Background: Bromodomain-containing protein (BRD) play a pivotal role in the development and progression of malignant tumours. This study aims to identify prognostic genes linked to BRD-related genes (BRDRGs) in patients with triple-negative breast cancer (TNBC) and to construct a novel prognostic model.
Methods: Data from TCGA-TNBC, GSE135565, and GSE161529 were retrieved from public databases. GSE161529 was used to identify key cell types. The BRDRGs score in TCGA-TNBC was calculated using single-sample Gene Set Enrichment Analysis (ssGSEA). Differential expression analysis was performed to identify differentially expressed genes (DEGs): DEGs1 in key cells, DEGs2 between tumours and controls and DEGs3 in high and low BRDRGs score subgroups in TCGA-TNBC. Differentially expressed BRDRGs (DE-BRDRGs) were determined by overlapping DEGs1, DEGs2 and DEGs3. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) network analysis were conducted to investigate active pathways and molecular interactions. Prognostic genes were selected through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses to construct a risk model and calculate risk scores. TNBC samples from TCGA-TNBC were classified into high and low-risk groups based on the median risk score. Additionally, correlations with clinical characteristics, Gene Set Enrichment Analysis (GSEA), immune analysis, and pseudotime analysis were performed.
Results: A total of 120 DE-BRDRGs were identified by overlapping 605 DEGs1 from four key cell types, 10,776 DEGs2, and 4,497 DEGs3. GO analysis revealed enriched terms such as 'apoptotic process,' 'immune response,' and 'regulation of the cell cycle,' while 56 KEGG pathways, including the 'MAPK signaling pathway,' were associated with DE-BRDRGs. A risk model comprising six prognostic genes (KRT6A, PGF, ABCA1, EDNRB, CTSD and GJA4) was constructed. A nomogram based on independent prognostic factors was also developed. Immune cell abundance was significantly higher in high-risk group. In both risk groups, TP53 exhibited the highest mutation frequency. The expression of KRT6A, ABCA1, EDNRB, and CTSD went decreased progressively in pseudotime.
Conclusion: A novel prognostic model for TNBC associated with BRDRGs was developed and validated, providing fresh insights into the relationship between BRD and TNBC.
Background: The prognosis of a plasma cell neoplasm (PCN) varies depending on the presence of genetic abnormalities. However, detecting sensitive genetic mutations poses challenges due to the heterogeneous nature of the cell population in bone marrow aspiration. The established gold standard for cell sorting is fluorescence-activated cell sorting (FACS), which is associated with lengthy processing times, substantial cell quantities, and expensive equipment. Magnetic-activated cell sorting (MACS) can be performed without the need for FACS equipment and allows for rapid sorting of many cells, making it a practical alternative. Our objective is to conduct a comparative analysis of these two sorting techniques to assess whether MACS can viably replace FACS in clinical applications.
Methods: Plasma cell purity, fluorescence in situ hybridization (FISH), and next-generation sequencing analyses were performed on FACS- and MACS-sorted bone marrow samples from 31 PCN patients.
Results: The MACS-sorted samples yielded a higher percentage of plasma cells than FACS-sorted samples under microscopy (p = 0.0156) and flow cytometry (p = 0.0313). FISH performed by two methods in 10 samples showed the same results, and the proportion of abnormal cells was significantly higher in MACS than in FACS (p = 0.001). Wilcoxon matched-pairs signed rank test analysis showed that the median of differences of variant allele frequency (VAF) of two methods (VAF of MACS minus VAF of FACS) in the DNMT3A, TET2, and ASXL1 (DTA) group was - 0.006555 (p = 0.0020), while that in the non-DTA group was 0.002805 (p = 0.0019). Ten copy number variants (CNVs) were found in both FACS- and MACS-sorted samples, eight were identified only in MACS-sorted samples, and one was detected only in FACS-sorted samples.
Conclusion: Our study demonstrates that MACS is a viable alternative for plasma cell sorting in bone marrow samples of patients with PCN.
Recently, there has been growing interest in the role of circular RNAs (circRNAs) in the progression of human cancers. Cellular senescence, a known anti-tumour mechanism, has been observed in several types of cancer. However, the regulatory interplay of circRNAs with cellular senescence in pancreatic cancer (PC) is still unknown. Therefore, we identified circHIF-1α, hsa_circ_0007976, which was downregulated in senescent cells using circRNA microarray analysis. Meanwhile, significantly upregulated expression of circHIF-1α in pancreatic cancer tissue detected by reverse transcription-polymerase chain reaction (RT-qPCR) and in situ hybridization (ISH). High circHIF-1α expression levels were found to independently predict poor survival outcomes. Subsequent treatments with DOX and H2O2 resulted in significantly lower levels of circHIF-1α. CircHIF-1α knockdown induces cellular senescence and suppresses PC proliferation in vitro experiments. The ability of circHIF-1α knockdown to suppress the progression of PC cells was further confirmed in vivo experiments. Our results showed that circHIF-1α is mainly presented in the nucleus of PC cells, also in the cytoplasm. Mechanistically, circHIF-1α inhibited senescence and accelerated the progression of PC cells through miR-375 sponging, thereby promoting HIF-1α expression levels. Nuclear circHIF-1α interacted with human antigen R protein (HUR) to increase HIF-1α expression. Thus, our results demonstrated that circHIF-1α ameliorates senescence and exacerbates growth in PC cells by increasing HIF-1α through targeting miR-375 and HUR, suggesting that targeting circHIF-1α offers a potential therapeutic candidate for PC.
Background: Methyltransferase-like (METTL) family protein plays a crucial role in the progression of malignancies. However, the function of METTL17 across pan-cancers, especially in hepatocellular carcinoma (HCC) is still poorly understood.
Methods: All original data were downloaded from TCGA, GTEx, HPA, UCSC databases and various data portals. First, we comprehensively analyzed RNA-seq data from the HPA database of 25 human tissues. An array of bioinformatics methods was employed to explore the potential oncogenic roles of METTL17, including analyzing its related prognosis, mutation, landscapes, tumor stemness index, immune cell infiltration, and other factors among different tumors. Additionally, gene set enrichment analysis (GSEA) was used to analyze pathways associated with METTL17 in HCC. Immunohistochemistry (IHC) was performed on clinical samples to validate the differential expression of METTL17 in HCC and normal tissues. Ultimately, we constructed a METTL17-related risk-score model of HCC and validated its prognostic classification efficiency. Survival rates were calculated using the Kaplan-Meier method. Statistical significance was defined as P < 0.05.
Results: METTL17 was differentially expressed in various cancers. METTL17 maintained strong correlations with the cancer patient's prognosis, genetic alterations, tumor stemness index, and immune-infiltrated cells, etc. In addition, IHC experiments verified that METTL expression was significantly decreased in liver tissues of HCC patients compared to normal liver tissue. GESA analysis indicated METTL17 mainly involves oncogenic and immune-related pathways among HCC. MRPS5, CHCHD2, NCBP1, LRPPRC, DAP3, and BMS1 were included in a prognostic model based on METTL17's interaction networks. Kaplan-Meier survival analysis of the prognostic model showed that the overall survival (OS) of the low-risk group was significantly better than that of the high-risk group (P < 0.001). The area under the receiver operating characteristic (ROC) curve (AUC) of the 1-year, 3-year, and 5-year OS were 0.747, 0.671, and 0.631, respectively.
Conclusions: METTL17 may serve as a novel prognostic marker and therapeutic target for human tumors, offering a theoretical foundation for formulating more effective and tailored clinical treatment options for cancers, particularly HCC.
Background: Patients with lung adenocarcinoma (LUAD) receiving drug treatment often have an unpredictive response and there is a lack of effective methods to predict treatment outcome for patients. Dendritic cells (DCs) play a significant role in the tumor microenvironment and the DCs-related gene signature may be used to predict treatment outcome. Here, we screened for DC-related genes to construct a prognostic signature to predict prognosis and response to immunotherapy in LUAD patients.
Methods: DC-related biological functions and genes were identified using single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing. DCs-related gene signature (DCRGS) was constructed using integrated machine learning algorithms. Expression of key genes in clinical samples was examined by real-time q-PCR. Performance of the prognostic model, DCRGS, for the prognostic evaluation, was assessed using a multiple time-dependent receiver operating characteristic (ROC) curve, the R package, "timeROC", and validated using GEO datasets.
Results: Analysis of scRNA-seq data showed that there is a significant upregulation of LGALS9 expression in DCs isolated from malignant pleural effusion samples. Leveraging the Coxboost and random survival forest combination algorithm, we filtered out six DC-related genes on which a prognostic prediction model, DCRGS, was established. A high predictive capability nomogram was constructed by combining DCRGS with clinical features. We found that patients with a high-DCRGS score had immunosuppression, activated tumor-associated pathways, and elevated somatic mutational load and copy number variant load. In contrast, patients in the low-DCRGS subgroup were resistant to chemotherapy but sensitive to the CTLA-4 immune checkpoint inhibitor and targeted therapy.
Conclusion: We have innovatively established a deep learning-based prediction model, DCRGS, for the prediction of the prognosis of patients with LUAD. The model possesses a strong prognostic prediction performance with high accuracy and sensitivity and could be clinically useful to guide the management of LUAD. Furthermore, the findings of this study could provide an important reference for individualized clinical treatment and prognostic prediction of patients with LUAD.
Background: Whether the intake of whole grain foods can protect against lung cancer is a long-standing question of considerable public health import, but the epidemiologic evidence has been limited. Therefore we aim to investigate the relationship between whole grain food consumption and lung cancer in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) cohort.
Methods: Diet was assessed with a self-administered Diet History Questionnaire (DHQ) at baseline. All incident lung cancer cases were pathologically verified. Hazard ratios and 95% confidence intervals for lung cancer risk associated with whole grain food consumption were estimated by Cox proportional hazards regression.
Results: During a median follow-up of 12.2 years, a total of 1,706 incident lung cancer events occurred, including 1,473 (86.3%) cases of non-small cell lung cancer (NSCLC) and 233 (13.7%) of small cell lung cancer (SCLC). After multivariate adjustment, comparing the highest quarter of consumption of whole grain foods to the lowest quarter, a 16% lower rate (HR 0.84, 95% CI 0.73-0.98) of lung cancer risks and a 17% lower rate (HR 0.83, 95% CI 0.69-0.98) for NSCLC were found, but no significant difference was shown for SCLC (HR 0.95, 95% CI 0.63-1.44). These results were consistently observed after a large range of subgroup and sensitivity analyses. A linear dose-response pattern was shown for lung cancer, NSCLC, and SCLC (P for non-linearity > 0.05).
Conclusions: In this large prospective cohort study, whole grain food consumption was associated with reduced lung cancer and NSCLC. Our findings suggest a potential protective role of whole grain foods against lung cancer.
The tertiary lymphoid structure (TLS) is recognized as a potential prognosis factor for breast cancer and is strongly associated with response to immunotherapy. Inducing TLS neogenesis can enhance the immunogenicity of tumors and improve the efficacy of immunotherapy. However, our understanding of TLS associated region at the single-cell level remains limited. Therefore, we employed high-resolution techniques, including single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST), and a TLS-specific signature to investigate TLS associated regions in breast cancer. We identified eighteen cell types within the TLS associated regions and calculated differential expression genes by comparing TLS associated regions with other areas. Notably, macrophages in the TLS associated regions exhibit lineage transformation, shifting from facilitators of immune activation to supporters of tumor cell growth. In terms of cell-cell communication within the TLS associated regions, KRT86+ CD8+ T cells, HISTIH4C+ cycling CD8+ T cells, IFNG+ CD8+ T cells, and IGKV3-20+ B cells demonstrate strong interactions with other cells. Additionally, we found that APOD+ fibroblast and CCL21+ fibroblast primarily recruit T and B cells through the CXCL12-CXCR4 ligand-receptor signaling pathway. We also validate these findings in four independent breast cancer datasets, which include one cell-level resolution dataset from the 10 × Xenium platform and three spot-level datasets from the 10 × Visium platform.
Deapioplatycodin D (DPD) is a triterpenoid saponin natural compound isolated from the Chinese herb Platycodon grandiflorum that has antiviral and antitumor properties. This study aimed to investigate the effects of DPD on glioblastoma (GBM) cells and to determine its intrinsic mechanism of action. Using a CCK8 assay, it was found that DPD significantly inhibited the growth of GBM cells. DPD-treated GBM cells contained swollen and degenerated mitochondria with empty vesicular bilayer membrane-like autophagic vesicle structures in the periphery of the mitochondria under transmission electron microscopy. DPD activated autophagy in GBM cells and induced a blockage of autophagic flux in the late stage. Transcriptomics identified differences in mitophagy-related genes, and analysis of the levels of the corresponding proteins indicated that mitophagy in GBM cells was induced mainly through BNIP3L. Increased expression of BNIP3L disrupts the Bcl-2-Beclin-1 complex, thereby releasing Beclin-1 and activating autophagy. Autophagy was inhibited after silencing of BNIP3L and overexpression of Bcl-2 in GBM cells, and the growth inhibitory effect of DPD was significantly reduced. This result demonstrated that DPD induces mitophagy in GBM cells through BNIP3L. Finally, activation of incomplete mitophagy in GBM cells by DPD through BNIP3L in vivo was demonstrated by establishing a mouse subcutaneous xenograft tumor model. In this study, in vitro and in vivo experiments established that DPD inhibited GBM cell growth by inducing BNIP3L-mediated incomplete mitophagy, which provides an experimental basis for studying new treatments of GBM.
Background: XB130, a classical adaptor protein, exerts a critical role in diverse cellular processes. Aberrant expression of XB130 is closely associated with tumorigenesis and aggressiveness. However, the mechanisms governing its expression regulation remain poorly understood. Heterogeneous nuclear ribonucleoprotein C (hnRNPC), as an RNA-binding protein, is known to modulate multiple aspects of RNA metabolism and has been implicated in the pathogenesis of various cancers. We have previously discovered that hnRNPC is one of the candidate proteins that interact with the 3' untranslated region (3'UTR) of XB130 in non-small cell lung cancer (NSCLC). Therefore, this study aims to comprehensively elucidate how hnRNPC regulates the expression of XB130 in NSCLC.
Materials and methods: We evaluated the expression of hnRNPC in cancer and assessed the correlation between hnRNPC expression and prognosis in cancer patients using public databases. Subsequently, several stable cell lines were constructed. The proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) of these cells were detected through Real-time cellular analysis, adherent colony formation, wound healing assay, invasion assay, and Western blotting. The specific regulatory manner between hnRNPC and XB130 was investigated by Real-time quantitative PCR, Western blotting, RNA pull‑down assay, dual‑luciferase reporter assay, RNA immunoprecipitation, and Co-Immunoprecipitation.
Results: We identified that hnRNPC expression is significantly elevated in NSCLC and correlates with poor prognosis in patients with lung adenocarcinoma. HnRNPC overexpression in NSCLC cells increased the expression of XB130, subsequently activating the PI3K/Akt signaling pathway and ultimately promoting cell proliferation and EMT. Additionally, overexpressing XB130 in hnRNPC-silenced cells partially restored cell proliferation and EMT. Mechanistically, hnRNPC specifically bound to the 3'UTR segments of XB130 mRNA, enhancing mRNA stability by inhibiting the recruitment of nucleases 5'-3' exoribonuclease 1 (XRN1) and DIS3-like 3'-5' exoribonuclease 2 (DIS3L2). Furthermore, hnRNPC simultaneously interacted with the eukaryotic initiation factor 4E (eIF4E), a component of the eIF4F complex, facilitating the circularization of XB130 mRNA and thereby increasing its translation efficiency.
Conclusions: HnRNPC overexpression promotes NSCLC progression by enhancing XB130 mRNA stability and translation, suggesting that hnRNPC might be a potential therapeutic and prognostic target for NSCLC.
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