Identification of a combined apoptosis and hypoxia gene signature for predicting prognosis and immune infiltration in 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.

NUP43 promotes PD-L1/nPD-L1/PD-L1 feedback loop via TM4SF1/JAK/STAT3 pathway in colorectal cancer progression and metastatsis

Programmed cell death-ligand 1 (PD-L1) has a significant role in tumor progression and metastasis, facilitating tumor cell evasion from immune surveillance. PD-L1 can be detected in the tumor cell nucleus and exert an oncogenic effect by nuclear translocation. Colorectal cancer (CRC) progression and liver metastasis (CCLM) are among the most lethal diseases worldwide, but the mechanism of PD-L1 nuclear translocation in CRC and CCLM remains to be fully understood. In this study, using CRISPR-Cas9-based genome-wide screening combined with RNA-seq, we found that the oncogenic factor NUP43 impacted the process of PD-L1 nuclear translocation by regulating the expression level of the PD-L1 chaperone protein IPO5. Subsequent investigation revealed that this process could stimulate the expression of tumor-promoting factor TM4SF1 and further activate the JAK/STAT3 signaling pathway, which ultimately enhanced the transcription of PD-L1, thus establishing a PD-L1-nPD-L1-PD-L1 feedback loop that ultimately promoted CRC progression and CCLM. In conclusion, our study reveals a novel role for nPD-L1 in CRC, identifies the PD-L1-nPD-L1-PD-L1 feedback loop in CRC, and provides a therapeutic strategy for CRC patients.

Oral cancers preceded by proliferative verrucous leukoplakia exhibit distinctive molecular features

OBJECTIVE

Proliferative verrucous leukoplakia (PVL) has high rates of malignant transformation into oral squamous cell carcinoma (OSCC), but the clinical and evolutionary pattern of OSCC from PVL (PVL-OSCC) is more favorable than that of OSCC not preceded by PVL (OSCC). Here, we aimed to explore the pathophysiologic differences between PVL-OSCC and OSCC through transcriptomic and DNA methylation analyses.

MATERIALS AND METHODS

In this case-control study, oral biopsies from 8 PVL-OSCC and 10 OSCC patients were obtained for global sequencing using RNAseq and a genome-wide DNA methylation analysis via the Infinium EPIC Platform (graphical abstract).

RESULTS

One hundred and thirty-three differentially expressed genes (DEGs) were detected, 94 of them upregulated in OSCC. Most of these genes were previously described in cancer and associated with prognosis. The integrative analysis revealed 26 DEGs, corresponding to 37 CpGs, whose promoters were regulated by DNA methylation. Twenty-nine of the CpGs were found as hypermethylated in PVL-OSCC. Only 5 of the genes that were aberrantly methylated and differentially expressed were upregulated in PVL-OSCC patients, whereas 21 were underexpressed.

CONCLUSIONS

PVL-OSCC patients presented lower expression of cancer-related genes. Hypermethylation of the promoter region of many genes was also noticed, indicating that DNA methylation could be a regulatory mechanism.

PIK3CA mutation-driven immune signature as a prognostic marker for evaluating the tumor immune microenvironment and therapeutic response in breast cancer

PURPOSE

Gene mutations drive tumor immune microenvironment (TIME) heterogeneity, in turn affecting prognosis and immunotherapy efficacy. PIK3CA is the most frequently mutated gene in breast cancer (BC), yet its relevance to BC prognosis remains controversial. Herein, we sought to determine the impact of PIK3CA mutation-driven immune genes (PDIGs) on BC prognosis in relation to TIME heterogeneity.

METHODS

PIK3CA mutation characteristics were compared and verified between the TCGA-BRCA dataset and a patient cohort from our hospital. PIK3CA mutation-driven differentially expressed genes were identified for consensus clustering and weighted gene co-expression network analysis to select the modules most relevant to the immune subtype. Thereafter, the two were intersected to obtain PDIGs. Univariate Cox, LASSO, and multivariate Cox regression analyses were sequentially performed on PDIGs to obtain a PIK3CA mutation-driven immune signature (PDIS), which was then validated using the Gene Expression Omnibus (GEO) database. Differences in functional enrichment, mutation landscape, immune infiltration, checkpoint gene expression, and drug response were compared between different risk groups.

RESULTS

PIK3CA mutation frequencies in the TCGA and validation cohorts were 34.49% and 40.83%, respectively. PIK3CA mutants were significantly associated with ER, PR, and molecular BC subtypes in our hospital cohort. The PDIS allowed for effective risk stratification and exhibited prognostic power in TCGA and GEO sets. The low-risk patients exhibited greater immune infiltration, higher expression of common immune checkpoint factors, and lower scores for tumor immune dysfunction and exclusion.

CONCLUSION

The PDIS can be used as an effective prognostic model for predicting immunotherapy response to guide clinical decision-making.

Construction of an oxidative stress-associated genes signature in breast cancer by machine learning algorithms

OBJECTIVE

To construct a prognostic model of a breast cancer-related oxidative stress-related gene (OSRG) signature using machine learning algorithms.

METHODS

The OSRGs of breast cancer were constructed by least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. The Cancer Genome Atlas (TCGA) was used to analyse the gene expression and prognostic value. The Human Protein Atlas was used to analyse the protein expression of hub genes. Receiver operating characteristic analysis, calibration curve and decision curve analysis were used to predict the stability of this model.

RESULTS

The area under the curve of 1-, 3- and 5-year overall survival were 0.751, 0.707 and 0.645 in the TCGA training dataset; and 0.692, 0.678 and 0.602 in the TCGA testing dataset, respectively. Calibration plot showed good agreement between predicted probabilities and observed outcomes. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) pathway analysis indicated that multiple cancer-related pathways were highly enriched in the high-risk group. Immune infiltration analysis showed immune cells and their functions may play a key role in the development and mechanism of breast cancer.

CONCLUSIONS

This new OSRG signature was associated with the immune infiltration and it might be useful in predicting the prognosis in patients with breast cancer.

Identification of somatic mutation-driven enhancers and their clinical utility in breast cancer

Somatic mutations contribute to cancer development by altering the activity of enhancers. In the study, a total of 135 mutation-driven enhancers, which displayed significant chromatin accessibility changes, were identified as candidate risk factors for breast cancer (BRCA). Furthermore, we identified four mutation-driven enhancers as independent prognostic factors for BRCA subtypes. In Her2 subtype, enhancer G > C mutation was associated with poorer prognosis through influencing its potential target genes FBXW9, TRIR, and WDR83. We identified aminoglutethimide and quinpirole as candidate drugs targeting the mutated enhancer. In normal subtype, enhancer G > A mutation was associated with poorer prognosis through influencing its target genes ALOX15B, LINC00324, and MPDU1. We identified eight candidate drugs such as erastin, colforsin, and STOCK1N-35874 targeting the mutated enhancer. Our findings suggest that somatic mutations contribute to breast cancer subtype progression by altering enhancer activity, which could be potential candidates for cancer therapy.

Identification of Breast Cancer Subtypes Based on Endoplasmic Reticulum Stress-Related Genes and Analysis of Prognosis and Immune Microenvironment in Breast Cancer Patients

Introduction: Endoplasmic reticulum stress (ERS) was a response to the accumulation of unfolded proteins and plays a crucial role in the development of tumors, including processes such as tumor cell invasion, metastasis, and immune evasion. However, the specific regulatory mechanisms of ERS in breast cancer (BC) remain unclear. Methods: In this study, we analyzed RNA sequencing data from The Cancer Genome Atlas (TCGA) for breast cancer and identified 8 core genes associated with ERS: ELOVL2, IFNG, MAP2K6, MZB1, PCSK6, PCSK9, IGF2BP1, and POP1. We evaluated their individual expression, independent diagnostic, and prognostic values in breast cancer patients. A multifactorial Cox analysis established a risk prognostic model, validated with an external dataset. Additionally, we conducted a comprehensive assessment of immune infiltration and drug sensitivity for these genes. Results: The results indicate that these eight core genes play a crucial role in regulating the immune microenvironment of breast cancer (BRCA) patients. Meanwhile, an independent diagnostic model based on the expression of these eight genes shows limited independent diagnostic value, and its independent prognostic value is unsatisfactory, with the time ROC AUC values generally below 0.5. According to the results of logistic regression neural networks and risk prognosis models, when these eight genes interact synergistically, they can serve as excellent biomarkers for the diagnosis and prognosis of breast cancer patients. Furthermore, the research findings have been confirmed through qPCR experiments and validation. Conclusion: In conclusion, we explored the mechanisms of ERS in BRCA patients and identified 8 outstanding biomolecular diagnostic markers and prognostic indicators. The research results were double-validated using the GEO database and qPCR.

Construction of a Novel Disulfidptosis-Related lncRNA Prognostic Signature in Pancreatic Cancer

Pancreatic cancer is a lethal, extremely aggressive gastrointestinal tumor with a poor prognosis and limited treatment alternatives. Disulfidptosis is a newly defined type of cell death with potential influence on cancer. Research on the association between disulfidptosis and pancreatic cancer is scarce. The expression data of disulfidptosis-related genes were downloaded from The Cancer Genome Atlas-Pancreatic Adenocarcinoma (TCGA). Disulfidptosis-related lncRNA signature (DRLS) was developed through the Cox and the least absolute shrinkage and selection operator (LASSO) analysis. Differences in enrichment functions, mutational landscape, immune microenvironment, and predicted therapeutic efficacy between high- and low-risk groups were assessed. Consensus clustering analysis was applied to identify the DRLS-related subtypes. Among 98 disulfidptosis-related lncRNAs, 5 lncRNAs were screened thus constructing a prognostic DRLS. DRLS showed high predictive accuracy and was an independent prognostic factor for pancreatic cancer. According to the risk scores calculated from the signature, samples were categorized into high- and low- risk groups. Overall, low-risk patients had a better prognosis, lower mutational occurrences, higher immune cell infiltration and more sensitivity to anti-tumor agents. The DRLS performed well in predicting prognosis and revealed intimate correlation with biological function, mutation status and immune infiltration landscape of pancreatic cancer, providing some insights for future research on the relationship between disulfidptosis and pancreatic cancer.

Insights into the potential of Sanguinarine as a promising therapeutic option for breast cancer

Breast cancer (BC) is one of the leading causes of cancer-related deaths in women worldwide. The tumor microenvironment (TME) plays a crucial role in the progression and metastasis of BC. A significant proportion of BC is characterized by a hypoxic TME, which contributes to the development of drug resistance and cancer recurrence. Sanguinarine (SAN), an isoquinoline alkaloid found in Papaver plants, has shown promise as an anticancer agent. The present review focuses on exploring the molecular mechanisms of hypoxic TME in BC and the potential of SAN as a therapeutic option. The review presents the current understanding of the hypoxic TME, its signaling pathways, and its impact on the progression of BC. Additionally, the review elaborates on the mechanisms of action of SAN in BC, including its effects on vital cellular processes such as proliferation, migration, drug resistance, and tumor-induced immune suppression. The review highlights the importance of addressing hypoxic TME in treating BC and the potential of SAN as a promising therapeutic option.

Prognosis-Related Molecular Subtypes and Immune Features Associated with Hepatocellular Carcinoma

Bioinformatics tools were used to identify prognosis-related molecular subtypes and biomarkers of hepatocellular carcinoma (HCC). Differential expression analysis of four datasets identified 3330 overlapping differentially expressed genes (DEGs) in the same direction in all four datasets. Those genes were involved in the cell cycle, FOXO signaling pathway, as well as complement and coagulation cascades. Based on non-negative matrix decomposition, two molecular subtypes of HCC with different prognoses were identified, with subtype C2 showing better overall survival than subtype C1. Cox regression and Kaplan-Meier analysis showed that 217 of the overlapping DEGs were closely associated with HCC prognosis. The subset of those genes showing an area under the curve >0.80 was used to construct random survival forest and least absolute shrinkage and selection operator models, which identified seven feature genes (SORBS2, DHRS1, SLC16A2, RCL1, IGFALS, GNA14, and FANCI) that may be involved in HCC occurrence and prognosis. Based on the feature genes, risk score and recurrence models were constructed, while a univariate Cox model identified FANCI as a key gene involved mainly in the cell cycle, DNA replication, and mismatch repair. Further analysis showed that FANCI had two mutation sites and that its gene may undergo methylation. Single-sample gene set enrichment analysis showed that Th2 and T helper cells are significantly upregulated in HCC patients compared to controls. Our results identify FANCI as a potential prognostic biomarker for HCC.