HOPX Is an Epigenetically Inactivated Tumor Suppressor and Overexpression of HOPX Induce Apoptosis and Cell Cycle Arrest in Breast Cancer

Research on the mechanism of HOPX-HDAC2 interaction inducing differentiation blockage in acute myeloid leukemia

Homeodomain-only protein homeobox (HOPX) mainly exerts its transcriptional repression by physically sequestering the serum co-repressor and recruiting histone deacetylase (HDAC), possessing important potential as a prognostic gene in acute myeloid leukemia (AML). HDACs play crucial roles in cell growth, gene regulation, and metabolism, and they are also important factors in promoting AML progression. Therefore, this project attempts to investigate whether HOPX affects AML progression by interacting with HDAC2 protein. Bioinformatics analysis was employed to identify potential prognostic genes in AML. Flow cytometry and MTT assays were performed to analyze the cellular biological functions of the AML prognostic marker HOPX. The interaction network of HOPX was analyzed using the Search Tool for the Retrieval of Interacting Genes database, and the interaction between HOPX and HDAC2 was observed using endogenous and exogenous immunoprecipitation. HOPX is highly expressed in AML cells. Further research uncovered that low expression of HOPX can repress the proliferation activity, anti-apoptotic ability, and differentiation blockage of AML cells. Moreover, mechanistically, HOPX induced AML differentiation blockage and malignant progression through interaction with HDAC. HOPX can serve as a prognostic marker for AML and can interact with HDAC2 to induce AML differentiation blockage and malignant progression.

Precision unveiled: Synergistic genomic landscapes in breast cancer-Integrating single-cell analysis and decoding drug toxicity for elite prognostication and tailored therapeutics

BACKGROUND

Globally, breast cancer, with diverse subtypes and prognoses, necessitates tailored therapies for enhanced survival rates. A key focus is glutamine metabolism, governed by select genes. This study explored genes associated with T cells and linked them to glutamine metabolism to construct a prognostic staging index for breast cancer patients for more precise medical treatment.

METHODS

Two frameworks, T-cell related genes (TRG) and glutamine metabolism (GM), stratified breast cancer patients. TRG analysis identified key genes via hdWGCNA and machine learning. T-cell communication and spatial transcriptomics emphasized TRG's clinical value. GM was defined using Cox analyses and the Lasso algorithm. Scores categorized patients as TRG_high+GM_high (HH), TRG_high+GM_low (HL), TRG_low+GM_high (LH), or TRG_low+GM_low (LL). Similarities between HL and LH birthed a "Mixed" class and the TRG_GM classifier. This classifier illuminated gene variations, immune profiles, mutations, and drug responses.

RESULTS

Utilizing a composite of two distinct criteria, we devised a typification index termed TRG_GM classifier, which exhibited robust prognostic potential for breast cancer patients. Our analysis elucidated distinct immunological attributes across the classifiers. Moreover, by scrutinizing the genetic variations across groups, we illuminated their unique genetic profiles. Insights into drug sensitivity further underscored avenues for tailored therapeutic interventions.

CONCLUSION

Utilizing TRG and GM, a robust TRG_GM classifier was developed, integrating clinical indicators to create an accurate predictive diagnostic map. Analysis of enrichment disparities, immune responses, and mutation patterns across different subtypes yields crucial subtype-specific characteristics essential for prognostic assessment, clinical decision-making, and personalized therapies. Further exploration is warranted into multiple fusions between metrics to uncover prognostic presentations across various dimensions.

hsa-miR-1301-3p Promotes the Proliferation and Migration of Nonsmall Cell Lung Cancer Cells and Reduces Radiosensitivity via Targeting Homeodomain-Only Protein Homeobox

Background: There is increasing evidence that abnormal expression of microRNAs is involved in the occurrence and progression of tumors. In previous experiments, we found that the content of hsa-miR-1301-3p in tumor tissues of patients with nonsmall cell lung cancer (NSCLC) showed an obvious upward trend compared with that in normal tissues. We performed a detailed study on the impact and underlying mechanism of hsa-miR-1301-3p in NSCLC cells. Methods: The impact of hsa-miR-1301-3p on NSCLC cell proliferation, apoptosis, migration, and invasion was examined using colony formation, flow cytometry, modified Boyden chamber, and wound healing assays. Different doses of radiation were applied to NSCLC cells to investigate their sensitivity to radiotherapy. The potential target gene of hsa-miR-1301-3p was determined by dual-luciferase reporter assay and immunoblotting. Result: hsa-miR-1301-3p was upregulated in NSCLC tissues and cells. hsa-miR-1301-3p effectively promoted the rapid proliferation, migration, and invasion of NSCLC cells, while inhibiting apoptosis. It also induced radioresistance in NSCLC cells. hsa-miR-1301-3p targeted the homeodomain-only protein homeobox (HOPX) mRNA 3' untranslated region and inhibited its transcription in NSCLC cells. Exogenous HOPX overexpression antagonized the mechanism by which hsa-miR-1301-3p regulates NSCLC cell proliferation, metastasis, and apoptosis. Conclusions: hsa-miR-1301-3p plays an oncogenic role in the occurrence and development of NSCLC. By targeting HOPX, hsa-miR-1301-3p can not only promote the proliferation and metastasis of NSCLC cells, but also alleviate apoptosis and reduce radiosensitivity.

HOPX is a tumor-suppressive biomarker that corresponds to T cell infiltration in skin cutaneous melanoma

BACKGROUND

Skin cutaneous melanoma (SKCM) is the most threatening type of skin cancer. Approximately 55,000 people lose their lives every year due to SKCM, illustrating that it seriously threatens human life and health. Homeodomain-only protein homeobox (HOPX) is the smallest member of the homeodomain family and is widely expressed in a variety of tissues. HOPX is involved in regulating the homeostasis of hematopoietic stem cells and is closely related to the development of tumors such as breast cancer, nasopharyngeal carcinoma, and head and neck squamous cell carcinoma. However, its function in SKCM is unclear, and further studies are needed.

METHODS

We used the R language to construct ROC (Receiver-Operating Characteristic) curves, KM (Kaplan‒Meier) curves and nomograms based on databases such as the TCGA and GEO to analyze the diagnostic and prognostic value of HOPX in SKCM patients. Enrichment analysis, immune scoring, GSVA (Gene Set Variation Analysis), and single-cell sequencing were used to verify the association between HOPX expression and immune infiltration. In vitro experiments were performed using A375 cells for phenotypic validation. Transcriptome sequencing was performed to further analyze HOPX gene-related genes and their signaling pathways.

RESULTS

Compared to normal cells, SKCM cells had low HOPX expression (p < 0.001). Patients with high HOPX expression had a better prognosis (p < 0.01), and the marker had good diagnostic efficacy (AUC = 0.744). GO/KEGG (Gene Ontology/ Kyoto Encyclopedia of Genes and Genomes) analysis, GSVA and single-cell sequencing analysis showed that HOPX expression is associated with immune processes and high enrichment of T cells and could serve as an immune checkpoint in SKCM. Furthermore, cellular assays verified that HOPX inhibits the proliferation, migration and invasion of A375 cells and promotes apoptosis and S-phase arrest. Interestingly, tumor drug sensitivity analysis revealed that HOPX also plays an important role in reducing clinical drug resistance.

CONCLUSION

These findings suggest that HOPX is a blocker of SKCM progression that inhibits the proliferation of SKCM cells and promotes apoptosis. Furthermore, it may be a new diagnostic and prognostic indicator and a novel target for immunotherapy in SKCM patients.

HOPX: A Unique Homeodomain Protein in Development and Tumor Suppression

Simple Summary

Homeobox (HOX) genes encode homeodomain proteins that regulate a wide range of molecular pathways. The homeodomain is highly conserved and binds to DNA. One exception is homeodomain-only protein (HOPX) that lacks DNA-binding capacity. HOPX plays a crucial role in development and its functional impairment is associated with a variety of diseases, including cancer. Loss of HOPX function occurs in a wide range of cancer types, where it functions as a tumor suppressor gene. Understanding the molecular mechanisms by which HOPX regulates carcinogenesis will likely lead to the development of new therapeutic approaches.

Abstract

Homeobox genes are master regulators of morphogenesis and differentiation by acting at the top of genetic hierarchies and their deregulation is associated with a variety of human diseases. They usually contain a highly conserved sequence that codes for the homeodomain of the protein, a specialized motif with three α helices and an N-terminal arm that aids in DNA binding. However, one homeodomain protein, HOPX, is unique among its family members in that it lacks the capacity to bind DNA and instead functions by interacting with transcriptional regulators. HOPX plays crucial roles in organogenesis and is expressed in both embryonic and adult stem cells. Loss of HOPX expression is common in cancer, where it functions primarily as a tumor suppressor gene. In this review, we describe the function of HOPX in development and discuss its role in carcinogenesis.

FRL: An Integrative Feature Selection Algorithm Based on the Fisher Score, Recursive Feature Elimination, and Logistic Regression to Identify Potential Genomic Biomarkers

Accurate screening on cancer biomarkers contributes to health assessment, drug screening, and targeted therapy for precision medicine. The rapid development of high-throughput sequencing technology has identified abundant genomic biomarkers, but most of them are limited to single-cancer analysis. Based on the combination of Fisher score, Recursive feature elimination, and Logistic regression (FRL), this paper proposes an integrative feature selection algorithm named FRL to explore potential cancer genomic biomarkers on cancer subsets. Fisher score is initially used to calculate the weights of genes to rapidly reduce the dimension. Recursive feature elimination and Logistic regression are then jointly employed to extract the optimal subset. Compared to the current differential expression analysis tool GEO2R based on the Limma algorithm, FRL has greater classification precision than Limma. Compared with five traditional feature selection algorithms, FRL exhibits excellent performance on accuracy (ACC) and F1-score and greatly improves computational efficiency. On high-noise datasets such as esophageal cancer, the ACC of FRL is 30% superior to the average ACC achieved with other traditional algorithms. As biomarkers found in multiple studies are more reliable and reproducible, and reveal stronger association on potential clinical value than single analysis, through literature review and spatial analyses of gene functional enrichment and functional pathways, we conduct cluster analysis on 10 diverse cancers with high mortality and form a potential biomarker module comprising 19 genes. All genes in this module can serve as potential biomarkers to provide more information on the overall oncogenesis mechanism for the detection of diverse early cancers and assist in targeted anticancer therapies for further developments in precision medicine.