Identification of long-term survival-associated gene in breast cancer

The potential mechanisms by which Xiaoyao Powder may exert therapeutic effects on thyroid cancer were examined at various levels

BACKGROUND

Thyroid cancer (TC) is the most prevalent endocrine malignancy, with a rising incidence necessitating safer treatment strategies to reduce overtreatment and its side effects. Xiaoyao Powder (XYP), a widely used herbal formula, shows promise in treating TC. This study aims to investigate the mechanisms by which XYP may affect TC.

METHODS

The components of XYP were identified through database retrieval, and targets related to TC were collected to construct a target network for key screening. GEO dataset samples analyzed immune cells and identified significantly differentially expressed core genes (SDECGs). Based on SDECG expression and clustering, samples were classified for comparison. WGCNA was employed to identify gene modules linked to clinical characteristics. ML models screened characteristic genes and constructed a nomogram validated using another GEO dataset. MR methods explored causal relationships between genes and TC.

RESULTS

The top ten active components of XYP were identified, along with 27 SDECGs that exhibited significant differences in immune cell infiltration between TC patients and normal controls. The nomogram effectively predicted TC risk, validated through ROC curves. Key characteristic genes included SMIM1, PPP1R16A, KIAA1462, DNAJC22, and EFNA5.

CONCLUSION

XYP may treat TC by regulating SMIM1, PPP1R16A, KIAA1462, DNAJC22, EFNA5, and associated immune pathways; this provides theoretical support for its potential mechanisms.

NS3 of hepatitis C virus drives hepatocellular carcinoma progression through a novel RNA-interference pathway

Hepatocellular carcinoma (HCC), a severe consequence of hepatitis C virus infection, is significantly influenced by the virus's non-structural protein 3 (NS3). This study employed transcriptome sequencing to explore the role of NS3 in promoting HCC progression by comparing gene expression profiles between HCV-infected HCC tissues and healthy liver controls. Key genes regulated by NS3 were identified and validated with quantitative reverse transcription PCR (RT-qPCR) and western blot analyses. Functionality assays, including CCK-8, BrdU, and Transwell migration and invasion tests, were performed to evaluate the effects of NS3 on HCC cell proliferation, migration, and invasion. Further investigation through a dual-luciferase reporter and RNA pull-down assays revealed that NS3 specifically upregulates circ_0001175. This circular RNA interacts with and inhibits miR-130a-5p, diminishing its regulatory impact on P53 by modulating the MDM4 pathway, thereby promoting oncogenic characteristics. The findings highlight the NS3-induced circ_0001175/miR-130a-5p/MDM4/P53 pathway as a potential therapeutic target, offering promising directions for treatment strategies in HCV-related HCC.

Kinesin family member 4 a drives Cancer stem cell characteristics in breast Cancer: Insights from scRNA-Seq and experimental validation

This study identifies cancer stem cell marker genes in breast cancer through an integrated analysis of single cell RNA sequencing and bulk RNA sequencing data. The breast cancer-related single-cell RNA sequencing dataset GSE180286 was analyzed to identify cancer stem cells populations and their marker genes, which were further validated using data from The Cancer Genome Atlas breast cancer dataset. Weighted gene co-expression network analysis revealed seven co-expression modules, with the Turquoise module showing a strong association with messenger RNA stemness indices. Venn diagram analysis identified 19 cancer stem cell marker genes, among which cyclin B1, cell division cycle associated 8, and kinesin family member 4 A were highlighted as core genes. Both in vitro and in vivo experiments demonstrated that silencing kinesin family member 4 A in breast cancer cell lines significantly reduced tumor sphere formation, proliferation, migration, and invasion. Additionally, the downregulation of kinesin family member 4 A in a nude mouse model markedly suppressed tumor growth. These findings suggest that cyclin B1, cell division cycle associated 8, and particularly kinesin family member 4 A are critical regulators of cancer stem cell properties in breast cancer and represent promising therapeutic targets.

SAA1 as a key mediator of immune inflammatory pathways in fungal keratitis through FOXO3a phosphorylation regulation

OBJECTIVE

Fungal keratitis (FK) is a severe ocular infection, with its underlying molecular mechanisms remaining incompletely understood. This study aimed to identify and investigate key genes involved in immune-inflammatory responses associated with FK pathogenesis using bioinformatics and in vitro assays.

METHODS

Transcriptomic data from the Gene Expression Omnibus (GEO) database (GSE58291) were analyzed using the limma package to identify differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to evaluate significant biological processes and pathways related to DEGs. Weighted gene co-expression network analysis (WGCNA) identified gene modules linked with FK-associated DEGs, and Venn diagram analysis highlighted core genes. Receiver operating characteristic (ROC) analysis assessed diagnostic potential. Immune cell composition was analyzed using CIBERSORT, and correlations between key genes and immune cells were evaluated. In vitro, human corneal epithelial cells (HCEC) were stimulated with Aspergillus fumigatus (A.F.), and pro-inflammatory cytokine expression (IL-1β, TNF-α, IL-6) was assessed using enzyme-linked immunosorbent assay (ELISA). Western blot and quantitative real-time polymerase chain reaction (RT-qPCR) analyzed FOXO3a phosphorylation and gene expression changes post-SAA1 siRNA transfection.

RESULTS

A total of 101 DEGs were identified, with WGCNA revealing 6 co-expression network modules, with significant associations noted in yellow and black modules. Nine shared genes were identified in DEGs and modules, with SAA1 strongly linked to FK pathogenesis. SAA1 expression was positively correlated with neutrophils, T cells CD4 memory activated, T cells gamma delta, and activated mast cells. Upon stimulation with A.F., cytokine expression increased, peaking at 24 h. Inhibition of SAA1 reduced FOXO3a phosphorylation and pro-inflammatory cytokine levels, underscoring SAA1's role in FK inflammation via FOXO3a regulation.

CONCLUSION

SAA1 is a key gene in FK, promoting inflammation by modulating FOXO3a phosphorylation. This highlights its potential as a therapeutic target in managing FK-related inflammation.

Targeting MS4A4A: A novel pathway to improve immunotherapy responses in glioblastoma

INTRODUCTION

Glioblastoma (GBM) remains a challenging brain tumor to treat, with limited response to PD-1 immunotherapy due to tumor-associated macrophages (TAMs), specifically the M2 phenotype. This study explores the potential of MS4A4A (membrane spanning four domains, subfamily A, member 4A) inhibition in driving M2 macrophage polarization toward the M1 phenotype via the ferroptosis pathway to enhance the effectiveness of immunotherapy in GBM.

METHODS

Single-cell RNA sequencing and spatial transcriptomic analyses were employed to characterize M2 macrophages and MS4A4A expression in GBM. In vitro studies utilizing TAM cultures, flow cytometry, and western blot validations were conducted to assess the impact of MS4A4A on the tumor immune microenvironment and M2 macrophage polarization. In vivo models, including subcutaneous and orthotopic transplantation in mice, were utilized to evaluate the effects of MS4A4A knockout and combined immune checkpoint blockade (ICB) therapy on tumor growth and response to PD-1 immunotherapy.

RESULTS

Distinct subsets of GBM-associated macrophages were identified, with spatial distribution in tumor tissue elucidated. In vivo experiments demonstrated that inhibiting MS4A4A and combining ICB therapy effectively inhibited tumor growth, reshaped the tumor immune microenvironment by reducing M2 TAM infiltration and enhancing CD8+ T-cell infiltration, ultimately leading to complete tumor eradication.

CONCLUSION

MS4A4A inhibition shows promise in converting M2 macrophages to M1 phenotype via ferroptosis, decreasing M2-TAM infiltration, and enhancing GBM response to PD-1 immunotherapy. These findings offer a novel approach to developing more effective immunotherapeutic strategies for GBM.

Pan-cancer exploration of oncogenic and clinical impacts revealed that HOXA9 is a diagnostic indicator of tumorigenesis

Homeodomain transcription factor A9 (HOXA9) is a member of the HOX cluster family of transcription factors that are crucially involved in embryo implantation, morphogenesis, body axis development, and endothelial cell differentiation. Despite numerous reports on its aberrant expression in a few malignancies, the molecular and functional complexity of HOXA9 across cancers remains obscure. We aimed to analyze the dynamic role of HOXA9 across cancers by identifying, analyzing, and understanding its multiple modes of regulation and functional implications and identifying possible therapeutic avenues. We conducted a comprehensive analysis to determine the role of HOXA9 across cancers. This approach involved the integration of large-scale datasets from public repositories such as the Genomic Data Commons, specifically the Cancer Genome Atlas (GDC-TCGA), across 33 different cancer types. The multiple modes of HOXA9 regulation by genetic and epigenetic factors were determined using online tools, which comprised experimentally validated observations. Furthermore, downstream pathways were identified by predicting the targets of HOXA9 and by performing functional enrichment analysis. We also assessed the clinical significance of HOXA9 in terms of prognosis and stage stratification. This study evaluated the correlation between HOXA9 and tumor-infiltrating molecules and discussed its association with therapeutically approved antineoplastic drugs. HOXA9 was significantly upregulated in 9 tumors and downregulated in 2 cancers. The deregulation of HOXA9 is primarily attributed to epigenetic factors, including promoter DNA methylation and noncoding RNAs (ncRNAs). The HOXA9 transcription factor interacts with PBX/MEIS cofactors and regulates multiple genes involved in cancer-associated EMT, autophagy, the cell cycle, metabolic pathways, Wnt signaling, TGF-β signaling, the AMPK pathway, PI3K/AKT signaling, and NF-κB signaling, thereby establishing control over downstream mechanisms. Differential expression in various clinical stages across cancers was shown to have prognostic significance and to be correlated with tumor-infiltrating immune molecules. The assessment of the correlation of HOXA9 expression with approved antineoplastic drugs revealed that targeting HOXA9 could be the most reliable strategy for preventing cancer progression. HOXA9 is upregulated in the majority of malignancies and drives cancer progression by regulating multiple signaling mechanisms. Hence, HOXA9 could be a reliable diagnostic indicator and a potential therapeutic candidate for solid cancer types.

Developing a Prognostic Model for Primary Biliary Cholangitis Based on a Random Survival Forest Model

Background: Primary biliary cholangitis (PBC) is a rare autoimmune liver disease with few effective treatments and a poor prognosis, and its incidence is on the rise. There is an urgent need for more targeted treatment strategies to accurately identify high-risk patients. The use of stochastic survival forest models in machine learning is an innovative approach to constructing a prognostic model for PBC that can improve the prognosis by identifying high-risk patients for targeted treatment. Method: Based on the inclusion and exclusion criteria, the clinical data and follow-up data of patients diagnosed with PBC-associated cirrhosis between January 2011 and December 2021 at Taizhou Hospital of Zhejiang Province were retrospectively collected and analyzed. Data analyses and random survival forest model construction were based on the R language. Result: Through a Cox univariate regression analysis of 90 included samples and 46 variables, 17 variables with p-values <0.1 were selected for initial model construction. The out-of-bag (OOB) performance error was 0.2094, and K-fold cross-validation yielded an internal validation C-index of 0.8182. Through model selection, cholinesterase, bile acid, the white blood cell count, total bilirubin, and albumin were chosen for the final predictive model, with a final OOB performance error of 0.2002 and C-index of 0.7805. Using the final model, patients were stratified into high- and low-risk groups, which showed significant differences with a P value <0.0001. The area under the curve was used to evaluate the predictive ability for patients in the first, third, and fifth years, with respective results of 0.9595, 0.8898, and 0.9088. Conclusion: The present study constructed a prognostic model for PBC-associated cirrhosis patients using a random survival forest model, which accurately stratified patients into low- and high-risk groups. Treatment strategies can thus be more targeted, leading to improved outcomes for high-risk patients.

FAM3L: Feature-Aware Multi-Modal Metric Learning for Integrative Survival Analysis of Human Cancers

Survival analysis is to estimate the survival time for an individual or a group of patients, which is a valid solution for cancer treatments. Recent studies suggested that the integrative analysis of histopathological images and genomic data can better predict the survival of cancer patients than simply using single bio-marker, for different bio-markers may provide complementary information. However, for the given multi-modal data that may contain irrelevant or redundant features, it is still challenge to design a distance metric that can simultaneously discover significant features and measure the difference of survival time among different patients. To solve this issue, we propose a Feature-Aware Multi-modal Metric Learning method (FAM3L), which not only learns the metric for distance constraints on patients' survival time, but also identifies important images and genomic features for survival analysis. Specifically, for each modality of data, we firstly design one feature-aware metric that can be decoupled into a traditional distance metric and a diagonal weight for important feature identification. Then, in order to explore the complex correlation across multiple modality data, we apply Hilbert-Schmidt Independence Criterion (HSIC) to jointly learn multiple metrics. Finally, based on the learned distance metrics, we apply the Cox proportional hazards model for prognosis prediction. We evaluate the performance of our proposed FAM3L method on three cancer cohorts derived from The Cancer Genome Atlas (TCGA), the experimental results demonstrate that our method can not only achieve superior performance for cancer prognosis, but also identify meaningful image and genomic features correlating strongly with cancer survival.

Ran GTPase and Its Importance in Cellular Signaling and Malignant Phenotype

Ran is a member of the Ras superfamily of proteins, which primarily regulates nucleocytoplasmic trafficking and mediates mitosis by regulating spindle formation and nuclear envelope (NE) reassembly. Therefore, Ran is an integral cell fate determinant. It has been demonstrated that aberrant Ran expression in cancer is a result of upstream dysregulation of the expression of various factors, such as osteopontin (OPN), and aberrant activation of various signaling pathways, including the extracellular-regulated kinase/mitogen-activated protein kinase (ERK/MEK) and phosphatidylinositol 3-kinase/Protein kinase B (PI3K/Akt) pathways. In vitro, Ran overexpression has severe effects on the cell phenotype, altering proliferation, adhesion, colony density, and invasion. Therefore, Ran overexpression has been identified in numerous types of cancer and has been shown to correlate with tumor grade and the degree of metastasis present in various cancers. The increased malignancy and invasiveness have been attributed to multiple mechanisms. Increased dependence on Ran for spindle formation and mitosis is a consequence of the upregulation of these pathways and the ensuing overexpression of Ran, which increases cellular dependence on Ran for survival. This increases the sensitivity of cells to changes in Ran concentration, with ablation being associated with aneuploidy, cell cycle arrest, and ultimately, cell death. It has also been demonstrated that Ran dysregulation influences nucleocytoplasmic transport, leading to transcription factor misallocation. Consequently, patients with tumors that overexpress Ran have been shown to have a higher malignancy rate and a shorter survival time compared to their counterparts.

WGCNA combined with GSVA to explore biomarkers of refractory neocortical epilepsy

About two-thirds of epilepsy patients relapse within five years after surgery. It is significant to note that the limitations of current treatments stem from a lack of understanding of molecular mechanisms. In this study, Weighted Gene Co-expression Network Analysis (WGCNA) and Gene set variation analysis (GSVA) methods were used to analyze the total RNA data from 20 surgical removal samples (epileptogenic zone and irritative zone, EZ and IZ) of 10 Chinese patients with refractory neocortical epilepsy downloaded from the original microarray dataset (GSE31718) of the National Center for Biological Information -Gene Expression Omnibus database (NCBI-GEO). The late stages of the estrogen response pathway, the IL6-JAK-STAT3-signal pathway and G2 checkpoints are correlated with the EZ, whereas the early stages of the estrogen response pathway and TGF-β signal are more strongly expressed in the IZ. The allogeneic rejection, apical surface and the TGF-β signal are relevant to the high seizure frequency, the unfolded protein response and MYC-target are mostly expressed in patients with low-frequency seizures. Genes with high gene significance(GS) values that were correlated with seizure frequency include OSR2, CABP4, CAPSL, CYP4F8, and FRK in the pink module, and SH3GLB2, CHAC1 and DDX23 in the yellow module. The occurrence of EZ and IZ act on different biological mechanisms. The upregulated genes associated with seizure frequency include OSR2, CABP4, CAPSL, CYP4F8, and FRK, and the downregulated genes include SH3GLB2, CHAC1 and DDX23. The evidence of key genes and differential pathways obtained by WGCNA and GSVA may be biomarkers for novel preventive and pharmacological interventions in clinical practice.

Identification of Specific Cervical Cancer Subtypes and Prognostic Gene Sets in Tumor and Nontumor Tissues Based on GSVA Analysis

Background

Cervical cancer is the fourth common cancer among women. Its prognosis needs our more attention. Our purpose was to identity new prognostic gene sets to help other researchers develop more effective treatment for cervical cancer patients and improve the prognosis of patients.

Methods

We used gene set variation analysis (GSVA) to calculate the enrichment scores of gene sets and identified three subtypes of cervical cancer through the Cox regression model, k-means clustering algorithm, and nonnegative matrix factorization method (NMF). Chi-square test was utilized to test whether a certain clinical characteristic is different among divided subtypes. We further screened the prognostic gene sets using differential analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) regression. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to analyze which pathways and function the genes from screened gene sets enriched. Search Tool for the Retrieval of Interacting Genes (STRING) was used to draw the protein-protein interaction network, and Cytoscape was used to visualize the hub genes of protein-protein interaction network.

Results

We identified three novel subtypes of cervical cancer in The Cancer Genome Atlas (TCGA) samples and validated in Gene Expression Omnibus (GEO) samples. There were significant variations between the three subtypes in histological type, T stage, M stage, and N stage. T_GSE36888_UNTREATED_VS_IL2_TREATED_STAT5_AB_KNOCKIN_TCELL_2H_UP and N_HALLMARK_ANGIOGENESIS were screened prognostic gene sets. The prognostic model was as follows: riskScore = T_GSE36888_UNTREATED_VS_IL2_TREATED_STAT5_AB_KNOCKIN_TCELL_2H_UP^∗ 2.617 + N_HALLMARK_ANGIOGENESIS^∗ 4.860. Survival analysis presented that in these two gene sets, high enrichment scores were all significantly related to worse overall survival. The hub genes from T gene set included CXCL1, CXCL2, CXCL8, ALDOA, TALDO1, LDHA, CCL4, FCAR, FCER1G, SAMSN1, LILRB1, SH3PXD2B, PPM1N, PKM, and FKBP4. As for N gene sets, the hub genes included ITGAV, PTK2, SPP1, THBD, and APOH.

Conclusions

Three novel subtypes and two prognostic gene sets were identified. 15 hub genes for T gene set and 5 hub genes for N gene set were discovered. Based on these findings, we can develop more and more effective treatments for cervical cancer patients. Based on the gene enriched pathways, we can development specific drugs targeting the pathways.

A Novel Ferroptosis-Related Gene Signature to Predict Prognosis of Esophageal Carcinoma

Objective

This study aimed to develop a novel ferroptosis-related gene-based prognostic signature for esophageal carcinoma (ESCA).

Methods

The TCGA-ESCA gene expression profiles and corresponding clinical data were downloaded from the TCGA database. Ferroptosis-related genes were identified from the literature and public databases, which were intersected with the differentially expressed genes between ESCA and normal samples. After univariate Cox regression and random forest analyses, several ferroptosis-related feature genes were identified and used to construct a prognostic signature. Then, the prognostic value of the complex value and the correlation of the complex value with immune cell infiltration were analyzed. Moreover, function analysis, mutation analysis, and molecular docking on the ferroptosis-related feature genes were performed.

Results

Based on the TCGA dataset and ferroptosis pathway genes, 1929 ferroptosis-related genes were preliminarily selected. Following univariate Cox regression analysis and survival analysis, 14 genes were obtained. Then, random forest analysis identified 10 ferroptosis key genes. These 10 genes were used to construct a prognostic complex value. It was found that low complex value indicated better prognosis compared with high complex value. In different ESCA datasets, there were similar differences in the proportion of immune cell distribution between the high and low complex value groups. Furthermore, TNKS1BP1, AC019100.7, KRI1, BCAP31, and RP11-408E5.5 were significantly correlated with ESCA tumor location, lymph node metastasis, and age of patients. KRI1 had the highest mutation frequency. BCAP31 had the strongest binding ability with small molecules DB12830, DB05812, and DB07307.

Conclusion

We constructed a novel ferroptosis-related gene signature, which has the potential to predict patient survival and tumor-infiltrating immune cells of ESCA.

A BIRC5High COD1Low Cancer Tissue Phenotype Indicates Poorer Prognosis of Metastatic Breast Cancer Patients

Extensive data research is helpful to find sensitive biomarkers for prognostic prediction of metastatic breast cancer. Through analyzing multiple GEO datasets, literature retrieval, and verified in GEPIA datasets, we identify BIRC5 (Baculoviral IAP repeat containing 5) and CDO1 (Cysteine dioxygenase type 1) as DEGs (differentially expressed genes) between breast tumor and normal tissue and DEGs between metastatic breast cancer and breast cancer in situ. Then, we performed a series of in silico studies on BIRC5 and CDO1 using online tools including the UALCAN, TIMER, TCGA-BRCA, LinkedOmics Kaplan-Meier Plotter, and an R script for analysis. To verify the association of 2 genes expression and patients’ clinical data, we detected BIRC5 and CDO1 mRNA in the tissue of 48 breast cancer patients. The results showed the tumor with BIRC5^high CDO1^low expression generally indicated patients’ shorter overall (OS) and relapse-free survival (RFS). Specifically, BIRC5 and CDO1 levels significantly affect OS or RFS in patients with Lymph node metastasis and molecular subtypes of TNBC (triple-negative breast cancer) and Luminal A. A BIRC5^high tumor displayed a purer tumor purity and expressed more KIR receptors on NK cells while activating more FOXP3⁺CD25⁺ Treg cells. The CDO1^low tumors infiltrated with more immunocytes leading to less tumor purity. In our verified experiment, BIRC5 mRNA level in patients with stage III and over was significantly higher than in patients with stage 0 to II, but there were no significant differences among molecular subtyping groups; TNBC tissue expressed lower CDO1 mRNA level than HER2⁺ and Luminal type cancer tissue. In conclusion, a BIRC5^high CDO1^low expression type in breast cancer tissue indicates a poorer prognosis of patients. The potential mechanism might be increased BIRC5 expression in cancer tissue is likely to accompany NK cells inhibition, activating more Treg cells, and lacking effective CD8⁺ T cells proliferation. Meanwhile, CDO1 level is positively related to more immunocytes infiltration.

Annexin-A1 deficiency attenuates stress-induced tumor growth via fatty acid metabolism in mice: an Integrated multiple omics analysis on the stress- microbiome-metabolite-epigenetic-oncology (SMMEO) axis

Background: High emotional or psychophysical stress levels have been correlated with an increased risk and progression of various diseases. How stress impacts the gut microbiota to influence metabolism and subsequent cancer progression is unclear. Methods: Feces and serum samples from BALB/c ANXA1+/+ and ANXA1-/- mice with or without chronic restraint stress were used for 16S rRNA gene sequencing and GC-MS metabolomics analysis to investigate the effect of stress on microbiome and metabolomics during stress and breast tumorigenesis. Breast tumors samples from stressed and non-stressed mice were used to perform Whole-Genome Bisulfite Sequencing (WGBS) and RNAseq analysis to construct the potential network from candidate hub genes. Finally, machine learning and integrated analysis were used to map the axis from chronic restraint stress to breast cancer development. Results: We report that chronic stress promotes breast tumor growth via a stress-microbiome-metabolite-epigenetic-oncology (SMMEO) axis. Chronic restraint stress in mice alters the microbiome composition and fatty acids metabolism and induces an epigenetic signature in tumors xenografted after stress. Subsequent machine learning and systemic modeling analyses identified a significant correlation among microbiome composition, metabolites, and differentially methylated regions in stressed tumors. Moreover, silencing Annexin-A1 inhibits the changes in the gut microbiome and fatty acid metabolism after stress as well as basal and stress-induced tumor growth. Conclusions: These data support a physiological axis linking the microbiome and metabolites to cancer epigenetics and inflammation. The identification of this axis could propel the next phase of experimental discovery in further understanding the underlying molecular mechanism of tumorigenesis caused by physiological stress.

Identification of CD4+ Conventional T Cells-Related lncRNA Signature to Improve the Prediction of Prognosis and Immunotherapy Response in Breast Cancer

BACKGROUND

Breast cancer (BC) is one of the most common malignancies in women, and long non-coding RNAs (lncRNAs) are key regulators of its development. T cells can recognize and kill cancer cells, and CD4+ T conventional (Tconv) cells are the main orchestrators of cancer immune function. However, research on CD4+ Tconv-related lncRNAs (CD4TLAs) prognostic signature in patients with BC is still lacking.

METHOD

A TCGA database and a GEO database were used to collect the BC patients. Through LASSO Cox regression analysis CD4TLAs-related prognostic models were further constructed, and risk scores (RS) were generated and developed a nomogram based on CD4TLAs. The accuracy of this model was validated in randomized cohorts and different clinical subgroups. Gene set enrichment analysis (GSEA) was used to explore potential signature-based functions. The role of RS has been further explored in the tumor microenvironment (TME), immunotherapy, and chemotherapy.

RESULT

A prognostic model based on 16 CD4TLAs was identified. High-RS was significantly associated with a poorer prognosis. RS was shown to be an independent prognostic indicator in BC patients. The low-RS group had a significant expression of immune infiltrating cells and significantly enriched immune-related functional pathways. In addition, the results of immunotherapy prediction indicated that patients with low-RS were more sensitive to immunotherapy.

CONCLUSIONS

Our signature has potential predictive value for BC prognosis and immunotherapy response. The findings of this work have greatly increased our understanding of CD4TLA in BC.

Oncogenic Potential of the Dual-Function Protein MEX3A

MEX3A belongs to the MEX3 (Muscle EXcess) protein family consisting of four members (MEX3A-D) in humans. Characteristic for MEX3 proteins is their domain structure with 2 HNRNPK homology (KH) domains mediating RNA binding and a C-terminal really interesting new gene (RING) domain that harbors E3 ligase function. In agreement with their domain composition, MEX3 proteins were reported to modulate both RNA fate and protein ubiquitination. MEX3 paralogs exhibit an oncofetal expression pattern, they are severely downregulated postnatally, and re-expression is observed in various malignancies. Enforced expression of MEX3 proteins in various cancers correlates with poor prognosis, emphasizing their oncogenic potential. The latter is supported by MEX3A's impact on proliferation, self-renewal as well as migration of tumor cells in vitro and tumor growth in xenograft studies.

Ceramide glycosylation and related enzymes in cancer signaling and therapy

Ceramides, the core of the sphingolipid metabolism, draw wide attention as tumor suppressor, and act directly on mitochondria to trigger apoptotic cell death. Ceramide-based therapies are being developed by using promote ceramide generating agents. The ceramide metabolism balance is regulated by multifaceted factors in cancer development. Ceramide metabolic enzymes can increase the elimination of ceramide and counteract the anti-tumor effects of ceramide. However, recent research showed that these metabolic enzymes were highly expressed in several cancers. Especially ceramide glycosyltransferases, they catalyze ceramide glycosylation and synthesis the skeleton of glycosphingolipids (GSLs), play an important role in regulating tumor progression and have a significant correlation with the poor prognosis of cancer patients. To further understand the biological characteristics of ceramide metabolism in tumor, this review focuses on the role of ceramide glycosylation and related enzymes in cancer signaling and therapy. Besides, the research on multidrug resistance and potential inhibitors of ceramide glycosyltransferases are also discussed. Advance study on the structure of ceramide glycosyltransferases and ceramide glycosylation signaling pathway will open the path to new therapies and treatments.

MEX3A suppresses proliferation and EMT via inhibiting Akt signaling pathway in cervical cancer

MEX3A, one member of the human MEX3 gene family, exerts different effects on a variety of human cancer cells. However, the biological functions and regulatory mechanism have not been explored in cervical cancer. In our study, we used multiple approaches to determine the functions and underlying molecular mechanism of MEX3A in cervical tumorigenesis, including CCK-8 assay, BrdU assay, FACS for cell cycle and apoptosis, wound healing assay, Transwell migration and invasion assays, immunohistochemistry (IHC) assay, Transfection, real-time RT-PCR and Western blotting analysis. IHC results showed that the expression levels of MEX3A were decreased in cervical cancer patients with advanced clinical stages and lymph node involvement. Moreover, upregulation of MEX3A attenuated cell proliferation, migration and invasion and induced cell cycle arrest at G0/G1 phase in human cervical cancer cells, whereas knockdown of MEX3A exhibited the opposite effects. Mechanistically, MEX3A exerted its tumor suppressive functions via inactivation of Akt signaling pathway and inhibiting epithelial to mesenchymal transition (EMT). Importantly, Akt activation by its activator SC79 reversed the biological functions of MEX3A overexpression. Furthermore, MEX3A inhibited tumor growth in xenograft models. Overall, our investigation suggested that MEX3A participated in antitumor activity in cervical cancer by inhibition of the Akt signaling pathway and EMT. Hence, targeting MEX3A might have a therapeutic potential to treat cervical cancer.