A gene expression signature in HER2+ breast cancer patients related to neoadjuvant chemotherapy resistance, overall survival, and disease-free survival

GNB4 Silencing Promotes Pyroptosis to Inhibit the Development of Glioma by Activating cGAS-STING Pathway

The induction of immunogenic cell death is a promising therapeutic option for gliomas. Pyroptosis is a type of programmed immunogenic cell death and its role in gliomas remains unclear. Differentially expressed genes (DEGs) were obtained from GSE4290 and GSE31262 datasets. Hub genes were screened from protein-protein interaction networks and assessed using principal component analysis and receiver operating characteristic curves. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to measure the mRNA expression of hub genes. Pyroptosis and pathway-related proteins were assessed using western blotting. Inflammatory factor levels were determined using enzyme-linked immunosorbent assay. The effect of guanine nucleotide-binding protein-4 (GNB4) on proliferation, migration, and invasion was evaluated using a cell viability test kit and wound-healing and transwell assays. In total, 202 DEGs were identified. Among them, F2R, GNG4, GNG3, PRKCB, and GNB4 were identified as hub genes in gliomas after comprehensive bioinformatics analysis. GNB4 was significantly upregulated in glioma cells compared to normal brain glial cells. Silencing GNB4 significantly inhibited proliferation, invasion, and migration of glioma cells. The expression of pyroptosis-related proteins increased after GNB4 silencing, with elevated levels of inflammatory factors. Pyroptosis inhibitors reversed the inhibitory effects of GNB4 silencing on cell proliferation, migration, and invasion. Additionally, GNB4 silencing activated the cGAS-STING pathway. Treatment with a cGAS-STING pathway inhibitor reversed the inhibition of proliferation, migration, and invasion while downregulating the expression of pyroptosis-related proteins. Silencing GNB4 promotes pyroptosis and thus inhibits the proliferation, migration, and invasion of glioma cells by activating the cGAS-STING pathway, which is a promising biomarker and therapeutic target for glioma.

Immune and Metabolic Reprogramming Induced by Paclitaxel, Capecitabine and Eribulin in Breast Cancer: Insights into Therapeutic Targets

Purpose

Chemotherapeutic agents are known to exert anti-tumor effects by not only invoking cytotoxic effects, but also by altering both the immune profile and metabolic milieu. These alterations to both the immune milieu and circulating metabolome may be leveraged for designing rationale drug combinations with immunotherapeutic agents, once chemotherapy fails.

Patients and Methods

Using publicly available transcriptomic data for breast cancer (BC) patients treated with neoadjuvant chemotherapy (GSE162187), we assessed transcriptional alterations that coincide with response to chemotherapy. To further study the metabolic and immune alterations associated with chemotherapeutic resistance, plasma samples from BC patients treated with eribulin, paclitaxel and capecitabine were obtained and assessed via Metabolomics and Luminex, at time of progression as compared to baseline.

Results

Transcriptomics analysis revealed enrichment of amino acid and lipid metabolic pathways, as well as immune pathways including B cells, complement cascade and T cells, in patients resistant to chemotherapy. To validate these findings and assess the differences among different chemotherapies, plasma samples were obtained at baseline and disease progression. Increases in IL-18; IL-22, amylin and IL-6 were observed at the time of disease progression on eribulin, capecitabine and paclitaxel, respectively. Metabolically, increases in docosahexaenoic acid and decreases in sphingomyelins; increases in triacylglycerides and decreases in fatty acids, and decreases in glutamic acid, lipids and phosphatidylcholines were observed on disease progression on eribulin, capecitabine and paclitaxel, respectively.

Conclusion

Distinct patterns of metabolic and immune dysregulation were associated with resistance to eribulin, capecitabine and paclitaxel. Varied immune and metabolic profiles were specific to each of the three chemotherapies, representing potential novel, and individualized, points of therapeutic leverage.

The RNA-binding protein LARP6 regulates the alternative splicing of related genes in MDA-MB-231 cells

Triple-negative breast cancer (TNBC) has the highest mortality rate of all breast cancer subtypes and currently lacks effective targeted therapies. LARP6 is an RNA-binding protein associated with cancer promotion, but its mechanism of action in TNBC remains unclear. We conducted RNA sequencing (RNA-seq) and improved RNA immunoprecipitation and sequencing (iRIP-seq) to identify the differentially expressed genes (DEGs) and alternative splice sites bound and regulated by LARP6 in MDA-MB-231 cells. Finally, both RT-qPCR and RIP-qPCR were employed for verification. Our study revealed that LARP6 overexpression altered the expression levels of 171 genes and that the number of regulated alternative splicing events (RASEs) exceeded 1000. The regulated alternative splicing genes (RASGs) corresponding to RASEs were enriched in biological processes such as DNA repair, the cell cycle, and the cellular response to DNA damage stimulus. In addition, we found that LARP6 tends to bind the CGACGAG motif. The intersection of peak-related genes with RASGs suggested that LARP6 can bind to 16 genes and regulate their alternative splicing (AS), thus playing an important role in TNBC progression. Our research indicated that LARP6 may promote the proliferation and invasion of TNBC cells by directly regulating the AS of related genes, providing new clues for targeted therapy for TNBC.

M6A modification regulates tumor suppressor DIRAS1 expression in cervical cancer cells

DIRAS family GTPase 1 (DIRAS1) has been reported as a potential tumor suppressor in other human cancer. However, its expression pattern and role in cervical cancer remain unknown. Knockdown of DIRAS1 significantly promoted the proliferation, growth, migration, and invasion of C33A and SiHa cells cultured in vitro. Overexpression of DIRAS1 significantly inhibited the viability and motility of C33A and SiHa cells. Compared with normal cervical tissues, DIRAS1 mRNA levels were significantly lower in cervical cancer tissues. DIRAS1 protein expression was also significantly reduced in cervical cancer tissues compared with para-cancerous tissues. In addition, DIRAS1 expression level in tumor tissues was significantly negatively correlated with the pathological grades of cervical cancer patients. DNA methylation inhibitor (5-Azacytidine) and histone deacetylation inhibitor (SAHA) resulted in a significant increase in DIRAS1 mRNA levels in C33A and SiHa cells, but did not affect DIRAS1 protein levels. FTO inhibitor (FB23-2) significantly down-regulated intracellular DIRAS1 mRNA levels, but significantly up-regulated DIRAS1 protein levels. Moreover, the down-regulation of METTL3 and METTL14 expression significantly inhibited DIRAS1 protein expression, whereas the down-regulation of FTO and ALKBH5 expression significantly increased DIRAS1 protein expression. In conclusion, DIRAS1 exerts a significant anti-oncogenic function and its expression is significantly downregulated in cervical cancer cells. The m6A modification may be a key mechanism to regulate DIRAS1 mRNA stability and protein translation efficiency in cervical cancer.

Single-cell analysis of matrisome-related genes in breast invasive carcinoma: new avenues for molecular subtyping and risk estimation

Background

The incidence of breast cancer remains high and severely affects human health. However, given the heterogeneity of tumor cells, identifying additional characteristics of breast cancer cells is essential for accurate treatment.

Purpose

This study aimed to analyze the relevant characteristics of matrix genes in breast cancer through the multigroup data of a breast cancer multi-database.

Methods

The related characteristics of matrix genes in breast cancer were analyzed using multigroup data from the breast cancer multi database in the Cancer Genome Atlas, and the differential genes of breast cancer matrix genes were identified using the elastic net penalty logic regression method. The risk characteristics of matrix genes in breast cancer were determined, and matrix gene expression in different breast cancer cells was evaluated using real-time fluorescent quantitative polymerase chain reaction (PCR). A consensus clustering algorithm was used to identify the biological characteristics of the population based on the matrix molecular subtypes in breast cancer, followed by gene mutation, immune correlation, pathway, and ligand-receptor analyses.

Results

This study reveals the genetic characteristics of cell matrix related to breast cancer. It is found that 18.1% of stromal genes are related to the prognosis of breast cancer, and these genes are mostly concentrated in the biological processes related to metabolism and cytokines in protein. Five different matrix-related molecular subtypes were identified by using the algorithm, and it was found that the five molecular subtypes were obviously different in prognosis, immune infiltration, gene mutation and drug-making gene analysis.

Conclusions

This study involved analyzing the characteristics of cell-matrix genes in breast cancer, guiding the precise prevention and treatment of the disease.

Secondary Transcriptomic Analysis of Triple-Negative Breast Cancer Reveals Reliable Universal and Subtype-Specific Mechanistic Markers

Background/Objectives: Breast cancer is diagnosed in 2.3 million women each year and kills 685,000 (~30% of patients) worldwide. The prognosis for many breast cancer subtypes has improved due to treatments targeting the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). In contrast, patients with triple-negative breast cancer (TNBC) tumors, which lack all three commonly targeted membrane markers, more frequently relapse and have lower survival rates due to a lack of tumor-selective TNBC treatments. We aim to investigate TNBC mechanistic markers that could be targeted for treatment. Methods: We performed a secondary TNBC analysis of 196 samples across 10 publicly available bulk RNA-sequencing studies to better understand the molecular mechanism(s) of disease and predict robust mechanistic markers that could be used to improve the mechanistic understanding of and diagnostic capabilities for TNBC. Results: Our analysis identified ~12,500 significant differentially expressed genes (FDR-adjusted p-value < 0.05), including KIF14 and ELMOD3, and two significantly modulated pathways. Additionally, our novel findings include highly accurate mechanistic markers identified using machine learning methods, including CIDEC (97.1% accuracy alone), CD300LG, ASPM, and RGS1 (98.9% combined accuracy), as well as TNBC subtype-differentiating mechanistic markers, including the targets PDE3B, CFD, IFNG, and ADM, which have associated therapeutics that can potentially be repurposed to improve treatment options. We then experimentally and computationally validated a subset of these findings. Conclusions: The results of our analyses can be used to better understand the mechanism(s) of disease and contribute to the development of improved diagnostics and/or treatments for TNBC.

Radiomic analysis reveals diverse prognostic and molecular insights into the response of breast cancer to neoadjuvant chemotherapy: a multicohort study

BACKGROUND

Breast cancer patients exhibit various response patterns to neoadjuvant chemotherapy (NAC). However, it is uncertain whether diverse tumor response patterns to NAC in breast cancer patients can predict survival outcomes. We aimed to develop and validate radiomic signatures indicative of tumor shrinkage and therapeutic response for improved survival analysis.

METHODS

This retrospective, multicohort study included three datasets. The development dataset, consisting of preoperative and early NAC DCE-MRI data from 255 patients, was used to create an imaging signature-based multitask model for predicting tumor shrinkage patterns and pathological complete response (pCR). Patients were categorized as pCR, nonpCR with concentric shrinkage (CS), or nonpCR with non-CS, with prediction performance measured by the area under the curve (AUC). The prognostic validation dataset (n = 174) was used to assess the prognostic value of the imaging signatures for overall survival (OS) and recurrence-free survival (RFS) using a multivariate Cox model. The gene expression data (genomic validation dataset, n = 112) were analyzed to determine the biological basis of the response patterns.

RESULTS

The multitask learning model, utilizing 17 radiomic signatures, achieved AUCs of 0.886 for predicting tumor shrinkage and 0.760 for predicting pCR. Patients who achieved pCR had the best survival outcomes, while nonpCR patients with a CS pattern had better survival than non-CS patients did, with significant differences in OS and RFS (p = 0.00012 and p = 0.00063, respectively). Gene expression analysis highlighted the involvement of the IL-17 and estrogen signaling pathways in response variability.

CONCLUSIONS

Radiomic signatures effectively predict NAC response patterns in breast cancer patients and are associated with specific survival outcomes. The CS pattern in nonpCR patients indicates better survival.

Single-cell sequencing analysis related to sphingolipid metabolism guides immunotherapy and prognosis of skin cutaneous melanoma

Background

We explore sphingolipid-related genes (SRGs) in skin melanoma (SKCM) to develop a prognostic indicator for patient outcomes. Dysregulated lipid metabolism is linked to aggressive behavior in various cancers, including SKCM. However, the exact role and mechanism of sphingolipid metabolism in melanoma remain partially understood.

Methods

We integrated scRNA-seq data from melanoma patients sourced from the GEO database. Through the utilization of the Seurat R package, we successfully identified distinct gene clusters associated with patient survival in the scRNA-seq data. Key prognostic genes were identified through single-factor Cox analysis and used to develop a prognostic model using LASSO and stepwise regression algorithms. Additionally, we evaluated the predictive potential of these genes within the immune microenvironment and their relevance to immunotherapy. Finally, we validated the functional significance of the high-risk gene IRX3 through in vitro experiments.

Results

Analysis of scRNA-seq data identified distinct expression patterns of 4 specific genes (SRGs) in diverse cell subpopulations. Re-clustering cells based on increased SRG expression revealed 7 subgroups with significant prognostic implications. Using marker genes, lasso, and Cox regression, we selected 11 genes to construct a risk signature. This signature demonstrated a strong correlation with immune cell infiltration and stromal scores, highlighting its relevance in the tumor microenvironment. Functional studies involving IRX3 knockdown in A375 and WM-115 cells showed significant reductions in cell viability, proliferation, and invasiveness.

Conclusion

SRG-based risk signature holds promise for precise melanoma prognosis. An in-depth exploration of SRG characteristics offers insights into immunotherapy response. Therapeutic targeting of the IRX3 gene may benefit melanoma patients.