KLF2 is a clinical diagnostic and treatment biomarker of breast cancer

Identification of the EBF1/ETS2/KLF2-miR-126-Gene Feed-Forward Loop in Breast Carcinogenesis and Stemness

MicroRNA (miR)-126 is frequently downregulated in malignancies, including breast cancer (BC). Despite its tumor-suppressive role, the mechanisms underlying miR-126 deregulation in BC remain elusive. Through silencing experiments, we identified Early B Cell Factor 1 (EBF1), ETS Proto-Oncogene 2 (ETS2), and Krüppel-Like Factor 2 (KLF2) as pivotal regulators of miR-126 expression. These transcription factors were found to be downregulated in BC due to epigenetic silencing or a "poised but not transcribed" promoter state, impairing miR-126 expression. Gene Ontology analysis of differentially expressed miR-126 target genes in the Cancer Genome Atlas: Breast Invasive Carcinoma (TCGA-BRCA) cohort revealed their involvement in cancer-related pathways, primarily signal transduction, chromatin remodeling/transcription, and differentiation/development. Furthermore, we defined interconnections among transcription factors, miR-126, and target genes, identifying a potential feed-forward loop (FFL) crucial in maintaining cellular identity and preventing the acquisition of stemness properties associated with cancer progression. Our findings propose that the dysregulation of the EBF1/ETS2/KLF2/miR-126 axis disrupts this FFL, promoting oncogenic transformation and progression in BC. This study provides new insights into the molecular mechanisms of miR-126 downregulation in BC and highlights potential targets for therapeutic intervention. Further research is warranted to clarify the role of this FFL in BC, and to identify novel therapeutic strategies aimed at modulating this network as a whole, rather than targeting individual signals, for cancer management.

Comprehensive Pan-Cancer Analysis of the Prognostic Role of KLF Transcription Factor 2 (KLF2) in Human Tumors

Background

KLF2 is a transcription factor expressed early in mammalian development that plays a role in many processes of development and disease. Recently, increasing studies revealed that KLF2 plays a key role in the occurrence and progression of cancer.

Purpose

The aim of this study was to explore the role of KLF2 in various tumor types using the Cancer Genome Atlas dataset.

Methods

Here, we set out to explore the role of KLF2 in 33 tumor types using TCGA (The Cancer Genome Atlas), GEO (Gene Expression Omnibus) dataset, Human Protein Atlas (HPA), UALCAN database, CancerSEA, GSCALite and several bioinformatic tools. Furthermore, we also performed immunohistochemistry and qPCR to further validate the role of KLF2 in multiple cancers and its correlation with prognosis.

Results

We found that KLF2 was underexpressed in most tumors and generally predicted poor OS in tumor patients. We found that amplification of KLF2 may be a risk factor for patients with OV (Ovarian serous cystadenocarcinoma). We also analyzed the abundance of checkpoints and markers of specific immune subsets including CD8+ T lymphocytes (T cells), CD4+ T cells, macrophages, and endothelial cells that significantly correlated with the expression level of KLF2 in pan-carcinoma tissues. In some cancers, KLF2 expression levels are positively correlated with gene promoter DNA methylation and drug sensitivity. In addition, we found that KLF2 is involved in single-cell level cell invasion in some cancers. In addition, KLF2 is co-expressed with several intracellular signal transduction genes involved in immune system processes. Immunohistochemistry and qPCR confirmed the low expression of KLF2 in STAD (stomach adenocarcinoma) and renal cancer.

Conclusion

Our pan-cancer analysis provides a comprehensive overview of the oncogenic roles of KLF2 in multiple human cancers and can be regarded as a potential prognostic marker and a novel target for cancer immunotherapy.

Machine Learning Reveals Impacts of Smoking on Gene Profiles of Different Cell Types in Lung

Smoking significantly elevates the risk of lung diseases such as chronic obstructive pulmonary disease (COPD) and lung cancer. This risk is attributed to the harmful chemicals in tobacco smoke that damage lung tissue and impair lung function. Current research on the impact of smoking on gene expression in specific lung cells is limited. This study addresses this gap by analyzing gene expression profiles at the single-cell level from 43,539 lung endothelial cells, 234,349 lung epithelial cells, 189,843 lung immune cells, and 16,031 lung stromal cells using advanced machine learning techniques. The data, categorized by different lung cell types, were classified into three smoking states: active smoker, former smoker, and never smoker. Each cell sample encompassed 28,024 feature genes. Employing an incremental feature selection method within a computational framework, several specific genes have been identified as potential markers of smoking status in different lung cell types. These include B2M, EEF1A1, and TPT1 in lung endothelial cells; FTL and MT-ATP8 in lung epithelial cells; HLA-B and HLA-C in lung immune cells; and HSP90B1 and LCN2 in lung stroma cells. Additionally, this study developed quantitative rules for representing the gene expression patterns related to smoking. This research highlights the potential of machine learning in oncology, enhancing our molecular understanding of smoking's harm and laying the groundwork for future mechanism-based studies.

MiR-15b-3p weakens bicalutamide sensitivity in prostate cancer via targeting KLF2 to suppress ferroptosis

Bicalutamide (BIC) resistance impedes the treatment of prostate cancer (PCa) and seems to involve ferroptosis; however, the underlying mechanism remains unclear. Our study aimed to explore how miR-15b-3p modulates ferroptosis in response to BIC resistance and determine whether the miRNA is suitable for early screening of PCa. Here, we found that PCa tissues had significantly higher miR-15b-3p expression than adjacent normal tissues. Analysis of blood samples in patients who underwent prostate-specific antigen (PSA) screening revealed that miR-15b-3p was a more accurate diagnostic than PSA (miR-15b-3p area under the curve [AUC] = 0.941, PSA AUC = 0.815). In vitro experiments then demonstrated that miR-15b-3p expression was markedly higher in LNCaP, PC-3, and DU145 cells than in RWPE-1 cells. Treatment with BIC decreased miR-15b-3p expression and progressive ferroptosis. Mechanistically, we identified KLF2 as the downstream target of miR-15b-3p. Overexpressing KLF2 facilitated ferroptosis via augmenting MDA and iron concentrations, in turn inhibiting the SLC7A11/GPX4 axis and decreasing GSH concentration. Through modulating ferroptosis, miR-15b-3p mimic and inhibitor weakened and enhanced BIC sensitivity, respectively. Furthermore, BIC treatment limited xenograft tumor volume in vivo, whereas agomir-15b-3p promoted tumor growth, indicating that miR-15b-3p attenuated the tumor-suppressive effects of BIC. Taken together, our results suggested that miR-15b-3p is crucial to BIC resistance, specifically via targeting KLF2 and thereby suppressing ferroptosis. High miR-15b-3p expression in early PCa screening should reflect a higher probability of cancer. In conclusion, miR-15b-3p has strong potential as a screening and diagnostic biomarker with reliable prospects for clinical application. Furthermore, because patients with high miR-15b-3p and low KLF2 expression have a greater risk of BIC resistance and malignant progression, targeting the miRNA and its downstream protein may be a new treatment strategy.

3D Printing for Cancer Diagnosis: What Unique Advantages Are Gained?

Cancer is a complex disease with global significance, necessitating continuous advancements in diagnostics and treatment. 3D printing technology has emerged as a revolutionary tool in cancer diagnostics, offering immense potential in detection and monitoring. Traditional diagnostic methods have limitations in providing molecular and genetic tumor information that is crucial for personalized treatment decisions. Biomarkers have become invaluable in cancer diagnostics, but their detection often requires specialized facilities and resources. 3D printing technology enables the fabrication of customized sensor arrays, enhancing the detection of multiple biomarkers specific to different types of cancer. These 3D-printed arrays offer improved sensitivity, allowing the detection of low levels of biomarkers, even in complex samples. Moreover, their specificity can be fine-tuned, reducing false-positive and false-negative results. The streamlined and cost-effective fabrication process of 3D printing makes these sensor arrays accessible, potentially improving cancer diagnostics on a global scale. By harnessing 3D printing, researchers and clinicians can enhance early detection, monitor treatment response, and improve patient outcomes. The integration of 3D printing in cancer diagnostics holds significant promise for the future of personalized cancer care.

Identification and validation of SERPINE1 as a prognostic and immunological biomarker in pan-cancer and in ccRCC

Background: SERPINE1, a serine protease inhibitor involved in the regulation of the plasminogen activation system, was recently identified as a cancer-related gene. However, its clinical significance and potential mechanisms in pan-cancer remain obscure. Methods: In pan-cancer multi-omics data from public datasets, including The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx), and online web tools were used to analyze the expression of SERPINE1 in different cancers and its correlation with prognosis, genetic alteration, DNA promoter methylation, biological processes, immunoregulator expression levels, immune cell infiltration into tumor, tumor mutation burden (TMB), microsatellite instability (MSI), immunotherapy response and drug sensitivity. Further, two single-cell databases, Tumor Immune Single-cell Hub 2 (TISCH2) and CancerSEA, were used to explore the expression and potential roles of SERPINE1 at a single-cell level. The aberrant expression of SERPINE1 was further verified in clear cell renal cell carcinoma (ccRCC) through qRT-PCR of clinical patient samples, validation in independent cohorts using The Gene Expression Omnibus (GEO) database, and proteomic validation using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Results: The expression of SERPINE1 was dysregulated in cancers and enriched in endothelial cells and fibroblasts. Copy number amplification and low DNA promoter methylation could be partly responsible for high SERPINE1 expression. High SERPINE1 expression was associated with poor prognosis in 21 cancers. The results of gene set enrichment analysis (GSEA) indicated SERPINE1 involvement in the immune response and tumor malignancy. SERPINE1 expression was also associated with the expression of several immunoregulators and immune cell infiltration and could play an immunosuppression role. Besides, SERPINE1 was found to be related with TMB, MSI, immunotherapy response and sensitivity to several drugs in cancers. Finally, the high expression of SERPINE1 in ccRCC was verified using qRT-PCR performed on patient samples, six independent GEO cohorts, and proteomic data from the CPTAC database. Conclusion: The findings of the present study revealed that SERPINE1 exhibits aberrant expression in various types of cancers and is associated with cancer immunity and tumor malignancy, providing novel insights for individualized cancer treatment.