patterns were grouped into a same gene module. Subsequently, the correlation between these modules and the traits of interest was calculated and visually represented as a heatmap. Modules exhibiting significant correlation (p < 0.05) were then chosen for further in-depth analysis.

Differentially expressed gene analysis

Using “limma” package of R (version 3.52.4), differentially expressed genes (DEGs) were identified [21]. To identify statistically significant DEGs, we applied stringent thresholds: an absolute log₂ fold change (|log₂FC|) greater than 1 (corresponding to a 2-fold change in expression) and an adjusted p-value of less than 0.05.

Functional enrichment analysis

Using the “clusterProfiler” R package (version 4.7.1.2), Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were conducted to explore the function and pathways of genes [22]. To identify significant diseases associated with Keratin 80 (KRT80), disease ontology (DO) analysis was employed using the “DOSE” R package (version 3.26.1) was employed [23]. A p-value of less than 0.05 was deemed statistically significant.

Protein-protein interaction (PPI) network analysis

For constructing the interaction network of genes, the STRING online tool (accessible at https://string-db.org/, version 11.0) utilized for predicting PPI network [24], and the Cytoscape (version 3.7.2) was used for visualization [25].

Survival analysis

The assessment of overall survival among patients between groups was conducted utilizing the R packages “survival” (https://CRAN.R-project.org/package=survival, version 3.5-5) and “survminer” (https://CRAN.R-project.org/package=survminer, version 0.4.9). The Kaplan-Meier method was employed to create the survival curve for the two groups, with statistical significance assessed via the log-rank test. The receiver operating characteristic (ROC) curves were generated with the “timeROC” R package (version 0.4) to analyze the efficacy of KRT80 in predicting prognosis [26]. To ascertain whether the KRT80 functions as an independent prognostic factor, multivariate Cox regression analysis was performed.

Immune cell infiltration analysis

To further explore the relationship between KRT80 expression and immune cells, the “CIBERSORT” was used to characterize the composition of immune cells between groups. The proportions of a total of 22 immune cells were calculated for each sample.

Drug sensitivity analysis

To evaluate the association between KRT80 expressionn and drug sensitivity, we analyzed data from the Genomics of Drug Sensitivity in Cancer (GDSC, http://www.cancerrxgene.org/). Using R package “oncoPredict” (version 0.2), we performed calculations to assess the relationships between the expression of KRT80 and the IC50 values of various drugs [27].

Cell culture and transfection

The normal breast cell line MCF-10A, the BC bone metastatic cell line BT549, and the BC highly metastatic cell line MDA-MB-231 were all obtained from Wuhan Pricella Biotechnology Co., Ltd (Wuhan, China). The BC low metastatic cell line MCF-7 was obtained from BeNa Culture Collection Co., Ltd (Henan, China). MCF-10A cell line was maintained in the DMEM/F12 with the addition of 5% horse serum (HS) and 1% penicillin-streptomycin (P/S). The MCF-7 cell line was cultured in MEM containing 10% FBS. The BT549 cell line was maintained in the RPMI 1640 with the addition of 10% fetal bovine serum (FBS) and 1% P/S. The MDA-MB-231 cell line was cultured in the DMEM/high-sugar with the addition of 1% P/S and 10% FBS. All culture mediums were at 37 °C in a humidified incubator containing 5% CO2.

For transient transfection, BT549 cells were transfected with siRNA negative control (si-NC), KRT80 siRNAs (siRNA1, siRNA2 and siRNA3), pcDNA3.1 negative control (OE-NC) and pcDNA3.1-KRT80 (OE-KRT80) plasmids using the Lipocat2000 reagent.

Quantitative real-time PCR (qPCR)

The total RNA was extracted from cells by TRNzol Universal Total RNA Extraction Reagent (DP424, Tiangen Biotech Co., Ltd., Beijing, China). Following the protocol in previous study, qPCR was performed [28]. GAPDH was the reference gene. In addition, the primer sequences were as follows: KRT80 forward (5’-GGCTGGCACTATCTCCAAGG-3’) and KRT80 reverse (5’-CCTTGCCAATTAGGGAGGCA-3’); GAPDH forward (5’-GAAGGTGAAGGTCGGAGTC-3’) and GAPDH reverse (5’-GAAGATGGTGATGGGATTTC-3’).

Western blot (WB)

Protein lysates were subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to polyvinylidene difluoride (PVDF) membranes. The membrane was then incubated overnight with primary antibodies (anti-KRT80, 16835-1-AP, Proteintech; anti-N-cadherin, 22018-1-AP, Proteintech; anti-E-cadherin, 20874-1-AP, Proteintech; anti-GAPDH, 60004-1-Ig, Proteintech) at 4℃ and subsequently incubated with secondary antibody for 1 h at room temperature. Protein bands were visualized by fully automated chemiluminescence image analysis system. Finally, band intensities were analyzed using the Image J software.

Cell counting kit-8 (CCK-8)

BT549 cells were seeded into a 96-well plate and maintained at 37℃. Cell viability was assessed using the CCK-8 reagent (Beyotime), according to the manufacturer’s instructions. Absorbance was measured at 450 nm for each well using a microplate reader.

Transwell migration and invasion assays

A suspension of BT549 cells (100 µl) was added to the upper chamber of a Transwell® insert, and the lower chamber was loaded with 300 µl of medium supplemented with 10% FBS. Following a 24-hour incubation, cells that migrated or invaded through the membrane were fixed in 4% paraformaldehyde, stained with crystal violet (Solarbio), and imaged using an inverted microscope. For cell invasion analysis, the Transwell insert was pre-coated at 37 °C for 2 h with Matrigel.

Statistical analysis

The Wilcox test was used for determining the difference in continuous variables across the two groups. The Pearson correlation was carried out using R function “cor”. A p-value of less than 0.05 was deemed statistically significant. All statistical evaluations were executed using R software (version 4.3.1).

For the experimental data, statistical analysis was performed using Student’s t-test for comparisons between two groups or one-way ANOVA for comparisons across three or more groups. All data are presented as mean ± standard deviation, and a p-value of less than 0.05 was defined as statistically significant.

Results

Identification of mBC-related modules using WGCNA

To discover potential modules linked to mBC, we conducted WGCNA using the GSE102484 dataset. The soft power of β = 5 was determined as soft-thresholding for constructing the gene network (Fig. 1A). As a result, nine gene modules were identified (Fig. 1B). Then, the correlation between each gene module and the two groups including mBC and pBC were calculated (Fig. 1C and D). The results showed that the brown, black, and pink modules had a significant correlation with mBC (p < 0.05). Therefore, these three modules were selected for downstream analysis, containing a total of 1,001 genes (module-genes) (Table S1).

Fig. 1.

Fig. 1

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Identification of mBC-related modules using WGCNA. (A). Determination of the soft-thresholding (power threshold β = 5). (B). Cluster dendrogram of gene modules. Each color represents a module, and the gray represents the gene set that cannot be aggregated to other modules. (C). Heatmap of eigengene adjacency. (D). Heatmap of the association of gene modules with mBC and pBC. Numbers in each cell indicate correlation and significance. (E, F). The top 10 KEGG pathway enrichment (E) and GO enrichment (F) of brown, black, and pink modules genes

To elucidate the biological functions of these genes, we carried out GO and KEGG enrichment analyses. The KEGG results showed that these genes were notably implicated in 26 pathways including cell cycle and ECM-receptor interaction (Fig. 1E, Table S1). The GO analysis uncovered a total of 786 GO terms that were significantly enriched, such as chromosome segregation and extracellular matrix structural constituent (Fig. 1F, Table S1).

Identification of KRT80 as a biomarker in mBC

Utilizing the GSE20685 dataset, we screened DEGs between mBC and pBC samples. Consequently, we identified 64 DEGs in the mBC group in comparison to the pBC group, composed of 30 up-regulated genes and 34 down-regulated genes (Fig. 2A). Following this, we identified 13 overlapping genes, including CXCL8, BCL2, CASP14, CX3CR1, EPHX2, KRT80, MYCN, RTN1, TMEM40, TREM1, EDIL3, EGLN3, and TMEM45A, by intersecting the 1,001 module-genes with the 64 DEGs (Fig. 2B, Tables S2).

Fig. 2.

Fig. 2

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Identification of candidate genes associated with mBC. (A). Volcano plots of DEGs between mBC and pBC samples in the GSE20685 dataset. (B). Venn diagram of the WGCNA module genes and DEGs. (C, D). The top 10 KEGG pathway enrichment (C) and GO enrichment (D) of 13 overlapping genes. (E). PPI network of candidate genes

The KEGG analysis showed that these 13 genes were linked to 23 pathways such as NF-kappa B signaling pathway and NOD-like receptor signaling pathway (Fig. 2C, Table S2). The GO analysis revealed significant involvement in 345 terms like leukocyte chemotaxis and humoral immune response (Fig. 2D, Table S2).

Next, we conducted a PPI analysis utilizing the online tool STRING, applying a threshold with a minimum interaction score exceeding 0.4. This analysis identified ten candidate genes (Fig. 2E).

KRT80 has been demonstrated to exhibit oncogenic properties in multiple malignancies, such as hepatocellular carcinoma [29] and lung cancer [30]. However, its biological function and clinical significance in BC remain poorly understood. Thus, we sought to investigate the expression profile of KRT80 in BC and evaluate its potential prognostic value.

High KRT80 expression significantly linked to BC malignant progression and metastasis

Utilizing the GSE102484, GSE20685, and GSE29431 datasets, we evaluated KRT80 levels between metastasis (mBC) and non metastasis (pBC) samples. The results showed that KRT80 was significantly up-regulated in mBC samples compared to pBC samples in all three datasets (Fig. 3A-C). Furthermore, qPCR and WB were employed to validate KRT80 expression in BC cells. The results showed that, in comparison to MCF-10A cells, KRT80 expression levels were notably upregulated in one low-metastatic cell line, MCF-7, and two high metastatic cell lines, BT549 and MDA-MB-231, with the highest expression observed in two high metastatic cell lines (Fig. 3D-E).

Fig. 3.

Fig. 3

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High KRT80 expression was significantly linked to BC progression and metastasis. (A-C). The KRT80 expression levels between mBC and pBC groups in the GSE102484 (A), GSE20685 (B), and GSE29431 (C) datasets. (D, E). Expression of KRT80 in mBC determined by qPCR (D) and western blot (E). (****p < 0.0001). (F-H). The KRT80 expression levels among different T stage (F), N stage (G), and M stage (H). (I-K). ROC curves of KRT80 expression in predicting the diagnosis of mBC based on the GSE102484 (I), GSE20685 (J), and GSE29431 (K) datasets

Furthermore, to investigate the role of KRT80 in the progression of BC, we analyzed its expression patterns in the T stage, N stage, and M stage. The results revealed that KRT80 expression levels exhibited a trend of gradual elevation with the advancement of both T stage and N stage, and there were significant disparities observed in the comparisons between T1 and T2, T1 and T3, T2 and T3 (Fig. 3F), as well as between N0 and N1, N0 and N2, and N0 and N3 (Fig. 3G) (p < 0.05). Notably, there were significant differences in KRT80 expression within the M stage, with a significantly elevated level in M1 (distant metastasis) compared to M0 (non-distant metastasis) (Fig. 3H). These findings suggested that the high expression of KRT80 may be linked to the malignant progression and distant metastasis of BC.

Based on the ROC analysis, we further investigated the diagnostic potential of KRT80 in mBC using the GSE102484, GSE20685, and GSE29431 datasets. Our findings demonstrated AUC values of 0.636, 0.685, and 0.705 across these three cohorts, respectively (Fig. 3I-K). These results suggested that KRT80 held promise for distinguishing mBC from the pBC samples.

KRT80 as an independent risk factor for the prognosis of BC

To further explore the expression differences of KRT80 in BC samples relative to control samples, we conducted analyses using the TCGA-BC and GSE42568 cohorts, we investigated the expression differences of KRT80 in BC samples compared to controls. Our findings revealed a significant up-regulation of KRT80 expression in BC samples in both datasets (Fig. 4A, B). Additionally, an examination of KRT80 expression among various BC subtypes via the GSE31448 dataset indicated that Luminal B, HER2-positive, and TNBC subtypes exhibited significantly elevated KRT80 expression levels compared to normal samples (Fig. 4C). Moreover, validation using the CCLE database for BC cell lines supported our findings, demonstrating a significantly higher KRT80 expression in BC cell lines compared to normal cell lines (Fig. 4D).

Fig. 4.

Fig. 4

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Exploration of the association of KRT80 expression with prognosis of BC patients. (A-B). The KRT80 expression levels between BC samples and controls in the TCGA (A), GSE42568 (B). (C). The KRT80 expression levels among various BC subtypes in the GSE31448 datasets. (D). The KRT80 expression levels between BC and non-cancerous groups in the CCLE database. (E, F). The Kaplan-Meier curves of KRT80 high expression and KRT80 low expression groups in the BC patients of TCGA (E) and GSE42568 (F) datasets. (G). The Kaplan-Meier curve of mBC and pBC groups in the GSE20685 datasets. (H, I). Time-dependent ROC curves of the KRT80 expression for predicting 1-, 3-, and 5-year survival in the GSE42568 (H) and GSE20685 (I) datasets. (J). Multivariate Cox regression analysis of KRT80 expression and clinical features, including age, gender, T stage, N stage, and M stage

To explore the association between KRT80 expression and BC prognosis, we conducted survival analysis on BC samples from the TCGA-BC and GSE42568 cohorts. Utilizing these two cohorts, patients were stratified into high (KRT80-H) and low (KRT80-L) expression groups based on median KRT80 levels. Notably, in both TCGA-BC and GSE42568 cohorts, the KRT80-H group experienced a worse OS in comparison to the KRT80-L group (Fig. 4E, F). In addition, analysis of the GSE20685 dataset revealed that mBC patients had worse OS compared to patients with pBC (Fig. 4G). Furthermore, we investigated the prognostic significance of KRT80 stratified by molecular subtypes (LumA, LumB, Her2, and Basal) within the TCGA-BC cohort. Our analysis revealed that high KRT80 expression was significantly associated with poorer prognosis in the HER2-enriched and Luminal B subtypes, while its prognostic impact was not significant in the Basal-like or Luminal A subtypes (Fig. S1).

Furthermore, according to the time-dependent ROC analysis, the AUC for 1-year, 3-year, and 5-year survival in GSE42568 dataset was 0.66, 0.60, and 0.72, respectively (Fig. 4H). Meanwhile, the AUC of 1-year, 3-year, and 5-year survival in GSE20685 dataset was 0.77, 0.74, and 0.72, respectively (Fig. 4I), indicating that KRT80 may be a reliable predictor of prognosis in BC patients.

To assess whether KRT80 could be an independent marker in predicting prognosis, we performed multivariate Cox regression analysis utilizing the TCGA-BC cohorts, incorporating five key clinical factors, including age, gender, T stage, N stage, and M stage. The analysis results demonstrated a significant correlation between KRT80 expression (HR = 1.135, P = 0.0295) and OS (Fig. 4J). Therefore, KRT80 may serve as an independent prognostic risk factor for BC.

Exploration of distinct functional information in different KRT80 expression groups

To gain deeper insights into the function of KRT80, we performed the GSEA using the TCGA-BC cohort, comparing the KRT80-H and KRT80-L groups. The results revealed that there were 177 pathways significantly enriched in the KRT80-H group relative to the KRT80-L group (Table S3). Particularly, JAK − STAT signaling pathway, NF − kappa B signaling pathway, PI3K − Akt signaling pathway, and TGF − beta signaling pathway were notably activated in the KRT80-H group (Fig. 5A). In addition, we conducted a DO analysis on the DEGs between KRT80-H and KRT80-L groups. This analysis revealed the enrichment of 217 DO terms, particularly linked to female cancers like ovarian cancer, ovary epithelial cancer, and breast carcinoma (Table S3, Fig. 5B).

Fig. 5.

Fig. 5

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Exploration of the biological significance of different KRT80 expression groups. (A). Five important pathways were activated in the high KRT80 expression group based on GSEA. (B). The top 20 DO terms enrichment of DEGs between the KRT80-H and KRT80-L groups

Distinct immune landscape characteristics between different KRT80 expression groups

Next, we further analyzed the immune landscapes of patients within the TCGA-BC cohort to investigate the association between KRT80 expression and immune status. By employing the CIBERSORT algorithm, we analyzed the levels of 22 immune cell types across various KRT80 expression levels. The findings indicated notable differences in the infiltration of eight immune cell types when comparing the KRT80-H and KRT80-L groups (Fig. 6A). Furthermore, we conducted a Pearson correlation analysis to assess the relationship between KRT80 expression and the infiltration of these eight immune cell types. Notably, we observed a significant negative correlation between KRT80 expression and the presence of resting mast cells (r = -0.14, p < 0.05), CD8 T cells (r = -0.13, p < 0.05), eosinophils (r = -0.13, p < 0.05), and activated NK cells (r = -0.076, p < 0.05). Conversely, a significant positive correlation was observed with resting NK cells (r = 0.063, p < 0.05), M1 macrophages (r = 0.085, p < 0.05), M0 macrophages (r = 0.15, p < 0.05), and activated dendritic cells (r = 0.15, p < 0.05) (Fig. 6B). In addition, within the GSE20685 dataset, we assessed immune cell infiltration between mBC and pBC groups, uncovering significant differences in gammadelt T cells, activated dendritic cells, and neutrophils between two categories (Fig. 6C).

Fig. 6.

Fig. 6

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The landscape of immune cell infiltration between different groups. (A). The immune cell infiltration in the KRT80-H and KRT80-L groups of TCGA-BC patients. (B). Correlations between the proportions of eight immune cell types and KRT80 expression. (C). The immune cell infiltration in the mBC and pBC groups of GSE20685 dataset. (D). Expression levels of immune checkpoint genes in the KRT80-H and KRT80-L groups. (E). Proportion of immune responses in KRT80-H and KRT80-L groups

Immunotherapy had gradually emerged as the important direction in tumor therapy. It has been shown that tumor mutation burden (TMB) had garnered significant attention as a predictive marker for immunotherapy. Therefore, we analyzed somatic mutations in the TCGA-BC cohort and calculated the TMB for each patient. Our results revealed that the KRT80-H group exhibited the highest frequency of TP53 mutation (44%) (Fig. S2A), while the KRT80-L group demonstrated the highest frequency of PIK3CA mutation (40%) (Fig. S2B). However, no significant difference in TMB was observed between the KRT80-H and KRT80-L groups (Fig. S2C). Furthermore, we further analyzed distinct expression patterns of eight immune checkpoint genes, including PD-1, CTLA-4, PDL-1, PDL-2, CD80, CD86, LAG-3, and TIGIT, across the KRT80-H and KRT80-L groups. Notably, we found that these genes were significantly upregulated in the KRT80-H group compared to the KRT80-L group (Fig. 6D).

Subsequently, we investigated the variations in immune response across KRT80-H and KRT80-L groups utilizing the IMvigor210 cohort. We discovered that the proportion of samples achieving complete response (CR) or partial response (PR) was higher in the KRT80-H group (26.8%) compared to the KRT80-L group (18.8%) (Fig. 6E). These results also suggested that BC patients with high KRT80 expression may be more appropriate candidates for immunotherapy.

Drug exploitation of mBC patients with high KRT80 expression

To establish a reference treatment protocol for BC patients, we explored the relationship between KRT80 expression and the IC50 values of various drugs using TCGA-BC cohort. We identified 46 drugs that exhibited a significant negative correlation with KRT80 expression, and 63 drugs displayed a significant positive correlation with KRT80 expression (Table S4). The IC50 values of Osimertinib_1919, Acetalax_1804, Lapatinib_1558, and Ibrutinib_1799 showed significant negative correlation with KRT80 expression (Fig. 7A), while the IC50 values of Sabutoclax_1849, Oxaliplatin_1089, SB505124_1194, and Elephantin_1835 exhibited marked positive correlation with KRT80 expression (Fig. 7B). Moreover, the IC50 values of Osimertinib_1919, Acetalax_1804, Lapatinib_1558, and Ibrutinib_1799 were significantly higher in the KRT80-L group compared to the KRT80-H group (Fig. 7C). Conversely, Sabutoclax_1849, Oxaliplatin_1089, SB505124_1194, and Elephantin_1835 exhibited significantly elevated IC50 values in the KRT80-H group (Fig. 7D). These findings indicated that patients with KRT80-H might be more sensitive to Osimertinib_1919, Acetalax_1804, Lapatinib_1558, and Ibrutinib_1799, while patients with KRT80-L might be more sensitive to Sabutoclax_1849, Oxaliplatin_1089, SB505124_1194, and Elephantin_1835.

Fig. 7.

Fig. 7

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Drug prediction of KRT80 expression based on TCGA dataset. (A, B). The IC50 values of four drugs demonstrated the most significant negative (A) and positive (B) correlation with the KRT80 expression. (C, D). IC50 values of the four drugs most negatively (C) and positively (D) associated with KRT80 expression in the KRT80-H and KRT80-L groups

KRT80 enhances the viability, migration, invasion and EMT process of BC cells

To investigate the role of KRT80 in BC progression, we knocked down KRT80 expression using siRNAs and also overexpressed KRT80 in BT549 cells (Fig. 8A and B). Among the three siRNAs, KRT80 siRNA1 demonstrated the highest efficacy in reducing KRT80 protein levels, which was utilized in subsequent assay (Fig. 8B). Significantly, downregulation of KRT80 resulted in a decrease in cell viability, migration, and invasion in BT549 cells; conversely, KRT80 overexpression produced opposite effects (Fig. 8C-G). Furthermore, downregulation of KRT80 notably reduced the protein levels of N-cadherin and increased the protein levels of E-cadherin in BT549 cells (Fig. 8H). Collectively, these findings suggest that KRT80 plays an oncogenic role in BC.

Fig. 8.

Fig. 8

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KRT80 enhances the viability, migration, invasion and EMT process of BC cells. (A, B) qPCR and western blot analyses of BT549 cells transfected with si-NC, si-KRT80-1, si-KRT80-2, si-KRT80-3, OE-NC and OE-KRT80. (C) BT549 cells were transfected with si-NC, si-KRT80-1, OE-NC and OE-KRT80. Cell viability was assessed using the CCK-8 assay. (D-G) Transwell assays were performed to evaluate cell migration and invasion. (H) Western blot was conducted to determine the protein expression of N-cadherin and E-cadherin in BT549 cells. *P < 0.05, **P < 0.01, ***P < 0.001

Discussion

BC was one of the primary causes of cancer-related deaths among women [1]. Furthermore, metastasis was the underlying cause of mortality in the majority of BC patients. Therapies specifically tailored for mBC remained inadequate [31]. The exploration of key biomarkers related to the prognosis of mBC could facilitate the development of therapeutic strategies.

In this study, we found that KRT80 expression was elevated in mBC samples compared to pBC samples. KRT80, a type II gene of the human epithelial intermediate filament, played a crucial role in cytoskeleton assembly and maintains cell morphology, motility, and adhesion [32]. Previous studies had demonstrated that KRT80 was over-expressed in numerous cancer types, promoting tumor cell proliferation and invasiveness. For instance, KRT80 was over-expressed in esophageal squamous cell carcinoma (ESCC) [33], and the proliferation, migration, and aggressiveness of ESCC cells were significantly reduced by inhibiting KRT80 expression [33]. In gastric cancer (GC), KRT80 played a pivotal role by activating the PI3K/AKT signaling pathway to drive GC proliferation [34]. A recent study showed that patients with lung adenocarcinoma had elevated KRT80 expression, and patients with high KRT80 expression levels had poorer clinical outcomes [30]. Similarly, in colorectal cancer (CRC), the KRT80 expression was significantly elevated, and patients with high KRT80 expression exhibited poorer prognosis [35]. Notably, KRT80 expression was significantly higher in advanced stages (III and IV) of CRC than in early stages (I and II) [36]. Interestingly, our study echoed the trend through correlation analysis between TNM stage and KRT80 expression, suggesting a close association between high KRT80 expression and the malignant progression of BC. Furthermore, our analysis revealed that KRT80 held promise as a diagnostic marker for mBC, with an AUC exceeding 0.6 in all three independent cohorts.

In female cancers, such as ovarian cancer (OC), KRT80 played a pivotal role. It was reported that KRT80 expression was significantly elevated in OC patients compared to both normal and benign groups, and a higher level of KRT80 expression was associated with a later FIGO stage and a higher rate of lymph node metastasis [37]. It was worth noting that previous studies had confirmed the involvement of KRT80 in BC. Perone et al. demonstrated that the over-expression of KRT80 led to an up-regulation of cytoskeleton-related genes, such as SEPT9, resulting in cytoskeletal rearrangement of BC cells and promoting their adhesion and invasiveness [38]. Xie et al. explored the prognostic role of KRT80 in breast invasive micropapillary carcinoma (IMPC, a subtype of BC), discovering that elevated KRT80 expression correlated with reduced overall survival in patients with IMPC [39]. However, there was still a significant gap in understanding the prognostic impact of KRT80 across broader BC types. Additionally, the biological function of KRT80 in BC cells remained largely unexplored. Our study bridged this gap by providing evidence that high KRT80 expression was significantly associated with a poor prognosis in BC patients. Additionally, overexpression of KRT80 could promote BC cell viability, migration and invasion, supporting its oncogenic role in BC. Furthermore, it was consistent with the conclusion found by Ouyang et al. that KRT80 promoted the proliferation and growth of GC cells by activating the PI3K-Akt signaling pathway [34]. Previous studies also have linked phosphorylated Akt to unfavorable clinical outcomes in cancer patients [40, 41]. Our results showed that PI3K-Akt signaling pathway was significantly activated in the high KRT80 expression group, suggesting that the oncogenic role of KRT80 in BC progression may be associated with PI3K-Akt signaling pathway. However, this hypothesis necessitates further validation in future experiments.

Through the functional enrichment analysis, we found that KRT80 was involved in immune-related pathways, such as NF-kappa B signaling pathway and NOD-like receptor signaling pathway. The NF-κB pathway is a well-established promoter of breast cancer progression, driving proliferation, metastasis, and inflammation [42, 43]. NOD-like receptor signaling pathway had been identified as a key regulator of inflammation-related tumorigenesis, angiogenesis, and chemoresistance [44]. It was reported that up-regulation of ubiquitin-specific protease 21 in BC promoted cellular tumorigenic capacity and was associated with NOD-like receptor signaling pathway [45]. Therefore, KRT80 may contribute to BC progression by modulating these two signaling pathways, although this potential mechanism requires further experimental validation.

Furthermore, our findings revealed a negative correlation between KRT80 expression levels and CD8 T cells and activated NK cells. NK cells and cytotoxic CD8 T cells represent two categories of immune cells capable of inducing target cell death [46]. Enhanced infiltration of T cells, particularly CD8 + T cells, as well as NK cells, plays a crucial role in facilitating anti-tumor immunity [47–49]. Additionally, an increased infiltration of NK cells and CD8 T cells was linked to improved overall survival [50, 51]. Our findings indicated that the KRT80-H group demonstrated reduced levels of activated NK cells and CD8 T cells in comparison to the KRT80-L group. This observation implies that the unfavorable prognosis observed in patients in the KRT80-H group may be associated with the diminished presence of these two essential anti-tumor immune cell types. However, the correlation coefficients between KRT80 expression and the infiltration levels of CD8⁺ T cells and activated NK cells were relatively low. Thus, while these results indicate a potential association, their clinical relevance remains uncertain. Further investigation is warranted to validate the relationship of KRT80 expression with CD8⁺ T cells and activated NK cells within the tumor immune microenvironment of BC.

Our results revealed a significant negative correlation between the IC50 of Lapatinib and KRT80 expression in BC, suggesting that patients with high KRT80 levels may be more sensitive to this drug, which is a standard treatment for HER2-positive BC [52, 53]. Previous studies have linked the expression of certain genes, such as HER2 and TMPRSS2, to Lapatinib sensitivity in HER2-positive BC [54, 55]. Collectively, these findings suggest that KRT80 may serve as a potential predictive biomarker for optimizing Lapatinib therapy in BC, thereby offering a new direction for treatment personalization. Further studies are warranted to validate its clinical utility.

Nevertheless, this research has certain limitations. First, one limitation of this study is the insufficient racial and regional diversity in the sample sources, which may restrict the generalizability of KRT80 as a biomarker in BC. Future research should include samples from diverse regions and racial backgrounds to validate our findings. Second, while our findings provide initial functional evidence, we acknowledge that the detailed molecular mechanisms of KRT80 in BC remain incompletely understood. Therefore, further studies, incorporating both in vitro and in vivo models, are warranted to further elucidate the mechanisms by which KRT80 promotes tumor progression and metastasis in BC. Third, although our data suggest a potential link between KRT80 expression and the response to immunotherapy, this observation in BC requires confirmation through future clinical trials or direct experimental evidence. Fourth, in the future, longitudinal studies are needed to evaluate the clinical utility of dynamic KRT80 expression changes as an indicator for monitoring disease recurrence and metastasis throughout BC progression. It would be valuable for future research to include immunohistochemical analysis of KRT80 expression in paired clinical specimens to better elucidate its expression pattern and clinical significance during recurrence or metastatic progression. Finally, although our multivariable analysis confirmed the independent prognostic value of KRT80, we were unable to explore the potential complex interactions between KRT80 and other clinical factors, such as detailed tumor size and lymph node status. Previous studies indicate that tumor size and lymph node status are correlated with tumor prognosis [56, 57]. Future studies that incorporate precise tumor measurements and detailed lymph node status are warranted to investigate whether the prognostic impact of KRT80 is modulated by these factors.

Conclusions

Our findings indicate that KRT80 is up-regulated in mBC samples compared to pBC samples, suggesting its potential diagnostic value for mBC. Importantly, elevated KRT80 expression is strongly correlated with unfavorable clinical outcomes in BC patients, establishing its potential prognostic significance in BC. Furthermore, overexpression of KRT80 could promote BT549 cell viability, migration and invasion in vitro. This study proposes that KRT80 may serve as a potential therapeutic target for mBC treatment.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (117KB, xlsx)
Supplementary Material 2 (38.1KB, xlsx)
Supplementary Material 3 (97.5KB, xlsx)
Supplementary Material 4 (14.7KB, xlsx)
Supplementary Material 6 (1.1MB, tif)
Supplementary Material 7 (3.4MB, tif)

Acknowledgements

Not applicable.

Author contributions

FS reviewed the literature, contributed to manuscript drafting and were responsible for the revision of the manuscript for important intellectual content. DW, MC reviewed the literature, interpreted the imaging findings. XL reviewed the literature and edited the manuscript. All authors issued final approval for the version to be submitted.

Funding

No funding was received for this research.

Data availability

The data that support the findings of this study are openly available in TCGA at https://tcga-data.nci.nih.gov/tcga/.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Informed consent

Informed consent was not required in this study.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

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References

  • 1.Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17–48. 10.3322/caac.21763. [DOI] [PubMed] [Google Scholar]
  • 2.Cao M, Li H, Sun D, Chen W. Cancer burden of major cancers in china: A need for sustainable actions. Cancer Commun (London England). 2020;40(5):205–10. 10.1002/cac2.12025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Wang Y, Yan Q, Fan C, Mo Y, Wang Y, Li X, et al. Overview and countermeasures of cancer burden in China. Sci China Life Sci. 2023;66(11):2515–26. 10.1007/s11427-022-2240-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Calar K, Plesselova S, Bhattacharya S, Jorgensen M, de la Puente P. Human Plasma-Derived 3D cultures model breast cancer treatment responses and predict clinically effective drug treatment concentrations. Cancers. 2020;12(7). 10.3390/cancers12071722. [DOI] [PMC free article] [PubMed]
  • 5.Wang S, Zhang Q, Yu C, Cao Y, Zuo Y, Yang L. Immune cell infiltration-based signature for prognosis and Immunogenomic analysis in breast cancer. Brief Bioinform. 2021;22(2):2020–31. 10.1093/bib/bbaa026. [DOI] [PubMed] [Google Scholar]
  • 6.Vargas-Rondon N, Perez-Mora E, Villegas VE, Rondon-Lagos M. Role of chromosomal instability and clonal heterogeneity in the therapy response of breast cancer cell lines. Cancer Biology Med. 2020;17(4):970–85. 10.20892/j.issn.2095-3941.2020.0028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Han B, Qu Y, Jin Y, Yu Y, Deng N, Wawrowsky K, et al. FOXC1 activates Smoothened-Independent Hedgehog signaling in Basal-like breast cancer. Cell Rep. 2015;13(5):1046–58. 10.1016/j.celrep.2015.09.063. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Bhattarai S, Sugita BM, Bortoletto SM, Fonseca AS, Cavalli LR, Aneja R. QNBC is associated with high genomic instability characterized by copy number alterations and MiRNA deregulation. Int J Mol Sci. 2021;22(21). 10.3390/ijms222111548. [DOI] [PMC free article] [PubMed]
  • 9.Maajani K, Jalali A, Alipour S, Khodadost M, Tohidinik HR, Yazdani K. The global and regional survival rate of women with breast cancer: A systematic review and Meta-analysis. Clin Breast Cancer. 2019;19(3):165–77. 10.1016/j.clbc.2019.01.006. [DOI] [PubMed] [Google Scholar]
  • 10.Liao M, Qin R, Huang W, Zhu HP, Peng F, Han B, et al. Targeting regulated cell death (RCD) with small-molecule compounds in triple-negative breast cancer: a revisited perspective from molecular mechanisms to targeted therapies. J Hematol Oncol. 2022;15(1):44. 10.1186/s13045-022-01260-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Mei D, Chen B, He B, Liu H, Lin Z, Lin J, et al. Actively priming autophagic cell death with novel transferrin receptor-targeted nanomedicine for synergistic chemotherapy against breast cancer. Acta Pharm Sin B. 2019;9(5):1061–77. 10.1016/j.apsb.2019.03.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Denkert C, Liedtke C, Tutt A, von Minckwitz G. Molecular alterations in triple-negative breast cancer-the road to new treatment strategies. Lancet. 2017;389(10087):2430–42. 10.1016/S0140-6736(16)32454-0. [DOI] [PubMed] [Google Scholar]
  • 13.Gonzalez-Angulo AM, Morales-Vasquez F, Hortobagyi GN. Overview of resistance to systemic therapy in patients with breast cancer. Adv Exp Med Biol. 2007;608:1–22. 10.1007/978-0-387-74039-3_1. [DOI] [PubMed] [Google Scholar]
  • 14.Li Y, Li D, Tao X, Ye Y, Zhang C, Xu Z, et al. Integrated prognostic model for young breast cancer patients: insights from SEER, METABRIC, and TCGA databases. Clin Breast Cancer. 2025. 10.1016/j.clbc.2025.07.015. [DOI] [PubMed] [Google Scholar]
  • 15.Song S, Sun L, Liu X, Zhang L, Li C, Liu Z, et al. Clinical prognosis and nomograms for hormone receptor-Positive and human epidermal growth factor receptor 2-Negative metastatic breast cancer patients treated with Palbociclib and endocrine therapy. Breast Cancer (Dove Med Press). 2025;17:669–81. 10.2147/BCTT.S523199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Zhang JN, Zhou XR, Yi ZL, Tian XY, Liu H. The construction of a new prognostic model of breast cancer and the exploration of drug sensitivity based on machine learning for Glycosylation-Related genes. Clin Breast Cancer. 2025;25(6):e756–64. 10.1016/j.clbc.2025.05.004. [DOI] [PubMed] [Google Scholar]
  • 17.Li K, Tan G, Zhang X, Lu W, Ren J, Si Y, et al. EIF4G1 is a potential prognostic biomarker of breast cancer. Biomolecules. 2022;12(12). 10.3390/biom12121756. [DOI] [PMC free article] [PubMed]
  • 18.Xie P, Ma Y, Yu S, An R, He J, Zhang H. Development of an Immune-Related prognostic signature in breast cancer. Front Genet. 2019;10:1390. 10.3389/fgene.2019.01390. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Tan W, Liu M, Wang L, Guo Y, Wei C, Zhang S, et al. Novel immune-related genes in the tumor microenvironment with prognostic value in breast cancer. BMC Cancer. 2021;21(1):126. 10.1186/s12885-021-07837-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559. 10.1186/1471-2105-9-559. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. 10.1093/nar/gkv007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Yu G, Wang LG, Han Y, He QY. ClusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–7. 10.1089/omi.2011.0118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Yu G, Wang LG, Yan GR, He QY. DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics. 2015;31(4):608–9. 10.1093/bioinformatics/btu684. [DOI] [PubMed] [Google Scholar]
  • 24.Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019;47(D1):D607–13. 10.1093/nar/gky1131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504. 10.1101/gr.1239303. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Blanche P, Dartigues JF, Jacqmin-Gadda H. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med. 2013;32(30):5381–97. 10.1002/sim.5958. [DOI] [PubMed] [Google Scholar]
  • 27.Maeser D, Gruener RF, Huang RS. OncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. Brief Bioinform. 2021;22(6). 10.1093/bib/bbab260. [DOI] [PMC free article] [PubMed]
  • 28.Li J, Yao J, Qi L. Identification of TUBB2A as a cancer-Immunity Cycle-Related therapeutic target in Triple-Negative breast cancer. Mol Biotechnol. 2023. 10.1007/s12033-023-00880-2. [DOI] [PubMed] [Google Scholar]
  • 29.Hua R, Yan X, He J, Wu N, Yu W, Yu P, et al. KRT80 in hepatocellular carcinoma plays oncogenic role via epithelial-mesenchymal transition and PI3K/AKT pathway. IUBMB Life. 2025;77(1):e2925. 10.1002/iub.2925. [DOI] [PubMed] [Google Scholar]
  • 30.Huang S, Tong W, Yang B, Ma L, Zhang J, Wang C, et al. KRT80 promotes lung adenocarcinoma progression and serves as a substrate for VCP. J Cancer. 2024;15(8):2229–44. 10.7150/jca.91753. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Li Z, Kang Y. Emerging therapeutic targets in metastatic progression: A focus on breast cancer. Pharmacol Ther. 2016;161:79–96. 10.1016/j.pharmthera.2016.03.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Wei XY, Zhao J, Tong HB, Cheng SJ, He N, Song FX. Characters of KRT80 and its roles in neoplasms diseases. Cancer Med. 2023;12(13):13991–4003. 10.1002/cam4.6040. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Wada M, Goto Y, Tanaka T, Okada R, Moriya S, Idichi T, et al. RNA sequencing-based MicroRNA expression signature in esophageal squamous cell carcinoma: oncogenic targets by antitumor miR-143-5p and miR-143-3p regulation. J Hum Genet. 2020;65(11):1019–34. 10.1038/s10038-020-0795-x. [DOI] [PubMed] [Google Scholar]
  • 34.Ouyang S, Zeng Z, Liu Z, Zhang Z, Sun J, Wang X, et al. OTUB2 regulates KRT80 stability via deubiquitination and promotes tumour proliferation in gastric cancer. Cell Death Discov. 2022;8(1):45. 10.1038/s41420-022-00839-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Li C, Liu X, Liu Y, Liu X, Wang R, Liao J, et al. Keratin 80 promotes migration and invasion of colorectal carcinoma by interacting with PRKDC via activating the AKT pathway. Cell Death Dis. 2018;9(10):1009. 10.1038/s41419-018-1030-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Wu YF, Wang CY, Tang WC, Lee YC, Ta HDK, Lin LC, et al. Expression profile and prognostic value of Wnt signaling pathway molecules in colorectal cancer. Biomedicines. 2021;9(10). 10.3390/biomedicines9101331. [DOI] [PMC free article] [PubMed]
  • 37.Liu O, Wang C, Wang S, Hu Y, Gou R, Dong H, et al. Keratin 80 regulated by miR-206/ETS1 promotes tumor progression via the MEK/ERK pathway in ovarian cancer. J Cancer. 2021;12(22):6835–50. 10.7150/jca.64031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Perone Y, Farrugia AJ, Rodriguez-Meira A, Gyorffy B, Ion C, Uggetti A, et al. SREBP1 drives Keratin-80-dependent cytoskeletal changes and invasive behavior in endocrine-resistant ERalpha breast cancer. Nat Commun. 2019;10(1):2115. 10.1038/s41467-019-09676-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Xie Y, Liu Z, Zhang J, Li G, Ni B, Shi C, et al. Deciphering the composition and key driver genes of breast invasive micropapillary carcinoma by multi-omics analysis. iScience. 2024;27(11):111178. 10.1016/j.isci.2024.111178. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Lu J, Zang H, Zheng H, Zhan Y, Yang Y, Zhang Y, et al. Overexpression of p-Akt, p-mTOR and p-eIF4E proteins associates with metastasis and unfavorable prognosis in non-small cell lung cancer. PLoS ONE. 2020;15(2):e0227768. 10.1371/journal.pone.0227768. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Xiaoping L, Xiaowei Z, Leizhen Z, Weijian G. Expression and significance of CD44 and p-AKT in pancreatic head cancer. World J Surg Oncol. 2015;13:334. 10.1186/s12957-015-0746-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Liang Y, Wang Y, Zhang Y, Ye F, Luo D, Li Y, et al. HSPB1 facilitates chemoresistance through inhibiting ferroptotic cancer cell death and regulating NF-kappaB signaling pathway in breast cancer. Cell Death Dis. 2023;14(7):434. 10.1038/s41419-023-05972-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Park YH. The nuclear factor-kappa B pathway and response to treatment in breast cancer. Pharmacogenomics. 2017;18(18):1697–709. 10.2217/pgs-2017-0044. [DOI] [PubMed] [Google Scholar]
  • 44.Liu P, Lu Z, Liu L, Li R, Liang Z, Shen M, et al. NOD-like receptor signaling in inflammation-associated cancers: from functions to targeted therapies. Phytomedicine. 2019;64:152925. 10.1016/j.phymed.2019.152925. [DOI] [PubMed] [Google Scholar]
  • 45.Peng L, Hu Y, Chen D, Linghu R, Wang Y, Kou X, et al. Ubiquitin specific protease 21 upregulation in breast cancer promotes cell tumorigenic capability and is associated with the NOD-like receptor signaling pathway. Oncol Lett. 2016;12(6):4531–7. 10.3892/ol.2016.5263. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Xie G, Dong H, Liang Y, Ham JD, Rizwan R, Chen J. CAR-NK cells: A promising cellular immunotherapy for cancer. EBioMedicine. 2020;59:102975. 10.1016/j.ebiom.2020.102975. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Fu Y, Peng Y, Zhao S, Mou J, Zeng L, Jiang X, et al. Combination foretinib and Anti-PD-1 antibody immunotherapy for colorectal carcinoma. Front Cell Dev Biol. 2021;9:689727. 10.3389/fcell.2021.689727. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Andre P, Denis C, Soulas C, Bourbon-Caillet C, Lopez J, Arnoux T, et al. Anti-NKG2A mAb is a checkpoint inhibitor that promotes Anti-tumor immunity by unleashing both T and NK cells. Cell. 2018;175(7):1731–e4313. 10.1016/j.cell.2018.10.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang TH et al. The Immune Landscape of Cancer. Immunity. 2018;48(4):812 – 30 e14. 10.1016/j.immuni.2018.03.023 [DOI] [PMC free article] [PubMed]
  • 50.Meng X, Xu H, Liang Y, Liang M, Song W, Zhou B, et al. Enhanced CT-based radiomics model to predict natural killer cell infiltration and clinical prognosis in non-small cell lung cancer. Front Immunol. 2023;14:1334886. 10.3389/fimmu.2023.1334886. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Hou YC, Chao YJ, Hsieh MH, Tung HL, Wang HC, Shan YS. Low CD8(+) T cell infiltration and high PD-L1 expression are associated with level of CD44(+)/CD133(+) cancer stem cells and predict an unfavorable prognosis in pancreatic cancer. Cancers. 2019;11(4). 10.3390/cancers11040541. [DOI] [PMC free article] [PubMed]
  • 52.Yuan Y, Liu X, Cai Y, Li W. Pyrotinib versus lapatinib therapy for HER2 positive metastatic breast cancer patients after first-line treatment failure: A meta-analysis and systematic review. PLoS ONE. 2023;18(1):e0279775. 10.1371/journal.pone.0279775. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Guarneri V, Griguolo G, Miglietta F, Conte PF, Dieci MV, Girardi F. Survival after neoadjuvant therapy with trastuzumab-lapatinib and chemotherapy in patients with HER2-positive early breast cancer: a meta-analysis of randomized trials. ESMO Open. 2022;7(2):100433. 10.1016/j.esmoop.2022.100433. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Ho CY, Wei CY, Zhao RW, Ye YL, Huang HC, Lee JC, et al. Artemisia argyi extracts overcome lapatinib resistance via enhancing TMPRSS2 activation in HER2-positive breast cancer. Environ Toxicol. 2024;39(6):3389–99. 10.1002/tox.24202. [DOI] [PubMed] [Google Scholar]
  • 55.Leis-Filho AF, de Faria Lainetti P, Emiko Kobayashi P, Fonseca-Alves CE, Laufer-Amorim R. Effects of lapatinib on HER2-Positive and HER2-Negative canine mammary carcinoma cells cultured in vitro. Pharmaceutics. 2021;13(6). 10.3390/pharmaceutics13060897. [DOI] [PMC free article] [PubMed]
  • 56.Tamai K, Tei M, Tsujimura N, Nishida K, Mori S, Yoshikawa Y, et al. Impact of small tumor size on prognosis in T3N1 colon cancer. World J Surg. 2025;49(2):343–52. 10.1002/wjs.12480. [DOI] [PubMed] [Google Scholar]
  • 57.Tomasicchio G, Mastropasqua MG, Picciariello A, Montanaro AE, Signorile D, Cirilli A, et al. A new possible Cut-Off of cytokeratin 19 mRNA copy number by OSNA in the Sentinel node of breast cancer patients to avoid unnecessary axillary dissection: A 10-Year experience in a tertiary breast unit. Cancers. 2022;14(14). 10.3390/cancers14143384. [DOI] [PMC free article] [PubMed]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (117KB, xlsx)
Supplementary Material 2 (38.1KB, xlsx)
Supplementary Material 3 (97.5KB, xlsx)
Supplementary Material 4 (14.7KB, xlsx)
Supplementary Material 6 (1.1MB, tif)
Supplementary Material 7 (3.4MB, tif)

Data Availability Statement

The data that support the findings of this study are openly available in TCGA at https://tcga-data.nci.nih.gov/tcga/.

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