Cancer Genome Research Articles

DPP2/7 is a Potential Predictor of Prognosis and Target in Immunotherapy in Colorectal Cancer: An Integrative Multi-omics Analysis.

Colorectal cancer (CRC) ranks among the leading causes of cancerrelated deaths. This study aimed to illuminate the relationship between DPP7 (also known as DPP2) and CRC through a combination of bioinformatics and experimental methodologies. A multi-dimensional bioinformatic analysis on DPP7 was executed, covering its expression, survival implications, clinical associations, functional roles, immune interactions, and drug sensitivities. Experimental validations involved siRNA-mediated DPP7 knockdown and various cellular assays. Data from the Cancer Genome Atlas (TCGA) identified high DPP7 expression in solid CRC tumors, with elevated levels adversely affecting patient prognosis. A shift from the N0 to the N2 stage in CRC was associated with increased DPP7 expression. Functional insights indicated the involvement of DPP7 in cancer progression, particularly in extracellular matrix disassembly. Immunological analyses showed its association with immunosuppressive entities, and in vitro experiments in CRC cell lines underscored its oncogenic attributes. DPP7 could serve as a CRC prognosis marker, functioning as an oncogene and representing a potential immunotherapeutic target.

A Metric to Characterize Differentially Methylated Region Sets Detected from Methylation Array Data

Background: Identifying differentially methylated region (DMR) is a basic but important task in epigenomics, which can help investigate the mechanisms of diseases and provide methylation biomarkers for screening diseases. A set of methods have been proposed to identify DMRs from methylation array data. However, it lacks effective metrics to characterize different DMR sets and enable a straight way for comparison. Methods: In this study, we introduce a metric, DMRn, to characterize DMR sets detected by different methods from methylation array data. To calculate DMRn, firstly, the methylation differences of DMRs are recalculated by incorporating the correlations between probes and their represented CpGs. Then, DMRn is calculated based on the number of probes and the dense of CpGs in DMRs with methylation differences falling in each interval. Result & Discussion: By comparing the DMRn of DMR sets predicted by seven methods on four scenario, the results demonstrate that DMRn can make an efficient guidance for selecting DMR sets, and provide new insights in cancer genomics studies by comparing the DMR sets from the related pathological states. For example, there are many regions with subtle methylation alteration in subtypes of prostate cancer are altered oppositely in the benign state, which may indicate a possible revision mechanism in benign prostate cancer. Conclusion: Futhermore, when applied to datasets that underwent different runs of batch effect removal, the DMRn can help to visualize the bias introduced by multi-runs of batch effect removal. The tool for calculating DMRn is available in the GitHub repository(https://github.com/xqpeng/DMRArrayMetric).

LINC00891 promotes tumorigenesis and metastasis of thyroid cancer by regulating SMAD2/3 via EZH2.

Thyroid cancer (TC), the most common endocrine malignant tumor, is increasingly causing a huge threat to our health nowadays. To explore the tumorigenesis mechanism of thyroid cancer, we identified that long intergenic non-coding RNA-00891 (LINC00891) was upregulated in TC using the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and local databases. LINC00891 expression correlated with histological type and lymph node metastasis (LNM). The high expression of LINC00891 could serve as a diagnostic marker for TC and its LNM. In vitro experiments demonstrated that LINC00891 knockdown could inhibit cell proliferation, migration, invasion apoptosis, and of TC cells. We also investigated the related mechanisms of LINC00891 promoting TC progression using RNA sequencing, Gene Set Enrichment Analysis, and Western blotting. Our experiments demonstrated that LINC00891 promoted TC progression via the EZH2-SMAD2/3 signaling axis. In addition, overexpression of EZH2 could reverse the suppressive epithelial-to-mesenchymal transition (EMT) caused by LINC00891 knockdown. In conclusion, the LINC00891/EZH2/SMAD2/3 regulatory axis participated in tumorigenesis and metastasis of thyroid cancer, which may provide a novel target for treatment.

3D genomic analysis reveals novel enhancer-hijacking caused by complex structural alterations that drive oncogene overexpression.

Cancer genomes are composed of many complex structural alterations on chromosomes and extrachromosomal DNA (ecDNA), making it difficult to identify non-coding enhancer regions that are hijacked to activate oncogene expression. Here, we describe a 3D genomics-based analysis called HAPI (Highly Active Promoter Interactions) to characterize enhancer hijacking. HAPI analysis of HiChIP data from 34 cancer cell lines identified enhancer hijacking events that activate both known and potentially novel oncogenes such as MYC, CCND1 , ETV1 , CRKL , and ID4 . Furthermore, we found enhancer hijacking among multiple oncogenes from different chromosomes, often including MYC , on the same complex amplicons such as ecDNA. We characterized a MYC - ERBB2 chimeric ecDNA, in which ERBB2 heavily hijacks MYC 's enhancers. Notably, CRISPRi of the MYC promoter led to increased interaction of ERBB2 with MYC enhancers and elevated ERBB2 expression. Our HAPI analysis tool provides a robust strategy to detect enhancer hijacking and reveals novel insights into oncogene activation.

Correlation between the immune microenvironment and bladder cancer based on a prognostic miRNA risk model

<p class="MsoNormal"><strong><span lang="EN-US" style="mso-bidi-font-size: 10.5pt; font-family: Nunito; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; color: #212529; background: white;">Background: </span></strong><span lang="EN-US" style="mso-bidi-font-size: 10.5pt; font-family: Nunito; color: #212529; background: white;">Bladder cancer (BLCA), particularly invasive BLCA, has become a medical burden worldwide as it is associated with recurrence and easy metastasis. There are specific differences in the expression of various miRNAs in tumor and normal tissues. Hence, miRNAs can be used as biomarkers for tumor diagnosis and prognostic evaluation. The current study aimed to predict the downstream target genes of BLCA-related miRNAs and explore their association with immune infiltration. Method: Data on BLCA-related mRNA and miRNA expression levels were downloaded from The Cancer Genome Atlas. Correlation analysis and Cox regression analysis were performed to validate the miRNA risk model. The infiltration of various immune cells should be compared to determine the distinct differences between the immunological microenvironment of the two risk groups. <strong><span style="font-family: Nunito; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi;">Results: </span></strong>A predictive framework of BLCA was established using the expression levels of two miRNAs. Cox regression analysis showed that the low-risk group had a better prognosis. Then, the target genes of miRNA were predicted, and the target genes were analyzed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Moreover, variations in immune cells and functions between the high- and low-risk groups were assessed. <strong><span style="font-family: Nunito; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi;">Conclusion:</span></strong> The prognostic features composed of two associated miRNAs (MIR-25, MIR-548AN) may help predict the overall survival of BLCA.</span></p>

Application of Genomic Medicine in Africa: 14th Conference of the African Society of Human Genetics and the 2nd International Congress of the Moroccan Society of Genomics and Human Genetics, Rabat, Morocco 2022.

The 14th African Society of Human Genetics (AfSHG) Morocco Meeting and 2nd International Congress of the Moroccan Society of Genomics and Human Genetics (SM2GH), held in Rabat, Morocco, from December 12 through 17, 2022, brought together 298 attendees from 23 countries, organized by the AfSHG in collaboration with the SM2GH. The conference's overarching theme was "Applications of Genomics Medicine in Africa," covering a wide range of topics, including population genetics, genetics of infectious diseases, hereditary disorders, cancer genetics, and translational genetics. The conference aimed to address the lag in the field of genetics in Africa and highlight the potential for genetic research and personalized medicine on the continent. The goal was to improve the health of African populations and global communities while nurturing the careers of young African scientists in the field. Distinguished scientists from around the world shared their recent findings in genetics, immunogenetics, genomics, genome editing, immunotherapy, and ethics genomics. Precongress activities included a 2-day bioinformatics workshop, "NGS Analysis for Monogenic Disease in African Populations," and a Young Investigators Forum, providing opportunities for young African researchers to showcase their work. The vast genetic diversity of the African continent poses a significant challenge in investigating and characterizing public health issues at the genetic and functional levels. Training, research, and the development of expertise in genetics, immunology, genomics, and bioinformatics are vital for addressing these challenges and advancing genetics in Africa. The AfSHG is committed to leading efforts to enhance genetic research, coordinate training, and foster research collaborations on the continent.

Clinical and molecular features of patients with IDH1 wild-type primary glioblastoma presenting unexpected short-term survival after gross total resection.

This study investigated the factors influencing short-term survivors (STS) after gross total resection (GTR) in patients with IDH1 wild-type primary glioblastoma. We analyzed five independent cohorts who underwent GTR, including 83 patients from Kitasato University (K-cohort), and four validation cohorts of 148 patients from co-investigators (V-cohort), 66 patients from the Kansai Molecular Diagnosis Network for the Central Nervous System tumors, 109 patients from the Cancer Genome Atlas, and 40 patients from the Glioma Longitudinal AnalySiS. The study defined STS as those who had an overall survival ≤ 12months after GTR with subsequent radiation therapy, and concurrent and adjuvant temozolomide (TMZ). The study included 446 patients with glioblastoma. All cohorts experienced unexpected STS after GTR, with a range of 15.0-23.9% of the cases. Molecular profiling revealed no significant difference in major genetic alterations between the STS and non-STS groups, including MGMT, TERT, EGFR, PTEN, and CDKN2A. Clinically, the STS group had a higher incidence of non-local recurrence early in their treatment course, with 60.0% of non-local recurrence in the K-cohort and 43.5% in the V-cohort. The study revealed that unexpected STS after GTR in patients with glioblastoma is not uncommon and such tumors tend to present early non-local recurrence. Interestingly, we did not find any significant genetic alterations in the STS group, indicating that such major alterations are characteristics of GB rather than being reliable predictors for recurrence patterns or development of unexpected STS.

ZEB family is a prognostic biomarker and correlates with anoikis and immune infiltration in kidney renal clear cell carcinoma

BackgroundZinc finger E-box binding homEeobox 1 (ZEB1) and ZEB2 are two anoikis-related transcription factors. The mRNA expressions of these two genes are significantly increased in kidney renal clear cell carcinoma (KIRC), which are associated with poor survival. Meanwhile, the mechanisms and clinical significance of ZEB1 and ZEB2 upregulation in KIRC remain unknown.MethodsThrough the Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database, expression profiles, prognostic value and receiver operating characteristic curves (ROCs) of ZEB1 and ZEB2 were evaluated. The correlations of ZEB1 and ZEB2 with anoikis were further assessed in TCGA-KIRC database. Next, miRTarBase, miRDB, and TargetScan were used to predict microRNAs targeting ZEB1 and ZEB2, and TCGA-KIRC database was utilized to discern differences in microRNAs and establish the association between microRNAs and ZEBs. TCGA, TIMER, TISIDB, and TISCH were used to analyze tumor immune infiltration.ResultsIt was found that ZEB1 and ZEB2 expression were related with histologic grade in KIRC patient. Kaplan-Meier survival analyses showed that KIRC patients with low ZEB1 or ZEB2 levels had a significantly lower survival rate. Meanwhile, ZEB1 and ZEB2 are closely related to anoikis and are regulated by microRNAs. We constructed a risk model using univariate Cox and LASSO regression analyses to identify two microRNAs (hsa-miR-130b-3p and hsa-miR-138-5p). Furthermore, ZEB1 and ZEB2 regulate immune cell invasion in KIRC tumor microenvironments.ConclusionsAnoikis, cytotoxic immune cell infiltration, and patient survival outcomes were correlated with ZEB1 and ZEB2 mRNA upregulation in KIRC. ZEB1 and ZEB2 are regulated by microRNAs.

Deep learning-based pathway-centric approach to characterize recurrent hepatocellular carcinoma after liver transplantation

BackgroundLiver transplantation (LT) is offered as a cure for Hepatocellular carcinoma (HCC), however 15–20% develop recurrence post-transplant which tends to be aggressive. In this study, we examined the transcriptome profiles of patients with recurrent HCC to identify differentially expressed genes (DEGs), the involved pathways, biological functions, and potential gene signatures of recurrent HCC post-transplant using deep machine learning (ML) methodology.Materials and methodsWe analyzed the transcriptomic profiles of primary and recurrent tumor samples from 7 pairs of patients who underwent LT. Following differential gene expression analysis, we performed pathway enrichment, gene ontology (GO) analyses and protein-protein interactions (PPIs) with top 10 hub gene networks. We also predicted the landscape of infiltrating immune cells using Cibersortx. We next develop pathway and GO term-based deep learning models leveraging primary tissue gene expression data from The Cancer Genome Atlas (TCGA) to identify gene signatures in recurrent HCC.ResultsThe PI3K/Akt signaling pathway and cytokine-mediated signaling pathway were particularly activated in HCC recurrence. The recurrent tumors exhibited upregulation of an immune-escape related gene, CD274, in the top 10 hub gene analysis. Significantly higher infiltration of monocytes and lower M1 macrophages were found in recurrent HCC tumors. Our deep learning approach identified a 20-gene signature in recurrent HCC. Amongst the 20 genes, through multiple analysis, IL6 was found to be significantly associated with HCC recurrence.ConclusionOur deep learning approach identified PI3K/Akt signaling as potentially regulating cytokine-mediated functions and the expression of immune escape genes, leading to alterations in the pattern of immune cell infiltration. In conclusion, IL6 was identified to play an important role in HCC recurrence.

Unlocking Clinical Insights: Lymphocyte-Specific Protein Tyrosine Kinase Candidates as Promising Therapeutic Targets for Pancreatic Cancer Risk Stratification.

Background: Uncover the pivotal link between lymphocyte-specific protein tyrosine kinase (Lck)-related genes and clinical risk stratification in pancreatic cancer. Methods: This study identifies shared genes between differentially expressed genes (DEGs) and Lck-related genes in pancreatic cancer using a methodological framework rooted in The Cancer Genome Atlas database. Feature gene selection is accomplished and a signature model is constructed. Statistical significant clinical endpoints such as overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) were defined. Results: After performing random survival forest, Lasso regression, and multivariate Cox regression model, 7 trait genes out of 272 Lck-associated DEGs are selected to create a signature model that is independent of other clinical factors and can predict OS and DSS. It appears that high-risk patients have activated the TP53 signaling pathway and the cell cycle signaling pathway. LAMA3 turned out to be the hub gene of the signature with high expression in pancreatic cancer. Patients with increased expression of LAMA3 had a short OS, DSS, and PFI in comparison. The candidate competing endogenous RNA network of LAMA3 turned out to be OPI5-AS1/hsa-miR-186-5p/LAMA3 axis. Conclusions: A characteristic signature of seven Lck-related genes, especially LAMA3, has been shown to be a key factor in clinical risk stratification for pancreatic cancer.

Analysis of PD-L1 promoter methylation combined with immunogenic context in pancreatic ductal adenocarcinoma

Despite the successful application of programmed cell death ligand 1 (PD-L1)-blocking strategies in some types of cancers and well-established prognostic indicators in pancreatic ductal adenocarcinoma (PDAC), the biological and clinical implications of the methylation status of PD-L1/PD-L2 in PDAC remain largely unknown. Therefore, this study aimed to explore the biological role of PD-L1/PD-L2 methylation and its association with clinicopathological features, clinical outcomes, and the immune microenvironment by analyzing the data on PD-L1/PD-L2 methylation and mRNA expression in PDAC cohorts obtained from the Cancer Genome Atlas and International Cancer Genome Consortium. The correlation between PD-L1 promoter methylation and PD-L1 expression and survival was further validated in an independent validation cohort (Peking Union Medical College Hospital [PUMCH] cohort) using pyrosequencing and immunohistochemistry. These results demonstrated that hypomethylation of the PD-L1 promoter was strongly associated with upregulated PD-L1 expression and shorter overall survival in PDAC. Multivariate Cox regression analyses revealed that the PD-L1 promoter methylation was an independent prognostic factor. PD-L1 promoter hypomethylation and high expression were related to aggressive clinical phenotypes. Moreover, both PD-L1 and PD-L2 methylation correlated with immune cell infiltration and the expression of immune checkpoint genes. PD-L1 promoter methylation status was further validated as an independent prognostic biomarker in patients with PDAC using the PUMCH cohort. The prognostic significance of PD-L1 promoter methylation was more discriminative in tumors with perineural/lymphovascular invasion and distant metastasis than in those without perineural/lymphovascular invasion and distant metastasis. In summary, the methylation status of the PD-L1 promoter is a promising biomarker for survival outcomes, immune infiltration, and the potential immune benefits of immunotherapy in PDAC.

Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition.

Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has revolutionized medical research, facilitating advancements in drug discovery and cancer diagnosis. ML identifies patterns in data, while DL employs neural networks for intricate processing. Predictive modeling challenges, such as data labeling, are addressed by transfer learning (TL), leveraging pre-existing models for faster training. TL shows potential in genetic research, improving tasks like gene expression analysis, mutation detection, genetic syndrome recognition, and genotype-phenotype association. This review explores the role of TL in overcoming challenges in mutation detection, genetic syndrome detection, gene expression, or phenotype-genotype association. TL has shown effectiveness in various aspects of genetic research. TL enhances the accuracy and efficiency of mutation detection, aiding in the identification of genetic abnormalities. TL can improve the diagnostic accuracy of syndrome-related genetic patterns. Moreover, TL plays a crucial role in gene expression analysis in order to accurately predict gene expression levels and their interactions. Additionally, TL enhances phenotype-genotype association studies by leveraging pre-trained models. In conclusion, TL enhances AI efficiency by improving mutation prediction, gene expression analysis, and genetic syndrome detection. Future studies should focus on increasing domain similarities, expanding databases, and incorporating clinical data for better predictions.

Identification of potential novel N6-methyladenosine effector-related lncRNA biomarkers for serous ovarian carcinoma: a machine learning-based exploration in the framework of 3P medicine.

Serous ovarian carcinoma (SOC) is considered the most lethal gynecological malignancy. The current lack of reliable prognostic biomarkers for SOC reduces the efficacy of predictive, preventive, and personalized medicine (PPPM/3PM) in patients with SOC, leading to unsatisfactory therapeutic outcomes. N6-methyladenosine (m6A) modification-associated long noncoding RNAs (lncRNAs) are effective predictors of SOC. In this study, an effective risk prediction model for SOC was constructed based on m6A modification-associated lncRNAs. Transcriptomic data and clinical information of patients with SOC were downloaded from The Cancer Genome Atlas. Candidate lncRNAs were identified using univariate and multivariate and least absolute shrinkage and selection operator-penalized Cox regression analyses. The molecular mechanisms of m6A effector-related lncRNAs were explored via Gene Ontology, pathway analysis, gene set enrichment analysis, and gene set variation analysis (GSVA). The extent of immune cell infiltration was assessed using various algorithms, including CIBERSORT, Microenvironment Cell Populations counter, xCell, European Prospective Investigation into Cancer and Nutrition, and GSVA. The calcPhenotype algorithm was used to predict responses to the drugs commonly used in ovarian carcinoma therapy. In vitro experiments, such as migration and invasion Transwell assays, wound healing assays, and dot blot assays, were conducted to elucidate the functional roles of candidate lncRNAs. Six m6A effector-related lncRNAs that were markedly associated with prognosis were used to establish an m6A effector-related lncRNA risk model (m6A-LRM) for SOC. Immune microenvironment analysis suggested that the high-risk group exhibited a proinflammatory state and displayed increased sensitivity to immunotherapy. A nomogram was constructed with the m6A effector-related lncRNAs to assess the prognostic value of the model. Sixteen drugs potentially targeting m6A effector-related lncRNAs were identified. Furthermore, we developed an online web application for clinicians and researchers (https://leley.shinyapps.io/OC_m6A_lnc/). Overexpression of the lncRNA RP11-508M8.1 promoted SOC cell migration and invasion. METTL3 is an upstream regulator of RP11-508M8.1. The preliminary regulatory axis METTL3/m6A/RP11-508M8.1/hsa-miR-1270/ARSD underlying SOC was identified via a combination of in vitro and bioinformatic analyses. In this study, we propose an innovative prognostic risk model and provide novel insights into the mechanism underlying the role of m6A-related lncRNAs in SOC. Incorporating the m6A-LRM into PPPM may help identify high-risk patients and personalize treatment as early as possible.

Increased Gremlin1 Expression in Pancreatic Ductal Adenocarcinoma Promotes a Fibrogenic Stromal Microenvironment.

The pancreatic ductal adenocarcinoma (PDAC) microenvironment is primarily composed of cancer-associated fibroblasts (CAFs) and immune cells. Gremlin1 (Grem1) is a profibrogenic factor that promotes tumorigenesis in several cancers. However, the role of Grem1 in the PDAC microenvironment is not adequately defined. We correlated Grem1 levels with activated stroma and immune cells in human PDAC using The Cancer Genome Atlas (TCGA) RNA-sequencing data and characterized the expression of Grem1 transcripts and isoforms in pancreatic cell lines and PDAC tissues. We assessed the role of Grem1 in the microenvironment by in vitro studies. Grem1 expression is associated with an activated stroma and increased M1 and M2 macrophages. Only full length Grem1 variant 1 and isoform 1 were detectable in human pancreatic cells, and remarkably high levels of Grem1 were observed in pancreatic fibroblasts (P < 0.05). Immunohistochemistry detected Grem1 protein in PDAC tumor cells and stromal cells, which correlated with infiltrating macrophages in PDAC tumors. Grem1 knockdown in CAFs suppressed transforming growth factor (TGF)-β-induced extracellular matrix proteins (P < 0.05). Grem1 recombinant protein treatment in vitro increased M1 and M2 macrophages (P < 0.05). Grem1 acts as a profibrogenic factor in the PDAC microenvironment via modulation of fibroblasts and macrophages. Grem1 may have the potential to be developed as a therapeutic target for PDAC.

Clinical significance of ribosomal protein S15 expression in patients with colorectal cancer liver metastases.

Liver metastasis is the most frequently observed distant metastasis of colorectal cancer, and the residual liver recurrence rate after hepatic resection is still high. To explore the mechanism of liver metastasis to discover potential new treatments, we assessed the relationship between the expression of differentially expressed genes (DEGs) and prognosis in patients with colorectal cancer liver metastasis (CRLM). The gene expression dataset was extracted from The Cancer Genome Atlas and the Gene Expression Omnibus. Significance analysis of DEGs between tumor and normal samples of colorectum, liver, and lung was conducted. A total of 80 CRLM patients were studied to assess the expression of RPS15, characteristics, and outcomes. We examined the relationships of RPS15 expression to cell viability and apoptosis invitro and vivo. Significance analysis identified 33 DEGs. In our cohorts, the overall survival rates were significantly lower in the high-RPS15-expression group, and high expression of RPS15 was an independent and unfavorable prognostic factor in recurrence-free survival and overall survival. Knockdown of RPS15 expression reduced the proliferative capacity of colorectal cancer cells and increased BAX-induced apoptotic cell death. RPS15 expression is an independent prognostic factor for CRLM patients and might be a novel therapeutic target for CRLM.

Case-Case Genome-Wide Analyses Identify Subtype-Informative Variants that Confer Risk for Breast Cancer.

Breast cancer includes several subtypes with distinct characteristic biological, pathological, and clinical features. Elucidating subtype-specific genetic etiology could provide insights into the heterogeneity of breast cancer to facilitate development of improved prevention and treatment approaches. Here, we conducted pairwise case-case comparisons among five breast cancer subtypes by applying a case-case GWAS (CC-GWAS) approach to summary statistics data of the Breast Cancer Association Consortium. The approach identified 13 statistically significant loci and eight suggestive loci, the majority of which were identified from comparisons between triple-negative breast cancer (TNBC) and luminal A breast cancer. Associations of lead variants in 12 loci remained statistically significant after accounting for previously reported breast cancer susceptibility variants, among which two were genome-wide significant. Fine mapping implicated putative functional/causal variants and risk genes at several loci, e.g., 3q26.31/TNFSF10, 8q22.3/NACAP1/GRHL2, and 8q23.3/LINC00536/TRPS1, for TNBC as compared to luminal cancer. Functional investigation further identified rs16867605 at 8q22.3 as a SNP that modulates enhancer activity of GRHL2. Subtype-informative polygenic risk scores (PRS) were derived, and patients with a high subtype-informative PRS had an up to 2-fold increased risk of being diagnosed with TNBC instead of luminal cancers. The CC-GWAS PRS remained statistically significant after adjusting for TNBC PRS derived from traditional case-control GWAS in The Cancer Genome Atlas and the African Ancestry Breast Cancer Genetic Consortium. The CC-GWAS PRS was also associated with overall survival and disease-specific survival among breast cancer patients. Overall, these findings have advanced our understanding of the genetic etiology of breast cancer subtypes, particularly for TNBC.

Suppressing mitochondrial inner membrane protein (IMMT) inhibits the proliferation of breast cancer cells through mitochondrial remodeling and metabolic regulation

Metabolic reprogramming is widely recognized as a hallmark of malignant tumors, and the targeting of metabolism has emerged as an appealing approach for cancer treatment. Mitochondria, as pivotal organelles, play a crucial role in the metabolic regulation of tumor cells, and their morphological and functional alterations are intricately linked to the biological characteristics of tumors. As a key regulatory subunit of mitochondria, mitochondrial inner membrane protein (IMMT), plays a vital role in degenerative diseases, but its role in tumor is almost unknown. The objective of this research was to investigate the roles that IMMT play in the development and progression of breast cancer (BC), as well as to elucidate the underlying biological mechanisms that drive these effects. In this study, it was confirmed that the expression of IMMT in BC tissues was significantly higher than that in normal tissues. The analysis of The Cancer Genome Atlas (TCGA) database revealed that IMMT can serve as an independent prognostic factor for BC patients. Additionally, verification in clinical specimens of BC demonstrated a positive association between high IMMT expression and larger tumor size (> 2 cm), Ki-67 expression (> 15%), and HER-2 status. Furthermore, in vitro experiments have substantiated that the suppression of IMMT expression resulted in a reduction in cell proliferation and alterations in mitochondrial cristae, concomitant with the liberation of cytochrome c, but it did not elicit mitochondrial apoptosis. Through Gene Set Enrichment Analysis (GSEA) analysis, we have predicted the associated metabolic genes and discovered that IMMT potentially modulates the advancement of BC through its interaction with 16 metabolic-related genes, and the changes in glycolysis related pathways have been validated in BC cell lines after IMMT inhibition. Consequently, this investigation furnishes compelling evidence supporting the classification of IMMT as prognostic marker in BC, and underscoring its prospective utility as a novel target for metabolic therapy.

Exosomal lncRNA SNHG12 promotes angiogenesis and breast cancer progression.

Breast cancer is one of the most prevalent malignancies in women. Exosomes are important mediators of intercellular communication; however, their regulatory mechanisms in human umbilical vein endothelial cells (HUVECs) angiogenesis in breast cancer remain unknown. We isolated and characterized breast cancer cell-derived exosomes and investigated their functions. Exosomal sequencing and the TCGA database were used to screen long non-coding RNA (lncRNA). In vitro and in vivo experiments were performed to investigate the role of exosomal lncRNA in HUVEC angiogenesis and tumor growth. Molecular methods were used to demonstrate the molecular mechanism of lncRNA. We demonstrated that breast cancer cell-derived exosomes promoted HUVEC proliferation, tube formation, and migration. Combining exosomal sequencing results with The Cancer Genome Atlas Breast Cancer database, we screened lncRNA small nucleolar RNA host gene 12 (SNHG12), which was highly expressed in breast cancer cells. SNHG12 was also upregulated in HUVECs co-cultured with exosome-overexpressed SNHG12. Moreover, overexpression of SNHG12 in exosomes increased HUVEC proliferation and migration, whereas deletion of SNHG12 in exosomes showed the opposite effects. In vivo experiments showed that SNHG12 knockdown in exosomes inhibited breast cancer tumor growth. Transcriptome sequencing identified MMP10 as the target gene of SNHG12. Functional experiments revealed that MMP10 overexpression promoted HUVEC angiogenesis. Mechanistically, SNHG12 blocked the interaction between PBRM1 and MMP10 by directly binding to PBRM1. Moreover, exosomal SNHG12 promoted HUVEC angiogenesis via PBRM1 and MMP10. In summary, our findings confirmed that exosomal SNHG12 promoted HUVEC angiogenesis via the PBRM1-MMP10 axis, leading to enhanced malignancy of breast cancer. Exosomal SNHG12 may be a novel therapeutic target for breast cancer.

Genomic Insights into the Role of TOP Gene Family in Soft-Tissue Sarcomas: Implications for Prognosis and Therapy.

This study focuses on the role of topoisomerases (TOPs) in sarcomas (SARCs), highlighting TOPs' influence on sarcoma prognosis through mRNA expression, genetic mutations, immune infiltration, and DNA methylation analysis using transcriptase sequencing and other techniques. The findings indicate that TOP gene mutations correlate with increased inflammation, immune cell infiltration, DNA repair abnormalities, and mitochondrial fusion genes alterations, all of which negatively affect sarcoma prognosis. Abnormal TOP expression may independently affect sarcoma patients' survival. Cutting-edge genomic tools such as Oncomine, gene expression profiling interactive analysis (GEPIA), and cBio Cancer Genomics Portal (cBioPortal) are utilizedto explore the TOP gene family (TOP1/1MT/2A/2B/3A/3B) in soft-tissue sarcomas (STSs). This in-depth analysis reveals a notable upregulation of TOP mRNA in STS patients arcoss various SARC subtypes, French Federation Nationale des Centres de Lutte Contre le Cancer classification (FNCLCC) grades, and specific molecular profiles correlating with poorer clinical outcomes. Furthermore, this investigation identifies distinct patterns of immune cell infiltration, genetic mutations, and somatic copy number variations linked to TOP genes that inversely affect patient survival rates. These findings underscore the diagnostic and therapeutic relevance of the TOP gene suite in STSs.

Inhibition of glycolysis enhances the efficacy of immunotherapy via PDK-mediated upregulation of PD-L1

BackgroundImmunotherapy for gastric cancer remains a challenge due to its limited efficacy. Metabolic reprogramming toward glycolysis has emerged as a promising avenue for enhancing the sensitivity of tumors to immunotherapy. Pyruvate dehydrogenase kinases (PDKs) play pivotal roles in regulating glycolysis. The importance of PDKs in the context of gastric cancer immunotherapy and their potential as therapeutic targets have not been fully explored.MethodsPDK and PD-L1 expression was analyzed using data from the GSE66229 and The Cancer Genome Atlas (TCGA) cohorts. Additionally, the Immune Checkpoint Blockade Therapy Atlas (ICBatlas) database was utilized to assess PDK expression in an immune checkpoint blockade (ICB) therapy group. Subsequently, the upregulation of PD-L1 and the enhancement of anticancer effects achieved by targeting PDK were validated through in vivo and in vitro assays. The impact of PDK on histone acetylation was investigated using ChIP‒qPCR to detect changes in histone acetylation levels.ResultsOur analysis revealed a notable negative correlation between PD-L1 and PDK expression. Downregulation of PDK led to a significant increase in PD-L1 expression. PDK inhibition increased histone acetylation levels by promoting acetyl-CoA generation. The augmentation of acetyl-CoA production and concurrent inhibition of histone deacetylation were found to upregulate PD-L1 expression in gastric cancer cells. Additionally, we observed a significant increase in the anticancer effect of PD-L1 antibodies following treatment with a PDK inhibitor.ConclusionsDownregulation of PDK in gastric cancer cells leads to an increase in PD-L1 expression levels, thus potentially improving the efficacy of PD-L1 immune checkpoint blockade therapy.