A Novel Glycolysis-Related Four-mRNA Signature for Predicting the Survival of Patients With Breast Cancer

Advances in the understanding of nuclear pore complexes in human diseases

BACKGROUND

Nuclear pore complexes (NPCs) are sophisticated and dynamic protein structures that straddle the nuclear envelope and act as gatekeepers for transporting molecules between the nucleus and the cytoplasm. NPCs comprise up to 30 different proteins known as nucleoporins (NUPs). However, a growing body of research has suggested that NPCs play important roles in gene regulation, viral infections, cancer, mitosis, genetic diseases, kidney diseases, immune system diseases, and degenerative neurological and muscular pathologies.

PURPOSE

In this review, we introduce the structure and function of NPCs. Then We described the physiological and pathological effects of each component of NPCs which provide a direction for future clinical applications.

METHODS

The literatures from PubMed have been reviewed for this article.

CONCLUSION

This review summarizes current studies on the implications of NPCs in human physiology and pathology, highlighting the mechanistic underpinnings of NPC-associated diseases.

Combination of 3PO analog PFK15 and siPFKL efficiently suppresses the migration, colony formation ability, and PFK-1 activity of triple-negative breast cancers by reducing the glycolysis

Among all the subtypes of breast cancer, triple-negative breast cancer (TNBC) has been associated with the worst prognosis. Recently, for many solid tumors (including breast cancer) metabolic reprogramming has appeared as a cancer cell hallmark, and the elevated glycolytic pathway has been linked to their aggressive phenotype. In the present study, we evaluated the prognostic and therapeutic relevance of PFKFB3 (6-phosphofructo-2- kinase/fructose-2,6-bisphosphatase) in TNBCs. Prognostic significance of PFKFB3 expression was evaluated in overall breast cancers as well as in TNBCs. PFKFB3 inhibitor (3PO potent analogue i.e., PFK15) cytotoxicity in TNBC cell lines (MDA-MB-231 and MDA-MB-468) was analyzed using an MTT (3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. Cancer cell physiological characteristics like clonogenicity and migration were also investigated after PFK15 treatment. As fructose-2,6-bisphosphate (F-2,6-BP), has been associated with increased PFK-1 activity, the effect of PFKFB3 inhibition by PFK15 was investigated on two major isoforms of phosphofructokinase-1 (PFK-1) in breast cancer, that is, phosphofructokinase-platelet type (PFKP) and phosphofructokinase-liver type (PFKL) (relevant to breast cancer). For PFKL inhibition, the siRNA approach was used. PFKFB3 expression was significantly correlated with inferior overall survival in breast cancer patients including TNBCs. PFK15 treatment in TNBC cells (i.e., MDA-MB-231 and MDA-MB-468) resulted in a decreased PFKP expression, thereby leading to reduced colony formation ability, migration rate, and extracellular lactate levels. However, to our surprise PFK15 treatment in both TNBC cells also resulted in elevated PFKL levels. Our results demonstrated that the combinatorial inhibition of PFK15 with siPFKL was more effective in TNBC cells, as it led to a decrease in colony formation ability, migration rate, extracellular lactate levels, and PFK-1 activity when compared with individual treatments. Using bona fide PFKFB3 inhibitor, that is, AZ67, we further show that AZ67 treatment to TNBC cells has no effect either on the expression of PFKP and PFKL, or on the lactate production. In summary, our present in vitro study demonstrated that 3PO derived PFK15 mechanism of action is totally different from AZ67 in TNBC cells. However, we advocate that the PFK15-mediated inhibition (along with PFKL) on the TNBCs migration, colony formation, and PFK-1 activity can be further explored for the therapeutic advantage of TNBC patients.

Identification of stromal cell proportion-related genes in the breast cancer tumor microenvironment using CorDelSFS feature selection: implications for tumor progression and prognosis

Background: The tumor microenvironment (TME) of breast cancer (BRCA) is a complex and dynamic micro-ecosystem that influences BRCA occurrence, progression, and prognosis through its cellular and molecular components. However, as the tumor progresses, the dynamic changes of stromal and immune cells in TME become unclear. Objective: The aim of this study was to identify differentially co-expressed genes (DCGs) associated with the proportion of stromal cells in TME of BRCA, to explore the patterns of cell proportion changes, and ultimately, their impact on prognosis. Methods: A new heuristic feature selection strategy (CorDelSFS) was combined with differential co-expression analysis to identify TME-key DCGs. The expression pattern and co-expression network of TME-key DCGs were analyzed across different TMEs. A prognostic model was constructed using six TME-key DCGs, and the correlation between the risk score and the proportion of stromal cells and immune cells in TME was evaluated. Results: TME-key DCGs mimicked the dynamic trend of BRCA TME and formed cell type-specific subnetworks. The IG gene-related subnetwork, plasmablast-specific expression, played a vital role in the BRCA TME through its adaptive immune function and tumor progression inhibition. The prognostic model showed that the risk score was significantly correlated with the proportion of stromal cells and immune cells in TME, and low-risk patients had stronger adaptive immune function. IGKV1D-39 was identified as a novel BRCA prognostic marker specifically expressed in plasmablasts and involved in adaptive immune responses. Conclusions: This study explores the role of proportionate-related genes in the tumor microenvironment using a machine learning approach and provides new insights for discovering the key biological processes in tumor progression and clinical prognosis.

Advances in Genomic Data and Biomarkers: Revolutionizing NSCLC Diagnosis and Treatment

Non-small cell lung cancer (NSCLC) is a significant public health concern with high mortality rates. Recent advancements in genomic data, bioinformatics tools, and the utilization of biomarkers have improved the possibilities for early diagnosis, effective treatment, and follow-up in NSCLC. Biomarkers play a crucial role in precision medicine by providing measurable indicators of disease characteristics, enabling tailored treatment strategies. The integration of big data and artificial intelligence (AI) further enhances the potential for personalized medicine through advanced biomarker analysis. However, challenges remain in the impact of new biomarkers on mortality and treatment efficacy due to limited evidence. Data analysis, interpretation, and the adoption of precision medicine approaches in clinical practice pose additional challenges and emphasize the integration of biomarkers with advanced technologies such as genomic data analysis and artificial intelligence (AI), which enhance the potential of precision medicine in NSCLC. Despite these obstacles, the integration of biomarkers into precision medicine has shown promising results in NSCLC, improving patient outcomes and enabling targeted therapies. Continued research and advancements in biomarker discovery, utilization, and evidence generation are necessary to overcome these challenges and further enhance the efficacy of precision medicine. Addressing these obstacles will contribute to the continued improvement of patient outcomes in non-small cell lung cancer.

A Novel Glycolysis-Related Signature for Predicting the Prognosis and Immune Infiltration of Uveal Melanoma

INTRODUCTION

As the most common aggressive intraocular cancer in adults, uveal melanoma (UVM) threatens the survival and vision of many people. Glycolysis is a novel hallmark of cancer, but the role of glycolysis-related genes in UVM prognosis remains unknown. The purpose of the study was to establish a glycolysis-related gene signature (GRGS) to predict UVM prognosis.

METHODS

Raw data were obtained from TCGA-UVM and GSE22138 datasets. The GRGS was established by univariate, LASSO, and multivariate Cox regression analyses. Kaplan-Meier survival and time-dependent receiver operating characteristic curves were used to evaluate the predictive ability of the GRGS. The relationships of the GRGS with infiltrating immune cell levels and mutations were analyzed with CIBERSORT and maftools.

RESULTS

A novel GRGS (risk score = 0.690861*ISG20 + 0.070991*MET - 0.227520*SDC2 + 0.690223*FBP1 + 0.048008*CLN6 - 0.128520*SDC3) was developed for predicting UVM prognosis. The GRGS had robust predictive stability in UVM. Enrichment annotation suggested that the high-risk group had stronger adaptive immune responses and that the low-risk group had more innate immune cell infiltration. Moreover, BAP1 mutation was related to high risk, and SF3B1 mutation was related to low risk.

CONCLUSIONS

This study developed and validated a novel GRGS to predict UVM prognosis and immune infiltration. The signature revealed an association between glycolysis-related genes and the tumor microenvironment, providing new insights into the role of glycolysis in UVM.

Effects of Glycolysis-Related Genes on Prognosis and the Tumor Microenvironment of Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is a common and deadly malignancy worldwide. Current treatment methods for hepatocellular carcinoma have many disadvantages; thus, it is urgent to improve the efficacy of these therapies. Glycolysis is critical in the occurrence and development of tumors. However, survival and prognosis biomarkers related to glycolysis in HCC patients remain to be fully identified.

Methods: Glycolysis-related genes (GRGs) were downloaded from “The Molecular Signatures Database” (MSigDB), and the mRNA expression profiles and clinical information of HCC patients were obtained from TCGA. Consensus clustering was performed to classify the HCC patients into two subgroups. We used the least absolute shrinkage and selection operator (LASSO) regression analysis to construct the risk signature model. Kaplan–Meier (K-M) survival analysis was performed to evaluate the prognostic significance of the risk model, and the receiver operating characteristic (ROC) curve analysis was used to evaluate the prediction accuracy. The independent prediction ability of the risk model was validated by univariate and multivariate Cox regression analyses. The differences of immune infiltrates and relevant oncogenic signaling between different risk groups were compared. Finally, biological experiments were performed to explore the functions of screened genes.

Results: HCC patients were classified into two subgroups, according to the expression of prognostic-related GRGs. Almost all GRGs categorized in cluster 2 showed upregulated expressions, whereas GRGs in cluster 1 conferred survival advantages. GSEA identified a positive correlation between cluster 2 and the glycolysis process. Ten genes were selected for risk signature construction. Patients were assigned to high-risk and low-risk groups based on the median risk score, and K-M survival analysis indicated that the high-risk group had a shorter survival time. Additionally, the risk gene signature can partially affect immune infiltrates within the HCC microenvironment, and many oncogenic pathways were enriched in the high-risk group, including glycolysis, hypoxia, and DNA repair. Finally, in vitro knockdown of ME1 suppressed proliferation, migration, and invasion of hepatocellular carcinoma cells.

Conclusion: In our study, we successfully constructed and verified a novel glycolysis-related risk signature for HCC prognosis prediction, which is meaningful for classifying HCC patients and offers potential targets for the treatment of hepatocellular carcinoma.

Crocodylus porosus Sera a Potential Source to Identify Novel Epigenetic Targets: In Silico Analysis

We have previously found that sera from Crocodylus porosus contain anticancer agents and the treatment of MCF7 cells with this serum resulted in the differential expression of 51 genes. The purpose of this study was to use in silico analysis to identify genes that might be epigenetically modulated in cells treated with crocodile serum and to understand the role of potential genes as novel candidates with epigenetic therapeutic potential. The findings report five proto-oncogenes (TUBA1B, SLC2A1, PGK1, CCND1, and NCAPD2) and two tumor suppressor genes (RPLP2, RPL37) as novel therapeutic targets. Furthermore, we present a comprehensive overview of relevant studies on epigenetic regulation of these genes along with an insight into their clinical implications. Therefore, elucidating the molecules present in the serum and gut bacteria of reptiles such as crocodiles may offer insights into the role of these genes on longevity, health, disease, and life expectancy.

Targeting PFKL with penfluridol inhibits glycolysis and suppresses esophageal cancer tumorigenesis in an AMPK/FOXO3a/BIM-dependent manner

As one of the hallmarks of cancer, metabolic reprogramming leads to cancer progression, and targeting glycolytic enzymes could be useful strategies for cancer therapy. By screening a small molecule library consisting of 1320 FDA-approved drugs, we found that penfluridol, an antipsychotic drug used to treat schizophrenia, could inhibit glycolysis and induce apoptosis in esophageal squamous cell carcinoma (ESCC). Gene profiling and Ingenuity Pathway Analysis suggested the important role of AMPK in action mechanism of penfluridol. By using drug affinity responsive target stability (DARTS) technology and proteomics, we identified phosphofructokinase, liver type (PFKL), a key enzyme in glycolysis, as a direct target of penfluridol. Penfluridol could not exhibit its anticancer property in PFKL-deficient cancer cells, illustrating that PFKL is essential for the bioactivity of penfluridol. High PFKL expression is correlated with advanced stages and poor survival of ESCC patients, and silencing of PFKL significantly suppressed tumor growth. Mechanistically, direct binding of penfluridol and PFKL inhibits glucose consumption, lactate and ATP production, leads to nuclear translocation of FOXO3a and subsequent transcriptional activation of BIM in an AMPK-dependent manner. Taken together, PFKL is a potential prognostic biomarker and therapeutic target in ESCC, and penfluridol may be a new therapeutic option for management of this lethal disease.

Expression Characteristics and Significant Prognostic Values of PGK1 in Breast Cancer

It was proven that PGK1 plays a vital role in the proliferation, migration, and invasion of human breast cancer. However, the correlation of PGK1 mRNA and protein expression with clinicopathologic characteristics and prognostic values according to various kinds of breast cancer patient classifications remains unsufficient. Here, we analyzed data from the Oncomine database, Breast cancer Gene-Expression Miner v4.5, TNMplot, MuTarget, PrognoScan database, and clinical bioinformatics to investigate PGK1 expression distribution and prognostic value in breast cancer patients. Our study revealed that the mRNA and protein expression levels of PGK1 were up-regulated in various clinicopathologic types of breast cancer. Moreover, the expression of PGK1 was correlated with mutations of common tumor suppressor genes TP53 and CDH1. In addition, we found that high mRNA level of PGK1 was significantly associated with poor OS, RFS, and DMFS. Notably, Cox regression analysis showed that PGK1 could be used as an independent prognostic marker. In summary, the aforementioned findings suggested that PGK1 might be not only explored as a potential biomarker, but also combined with TP53/CDH1 for chemotherapy in breast cancer.