Identification of ZG16B as a prognostic biomarker in breast cancer

Transcriptomics in idiopathic pulmonary fibrosis unveiled: a new perspective from differentially expressed genes to therapeutic targets

Background

The underlying molecular pathways of idiopathic pulmonary fibrosis (IPF), a progressive lung condition with a high death rate, are still mostly unknown. By using microarray datasets, this study aims to identify new genetic targets for IPF and provide light on the genetic factors that contribute to the development of IPF.

Method

We conducted a comprehensive analysis of three independent IPF datasets from the Gene Expression Omnibus (GEO) database, employing R software for data handling and normalization. Our evaluation of the relationships between differentially expressed genes (DEGs) and IPF included differential expression analysis, expression quantitative trait loci (eQTL) analysis, and Mendelian Randomization(MR) analyses. Additionally, we used Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to explore the functional roles and pathways of these genes. Finally, we validated the results obtained for the target genes.

Results

We identified 486 highly expressed genes and 468 lowly expressed genes that play important roles in IPF. MR analysis identified six significantly co-expressed genes associated with IPF, specifically C12orf75, SPP1, ZG16B, LIN7A, PPP1R14A, and TLR2. These genes participate in essential biological processes and pathways, including macrophage activation and neural system regulation. Additionally, CIBERSORT analysis indicated a unique immune cell distribution in IPF, emphasized the significance of immunological processes in the disease. The MR analysis was consistent with the results of the analysis of variance in the validation cohort, which strengthens the reliability of our MR findings.

Conclusion

Our findings provide new insights into the molecular basis of IPF and highlight the promise of therapeutic interventions. They emphasize the potential of targeting specific molecular pathways for the treatment of IPF, laying the foundation for further research and clinical work.

Ferroptosis, Metabolic Rewiring, and Endometrial Cancer

Ferroptosis is a newly discovered form of regulated cell death. The main feature of ferroptosis is excessive membrane lipid peroxidation caused by iron-mediated chemical and enzymatic reactions. In normal cells, harmful lipid peroxides are neutralized by glutathione peroxidase 4 (GPX4). When GPX4 is inhibited, ferroptosis occurs. In mammalian cells, ferroptosis serves as a tumor suppression mechanism. Not surprisingly, in recent years, ferroptosis induction has gained attention as a potential anticancer strategy, alone or in combination with other conventional therapies. However, sensitivity to ferroptosis inducers depends on the metabolic state of the cell. Endometrial cancer (EC) is the sixth most common cancer in the world, with more than 66,000 new cases diagnosed every year. Out of all gynecological cancers, carcinogenesis of EC is mostly dependent on metabolic abnormalities. Changes in the uptake and catabolism of iron, lipids, glucose, and glutamine affect the redox capacity of EC cells and, consequently, their sensitivity to ferroptosis-inducing agents. In addition to this, in EC cells, ferroptosis-related genes are usually mutated and overexpressed, which makes ferroptosis a promising target for EC prediction, diagnosis, and therapy. However, for a successful application of ferroptosis, the connection between metabolic rewiring and ferroptosis in EC needs to be deciphered, which is the focus of this review.

Molecular Subtypes and Machine Learning-Based Predictive Models for Intracranial Aneurysm Rupture

BACKGROUND

The determination of biological mechanisms and biomarkers related to intracranial aneurysm (IA) rupture is of utmost significance for the development of effective preventive and therapeutic strategies in the clinical field.

METHODS

GSE122897 and GSE13353 datasets were downloaded from Gene Expression Omnibus. Data extracted from GSE122897 were used for analyzing differential gene expression, and consensus clustering was performed to identify stable molecular subtypes. Clinical characteristics were compared between subgroups, and fast gene set enrichment analysis and weighted gene coexpression network analysis were performed. Hub genes were identified via least absolute shrinkage and selection operator analysis. Predictive models were constructed based on hub genes using the Light Gradient Boosting Machine, eXtreme Gradient Boosting, and logistic regression algorithm. Immune cell infiltration in IA samples was analyzed using Microenvironment Cell Population counter, CIBERSORT, and xCell algorithm. The correlation between hub genes and immune cells was analyzed. The predictive model and immune cell infiltration were validated using data from the GSE13353 dataset.

RESULTS

A total of 43 IA samples were classified into 2 subgroups based on gene expression profiles. Subgroup I had a higher risk of rupture, while 70% of subgroup II remained unruptured. In subgroup I, specific genes were associated with inflammation and immunity, and weighted gene coexpression network analysis revealed that the black module genes were linked to IA rupture. We identified 4 hub genes (spermine synthase, macrophage receptor with collagenous structure, zymogen granule protein 16B, and LIM and calponin-homology domains 1), which constructed predictive models with good diagnostic performance in differentiating between ruptured and unruptured IA samples. Monocytic lineage was found to be a significant factor in IA rupture, and the 4 hub genes were linked to monocytic lineage (P < 0.05).

CONCLUSIONS

We reveal a new molecular subtype that can reflect the actual pathological state of IA rupture, and our predictive models constructed by machine learning algorithms can efficiently predict IA rupture.

An emphasis on the interaction of signaling pathways highlights the role of miRNAs in the etiology and treatment resistance of gastric cancer

Gastric cancer (GC) is 4th in incidence and mortality rates globally. Several genetic and epigenetic factors, including microRNAs (miRNAs), affect its initiation and progression. miRNAs are short chains of nucleic acids that can regulate several cellular processes by controlling their gene expression. So, dysregulation of miRNAs expressions is associated with GC initiation, progression, invasion capacity, apoptosis evasions, angiogenesis, promotion and EMT enhancement. Of important pathways in GC and controlled by miRNAs are Wnt/β-catenin signaling, HMGA2/mTOR/P-gp, PI3K/AKT/c-Myc, VEGFR and TGFb signaling. Hence, this review was conducted to review an updated view of the role of miRNAs in GC pathogenesis and their modulatory effects on responses to different GC treatment modalities.

Underlying mechanisms of epithelial splicing regulatory proteins in cancer progression

Cancer is the second-leading disease-related cause of global mortality after cardiovascular disease. Despite significant advances in cancer therapeutic strategies, cancer remains one of the major obstacles to human life extension. Cancer pathogenesis is extremely complicated and not fully understood. Epithelial splicing regulatory proteins (ESRPs), including ESRP1 and ESRP2, belong to the heterogeneous nuclear ribonucleoprotein family of RNA-binding proteins and are crucial regulators of the alternative splicing of messenger RNAs (mRNAs). The expression and activity of ESRPs are modulated by various mechanisms, including post-translational modifications and non-coding RNAs. Although a growing body of evidence suggests that ESRP dysregulation is closely associated with cancer progression, the detailed mechanisms remain inconclusive. In this review, we summarize recent findings on the structures, functions, and regulatory mechanisms of ESRPs and focus on their underlying mechanisms in cancer progression. We also highlight the clinical implications of ESRPs as prognostic biomarkers and therapeutic targets in cancer treatment. The information reviewed herein could be extremely beneficial to the development of individualized therapeutic strategies for cancer patients.

A six-gene prognostic signature for both adult and pediatric acute myeloid leukemia identified with machine learning

BACKGROUND

Although it is well-known that adult and pediatric acute myeloid leukemias (AMLs) are genetically distinct diseases, they still share certain gene expression profiles. The age-related genetic heterogeneities of AMLs have been well-studied, but the common prognostic signatures and molecular mechanisms of adult and pediatric AMLs are less investigated.

AIM

To identify genes and pathways that are associated with both pediatric and adult AMLs and discover a gene signature for overall survival (OS) prediction.

METHODS

Through mining the transcriptome profiles of The Cancer Genome Atlas (TCGA) data sets of adult cancers and The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) data of pediatric cancers, we identified genes that are commonly dysregulated in both pediatric and adult AMLs, further discovered a common gene signature, and built two risk score models for TCGA and TARGET cohorts, respectively with L ₀ regularized global AUC (area under the receiver operating characteristic curve) summary maximization.

RESULTS

We identified 57 genes that are differentially expressed and prognostically significant in both adult and childhood AMLs. The top 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched with those 57 genes include transcriptional misregulation, focal adhesion, PI3K-Akt signaling pathway, and signaling pathways regulating pluripotency of stem cells. We further identified a 6-gene signature including genes of ADAMTS3, DNMT3B, NYNRIN, SORT1, ZFHX3, and ZG16B for risk prediction. We constructed a risk score model with one dataset (either TCGA or TARGET) and evaluated its performance with the other. The test AUCs for the risk prediction of TCGA data with a 2-year and 5-year OS cutoffs are 0.762 (P = 2.33e-13, 95% CI: 0.69-0.83) and 0.759 (P = 7.26e-08, 95% CI: 0.66-0.85), respectively, while the test AUCs of TARGET data with the same cutoffs are 0.71 (P = 3.3e-07, 95% CI: 0.62-0.79) and 0.72 (P= 5.25e-09, 95% CI: 0.65-0.80), respectively. We further stratified patients into 3 equal sized prognostic subtypes with the 6-gene risk scores. The P-values of the tertile partitions are 1.74e-07 and 3.28e-08 for the TARGET and TCGA cohorts, respectively, which are significantly better than the standard cytogenetic risk stratification of both cohorts (TARGET: P = 1.64e-06; TCGA: P = 1.79e-05). When validated with two other independent cohorts, the 6-gene risk score models remain a significant predictor for OS. Investigating the common gene expression program is significant in that we may extrapolate the findings from adults to children and avoid unnecessary pediatric clinical trials.

PredictiveNetwork: predictive gene network estimation with application to gastric cancer drug response-predictive network analysis

Background

Gene regulatory networks have garnered a large amount of attention to understand disease mechanisms caused by complex molecular network interactions. These networks have been applied to predict specific clinical characteristics, e.g., cancer, pathogenicity, and anti-cancer drug sensitivity. However, in most previous studies using network-based prediction, the gene networks were estimated first, and predicted clinical characteristics based on pre-estimated networks. Thus, the estimated networks cannot describe clinical characteristic-specific gene regulatory systems. Furthermore, existing computational methods were developed from algorithmic and mathematics viewpoints, without considering network biology.

Results

To effectively predict clinical characteristics and estimate gene networks that provide critical insights into understanding the biological mechanisms involved in a clinical characteristic, we propose a novel strategy for predictive gene network estimation. The proposed strategy simultaneously performs gene network estimation and prediction of the clinical characteristic. In this strategy, the gene network is estimated with minimal network estimation and prediction errors. We incorporate network biology by assuming that neighboring genes in a network have similar biological functions, while hub genes play key roles in biological processes. Thus, the proposed method provides interpretable prediction results and enables us to uncover biologically reliable marker identification. Monte Carlo simulations shows the effectiveness of our method for feature selection in gene estimation and prediction with excellent prediction accuracy. We applied the proposed strategy to construct gastric cancer drug-responsive networks.

Conclusion

We identified gastric drug response predictive markers and drug sensitivity/resistance-specific markers, AKR1B10, AKR1C3, ANXA10, and ZNF165, based on GDSC data analysis. Our results for identifying drug sensitive and resistant specific molecular interplay are strongly supported by previous studies. We expect that the proposed strategy will be a useful tool for uncovering crucial molecular interactions involved a specific biological mechanism, such as cancer progression or acquired drug resistance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04871-z.

PAUF as a Target for Treatment of High PAUF-Expressing Ovarian Cancer

Pancreatic adenocarcinoma up-regulated factor (PAUF) plays an important role in tumor growth, metastasis, and immune evasion in the pancreatic tumor microenvironment, and recent studies suggest an association between PAUF expression and poor prognosis in ovarian cancer patients. The current study aimed 1) to characterize the potential tumor-promoting role of PAUF in ovarian cancer, using in vitro and in vivo models, including a PAUF-knockout OVCAR-5 cell line, and 2) to explore the potential therapeutic effects of an anti-PAUF antibody for ovarian cancer. Recombinant PAUF significantly increased tumor metastatic capacity (migration, invasion, and adhesion) in all the ovarian cancer cell lines tested, except for the OVCAR-5 cell line which expresses PAUF at a much higher level than the other cells. PAUF-knockout in the OVCAR-5 cell line led to apparently delayed tumor growth in vitro and in vivo. Furthermore, the administration of an anti-PAUF antibody exhibited notable sensitizing and synchronizing effects on docetaxel in mice bearing the OVCAR-5 xenograft tumors. Taken together, this study shows that the expression level of PAUF is an independent factor determining malignant behaviors of ovarian cancer and, for the first time, it suggests that PAUF may be a promising therapeutic target for high PAUF-expressing ovarian cancer.

Shotgun Proteomics for the Identification and Profiling of the Tear Proteome of Keratoconus Patients

Purpose

The qualitative approach followed in this study aims to obtain an extensive view of the keratoconus (KC) tear proteome, which could highlight proteins previously undetected and enlarge our knowledge of the disease's pathophysiology.

Methods

Twenty-five patients diagnosed with KC and 25 control subjects were studied in a prospective, cross-sectional study. KC screening examinations, including clinical and tomographic examinations, were performed on all participants. Tear samples were collected using Schirmer strips and analyzed by liquid chromatography-tandem mass spectrometry in a data-dependent workflow. A spectral count was used as a semiquantification tool. The tear proteomes of both groups were identified and profiled, and the functional interactions and biological characterization of differential proteins were analyzed using in silico tools.

Results

We identified a total of 232 proteins, of whom 133 were expressed in both groups’ samples; 41 were observed only in control samples and 58 were identified just in tears of patients with KC. A semiquantitative analysis showed the dysregulation of 17 proteins in the KC samples. An in silico analysis linked proteins only expressed in KC samples to oxidative stress, skin development, and apoptosis. The dysregulation of proteins involved in iron transport, inflammation, oxidative stress, and protease inhibition was observed in the semiquantitative results.

Conclusions

A shotgun analysis showed that the tear proteome of patients with KC differed from controls by more than one-third of the total proteins identified, highlighting the relationship of the proteins only expressed in KC tears with processes of cell death, oxidative damage, and inflammation. The underexpression of proteins involved in iron pathways might support the iron imbalance as a contributing factor to cellular damage and death in KC disease.

Fully Automated Computational Approach for Precisely Measuring Organelle Acidification with Optical pH Sensors

pH balance and regulation within organelles are fundamental to cell homeostasis and proliferation. The ability to track pH in cells becomes significantly important to understand these processes in detail. Fluorescent sensors based on micro- and nanoparticles have been applied to measure intracellular pH; however, an accurate methodology to precisely monitor acidification kinetics of organelles in living cells has not been established, limiting the scope of this class of sensors. Here, silica-based fluorescent microparticles were utilized to probe the pH of intracellular organelles in MDA-MB-231 and MCF-7 breast cancer cells. In addition to the robust, ratiometric, trackable, and bioinert pH sensors, we developed a novel dimensionality reduction algorithm to automatically track and screen massive internalization events of pH sensors. We found that the mean acidification time is comparable among the two cell lines (ΔT_MCF-7 = 16.3 min; ΔT_MDA-MB-231 = 19.5 min); however, MCF-7 cells showed a much broader heterogeneity in comparison to MDA-MB-231 cells. The use of pH sensors and ratiometric imaging of living cells in combination with a novel computational approach allow analysis of thousands of events in a computationally inexpensive and faster way than the standard routes. The reported methodology can potentially be used to monitor pH as well as several other parameters associated with endocytosis.

Biogenesis, functions, and clinical implications of circular RNAs in non-small cell lung cancer

Lung cancer (LC) is the leading cause of cancer-related deaths worldwide, with high morbidity and mortality. Non-small cell lung cancer (NSCLC) is a major pathological type of LC and accounts for more than 80% of all cases. Circular RNAs (circRNAs) are a large class of non-coding RNAs (ncRNAs) with covalently closed-loop structures, a high abundance, and tissue-specific expression patterns. They participate in various pathophysiological processes by regulating complex gene networks involved in proliferation, apoptosis, migration, and epithelial-to-mesenchymal transition (EMT), as well as metastasis. A growing number of studies have revealed that the dysregulation of circRNAs contributes to many aspects of cancer progression, such as its occurrence, metastasis, and recurrence, suggesting their great potential as efficient and specific biomarkers in the diagnosis, prognosis, and therapeutic targeting of NSCLC. In this review, we systematically elucidate the characteristics, biogenesis, and functions of circRNAs and focus on their molecular mechanisms in NSCLC progression. Moreover, we highlight their clinical implications in NSCLC treatment.

Long Non-Coding RNA in Gastric Cancer: Mechanisms and Clinical Implications for Drug Resistance

Gastric cancer (GC) is the third leading cause of cancer-related deaths worldwide, with high recurrence and mortality rate. Chemotherapy, including 5-fluorouracil (5-FU), adriamycin (ADR), vincristine (VCR), paclitaxel (PTX), and platinum drugs, remains one of the fundamental methods of GC treatment and has efficiently improved patients’ prognosis. However, most patients eventually develop resistance to chemotherapeutic agents, leading to the failure of clinical treatment and patients’ death. Recent studies suggest that long non-coding RNAs (lncRNAs) are involved in the drug resistance of GC by modulating the expression of drug resistance-related genes via sponging microRNAs (miRNAs). Moreover, lncRNAs also play crucial roles in GC drug resistance via a variety of mechanisms, such as the regulation of the oncogenic signaling pathways, inhibition of apoptosis, induction of autophagy, modulation of cancer stem cells (CSCs), and promotion of the epithelial-to-mesenchymal transition (EMT) process. Some of lncRNAs exhibit great potential as diagnostic and prognostic biomarkers, as well as therapeutic targets for GC patients. Therefore, understanding the role of lncRNAs and their mechanisms in GC drug resistance may provide us with novel insights for developing strategies for individual diagnosis and therapy. In this review, we summarize the recent findings on the mechanisms underlying GC drug resistance regulated by lncRNAs. We also discuss the potential clinical applications of lncRNAs as biomarkers and therapeutic targets in GC.

Impact of the Glycemic Level on the Salivary Proteome of Middle-Aged and Elderly People With Type 2 Diabetes Mellitus: An Observational Study

Type 2 diabetes mellitus (T2DM) is an increasing global public health concern, but its impact on the salivary proteome is still unclear. To evaluate the effect of glycemic levels in middle-aged and elderly individuals with T2DM on salivary proteomics, we compared the differences by liquid chromatography tandem mass spectrometry (LC–MS/MS). Unstimulated whole saliva samples from 8 T2DM patients with good glycemic control (G group, HbA1c <6.5%) and 16 patients with poor control (P group, HbA1c ≥6.5%) were analyzed by LC–MS/MS in the data-independent acquisition mode (Clinical register number: ChiCTR1900023582.). After functional annotation, cluster analysis and receiver operating characteristic (ROC) curve analysis were carried out to screen and evaluate candidate proteins. A total of 5,721 proteins were quantified, while 40 proteins differed significantly. In the P group, proteins involved in oxidative stress-related processes were upregulated, whereas proteins related to salivary secretion were downregulated. The combination of thioredoxin domain-containing protein 17, zymogen granule protein 16B, and FAM3 metabolism regulating signaling molecule D yielded an area under the curve of 0.917 which showed a robust ability to distinguish the P and G groups. In conclusion, poorly controlled hyperglycemia may affect salivary proteins through various pathways, including oxidative stress and glandular secretion. Furthermore, the differentially expressed proteins, especially the three proteins with the best differentiation, might serve as an anchor point for the further study of hyperglycemia and oral diseases.

Long non-coding RNAs: Biogenesis, functions, and clinical significance in gastric cancer

Gastric cancer (GC) is one of the most prevalent malignant tumor types and the third leading cause of cancer-related death worldwide. Its morbidity and mortality are very high due to a lack of understanding about its pathogenesis and the slow development of novel therapeutic strategies. Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs with a length of more than 200 nt. They play crucial roles in a wide spectrum of physiological and pathological processes by regulating the expression of genes involved in proliferation, differentiation, apoptosis, cell cycle, invasion, metastasis, DNA damage, and carcinogenesis. The aberrant expression of lncRNAs has been found in various cancer types. A growing amount of evidence demonstrates that lncRNAs are involved in many aspects of GC pathogenesis, including its occurrence, metastasis, and recurrence, indicating their potential role as novel biomarkers in the diagnosis, prognosis, and therapeutic targets of GC. This review systematically summarizes the biogenesis, biological properties, and functions of lncRNAs and highlights their critical role and clinical significance in GC. This information may contribute to the development of better diagnostics and treatments for GC.

Mechanisms of Action And Clinical Implications of MicroRNAs in the Drug Resistance of Gastric Cancer

Gastric cancer (GC) is one of the most common malignant tumors of digestive systems worldwide, with high recurrence and mortality. Chemotherapy is still the standard treatment option for GC and can effectively improve the survival and life quality of GC patients. However, with the emergence of drug resistance, the clinical application of chemotherapeutic agents has been seriously restricted in GC patients. Although the mechanisms of drug resistance have been broadly investigated, they are still largely unknown. MicroRNAs (miRNAs) are a large group of small non-coding RNAs (ncRNAs) widely involved in the occurrence and progression of many cancer types, including GC. An increasing amount of evidence suggests that miRNAs may play crucial roles in the development of drug resistance by regulating some drug resistance-related proteins as well as gene expression. Some also exhibit great potential as novel biomarkers for predicting drug response to chemotherapy and therapeutic targets for GC patients. In this review, we systematically summarize recent advances in miRNAs and focus on their molecular mechanisms in the development of drug resistance in GC progression. We also highlight the potential of drug resistance-related miRNAs as biomarkers and therapeutic targets for GC patients.

Bioinformatics analysis of structures and ligand-bindings of predicted zymogen granule protein observed on Bali cattle (Bos javanicus) saliva

Objective

Previously, we have shown that predicted zymogen granule protein 16 homolog B (P-G3MZ19) existed in Bali cattle (Bos javanicus) saliva. It was suggested that P-G3MZ19 is a member of the mannose-binding lectin family that plays an essential role in innate immunity. In the present study, we aimed to analyze the structure and ligand-binding of P-3MZ19 in Bali cattle saliva.

Materials and Methods

Saliva of four adult healthy Bali cattle was collected, lyophilized, and subjected to two-dimensional (2-D) gel electrophoresis. The target spot of around 17 kDa related to P-G3MZ19 was excised for matrix-assisted laser desorption ionization time-of-flight mass spectrometer/time-of-flight mass spectrometer mass spectrometry analysis and sequencing. The structure and the ligand-binding of P-3MZ19 were analyzed using bioinformatics software programs published elsewhere.

Results

Based on Iterative Threading ASSEmbly Refinement the 3D model of P-G3MZ19 was suggested to have similarities to exo-alpha-sialidase (EC 3.2.1.18); while its ligand-binding sites consisted of seven residues, i.e., 25aa-26aa (Gly-Gly), 95aa (Phe), 138aa (Tyr), 140aa (Leu), 141aa (Gly), and 143aa (Thr).

Conclusion

The structure of P-G3MZ19 of Bali cattle saliva and its ligand-binding sites have been successfully determined by using bioinformatics techniques. The biological and immunological roles of the peptide are currently under investigation based on P-G3MZ19 synthetic peptides.

Epithelial Splicing Regulatory Protein 1 Is Overexpressed in Breast Cancer and Predicts Poor Prognosis for Breast Cancer Patients

Background

Epithelial splicing regulatory proteins (ESRPs), including ESRP1 and ESRP2, are important proteins for alternative splicing of mRNAs and are reported to promote or inhibit the progression of some tumors. However, the effects of ESRPs in breast cancer are still unknown.

Material/Methods

In this study, we detected the transcriptional level and alterations of ESRP1 in patients with breast cancer based on the Oncomine, Gene Expression Profiling Interactive Analysis, Gene Expression-Based Outcome for Breast Cancer Online, and cBioPortal databases. Using immunohistochemistry and quantitative polymerase chain reaction, the expression pattern of ESRP1 in breast cancer was analyzed. Analysis of the clinicopathological characteristics and function of ESRP1 in breast cancer were actualized through the University of Alabama Cancer database and Database for Annotation, Visualization and Integrated Discovery. Using the Kaplan-Meier plotter, the prognostic values of ESRP1 in patients with breast cancer were analyzed. The Encyclopedia of RNA Interactomes database was used to predict miRNAs that regulated ESRP1.

Results

We found that ESRP1 was significantly overexpressed in patients with breast cancer, compared with patients without breast cancer, and had statistically significant clinicopathological characteristics. Kaplan-Meier plotter analysis indicated that the elevated expression of ESRP1 was associated with poor prognosis in patients with breast cancer. Furthermore, hsa-miR-181c-5p was identified to be potentially involved in the regulation of ESRP1.

Conclusions

These results suggest that ESRP1 is a valuable target for the precise treatment of breast cancer and a potential biomarker for the prognosis of patients with breast cancer.