Biology subjects

Guha, S.

Publications and source records attributed to Guha, S..

At least 19 recordsLinked to original sources

bioRxiv2026

Natural isolates of Sinorhizobium meliloti with dry, as opposed to mucoid, colony morphologies have higher soil fitness, slightly lower host fitness, and reduced nitrogen fixation

Sinorhizobium meliloti is a nitrogen-fixing soil bacterium that forms a facultative mutualism with its host alfalfa. S. meliloti strains exhibit diverse colony morphologies on solid culture media that span a continuum from dry to mucoid. Studies on how this trait affects symbiosis rely on mutations in exopolysaccharide II production genes; however, whether natural variation in morphology confers a selective advantage on rhizobia in their natural habitats remains unknown. Here, we used 90 mucoid and 27 dry natural isolates to test whether colony morphology is associated with rhizobial fitness in the soil and in the host, or with the nitrogen-fixation benefits they provide to the plant. Colony morphologies were widely distributed across a phylogenetic tree, suggesting that morphological shifts occurred repeatedly and no single genetic variant was strongly associated with this trait. We inoculated soil mesocosms and plants with a mixture of dry and mucoid bacteria. We observed that dry rhizobia have higher fitness, particularly at 22{degrees}C and 32{degrees}C, but not under cold or drought conditions, independent of the initial rhizobial load. Conversely, mucoid bacteria have a modestly higher relative fitness in host nodules, and single inoculations showed that they tend to confer greater benefits to all three alfalfa varieties. Over time, we also observed rare, repeated conversion from mucoid to dry morphologies in soil and host plants. Taken together, our study reveals that morphological variation within a species in a single agricultural field is important for bacterial fitness in the soil and can have cascading effects on the rhizobia's plant benefits.

ecology↗

bioRxiv2026

Scaling of structural variability of ecDNA polymer condensates with copy number boosts and stabilises oncogene regulatory contacts

Extrachromosomal DNAs (ecDNAs) form highly heterogeneous condensates in cancer cells that drive oncogene overexpression, yet how structural variability coexists with stable gene regulation remains unclear. Here, we develop a minimal polymer physics model of MYC-harbouring COLO320-DM ecDNAs, where BRD4-like complexes bind and bridge cognate sites along ecDNA rings. Above a critical binder concentration, ecDNAs phase separate into condensates exhibiting diverse conformations because of their thermodynamic folding degeneracy. Despite this variability, condensates retain conserved interaction scaffolds that give rise to reproducible contact patterns, including in-trans associated domains (I-TADs), genomic regions enriched in intermolecular regulatory contacts between distinct ecDNAs. We find that condensate 3D architecture follows universal scaling relations with ecDNA copy number, n, remaining robust to model parameter changes. Regulatory contacts within I TADs increase linearly with n, yet they are one order of magnitude stronger than in size matched control regions outside I TADs, whereas their relative fluctuations are markedly suppressed as n increases. This scaling produces enhanced, low-noise regulatory environments for oncogenes embedded within I-TADs, such as PVT1-MYC fusions, whereas the canonical MYC copy, located outside, is less amplified as experimentally observed. Our findings reveal universal polymer physics principles underlying ecDNA condensate organization, offering a mechanistic basis for selective oncogene amplification and potential advantages in cancer progression.

biophysics↗

bioRxiv2026

Enhancer RNA like function of intergenic inherited lncRNAs during maternal to zygotic transition in zebrafish

The maternal-to-zygotic transition (MZT) is a major developmental event during which inherited transcripts are remodeled and zygotic transcription is established. Although parentally inherited long noncoding RNAs (lncRNAs) are present in early embryos, they have been thought to be dispensable. We have identified more than 2000 inherited lncRNAs in zebrafish embryos, but how these RNAs participate in regulatory programs during early development has remained unexplored. Here, the inheritance of selected zebrafish lncRNAs spanning a broad expression range were confirmed at the pre-MZT stage and full-length sequences were captured by Direct RNA nanopore sequencing. We show that 30% inherited intergenic lncRNAs are preferentially associated with active enhancers, annotated as such in DANIO CODE, whereas non-inherited intergenic lncRNAs rarely overlap with enhancers. Perturbation of five inherited intergenic lncRNAs, individually, using antisense oligonucleotides reduced the expression of their respective neighboring genes at 2.5, 4.3, and/or 6 hours post fertilization, indicating that these RNAs act as positive local regulators during MZT. Together, these findings identify inherited intergenic lncRNAs as enhancer-associated regulators with elncRNA-like properties during early embryogenesis.

developmental biology↗

bioRxiv2026

Alfalfa varieties can weakly choose beneficial nitrogen-fixing bacteria from a population isolated from a single field

O_LIIn natural and agricultural systems, legumes recruit rhizobia from diverse soil populations to fix nitrogen in root nodules. A few legumes, including the model legume Medicago truncatula, can select and enrich beneficial rhizobia. Here, we investigated whether its perennial relative, Medicago sativa (alfalfa), a globally important forage crop, also possesses this ability. C_LIO_LIWe developed a genetically variable collection of 117 Sinorhizobium meliloti strains sampled from three field-grown alfalfa varieties, performed multi-strain and single-strain inoculations in a nitrogen-free greenhouse experiment across the same hosts, and evaluated plant benefits and relative strain fitness in nodules. C_LIO_LIAlfalfa varieties differed in which strains best promoted plant growth and which strains had high fitness in nodules. Regressing strain fitness and host benefit revealed that two of three alfalfa varieties selected and enriched more beneficial strains during symbiosis, though the strength of selection was weak. In alignment with these results, no variety produced as much biomass in mixed inoculation as it did with the best-performing single strain. C_LIO_LILegumes ability to enrich beneficial rhizobial populations from field-representative strain diversity warrants further study to develop optimized varieties. Ultimately, identifying crop varieties that naturally select for beneficial bacteria could reduce the need for repeated inoculant applications. C_LI

Plant Biology↗

bioRxiv2026

NeuroPupil: A generalization-first framework for scalable and biologically informative cross-species pupillometry

Quantitative pupillometry provides a noninvasive window into brain state and neurological function, but its broader use across experimental and clinical settings is limited by challenges in achieving accurate, scalable, and generalizable measurements. Here, we present NeuroPupil, a deep learning framework for high-throughput, cross-species pupillometry that emphasizes robust generalization across subjects, behavioral contexts, and imaging conditions. Through systematic benchmarking of training strategies and network architectures, we identify pooled multi-subject training combined with an optimized U-Net architecture as a key determinant of reliable and transferable pupil tracking performance. Across diverse mouse and human datasets, NeuroPupil achieves improved accuracy and substantial gains in computational efficiency compared to existing approaches, enabling practical analysis of large-scale datasets. We further demonstrate that improved pupil tracking fidelity enhances downstream biological inference: NeuroPupil-derived pupil features significantly improve prediction of distributed cortical activity in behaving mice and preserve diagnostically relevant temporal structure in human clinical recordings. These findings highlight the importance of precise and scalable measurement for linking pupil dynamics to brain activity and clinical phenotypes. By integrating benchmarking, scalability, and accessible software tools, NeuroPupil provides a reproducible framework for large-scale pupillometry and facilitates its application in systems and translational neuroscience.

neuroscience↗

bioRxiv2025

Regulation of lipid metabolism is a primordial function of STING

The stimulator of interferon genes (STING) is a pivotal regulator of type I interferon (IFN) responses. Although the IFN system is confined to vertebrates, STING is present across metazoans and in some unicellular eukaryotes, suggesting involvement in distinct functions prior to vertebrate divergence. We here explored the conservation of STING-mediated regulation of polyunsaturated fatty acid (PUFA) metabolism. We found that tested STING homologs from vertebrates, invertebrates, and unicellular eukaryotes interacted with the fatty acid desaturase 2 (FADS2) rate-limiting enzyme in PUFA metabolism and subsequent functional outputs. The ability to regulate lipid metabolism did not correlate with antiviral activity, suggesting that the cooptation of this metabolic pathway by the IFN-based immune system is independent of STING-associated immune responses. Thus, STING-mediated metabolic regulation is an evolutionarily conserved feature and a primordial STING function.

immunology↗

bioRxiv2025

Androgen receptor activation stabilizes a hybrid epithelial/mesenchymal phenotype in presence of Notch-Jagged signaling

Metastasis is a key cause of mortality in cancer. It is driven majorly by clusters of circulating tumor cells which are often comprised of cells in a hybrid epithelial/mesenchymal (E/M) phenotype. Hybrid E/M phenotype has also been shown to be more tumor-initiating and therapy-resistant, but how cells maintain the hybrid E/M phenotype(s) remains an active area of investigation. Here, we develop a mathematical model that couples the intracellular dynamics of key players of Epithelial-Mesenchymal Transition (EMT) with the Androgen Receptor (AR), and cell-cell communication through Notch-Delta-Jagged signaling, in the context of prostate cancer. Our simulations show that AR can stabilize a hybrid E/M phenotype predominantly in the presence of Notch-Jagged, but not Notch-Delta, signaling. Implementing this model on a multi-cellular lattice, we observed that AR can alter the fraction of cells exhibiting a hybrid E/M and a mesenchymal phenotype. Finally, through analysis of transcriptomic and patient survival data, we found that the co-expression of AR and Jagged correlated with worse out-comes. Together, these results highlight the outcome of emergent dynamics between the Notch and AR signaling axis in promoting prostate cancer aggressiveness.

systems biology↗

bioRxiv2025

Nodule branching, size, and symbiosis outcomes are shaped by natural genetic variation in rhizobial bacteria and alfalfa hosts

In many host-microbe symbioses, hosts develop specialized organs that house microbial symbionts. In the legume-rhizobium symbiosis, these organs are called root nodules. While many studies have focused on host traits like plant growth, nodule number, and their structural changes, nodule morphology may also influence rhizobial benefits (e.g., bacterial population size). Here, we investigated how variation in alfalfa nodule morphologies influences the fitness outcome of both partners. Specifically, we measured how host and strain genetic variation influenced 1) nodule branch number, area, and rhizobial fitness at the individual nodule level and 2) the proportion of branched nodules, nodule number, and plant benefits at the whole plant level. From the rhizobial perspective, host identity had a stronger effect on rhizobial fitness, and larger or more branched nodules tend to release more rhizobia. From the host perspective, the proportion of branched nodules was strongly negatively correlated with the total nodule number and positively correlated with host benefit across multiple alfalfa genotypes. However, the strength of the relationship differed among hosts. Together, these results provide insight into the host and rhizobial genetic contributions to nodule morphology and symbiotic outcomes, suggesting a novel direction for further research in legume-rhizobium mutualism. HighlightPlant and bacterial symbiotic effectiveness can be gauged by the number of nodule branches formed in indeterminant legumes-rhizobium systems along with traditional metrics such as nodule count.

plant biology↗

bioRxiv2025

Capture, Confine, Characterize: High-Throughput Dielectrophoresis-Based Single-Cell Microfluidics Platform to Analyze Mammalian and Yeast Cells Using Raman Spectroscopy

Single-cell analysis technologies are pivotal in unraveling complex biological mechanisms, yet existing platforms are often limited to sequencing-based end-point measurements, which fail to capture live cell dynamics. Here, we present a microfluidic- microelectronic device, the Microfluidic dielectrophoretic Arresting System (MiDAS) that employs dielectrophoresis (DEP) for high-throughput single-cell and droplet trapping in a compact array. We tested multiple trap geometries, including a 20 m-diameter DEP trap for polymer microbeads, fungal and mammalian cells, and a 40 m-diameter trap for water-in-oil droplets. The platform demonstrates broad sample compatibility, reliably immobilizing cells and beads of varying sizes. By integrating optical imaging and Raman spectroscopy, we enable rapid, non-destructive interrogation of individual cells with temporal resolution. We describe different modes of MiDAS operation to trap and manipulate single-cells or reverse emulsion droplets on demand, with applications in droplet microfluidics. Our MiDAS platforms simple fabrication, robust performance, and broad compatibility with diverse sample types position it as a versatile tool with transformative potential for single-cell analysis, offering researchers an innovative approach to interrogate cellular dynamics with unprecedented precision and throughput.

bioengineering↗

bioRxiv2025

Development of a method for large-scale single-molecule analysis of tau proteoforms

Proteins exist as diverse proteoforms resulting from a combination of genetic variation, alternative splicing, and post-translational modifications. Current methods struggle to capture this complexity at the single-molecule level. Here we introduce Iterative Mapping of Proteoforms (IMaP), a method that enables the massively-parallel interrogation of millions to billions of single-protein molecules through iterative probing with fluorescently labeled antibodies. Using 12 site-specific antibodies, the method is capable of measuring 212 (4,096) potential proteoform groups. We used IMaP to measure proteoform group profiles of the tau protein, a key player in neurodegenerative diseases, using two pan anti-tau antibodies (Tau-13, Tau-216), three isoform-specific antibodies (Anti-0N, Anti-2N, Anti-4R), and seven phosphosite-specific antibodies (Anti-pT181, Anti-pS202+pT205, Anti-pT205, Anti-pS214, Anti-pT217, Anti-pT231, and Anti-pS396). The method demonstrates high sensitivity (detecting proteoforms at 0.1% abundance), high reproducibility (median CV <5.5%), and broad dynamic range (>3 orders of magnitude), outperforming conventional techniques in resolving closely related proteoform groups. We demonstrated that the method can be used on relevant biological samples by examining various neuronal models (iNeuron cells, organoids, MiBrains, and mouse brains) and human samples. This examination revealed 130 distinct tau proteoform groups with as many as six phosphorylation events. The non-random distribution of these phosphorylation events suggests ordered and site-specific modification processes rather than random, stochastic accumulation. Certain combinations of phosphorylation events were more abundant than others; for example, pT217 preferentially co-occurred with pT181. In validating the applicability of the assay to human disease samples, we noted a specific pattern of multiple phosphorylation events in an advanced Alzheimers disease patient that suggests a sequential pathway of pathological tau modification. Iterative Mapping of Proteoforms provides insights into proteoform complexity at the single-molecule level, with significant implications for understanding protein regulation in neurodegenerative diseases and beyond.

biochemistry↗

bioRxiv2025

Physical principles of phase-separation action on chromatin looping associated to pathogenic gene activation

Phase-separation of chimeric proteins resulting from genetic mutations has been shown to trigger aberrant chromatin looping, contributing to disease development, including cancer. However, the physical mechanisms regulating these processes are not yet fully understood. In this study, we employ polymer physics models of chromatin and numerical simulations to investigate the relationship between phase-separation of proteins and chromatin structure. We demonstrate that a simple model, including only protein-protein and protein-chromatin interactions, effectively explains the aberrant looping observed around oncogenes, such as PBX3, in cells expressing the NUP98-HOXA9 chimeric protein, which is associated with leukemia. In this scenario, looping occurs through a switch-like mechanism controlled by the concentration of the chimera and its affinity with chromatin. Moreover, when incorporating the presence of extruding factors in a more complex model, similar results are observed, indicating a mild dependence of this looping mechanism on loop-extrusion activity. Finally, leveraging our numerical simulations, we propose potential strategies to inhibit the formation of enhancer-gene loops by directly targeting the chimeric protein with interfering molecules.

biophysics↗

bioRxiv2025

The methyl-CpG-binding protein 2 inhibits cGAS-associated signaling

The detection of cytosolic dsDNA is tightly regulated to avoid pathological inflammatory responses. A major pathway involved in their detection relies on the cyclic GMP-AMP synthase (cGAS) that triggers activation of the Stimulator of interferon genes (STING) which subsequently drives the expression of inflammatory genes and type I Interferons (IFNs). Here, we show that the methyl-CpG-binding protein 2 (MECP2), a major transcriptional regulator, controls dsDNA-associated inflammatory responses. We show that the presence of cytosolic dsDNA promotes MECP2 export from the nucleus to the cytosol where it interacts with dsDNA, dampening cGAS activation. Our data also indicate that MECP2 export from the nucleus partially phenocopies MECP2 deficiency, leading to the expression of inflammatory and interferon stimulated genes, enforcing an antiviral state. Finally, we also show that MECP2 displacement from the nucleus following dsDNA stimulation is sufficient to disrupt its canonical function, leading to the reactivation of otherwise repressed genes, such endogenous retroelements of the Long interspersed nuclear element-1 (LINE-1) family. Re-expression of the latter led to the accumulation of DNA species feeding cGAS-dependent signaling and can be dampened by reverse transcriptase inhibitors. We thus establish a previously unforeseen direct role of MECP2 in the regulation of the breadth and nature of dsDNA-associated inflammatory responses. Furthermore, our results suggest that targeting dsDNA-associated pathways or pharmacological inhibition of LINE-1 may bear therapeutic hopes for Rett syndrome (RTT) patients that present with MECP2 deficiency.

immunology↗

bioRxiv2024

The activity of soil microbial taxa in the rhizosphere predicts the success of root colonization

Plant-beneficial microbes have great potential to improve sustainability in agriculture. Still, managing beneficial microbes is challenging because the impact of microbial dormancy on community assembly across the soil, rhizosphere, and endosphere is poorly understood. We address this gap with the first documented use of Biorthogonal Non-Canonical Amino Acid Tagging (BONCAT) to probe active microbes in the soil-to-root gradient. Using nodule-forming legume Trifolium incarnatum, we confirmed that BONCAT is suitable for labeling endospheric microbes with microscopy. Next, we coupled BONCAT to Flow Cytometer Activated Cell Sorting (FACS) and 16S rRNA amplicon sequencing to probe patterns of microbial activity and the structure of the active microbial community across the soil, rhizosphere, root, and nodule with a native soil microbial community. As expected, we found x10 higher microbial activity in the endosphere than in the rhizosphere or bulk soil, likely due to increased plant resources. Finally, we revealed that microbial activity in the rhizosphere was significantly associated with successful endosphere colonization, more so than microbial abundance alone. This last finding has implications for the development of microbial inoculants, suggesting colonizing plant roots is linked to a microbes ability to overcome dormancy once deployed in the soil.

microbiology↗

bioRxiv2024

Optimal Prescriptive Treatments for Ovarian Cancer with Genetic Data

Among the cancers affecting the female population, Ovarian Cancer (OC), while being relatively rare, is the leading cause of gynecological cancer-related deaths, with overall 5-year survival rates of approximately 50% for all stages combined. This is because of the challenges associated with the diagnosis, resulting in detection at advanced stages of OC, coupled with the slow progress in effective treatment options since the approval of platinum-based chemotherapy in the late 1970s. There has been a relative lack of sophisticated methods based on Machine Learning (ML) models that use genetic data for better prediction of Ovarian Cancer outcomes and result in more effective treatment recommendations. Therefore, there is an unmet clinical need to create models that allow physicians to make informed decisions based on all available data, including patient demographic, social, health, and genomic data. Hence, we develop new techniques for leveraging genetic information in prescribing optimal treatments for patients with OC, using a publicly available dataset from the Prostate, Lung, Colorectal and Ovarian Cancer (PLCO) trial. Our approach is able to transform genotype sequencing information into a simple tabular form that can then be used as the input to any ML model. Coupled with the recorded treatment regimen and clinical parameters of matched patients from the genetic dataset, we estimate the treatment effect in terms of mortality prediction and use it to prescribe the optimal treatment for any given patient. By including the genetic features engineered through our proposed method, our models have a higher accuracy than the models without genetic information embedded. The increase in predictive accuracy demonstrates the improved efficacy of our method in the predictive setting. Furthermore, in the prescriptive setting, the models including genetic features output different treatment choices for patients, showing the impact of their inclusion. This is further highlighted by the feature importance of the genetic features such as mutations in the FAT3, BRCA1, BRCA2, and NF1 genes, where they rank highly with a tighter aggregation of the top features, relative to the sharp drop-off in feature importance after the top feature in the models without genetic data. Taken together, in summary, our models will allow oncologists to make more informed and accurate decisions, incorporating a patients genetic data with all other available clinical information, which has the potential for improved prognosis and better long-term survival outcomes for Ovarian Cancer patients.

cancer biology↗

bioRxiv2024

In multi-strain inoculations, mutation of Medicago symbiosis genes creates a complex selective landscape for naturally-occurring genetic variation in rhizobia

In the mutualism between leguminous plants and rhizobia bacteria, rhizobia live inside root nodules, creating potential for host genes to shape the rhizobial selective environment. Many host genes that affect symbiosis have been identified; however, the extent to which these genes affect selection acting on rhizobia is unknown. In this study, we inoculated 18 Medicago truncatula symbiotic mutants (including mutants that alter Nodule Cysteine-Rich (NCR) peptide production, plant defense, and nodule number regulation) with a mixture of 86 Sinorhizobium meliloti strains. Most mutations resulted in reduced host benefits, but the effects on rhizobial benefit (i.e., relative strain fitness) varied widely, revealing widespread host-by-strain fitness interactions. Genome-wide association analyses identified variants on rhizobial replicons pSymA and pSymB as important in mediating strain fitness responses to host mutations. Whereas most top variants affected rhizobial fitness with one host mutant (limited effect variants), nine affected fitness across six or more host mutations. These pervasive variants occurred primarily on pSymA, the symbiotic replicon, and include fixL and some metabolic genes. In contrast to the limited effect variants, variants with pervasive positive effects on strain fitness when host genes were mutated tended to adversely affect fitness in wild-type hosts. Competition assays across Medicago genotypes confirmed a pervasive role for one candidate (malonyl-CoA synthase), and AlphaFold multimer modelling suggests that many rhizobial top candidates could interact with host NCR peptides. Our results reveal how host genetic variation affects rhizobial fitness, setting the stage for improving rhizobial inoculants and breeding legume hosts better adapted to multi-strain environments.

evolutionary biology↗

bioRxiv2024

Binder and monomer valencies determine the extent of collapse and reswelling of chromatin

Multivalent DNA-bridging proteins mediated collapse of chromatin polymer has long been established as one of the driving factors in chromatin organisation inside cells. These multivalent proteins can bind to distant binding sites along the chromatin backbone and bring them together in spatial proximity, leading to collapsed conformations. Recently, it has been suggested that these proteins not only drive collapse of the chromatin polymer, but also reswelling at higher concentrations. In this study, we investigate the physical mechanisms underlying this unexpected reswelling behaviour. We use Langevin dynamics simulation of a coarse-grained homopolymer to investigate the effects of the valencies of both the binders and the monomers on the polymer conformations. We find that while the extent of collapse of the polymer is strongly dependent on the binder valency, the extent of reswelling is largely determined by the monomer valency. Furthermore, we also discover two different physical mechanisms that drive reswelling of the polymer - excluded volume effects and loss of long-range loops. Finally, we obtain a classification map to determine the regimes in which each of these mechanisms is the dominant factor leading to polymer reswelling.

biophysics↗

bioRxiv2024

Decreased CREB phosphorylation impairs embryonic retinal neurogenesis in the Oa1-/- mouse model of Ocular albinism

Mutations in the human Ocular albinism type-1 gene OA1 are associated with abnormal retinal pigment epithelium (RPE) melanogenesis and poor binocular vision resulting from misrouting of ipsilateral retinal ganglion cell (iRGC) axons to the brain. We studied the latter using wild-type (WT) and Oa1-/- mouse eyes. At embryonic stages, the WT RPE-specific Oa1 protein signals through cAMP/Epac1-Erk2-CREB. Following CREB phosphorylation, a pCREB gradient extends from the RPE to the differentiating retinal amacrine and RGCs. In contrast to WT, the Oa1-/- RPE and ventral ciliary-margin-zone, a niche for iRGCs, express less pCREB while their retinas have a disrupted pCREB gradient, indicating Oa1s involvement in pCREB maintenance. Oa1-/- retinas also show hyperproliferation, enlarged nuclei, reduced differentiation, and fewer newborn amacrine and RGCs than WT retinas. Our results demonstrate that Oa1s absence leads to reduced binocular vision through a hyperproliferation-associated block in differentiation that impairs neurogenesis. This may affect iRGC axons routing to the brain.

developmental biology↗

bioRxiv2024

LASSO Based Analysis for Prediction of Prognostic Signature Genes Associated with Breast Cancer

BackgroundCancer is a genetic disease, where gene alterations play a significant role in the disease onset and pathogenesis. Analysis of the underlying gene interaction pathways could reveal new biomarkers and could also potentially help in the development of targeted drugs for therapeutics. Microarray techniques have emerged as powerful tools capable of simultaneously measuring the expression levels of thousands of genes, making them invaluable in cancer biology research. However, the processing of the resultant datasets poses significant challenges due to their high dimensionality. Also, feature extraction becomes essential to discern the crucial features within these extensive datasets. To mitigate these difficulties advanced computational techniques like Machine Learning (ML) could be instrumental. LASSO-regression-based classification is an advanced ML technique that can help in feature selection by evaluating individual parameters like genes. MethodsThis study focuses on uncovering key prognostic genes for breast cancer using a combination of LASSO regression-based classifier and statistical bioinformatics models. Differentially expressed genes (DEGs) were identified using the "Limma" package in R, and significant genes were further filtered using the LASSO-based classifier significance coefficient. Genes common to both methods were considered as the focus of this study. Additionally, Protein-Protein Interaction (PPI) networks of these key genes were constructed using STRING, and hub genes, significant modules, and associated genes were identified using Cytoscape. ResultsThis study identified CCR8, CXCL11, CCL23, CCL24, CCL28, and CCL21 as signature prognostic genes for breast cancer, revealing a strong association between chemokines and breast cancer pathogenesis. Extensive literature searches were conducted to validate and confirm their prognostic significance in the disease. ConclusionThese findings are pivotal for enhancing our comprehension of the pathways involved in breast cancer. Additionally, they hold promise as novel biomarkers for diagnostic purposes and may also reveal significant therapeutic targets for the management of breast cancer.

cancer biology↗