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Article

Paired-Box (PAX) Gene Signatures as a Biomarker of Breast Cancer Progression

by
Manuel Scimeca
1,†,
Maria Paola Scioli
1,†,
Valeria Palumbo
1,
Lukas Funke
2,
Jonathan Woodsmith
2,
Francesca Servadei
1,
Erica Giacobbi
1,
Christian Seghetti
1,
Oreste Claudio Buonomo
3,4,
Eleonora Candi
1,5,
Michele Treglia
6,7,
Luigi Tonino Marsella
6,7,
Gerry Melino
1,
Alessandro Mauriello
1,* and
Rita Bonfiglio
1
1
Department of Experimental Medicine, TOR, University of Rome “Tor Vergata”, 00133 Rome, Italy
2
Indivumed GmbH, Falkenried, 20251 Hamburg, Germany
3
Breast Unit Policlinico Tor Vergata, Department of Surgical Science, Tor Vergata University, Viale Oxford 81, 00133 Rome, Italy
4
Department of Health Science, University of Basilicata, Via Nazario Sauro, 85, 85100 Potenza, Italy
5
Biochemistry Laboratory, Istituto Dermopatico Immacolata (IDI-IRCCS), Via Monti di Creta n.106, 00166 Rome, Italy
6
Laif (Laboratorio di Antropologia e Invecchiamento Forense), Sezione di Medicina Legale, Sicurezza Sociale e Tossicologia Forense, University of Tor Vergata, 00133 Rome, Italy
7
Department of Biomedicine and Prevention, University of Tor Vergata, 00133 Rome, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Int. J. Mol. Sci. 2026, 27(4), 1988; https://doi.org/10.3390/ijms27041988
Submission received: 14 January 2026 / Revised: 6 February 2026 / Accepted: 17 February 2026 / Published: 19 February 2026
(This article belongs to the Special Issue Current Research on Cancer Biology and Therapeutics: Fourth Edition)
Browse Figures
Versions Notes

Abstract

Breast cancer is the leading cause of cancer-related death in women, and despite advances in preventive screening as well as in molecular classification, many patients still do not benefit from existing therapies, highlighting the importance of identifying new molecular determinants of treatment resistance. The Paired-box (PAX) family of developmental transcription factors are central regulators of tissue morphogenesis and lineage specification, yet their reactivation in tumors and contribution to breast cancer progression remain only partially defined. Here, a multi-level analysis integrating RNA sequencing and protein profiling in twenty-one primary breast carcinomas shows that distinct PAX members are directly correlated to distinct fundamental cancer hallmarks, including proliferation, cell death, epithelial–mesenchymal transition, immune evasion, and genomic instability. Specifically, PAX1 and PAX9 correlates with both cell death and proliferative markers, indicating dual roles in the regulation of cell fate. PAX1 and PAX9 correlate with both proliferative and apoptotic markers, indicating dual roles in cell fate regulation. PAX3, PAX5, and PAX8 are mainly associated with immune checkpoint expression, including PD-L1 and TIGIT, while PAX6 is linked to microsatellite instability and tumor mutational burden, implicating it in genomic dysregulation. Therefore, PAX-based molecular signatures identify that accurately predict lymph node metastasis at transcriptomic (PAX2–PAX7) levels. These findings establish PAX transcription factors as key modulators of breast cancer biology and support their potential as clinically relevant biomarkers for prognostic refinement and therapeutic stratification.

1. Introduction

Breast cancer is the most commonly diagnosed malignancy and the leading cause of cancer-related death among women worldwide [1,2]. Despite significant advances in early detection and the development of molecular targeted therapies, including endocrine modulators, anti-HER2 agents, and CDK4/6 inhibitors [3,4,5], a substantial proportion of patients suffer from disease recurrence, distant metastases, or resistance to standard treatments [6]. This heterogeneity of clinical responses underscores a persistent gap in our understanding of the molecular determinants of tumor progression and therapeutic response [7,8,9,10]. As a result, there is an urgent need to identify novel and reliable biomarkers that can enhance prognostic accuracy and guide individualized treatment decisions beyond current molecular classifications [11,12].
In recent years, developmental transcription factors have emerged as central players in oncogenesis, driving processes such as cellular plasticity, immune modulation, and metastatic spread.
The Paired-box (PAX) gene family, comprising nine evolutionarily conserved genes critical for embryonic development and tissue morphogenesis, has attracted growing attention in cancer biology [13,14,15,16]. In physiological contexts, PAX genes regulate lineage commitment, stem cell maintenance, and epithelial–mesenchymal dynamics. However, their ectopic reactivation in adult tissues, particularly within tumors, suggests a potential role in reprogramming differentiation pathways and promoting malignant phenotypes [13,14,15,16]. Although various studies have implicated specific PAX genes as context-dependent oncogenes or tumor suppressors [17,18], current evidence in human breast cancer remains limited. PAX5, traditionally known for its role in B-cell lineage commitment, has been implicated in regulating epithelial-to-mesenchymal transition (EMT) and tumor invasiveness through epigenetic regulation and miRNA-mediated networks [19,20]. Its downregulation, often through promoter hypermethylation, has been associated with aggressive tumor phenotypes and loss of epithelial markers such as E-cadherin [19,20]. Conversely, PAX6 displays a tumor suppressor or oncogene depending on the molecular context [21]. However, most existing data about the role of PAX genes in breast cancer progression are fragmented, relying on single-gene approaches or transcriptomic meta-analyses, often without direct validation at the protein level. Moreover, the functional implications of PAX expression in the tumor immune landscape, epithelial plasticity, and clinical behaviour remain poorly defined. To address this gap, we performed a multi-omic investigation of PAX gene expression across transcriptomic and protein levels in twenty-one primary breast carcinomas. By correlating PAX signatures with distinct cancer hallmarks, we provide evidence for a potential role for specific PAX members in modulating the tumor microenvironment and metastatic dissemination. We also uncover a transcriptional and immunohistochemical PAX-based signature with high predictive accuracy for lymph node metastasis, underscoring their potential as clinically reliable biomarkers.

2. Results

2.1. Histopathological Assessment

Twenty-one invasive breast cancer cases from female patients aged 41 to 87 years (mean ± SD: 58.7 ± 3.0 years) have been collected at the University of Rome Tor Vergata. According to Nottingham grading, invasive ductal carcinoma has been classified as G3 (15/21), G2 (4/21) and G1 (1/21). One case was classified as mixed invasive carcinoma with lobular and ductal mixed pattern. Molecular classification by the PAM-50 multigene test allowed us to identify the following subtypes: 4/21 Luminal-A, 11/21 Luminal-B, 2/21 Basal-like, 1/21 Normal-like and 3/21 Unclear.

2.2. Systematic PAX Expression and Prognostic Association Analysis

To investigate the potential role of PAX family members in breast cancer (Figure 1), a bioinformatic analysis was performed using the UCSC Xena tool based on TCGA Breast Cancer (BRCA) cohort data [22]. The data showed that PAX3, PAX6 and PAX8 expressions are associated with overall survival (OS) in breast cancer patients. Specifically, higher expression of PAX3 (Figure 1C) and PAX6 is negatively associated with OS (Figure 1F), whereas patients with high levels of PAX8 expression exhibit improved OS. No difference in OS was observed for the expression of PAX1, PAX2, PAX4, PAX5, PAX7 and PAX9.
Analysis of PAX family member expression in normal, tumoral, and metastatic breast tissues revealed a significant increase in PAX1 (Figure 1A), PAX2 (Figure 1B), and PAX9 (Figure 1I) in tumor samples compared to normal ones. Noteworthy, PAX5 (Figure 1E) expression was higher in metastatic tissues as compared to both normal and tumoral samples.

2.3. PAXs Gene Expression and Key Molecular Features of Breast Cancers: Cell Proliferation and Cell Death

To investigate the potential role of PAX genes in breast cancer, RNA-seq data from all 21 tumour samples were analysed and compared with key molecular features of tumour lesions, including genes involved in immune evasion, cell death pathways, EMT, hypoxia, and the tumour microenvironment. Thus, we aimed to validate the observed associations in an independent cohort and to explore potential links between PAX gene expression and major biological processes underlying tumour progression and therapeutic resistance.
As a results, PAX1 and PAX9 displayed significant positive correlations with key markers related to cell death such as BARD1 (PAX1: ρ = 0.66, p = 0.002; PAX9: ρ = 0.66, p = 0.001) and BAX (PAX1: ρ = 0.58, p = 0.008; PAX9: ρ = 0.41, p = 0.064). These associations suggest that PAX proteins may actively influence cell fate decisions, balancing apoptotic and survival pathways within the tumor microenvironment [23]. In addition, BAX expression correlated positively with PAX5, further implicating this transcription factor in the modulation of pro-apoptotic signalling (Figure 2). Notably, PAX9 expression showed a significant positive association with both hypoxia pan cancer (ρ = 0.55, p = 0.01) and proliferation scores (ρ = 0.54, p = 0.012) (Figure 2), highlighting its dual involvement in sustaining tumor growth under stress conditions (Figure 2 and Figure 3).

2.4. PAXs Expression and Immune Evasion

Several PAX family members showed significant correlations with key immune checkpoint molecules (Figure 3). Specifically, PAX3, PAX5, and PAX8 exhibited positive associations with PD-L1 expression (PAX3: ρ = 0.50, p = 0.02; PAX5: ρ = 0.60, p = 0.005; PAX8: ρ = 0.45, p = 0.04). Notably, the association between PD-L1 and PAX5 has also been observed in a large cohort of breast infiltrating carcinomas (n = 1091) from KM database (see Supplementary Figure S1A). Similarly, significant correlations were observed with TIGIT for PAX3 (ρ = 0.48, p = 0.03) and PAX5 (ρ = 0.53, p = 0.01). These findings suggest that PAX gene expression may contribute to immune evasion through upregulation of immune checkpoint pathways. In particular, PAX5 displayed the most extensive pattern of immune associations. Positive correlations were identified with B cell scores (ρ = 0.52, p = 0.02), cytotoxic lymphocyte infiltration (ρ = 0.54, p = 0.01), and NK cell markers (ρ = 0.48, p = 0.03).

2.5. PAXs Expression and Epithelial–Mesenchymal Transition and Functional Clustering

In the context of EMT, PAX genes have been associated with the expression of several genes involved in the EMT of breast carcinomas such as ZEB1 [24], FN1 [25,26,27], MMPs [28] and CDH1 [29]. PAX1 displayed strong positive correlations with several canonical EMT mediators, including FN1 (ρ = 0.66, p = 0.002), ZEB1 (ρ = 0.57, p = 0.009), and MMP2 (ρ = 0.58, p = 0.008), as well as with the pan-cancer EMT signature (Figure 4). The associations between PAX1 and FN1, ZEB1 and MMP2 were also validated in a large cohort of breast infiltrating carcinomas (n = 1091) from KM database (see Supplementary Figure S1B). These associations suggest that PAX1 expression accompanies a mesenchymal transcriptional program characterized by extracellular matrix remodelling and enhanced cellular motility. Conversely, PAX2 exhibited a distinct correlation pattern, showing a robust positive association with CDH1 (ρ = 0.67, p = 0.001), a key epithelial marker, and a negative correlation with the EMT composite score (ρ = –0.37, p = 0.097). This opposite trend highlights a potential antagonistic role between PAX1 and PAX2 in EMT dynamics, where PAX1 may promote mesenchymal traits while PAX2 preserves epithelial identity. Hierarchical clustering of correlation coefficients highlighted distinct profiles for PAX5 and PAX6, which clustered more closely with immune evasion, EMT and cell death signatures. Moreover, several PAX genes displayed significant co-expression with one another (Figure 2, Figure 3 and Figure 4), suggesting shared transcriptional regulation or involvement in coordinated oncogenic pathways.

2.6. Immunohistochemical Analysis

Immunohistochemical analysis revealed distinct expression patterns of PAX family members in breast cancer (Figure 5). PAX1 showed strong cytoplasmic positivity in most tumor samples, with a few cases also displaying nuclear staining (Figure 5A). PAX2 was consistently expressed in the nucleus with very strong positivity, while many cases also showed faint cytoplasmic staining (Figure 5B). PAX3 was detected only in the cytoplasm, with moderate staining intensity (Figure 5C). PAX4 (Figure 5D), PAX8 (Figure 5H), and PAX9 (Figure 5I) exhibited variable expression, with both nuclear and cytoplasmic positivity. Cytoplasmic localization of PAX proteins may reflect dysregulation or post-translational modifications affecting nuclear import or protein stability. Since PAX proteins are transcription factors, nuclear staining is generally associated with transcriptional activity, whereas the presence in both compartments could indicate shuttling, partial activation, or heterogeneous distribution within tumor cells.
No immunohistochemical positivity was observed for PAX5 (Figure 5E), PAX6 (Figure 5F), and PAX7 (Figure 5G) in tumor cells. However, PAX5 was strongly expressed in tumor-infiltrating lymphocytes (Figure 5E). The absence of staining for some PAX proteins may be due to post-transcriptional regulation, preventing translation, or to protein levels below the detection threshold of immunohistochemistry.
The association between RNA and protein expression plays a critical role in the development and interpretation of molecular signatures (Figure 6A). mRNA levels do not always correlate directly with protein abundance due to post-transcriptional, translational, and post-translational regulatory mechanisms. As a result, integrating both RNA and protein expression data can provide a more accurate and functionally relevant representation of cellular states. Moreover, mRNA and protein expression levels may serve as distinct biomarkers, depending on the biological or clinical context. In our cohort no significant correlation between mRNA and immunohistochemical signals of PAXs family members has been observed. However, the two approaches could be employed in a complementary manner to identify distinct prognostic biomarkers, reflecting different layers of tumor biology.

2.7. Prognostic Value of PAX Family

Transcriptomic data have been used to evaluate the ability of individual PAX genes, as well as their combined expression profiles, to predict lymph node metastasis in breast carcinomas (Figure 6A). Among the genes analysed by RNA-seq, PAX7 and PAX2 were identified as the most informative predictors (AUC 0.78): PAX7 was positively associated with nodal metastasis, whereas PAX2 showed an inverse correlation (Figure 6B).

3. Discussion

Through an integrative multi-level approach, this study highlights a subset of PAX genes as central regulators of cancer hallmarks, including cell death, immune evasion, and epithelial plasticity, in breast cancers. Notably, a specific PAX gene expression signature demonstrated an exceptional ability to predict lymph node metastasis, positioning these factors not only as potential molecular drivers of tumor dissemination, but more importantly, as promising biomarkers of tumor progression. Specifically, a two-gene RNA-seq model based on high PAX7 and low PAX2 expression yielded exceptional predictive accuracy (AUC-ROC = 0.78). These findings not only expand the oncogenic landscape of PAX genes but also lay the foundation for their integration into prognostic frameworks and future therapeutic stratification.
TCGA datasets uncovered distinct survival associations across PAX genes, with high expression of PAX3 and PAX6 correlating with poorer overall survival.
At the molecular level, the expression of PAX3, PAX5 and PAX8 demonstrated significant associations with immune checkpoint regulators, such as PD-L1 and TIGIT, suggesting a putative role in immune evasion. PAX5, in particular, also exhibited broad immunological correlations, including positive associations with B cell signatures and NK cell markers [30,31]. These results suggest a potential immunomodulatory function for PAX5, echoing emerging evidence of transcription factors shaping the immune microenvironment in solid tumors. This evidence is particularly important given that breast cancer is generally considered a low-immunogenic tumor. However, PAX5 expression may help identify a subgroup of breast cancer patients who could benefit from immunotherapy, mainly targeting specific immune checkpoints [32,33]. In a recent paper it has been demonstrated that a combined therapy with TIGIT and PD-1 inhibitors have a synergic effect on breast cancer model opening new perspectives for patients [34]. PAX1 appears to promote a mesenchymal transcriptional program characterized by extracellular matrix remodelling and increased cellular motility, in line with its strong associations with FN1, ZEB1, and MMP2. In contrast, PAX2 shows a robust positive correlation with CDH1 and an inverse association with the EMT composite score, suggesting a role in maintaining epithelial integrity. This antagonistic behavior positions PAX1 and PAX2 on opposite ends of the EMT spectrum, reflecting a potential transcriptional switch that modulates epithelial plasticity in breast cancer. In the context of cell proliferation and cell death, the observed associations between PAX1 and PAX9 and key apoptotic mediators such as BARD1 and BAX suggest that these transcription factors participate in the fine-tuning of cell death programs within the tumor microenvironment. Rather than acting as simple inducers or repressors of apoptosis, PAX proteins may modulate the dynamic equilibrium between survival and programmed cell death, thereby influencing tumor cell adaptability. The positive correlation between PAX5 and BAX further supports a broader role for PAX family members in apoptotic regulation, potentially through transcriptional control of pro-death genes [35]. Interestingly, the concomitant association of PAX9 with hypoxia and proliferation pathways indicates that apoptotic regulation by PAX factors is tightly integrated with stress adaptation and growth-promoting mechanisms.
Immunohistochemistry analysis revealed distinct subcellular localization patterns. Nuclear expression, expected for transcription factors, was prominent for PAX2, while PAX1, PAX3, PAX4, PAX8, and PAX9 displayed varied cytoplasmic and nuclear distribution. Such heterogeneity may reflect post-translational modifications, altered nuclear import mechanisms, or compartmental shuttling, raising important questions regarding the functional status of cytoplasmic PAX proteins. This may reflect translational repression, rapid protein turnover, and aligns with increasing recognition that mRNA levels do not consistently predict protein abundance. In fact, comprehensive clinical proteogenomic analysis highlighted that RNA and protein data often diverge in tumors, and that integration of both is critical for accurate phenotypic and biomarker interpretation [36,37,38].
The presence of cytoplasmic PAX proteins, typically nuclear transcription factors, suggests mislocalization or post-translational modification, such as phosphorylation and acetylation, that impedes nuclear import. This phenomenon has been reported for other transcription factors and may signal altered functional states. For instance, cytoplasmic localization of RUNX3 in gastric and breast cancers results in loss of its tumor-suppressive function, while β-catenin requires nuclear accumulation to mediate oncogenic Wnt signalling [39,40,41]. By analogy, the exclusive cytoplasmic localization of PAX3 observed in our breast cancer cohort likely reflects impaired nuclear import, limiting its transcriptional capacity and pointing to functional inactivation due to a different localization. Notably, PAX5, PAX6, and PAX7 were undetectable at the protein level, despite measurable RNA expression.
The complex and significant association between PAX genes and fundamental hallmarks of cancer lay the foundation for innovative therapeutic strategies based on the direct or indirect modulation of PAX family members. In fact, given their transcriptional nature, PAX proteins represent challenging but potentially high-yield targets. Although direct pharmacological inhibition of PAX factors remains elusive due to intrinsic disorder and nuclear localization [14], several indirect targeting strategies are emerging. For instance, the compound BG-1 has been reported to disrupt PAX2–histone methyltransferase interactions, leading to impaired transcriptional activity and reduced proliferation in renal carcinoma cells [16]. Similarly, HDAC inhibitors such as Entinostat and SAHA have been shown to suppress the oncogenic activity of PAX3:FOXO1 fusion proteins in rhabdomyosarcoma models [42,43].
Beyond indirect inhibition, efforts in structure-based virtual screening have identified small molecules (e.g., EG1) capable of impairing PAX DNA-binding function, providing proof of concept for direct interference with PAX transcriptional activity [44,45]. Biolayer interferometry (BLI) and surface plasmon resonance are emerging as useful platforms for drug screening against recombinant PAX proteins, allowing identification of lead compounds based on binding affinity and functional suppression in luciferase assays [45].
Additionally, oligonucleotide-based approaches, including antisense oligonucleotides (AONs) and RNA interference, offer another strategy to modulate PAX expression [46]. While delivery and stability remain significant hurdles, rapid progress in oligonucleotide chemistry (e.g., morpholino and PEG-conjugated compounds) may enhance clinical feasibility [47].
Despite the strength of an integrative multi-omic design and validation at both transcriptomic and protein levels, this study has limitations. The relatively small sample size of our institutional cohort may limit the generalizability of the findings. Moreover, the retrospective nature of the study may introduce potential biases related to patient selection, sample preservation, and data completeness. Although integration with external datasets such as Indivumed and TCGA partially mitigates these constraints, prospective validation in larger, independent cohorts will be essential.

4. Methods

4.1. Sample Collection

A total number of 21 samples of breast tumors samples were collected. All tissue samples were used for histological, immunohistochemical and molecular investigations. The study protocol was approved by the Institutional Ethical Committee of the “Policlinico Tor Vergata” (reference number # 96-19, 17 July 2019). All experimental procedures were conducted in accordance with the Code of Ethics of the World Medical Association, specifically the Declaration of Helsinki.

4.2. Construction of TMA

For the construction of the tissue microarray (TMA), 21 breast cancer cases were selected. Histological slides were cut from archival FFPE tumor blocks, stained with hematoxylin and eosin (H&E), and independently evaluated by two pathologists to assess tissue quality and tumor representativeness. H&E-stained slides were first digitized to enable objective identification of regions of interest.
TMA construction was subsequently performed using the fully automated TMA Grand Master system (3DHISTECH, Budapest, Hungary). Through the associated control software, digital slide images were accurately matched to the corresponding donor blocks and used to guide tissue sampling. A single 1.5 mm-diameter tissue core per case was automatically extracted based on predefined annotations and precisely arrayed into the recipient block.
From the completed TMA block, 3 µm-thick sections were cut, stained with H&E, and rescanned to verify sample integrity and alignment. For downstream digital image analysis and immunohistochemical quantification, a standardized annotation area of 1.22 mm2 was defined within each TMA spot using calibrated image-analysis software. This region of interest represents a fixed analytical area, independent of the original core diameter, and was applied uniformly across all samples to ensure consistent and comparable quantification.

4.3. Immunohistochemistry

Immunohistochemical analyses were conducted to investigate the expression of all the nine PAX proteins in breast cancers. Briefly, TMA sections were subjected to antigen retrieval by treating them with EDTA citrate pH 7.8 (PAX1, PAX3 and PAX9) or EDTA citrate pH 6.0 (PAX2, PAX4, PAX5, PAX6, PAX7 and PAX8) at 95 ◦C for 30 min. Subsequently, the sections were incubated with the following antibodies: a rabbit polyclonal anti-PAX1 antibody (dilution 1:100, ab203065, AbCam, Cambridge, UK), a mouse monoclonal anti-PAX2 antibody (dilution 1:500, clone PAX2/1104, A2522739, Antibodies.com, Cambridge, UK), a mouse monoclonal anti-PAX3 antibody (dilution 1:250 clone PAX3/8426, A316851 Antibodies.com, Cambridge, UK), a rabbit polyclonal anti-PAX4 antibody (dilution 1:250, PA1108, Termo Fischer, Cambridge, UK), a mouse monoclonal antibody anti-PAX5 (pre-diluted, clone PAX5-L, NCL-L-PAX-5, Leica), a mouse monoclonal antibody anti-PAX6 (dilution 1:100, clone AD2.38, ab78545, Abcam, Cambridge, UK), a mouse monoclonal antibody anti-PAX7 (dilution 1:500, clone PAX7/1187, ab218472, Abcam, Cambridge; UK), a mouse monoclonal anti-PAX8 antibody (pre-diluted, clone MRQ-50, MRQ-50 Mab, Leica) and a rabbit monoclonal anti-PAX9 antibody (dilution 1:100, clone S6MR, STJ11103106, St Jhons’s Laboratory Ltd., London, UK) for 1 h at room temperature. Washing was performed using PBS/Tween20 pH 7.6. The reactions were visualized using the HRP-DAB Detection Kit (UCS Diagnostic, Rome, Italy). Immunostained TMA slides were digitized using a Pannoramic Midi II Rx scanner (Epredia, Portsmouth, New Hampshire, USA) and analyzed with AI-assisted image analysis software (SlideViewer Quant Center; 3DHISTECH, Budapest, Hungary). Immunoreactivity was quantified by counting the number of positively stained cancer cells within a predefined annotation area of 1.22 mm2 for each TMA spot. The software enabled accurate identification and classification of positive cells. Negative (no primary antibody incubation) and positive controls have been performed for each reaction.

4.4. Nucleic Acid Extraction and Quality Assessment

Frozen tissue fragments were lysed in sample buffer supplemented with β-mercaptoethanol and mechanically homogenized using the BeadBug system. Nucleic acids were isolated simultaneously from each preparation using the Qiagen AllPrep Universal Kit (QIAGEN, Hilden, Germany) following the manufacturer’s protocol. DNA and RNA yields were quantified via Qubit fluorometry (dsDNA BR or RNA BR assays, respectively). Quality assessment was performed on an Agilent Tapestation using the Genomic DNA and High-Sensitivity RNA ScreenTape kits (Agilent Technologies, Santa Clara, CA, USA). RNA samples were considered suitable for library preparation only if exhibiting a RIN  ≥  4 or DV200  ≥  60.

4.5. Library Preparation and NGS Sequencing

Whole-genome sequencing (WGS) libraries were generated using the PCR-free KAPA Hyper Prep Kit (Roche, Basel, Switzerland). For transcriptomic profiling, ribosomal RNA was removed with the Ribo Zero Kit (Illumina, San Diego, CA, USA) prior to library construction using the TruSeq Stranded Total RNA Kit (Qiagen, Hilden, Germany). All procedures were carried out following the manufacturers’ guidelines. Sequencing was conducted on an Illumina NovaSeq 6000 platform with 150 bp paired-end reads. WGS achieved mean coverage ≥60× in tumor samples and ≥30× in matched normal tissues, with ≥95% genomic breadth of coverage. RNA-seq libraries yielded ≥100 million total reads, with <20% rRNA content and ≥20 million reads mapping to mRNA transcripts based on the Ensembl annotation. Ribosomal depletion targeted both nuclear and mitochondrial rRNA species.

4.6. NGS Data Processing

NGS data were aligned to the GRCh38 human reference genome. Detection and annotation of short variants in normal DNA were performed using Haplotype Caller within the GATK pipeline (GATK v4.2.6.1) [48]. Germline alterations from WGS were annotated with SnpEff [49], while somatic variants were identified through a consensus approach incorporating Mutect2, Strelka [50], VarScan [51], and SomaticSniper [52]. Structural variants were inferred using the TitanCNA [53] and DellyCNV [54] R packages. RNA-seq expression values were normalized as transcripts per million (TPM). Immune and stromal cell infiltration within tumor samples was estimated using MCPCounter algorithms [55,56]. Pathway activity scores for JAK–STAT signalling, estrogen receptor activity, TGF-β signalling, and TP53 signalling were inferred using multi-gene signatures implemented in the PROGENy bioinformatic framework [57]. The proliferation score was calculated by gene set variation analysis (GSVA) using the R package GSVA and a predefined set of 40 cell cycle-related genes (E2F5, TYMS, CCNE1, CCNA2, CCNE2, CCNF, CDC25A, CENPF, CDC45L, TOP2A, CDC6, BIRC5, CDKN3, BUB1, E2F1, BUB1B, MCM2, CCNB1, MCM6, CCNB2, NPAT, CDC2, PCNA, CDC20, SLBP, CDC25B, BRCA1, CDC25C, CDKN2C, CDKN2D, DHFR, CENPA, MSH2, CKS1, NASP, CKS2, RRM1, PLK1, RRM2, STK15) [58].
The Gene70 signature, comprising 70 genes, was used to generate a prognostic profile using the R package genefu [59]. The pan-cancer hypoxia scores were derived from the 52-gene expression signature originally developed by Buffa et al. [60], while the pan-cancer EMT score was based on the gene expression signature reported by Mak et al. [61]. IFN-gamma Score were calculated based on Ayers M. et al. [62].

4.7. Bioinformatic Analysis

A bioinformatics analysis was conducted using publicly available datasets through the UCSC Xena platform (https://xena.ucsc.edu/compare-tissue/) [22], accessed on 22 July 2025. To assess the potential prognostic value of PAX genes in breast cancers. A one-way ANOVA or Student’s t-test was performed to associate PAX genes expression in normal, tumor and metastatic samples (*** p < 0.001; **** p < 0.0001). In addition to investigating gene–gene associations, the KM tool was interrogated (https://kmplot.com/analysis/ accessed on 30 January 2026). Using a cohort of 1091 infiltrating breast carcinomas, the significant associations identified in our cohort were further evaluated [63].

4.8. Statistical Analysis

All statistical analyses were conducted using Python (v3.10). Continuous variables, including RNA-seq expression levels of PAX genes and IHC scores, were first assessed for normality using the Shapiro–Wilk test. Based on the distribution, either Spearman’s rank correlation coefficient (for non-normally distributed or mixed pairs) or Pearson’s correlation coefficient (for normally distributed pairs) was calculated to assess the strength and direction of associations. Correlation analyses were restricted to pairwise complete cases without imputation. Associations were considered statistically significant at a p-value < 0.05. Results are reported as correlation coefficients (ρ) with corresponding p-values. Visualizations were generated using annotated heatmaps to facilitate interpretation of the correlation matrices. To assess the predictive role of PAX gene expression in lymph node metastasis, we conducted a multistep analysis using RNA-seq data. Data were standardized using z-score normalization and implemented a multivariate logistic regression model with L2 regularization. The model was trained on all nine PAX genes and evaluated using 5-fold stratified cross-validation. Discriminative performance was quantified by the area under the receiver operating characteristic curve (AUC-ROC). To identify the most influential predictors, coefficients were extracted and ranked by magnitude. We additionally simulated the predicted probability of metastasis for a patient expressing high levels (75th percentile) of all PAX genes.

5. Conclusions

In conclusion, we provide an integrative multiomics analysis of the role of PAX transcription factors in breast cancer through transcriptomic and immunohistochemical approaches. These findings lay the foundation for the integration of PAX signature into prognostic frameworks and future therapeutic stratification. This is particularly relevant in the current clinical context, where, despite the advent of molecular subtyping in breast cancer, with the delineation of distinct groups defined by specific prognoses and therapeutic responses, a substantial proportion of patients still fail to benefit from standard treatments.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms27041988/s1.

Author Contributions

A.M., R.B. and M.S. conceived the project. M.P.S. and M.S. prepared the first draft. All authors contributed to writing the manuscript. M.P.S. and M.S. were responsible for the methodology. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Ministry of Health—HUB LIFE SCIENCE—Advanced Diagnostic–Italian network of excellence for advanced diagnosis (INNOVA; PNC-E3-2022-23683266) to AM, GM, EC and MS. The work was also supported by the European Union NextGenerationEU via MUR-PNRR M4C2-II.3 PE6 project PE00000019 Heal Italia (CUP: E83C22004670001) to G.M., A.M. and E.C., Associazione Italiana per la Ricerca contro il Cancro (AIRC) to GM (IG 2022 ID 27366; 2023–2027), to EC (IG#31044; 2024–2029), RC/Fondazione Luigi Maria Monti IDI-IRCCS to E.C. and the “Ministero dell’Università e della Ricerca” PRIN2022 (2022TXHFSA) by M.S.

Institutional Review Board Statement

All the procedures carried out in the research with participation of humans were in compliance with the ethical standards of the institutional and/or national ethics committee and with the Helsinki Declaration of 1964 and its subsequent changes or with comparable ethics standards. Informed voluntary consent was obtained from every participant of the study: Approval on reference number # 96-19, 17 July 2019.

Informed Consent Statement

Written informed consent was obtained from all patients prior to inclusion in the study.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

Dr. Lucas Funke and Dr. Jonathan Woodsmith were employed in Indivumed GmbH. The authors declare that this research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. Available online: https://gco.iarc.fr/en (accessed on 5 January 2026).
  2. Kim, J.; Harper, A.; McCormack, V.; Sung, H.; Houssami, N.; Morgan, E.; Mutebi, M.; Garvey, G.; Soerjomataram, I.; Fidler-Benaoudia, M.M. Global patterns and trends in breast cancer incidence and mortality across 185 countries. Nat. Med. 2025, 31, 1154–1162. [Google Scholar] [CrossRef] [PubMed]
  3. Allison, K.H.; Hammond, M.E.H.; Dowsett, M.; McKernin, S.E.; Carey, L.A.; Fitzgibbons, P.L.; Hayes, D.F.; Lakhani, S.R.; Chavez-MacGregor, M.; Perlmutter, J.; et al. Estrogen and Progesterone Receptor Testing in Breast Cancer: ASCO/CAP Guideline Update. J. Clin. Oncol. 2020, 38, 1346–1366. [Google Scholar] [CrossRef]
  4. Wolff, A.C.; Somerfield, M.R.; Dowsett, M.; Hammond, M.E.H.; Hayes, D.F.; McShane, L.M.; Saphner, T.J.; Spears, P.A.; Allison, K.H. Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer: ASCO-College of American Pathologists Guideline Update. J. Clin. Oncol. 2023, 41, 3867–3872. [Google Scholar] [CrossRef] [PubMed]
  5. Yang, X.; Smirnov, A.; Buonomo, O.C.; Mauriello, A.; Shi, Y.; Bischof, J.; Woodsmith, J.; TOR CENTRE; Melino, G.; Candi, E.; et al. A primary luminal/HER2 negative breast cancer patient with mismatch repair deficiency. Cell Death Discov. 2023, 9, 365. [Google Scholar] [CrossRef]
  6. Abderrahman, B.; Jordan, V.C. Telling details of breast-cancer recurrence. Nature 2018, 553, 155. [Google Scholar] [CrossRef] [PubMed]
  7. Carlino, F.; Solinas, C.; Orditura, M.; Bisceglia, M.D.; Pellegrino, B.; Diana, A. Editorial: Heterogeneity in breast cancer: Clinical and therapeutic implications. Front. Oncol. 2024, 14, 1321654. [Google Scholar] [CrossRef]
  8. Guo, L.; Kong, D.; Liu, J.; Zhan, L.; Luo, L.; Zheng, W.; Zheng, Q.; Chen, C.; Sun, S. Breast cancer heterogeneity and its implication in personalized precision therapy. Exp. Hematol. Oncol. 2023, 12, 3, Erratum in Exp. Hematol. Oncol. 2024, 13, 7.. [Google Scholar] [CrossRef]
  9. Scimeca, M.; Trivigno, D.; Bonfiglio, R.; Ciuffa, S.; Urbano, N.; Schillaci, O.; Bonanno, E. Breast cancer metastasis to bone: From epithelial to mesenchymal transition to breast osteoblast-like cells. Semin. Cancer Biol. 2021, 72, 155–164. [Google Scholar] [CrossRef]
  10. Amelio, I.; Bertolo, R.; Bove, P.; Candi, E.; Chiocchi, M.; Cipriani, C.; Di Daniele, N.; Ganini, C.; Juhl, H.; Mauriello, A.; et al. Cancer predictive studies. Biol. Direct. 2020, 15, 18. [Google Scholar] [CrossRef]
  11. Scimeca, M.; Urbano, N.; Bonfiglio, R.; Schillaci, O.; Bonanno, E. Management of oncological patients in the digital era: Anatomic pathology and nuclear medicine teamwork. Future Oncol. 2018, 14, 1013–1015. [Google Scholar] [CrossRef]
  12. Melino, G.; Bischof, J.; Chen, W.L.; Jia, W.; Juhl, H.; Kopeina, G.S.; Mauriello, A.; Novelli, F.; Scimeca, M.; Shi, Y.; et al. New hope for the world cancer day. Biol. Direct 2025, 20, 14. [Google Scholar] [CrossRef]
  13. Giacobbi, E.; Scioli, M.P.; Servadei, F.; Palumbo, V.; Bonfiglio, R.; Bove, P.; Mauriello, A.; Scimeca, M. PAX Family, Master Regulator in Cancer. Diagnostics 2025, 15, 1420. [Google Scholar] [CrossRef]
  14. Shaw, T.; Barr, F.G.; Üren, A. The PAX Genes: Roles in Development, Cancer, and Other Diseases. Cancers 2024, 16, 1022. [Google Scholar] [CrossRef]
  15. Robson, E.J.; He, S.J.; Eccles, M.R. A PANorama of PAX genes in cancer and development. Nat. Rev. Cancer 2006, 6, 52–62. [Google Scholar] [CrossRef]
  16. Bradford, S.T.J.; Grimley, E.; Laszczyk, A.M.; Lee, P.H.; Patel, S.R.; Dressler, G.R. Identification of Pax protein inhibitors that suppress target gene expression and cancer cell proliferation. Cell Chem. Biol. 2022, 29, 412–422.e4. [Google Scholar] [CrossRef]
  17. Shyr, C.R.; Tsai, M.Y.; Yeh, S.; Kang, H.Y.; Chang, Y.C.; Wong, P.L.; Huang, C.C.; Huang, K.E.; Chang, C. Tumor suppressor PAX6 functions as androgen receptor co-repressor to inhibit prostate cancer growth. Prostate 2010, 70, 190–199. [Google Scholar] [CrossRef] [PubMed]
  18. Wachtel, M.; Schäfer, B.W. Unpeaceful roles of mutant PAX proteins in cancer. Semin. Cell Dev. Biol. 2015, 44, 126–134. [Google Scholar] [CrossRef] [PubMed]
  19. Benzina, S.; Beauregard, A.P.; Guerrette, R.; Jean, S.; Faye, M.D.; Laflamme, M.; Maïcas, E.; Crapoulet, N.; Ouellette, R.J.; Robichaud, G.A. Pax-5 is a potent regulator of E-cadherin and breast cancer malignant processes. Oncotarget 2017, 8, 12052–12066. [Google Scholar] [CrossRef]
  20. Benzina, S.; Harquail, J.; Guerrette, R.; O’Brien, P.; Jean, S.; Crapoulet, N.; Robichaud, G.A. Breast Cancer Malignant Processes are Regulated by Pax-5 Through the Disruption of FAK Signaling Pathways. J. Cancer 2016, 7, 2035–2044. [Google Scholar] [CrossRef] [PubMed]
  21. Jin, M.; Gao, D.; Wang, R.; Sik, A.; Liu, K. Possible involvement of TGF-β-SMAD-mediated epithelial-mesenchymal transition in pro-metastatic property of PAX6. Oncol. Rep. 2020, 44, 555–564. [Google Scholar] [CrossRef]
  22. Available online: https://xena.ucsc.edu/compare-tissue/ (accessed on 10 December 2025).
  23. Feki, A.; Jefford, C.E.; Berardi, P.; Wu, J.Y.; Cartier, L.; Krause, K.H.; Irminger-Finger, I. BARD1 induces apoptosis by catalysing phosphorylation of p53 by DNA-damage response kinase. Oncogene 2005, 24, 3726–3736. [Google Scholar] [CrossRef] [PubMed]
  24. Mohammadi Ghahhari, N.; Sznurkowska, M.K.; Hulo, N.; Bernasconi, L.; Aceto, N.; Picard, D. Cooperative interaction between ERα and the EMT-inducer ZEB1 reprograms breast cancer cells for bone metastasis. Nat. Commun. 2022, 13, 2104. [Google Scholar] [CrossRef]
  25. Li, B.; Shen, W.; Peng, H.; Li, Y.; Chen, F.; Zheng, L.; Xu, J.; Jia, L. Fibronectin 1 promotes melanoma proliferation and metastasis by inhibiting apoptosis and regulating EMT. Onco Targets Ther. 2019, 12, 3207–3221. [Google Scholar] [CrossRef]
  26. Zhou, J.; Cheng, A.; Guo, J.; Liu, Y.; Li, X.; Chen, M.; Hu, D.; Wu, J. Targeting PRDX2 to inhibit tumor growth and metastasis in triple-negative breast cancer: The role of FN1 and the PI3K/AKT/SP1 pathway. J. Transl. Med. 2025, 23, 434. [Google Scholar] [CrossRef]
  27. Liu, X.; Meng, L.; Li, X.; Li, D.; Liu, Q.; Chen, Y.; Li, X.; Bu, W.; Sun, H. Regulation of FN1 degradation by the p62/SQSTM1-dependent autophagy-lysosome pathway in HNSCC. Int. J. Oral. Sci. 2020, 12, 34, Correction in Int. J. Oral Sci. 2021, 13, 34.. [Google Scholar] [CrossRef]
  28. Radisky, E.S.; Radisky, D.C. Matrix metalloproteinase-induced epithelial-mesenchymal transition in breast cancer. J. Mammary Gland. Biol. Neoplasia. 2010, 15, 201–212. [Google Scholar] [CrossRef]
  29. Serrano-Gomez, S.J.; Maziveyi, M.; Alahari, S.K. Regulation of epithelial-mesenchymal transition through epigenetic and post-translational modifications. Mol. Cancer 2016, 15, 18. [Google Scholar] [CrossRef]
  30. Carotata, S.; Brady, J.; Wu, L.; Nutt, S.L. Transient Notch signaling induces NK cell potential in Pax5-deficient pro-B cells. Eur. J. Immunol. 2006, 36, 3294–3304. [Google Scholar] [CrossRef]
  31. Khan, M.R.; Ahmad, A.; Kayani, N.; Minhas, K. Expression of PAX-5 in B Cell Hodgkin and Non-Hodgkin Lymphoma. Asian Pac. J. Cancer Prev. 2018, 19, 3463–3466. [Google Scholar] [CrossRef]
  32. Cortes, J.; Cescon, D.W.; Rugo, H.S.; Nowecki, Z.; Im, S.A.; Yusof, M.M.; Gallardo, C.; Lipatov, O.; Barrios, C.H.; Holgado, E.; et al. KEYNOTE-355 Investigators. Pembrolizumab plus chemotherapy versus placebo plus chemotherapy for previously untreated locally recurrent inoperable or metastatic triple-negative breast cancer (KEYNOTE-355): A randomised, placebo-controlled, double-blind, phase 3 clinical trial. Lancet 2020, 396, 1817–1828. [Google Scholar]
  33. Han, H.S.; Jeong, S.; Kim, H.; Kim, H.D.; Kim, A.R.; Kwon, M.; Park, S.H.; Woo, C.G.; Kim, H.K.; Lee, K.H.; et al. TOX-expressing terminally exhausted tumor-infiltrating CD8+ T cells are reinvigorated by co-blockade of PD-1 and TIGIT in bladder cancer. Cancer Lett. 2021, 499, 137–147. [Google Scholar] [CrossRef]
  34. Kim, S.; Jeon, S.H.; Kim, Y.; Park, N.; Kim, I.A. TIGIT blockade increases efficacy of PD-1 blockade combined with radiation therapy in triple-negative breast cancer model. Radiother. Oncol. 2025, 208, 110932. [Google Scholar] [CrossRef]
  35. Lang, D.; Powell, S.K.; Plummer, R.S.; Young, K.P.; Ruggeri, B.A. PAX genes: Roles in development, pathophysiology, and cancer. Biochem. Pharmacol. 2007, 73, 1–14. [Google Scholar] [CrossRef] [PubMed]
  36. Upadhya, S.R.; Ryan, C.J. Experimental reproducibility limits the correlation between mRNA and protein abundances in tumor proteomic profiles. Cell Rep. Methods 2022, 2, 100288. [Google Scholar] [CrossRef]
  37. Arad, G.; Geiger, T. Functional Impact of Protein-RNA Variation in Clinical Cancer Analyses. Mol. Cell Proteom. 2023, 22, 100587. [Google Scholar] [CrossRef]
  38. Eraslan, B.; Wang, D.; Gusic, M.; Prokisch, H.; Hallström, B.M.; Uhlén, M.; Asplund, A.; Pontén, F.; Wieland, T.; Hopf, T.; et al. Quantification and discovery of sequence determinants of protein-per-mRNA amount in 29 human tissues. Mol. Syst. Biol. 2019, 15, e8513. [Google Scholar] [CrossRef]
  39. Cong, F.; Schweizer, L.; Chamorro, M.; Varmus, H. Requirement for a nuclear function of beta-catenin in Wnt signaling. Mol. Cell Biol. 2003, 23, 8462–8470. [Google Scholar] [CrossRef]
  40. Chen, L.F. Tumor suppressor function of RUNX3 in breast cancer. J. Cell Biochem. 2012, 113, 1470–1477. [Google Scholar] [CrossRef]
  41. Ito, K.; Liu, Q.; Salto-Tellez, M.; Yano, T.; Tada, K.; Ida, H.; Huang, C.; Shah, N.; Inoue, M.; Rajnakova, A.; et al. RUNX3, a novel tumor suppressor, is frequently inactivated in gastric cancer by protein mislocalization. Cancer Res. 2005, 65, 7743–7750. [Google Scholar] [CrossRef]
  42. Ghayad, S.E.; Rammal, G.; Sarkis, O.; Basma, H.; Ghamloush, F.; Fahs, A.; Karam, M.; Harajli, M.; Rabeh, W.; Mouawad, J.E.; et al. The histone deacetylase inhibitor Suberoylanilide Hydroxamic Acid (SAHA) as a therapeutic agent in rhabdomyosarcoma. Cancer Biol. Ther. 2019, 20, 272–283. [Google Scholar] [CrossRef]
  43. Herrero Martín, D.; Boro, A.; Schäfer, B.W. Cell-based small-molecule compound screen identifies fenretinide as potential therapeutic for translocation-positive rhabdomyosarcoma. PLoS ONE 2013, 8, e55072. [Google Scholar] [CrossRef]
  44. Grimley, E.; Liao, C.; Ranghini, E.J.; Nikolovska-Coleska, Z.; Dressler, G.R. Inhibition of Pax2 Transcription Activation with a Small Molecule that Targets the DNA Binding Domain. ACS Chem. Biol. 2017, 12, 724–734. [Google Scholar] [CrossRef]
  45. Nakazawa, K.; Shaw, T.; Song, Y.K.; Kouassi-Brou, M.; Molotkova, A.; Tiwari, P.B.; Chou, H.C.; Wen, X.; Wei, J.S.; Deniz, E.; et al. Piperacetazine Directly Binds to the PAX3::FOXO1 Fusion Protein and Inhibits Its Transcriptional Activity. Cancer Res. Commun. 2023, 3, 2030–2043. [Google Scholar] [CrossRef]
  46. Zhang, L.; Wang, C. Identification of a new class of PAX3-FKHR target promoters: A role of the Pax3 paired box DNA binding domain. Oncogene 2007, 26, 1595–1605. [Google Scholar] [CrossRef]
  47. Thakur, S.; Sinhari, A.; Jain, P.; Jadhav, H.R. A perspective on oligonucleotide therapy: Approaches to patient customization. Front. Pharmacol. 2022, 13, 1006304. [Google Scholar] [CrossRef]
  48. McKenna, A.; Hanna, M.; Banks, E.; Sivachenko, A.; Cibulskis, K.; Kernytsky, A.; Garimella, K.; Altshuler, D.; Gabriel, S.; Daly, M.; et al. The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010, 20, 1297–1303. [Google Scholar] [CrossRef]
  49. Cingolani, P.; Platts, A.; Wang, L.; Coon, M.; Nguyen, T.; Wang, L.; Land, S.J.; Lu, X.; Ruden, D.M. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 2012, 6, 80–92. [Google Scholar] [CrossRef]
  50. Kim, S.; Scheffler, K.; Halpern, A.L.; Bekritsky, M.A.; Noh, E.; Källberg, M.; Chen, X.; Kim, Y.; Beyter, D.; Krusche, P.; et al. Strelka2, fast and accurate calling of germline and somatic variants. Nat. Methods 2018, 15, 591–594. [Google Scholar] [CrossRef]
  51. Koboldt, D.C.; Chen, K.; Wylie, T.; Larson, D.E.; McLellan, M.D.; Mardis, E.R.; Weinstock, G.M.; Wilson, R.K.; Ding, L. VarScan: Variant detection in massively parallel sequencing of individual and pooled samples. Bioinformatics 2009, 25, 2283–2285. [Google Scholar] [CrossRef]
  52. Larson, D.E.; Harris, C.C.; Chen, K.; Koboldt, D.C.; Abbott, T.E.; Dooling, D.J.; Ley, T.J.; Mardis, E.R.; Wilson, R.K.; Ding, L. SomaticSniper: Identification of somatic point mutations in whole genome sequencing data. Bioinformatics 2012, 28, 311–317. [Google Scholar] [CrossRef]
  53. Ha, G.; Roth, A.; Khattra, J.; Ho, J.; Yap, D.; Prentice, L.M.; Melnyk, N.; McPherson, A.; Bashashati, A.; Laks, E.; et al. TITAN: Inference of copy number architectures in clonal cell populations from tumor whole-genome sequence data. Genome Res. 2014, 24, 1881–1893. [Google Scholar] [CrossRef]
  54. Rausch, T.; Zichner, T.; Schlattl, A.; Stütz, A.M.; Benes, V.; Korbel, J.O. DELLY: Structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 2012, 28, i333–i339. [Google Scholar] [CrossRef]
  55. Becht, E.; Giraldo, N.A.; Lacroix, L.; Buttard, B.; Elarouci, N.; Petitprez, F.; Selves, J.; Laurent-Puig, P.; Sautès-Fridman, C.; Fridman, W.H.; et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 2016, 17, 218, Erratum in Genome Biol. 2016, 17, 249.. [Google Scholar] [CrossRef]
  56. Concetti, L.; Scimeca, M.; Bischof, J.; Woodsmith, J.; Agostini, M.; Fiorani, C.; Shi, Y.; Candi, E.; Melino, G.; Mauriello, A.; et al. Multi-omic characterization of consensus molecular subtype 1 (CMS1) colorectal cancer with dampened immune response improves precision medicine. Mol Oncol. 2025, 19, 3486–3498. [Google Scholar] [CrossRef]
  57. Schubert, M.; Klinger, B.; Klünemann, M.; Sieber, A.; Uhlitz, F.; Sauer, S.; Garnett, M.J.; Blüthgen, N.; Saez-Rodriguez, J. Perturbation-response genes reveal signaling footprints in cancer gene expression. Nat. Commun. 2018, 9, 20. [Google Scholar] [CrossRef]
  58. Yuan, J.; Levitin, H.M.; Frattini, V.; Bush, E.C.; Boyett, D.M.; Samanamud, J.; Ceccarelli, M.; Dovas, A.; Zanazzi, G.; Canoll, P.; et al. Single-cell transcriptome analysis of lineage diversity in high-grade glioma. Genome Med. 2018, 10, 57. [Google Scholar] [CrossRef]
  59. Gendoo, D.M.; Ratanasirigulchai, N.; Schröder, M.S.; Paré, L.; Parker, J.S.; Prat, A.; Haibe-Kains, B. Genefu: An R/Bioconductor package for computation of gene expression-based signatures in breast cancer. Bioinformatics 2016, 32, 1097–1099. [Google Scholar] [CrossRef]
  60. Buffa, F.M.; Harris, A.L.; West, C.M.; Miller, C.J. Large meta-analysis of multiple cancers reveals a common, compact and highly prognostic hypoxia metagene. Br. J. Cancer 2010, 102, 428–435, Erratum in Br. J. Cancer 2010, 103, 1136.. [Google Scholar] [CrossRef]
  61. Mak, M.P.; Tong, P.; Diao, L.; Cardnell, R.J.; Gibbons, D.L.; William, W.N.; Skoulidis, F.; Parra, E.R.; Rodriguez-Canales, J.; Wistuba, I.I.; et al. A Patient-Derived, Pan-Cancer EMT Signature Identifies Global Molecular Alterations and Immune Target Enrichment Following Epithelial-to-Mesenchymal Transition. Clin. Cancer Res. 2016, 22, 609–620. [Google Scholar] [CrossRef]
  62. Ayers, M.; Lunceford, J.; Nebozhyn, M.; Murphy, E.; Loboda, A.; Kaufman, D.R.; Albright, A.; Cheng, J.D.; Kang, S.P.; Shankaran, V.; et al. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J. Clin. Invest. 2017, 127, 2930–2940. [Google Scholar] [CrossRef]
  63. Posta, M.; Győrffy, B. Pathway-level mutational signatures predict breast cancer outcomes and reveal therapeutic targets. Br. J. Pharmacol. 2025, 182, 5734–5747. [Google Scholar] [CrossRef] [PubMed]
Ijms 27 01988 g001
Figure 1. Bioinformatic analysis of PAX genes expression in breast carcinomas. Graphs show overall survival and expression levels in normal, tumor, and metastatic breast tissues for PAX1 (A), PAX2 (B), PAX3 (C), PAX4 (D), PAX5 (E), PAX6 (F), PAX7 (G), PAX8 (H), and PAX9 (I). Asterisks indicate levels of statistical significance: * p < 0.05; ** p < 0.01; **** p < 0.0001.
Figure 1. Bioinformatic analysis of PAX genes expression in breast carcinomas. Graphs show overall survival and expression levels in normal, tumor, and metastatic breast tissues for PAX1 (A), PAX2 (B), PAX3 (C), PAX4 (D), PAX5 (E), PAX6 (F), PAX7 (G), PAX8 (H), and PAX9 (I). Asterisks indicate levels of statistical significance: * p < 0.05; ** p < 0.01; **** p < 0.0001.
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Figure 2. Heatmap showing correlations between PAX gene expression involved in cell death, proliferation and hypoxia in breast cancer. Strong positive associations are shown in blue, while negative associations are shown in red.
Figure 2. Heatmap showing correlations between PAX gene expression involved in cell death, proliferation and hypoxia in breast cancer. Strong positive associations are shown in blue, while negative associations are shown in red.
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Figure 3. Heatmap showing correlations between PAX gene expression involved in cancer immune evasion in breast cancer. Strong positive associations are shown in blue, while negative associations are shown in red.
Figure 3. Heatmap showing correlations between PAX gene expression involved in cancer immune evasion in breast cancer. Strong positive associations are shown in blue, while negative associations are shown in red.
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Figure 4. Heatmap showing correlations between PAX gene expression involved in the epithelial to mesenchymal transition (EMT) in breast cancer. Strong positive associations are shown in blue, while negative associations are shown in red.
Figure 4. Heatmap showing correlations between PAX gene expression involved in the epithelial to mesenchymal transition (EMT) in breast cancer. Strong positive associations are shown in blue, while negative associations are shown in red.
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Figure 5. Immunohistochemical staining for (A) PAX1, (B) PAX2, (C) PAX3, (D) PAX4, (E) PAX5, (F) PAX6, (G) PAX7, (H) PAX8 and (I) PAX9 of tumor cells in representative breast cancer tissues. In tumor cells, the positivity was detected in the nucleus, cytoplasm or both compartments. Scale bar represents 100 µm.
Figure 5. Immunohistochemical staining for (A) PAX1, (B) PAX2, (C) PAX3, (D) PAX4, (E) PAX5, (F) PAX6, (G) PAX7, (H) PAX8 and (I) PAX9 of tumor cells in representative breast cancer tissues. In tumor cells, the positivity was detected in the nucleus, cytoplasm or both compartments. Scale bar represents 100 µm.
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Figure 6. (A) Heatmap shows associations between PAX gene expression at the mRNA and protein (IHC) levels. Strong positive correlations are shown in blue, whereas negative correlations are shown in red. (B) ROC Curve in which PAX2 and PAX7 achieved an AUC-RC of 0.78. The dotted diagonal line indicates the performance of a random classifier (AUC = 0.5).
Figure 6. (A) Heatmap shows associations between PAX gene expression at the mRNA and protein (IHC) levels. Strong positive correlations are shown in blue, whereas negative correlations are shown in red. (B) ROC Curve in which PAX2 and PAX7 achieved an AUC-RC of 0.78. The dotted diagonal line indicates the performance of a random classifier (AUC = 0.5).
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Scimeca, M.; Scioli, M.P.; Palumbo, V.; Funke, L.; Woodsmith, J.; Servadei, F.; Giacobbi, E.; Seghetti, C.; Buonomo, O.C.; Candi, E.; et al. Paired-Box (PAX) Gene Signatures as a Biomarker of Breast Cancer Progression. Int. J. Mol. Sci. 2026, 27, 1988. https://doi.org/10.3390/ijms27041988

AMA Style

Scimeca M, Scioli MP, Palumbo V, Funke L, Woodsmith J, Servadei F, Giacobbi E, Seghetti C, Buonomo OC, Candi E, et al. Paired-Box (PAX) Gene Signatures as a Biomarker of Breast Cancer Progression. International Journal of Molecular Sciences. 2026; 27(4):1988. https://doi.org/10.3390/ijms27041988

Chicago/Turabian Style

Scimeca, Manuel, Maria Paola Scioli, Valeria Palumbo, Lukas Funke, Jonathan Woodsmith, Francesca Servadei, Erica Giacobbi, Christian Seghetti, Oreste Claudio Buonomo, Eleonora Candi, and et al. 2026. "Paired-Box (PAX) Gene Signatures as a Biomarker of Breast Cancer Progression" International Journal of Molecular Sciences 27, no. 4: 1988. https://doi.org/10.3390/ijms27041988

APA Style

Scimeca, M., Scioli, M. P., Palumbo, V., Funke, L., Woodsmith, J., Servadei, F., Giacobbi, E., Seghetti, C., Buonomo, O. C., Candi, E., Treglia, M., Marsella, L. T., Melino, G., Mauriello, A., & Bonfiglio, R. (2026). Paired-Box (PAX) Gene Signatures as a Biomarker of Breast Cancer Progression. International Journal of Molecular Sciences, 27(4), 1988. https://doi.org/10.3390/ijms27041988

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