Genome Gene Expression Research Articles

Targeting FDFT1 Reduces Cholesterol and Bile Acid Production and Delays Hepatocellular Carcinoma Progression Through the HNF4A/ALDOB/AKT1 Axis.

Targeting cholesterol metabolism is a novel direction for tumor therapy. Unfortunately, the current use of statins for hepatocellular carcinoma (HCC) is controversial. Herein, farnesyl-diphosphate farnesyltransferase 1 (FDFT1) is identified as a novel target for treating HCC and a potential alternative to statins. Twenty-three key genes in cholesterol biosynthesis are screened, and FDFT1 is identified via public databases (The Cancer Genome Atlas, International Cancer Genome Consortium and Gene Expression Omnibus). Clinical samples reveal that FDFT1 is highly expressed in HCC tissues, and this phenotype is strongly associated with a poor prognosis. Functionally, FDFT1 knockdown inhibits the proliferation and metastasis of HCC cells and suppresses hepatocarcinogenesis in vitro and in vivo, whereas FDFT1 overexpression promotes HCC cell proliferation and metastasis. Mechanistically, FDFT1 downregulation decreases cholesterol and bile acid levels and then increases hepatocyte nuclear factor 4 alpha (HNF4A) transcriptional activity. Experiments indicate that HNF4A combines with the promoter of aldolase B (ALDOB)and promotes the ALDOB transcription and that ALDOB combines with AKT serine/threonine kinase 1 (AKT1) and inhibits AKT1 phosphorylation. Moreover, FDFT1 knockdown combined with AKT inhibitor (AZD5363) treatment shows remarkable therapeutic potential. FDFT1 inhibition reduces cholesterol and bile acid levels to delay HCC progression through the HNF4A/ALDOB/AKT1 axis. Thus, targeting FDFT1 may be a novel potential strategy for treating HCC.

Prognostic value of FCER1G expression and M2 macrophage infiltration in esophageal squamous cell carcinoma

BackgroundFCER1G as an immune-associated protein, which belongs to the immunoglobulin superfamily and is involved in mediating and executing antibody-mediated immune responses. However, the role of FCER1G in cancers remains controversial. Our objectives were to study the association between FCER1G and tumor- infiltrating immune cells (TIICs) as well as the predictive significance of FCER1G.MethodsThe expression of FCER1G and its prognostic value in ESCC was examined by The Cancer Genome Atlas and Gene Expression Omnibus databases. We also evaluated the relationship between FCER1G expression and 22 TIICs. Immunohistochemistry was used to detect the expression and distribution of FCER1G. Double immunofluorescence was used to detect the co-expression of FCER1G and CD163 positive cells. Kaplan–Meier survival curves and Cox regression analysis was performed to determine the prognostic significance of FCER1G and CD163.ResultsThe analysis revealed that FCER1G was upregulated in ESCC, which was distributed more in the intra-tumor mesenchyme than in the cancer nests. The more infiltration in intra-tumor mesenchyme the worse the overall survival (OS) for patients with ESCC. The infiltration of FCER1G+ cells was positively correlated with that of M2 macrophages and most of the CD163+ M2 macrophages expressed FCER1G. The more the infiltration of FCER1G+ M2 macrophages, the worse the OS of ESCC patients. FCER1G and TNM stage were identified as independent risk factors affecting the OS of ESCC patients.ConclusionsFCER1G+ cells infiltration may help to predict the prognosis of ESCC. The combined detection of FCER1G and CD163 has a higher prognostic value.

Genomic 8-oxoguanine modulates gene transcription independent of its repair by DNA glycosylases OGG1 and MUTYH.

8-oxo-7,8-dihydroguanine (OG) is one of the most abundant oxidative lesions in the genome and is associated with genome instability. Its mutagenic potential is counteracted by a concerted action of 8-oxoguanine DNA glycosylase (OGG1) and mutY homolog DNA glycosylase (MUTYH). It has been suggested that OG and its repair has epigenetic-like properties and mediates transcription, but genome-wide evidence of this interdependence is lacking. Here, we applied an improved OG-sequencing approach reducing artificial background oxidation and RNA-sequencing to correlate genome-wide distribution of OG with gene transcription in OGG1 and/or MUTYH-deficient cells. Our data identified moderate enrichment of OG in the genome that is mainly dependent on the genomic context and not affected by DNA glycosylase-initiated repair. Interestingly, no association was found between genomic OG deposition and gene expression changes upon loss of OGG1 and MUTYH. Regardless of DNA glycosylase activity, OG in promoter regions correlated with expression of genes related to metabolic processes and damage response pathways indicating that OG functions as a cellular stress sensor to regulate transcription. Our work provides novel insights into the mechanism underlying transcriptional regulation by OG and DNA glycosylases OGG1 and MUTYH and suggests that oxidative DNA damage accumulation and its repair utilize different pathways.

Regulation of pancreatic cancer cells by suppressing KIN17 through the PI3K/AKT/mTOR signaling pathway.

Pancreatic cancer is an aggressive tumor, which is often associated with a poor clinical prognosis and resistance to conventional chemotherapy. Therefore, there is a need to identify new therapeutic markers for pancreatic cancer. Although KIN17 is a highly expressed DNA‑ and RNA‑binding protein in a number of types of human cancer, its role in pancreatic cancer development, especially in relation to progression, is currently unknown. The present study verified the upregulation of KIN17 in pancreatic cancer using The Cancer Genome Atlas and Gene Expression Omnibus databases (GSE15471, GSE71989 and GSE62165), and identified an association between the PI3K/Akt/mTOR pathway and patient prognosis using publicly available datasets (Gene Expression Profiling Interactive Analysis). Immunohistochemistry was performed to determine the association between KIN17 and the pathological features of clinical pancreatic cancer samples. Furthermore, knockdown of KIN17 was shown to inhibit the migration and invasion of pancreatic cancer cells, and to reverse epithelial‑mesenchymal transition in pancreatic cancer cells through downregulation of Vimentin and N‑cadherin, and upregulation of E‑cadherin. Through various cellular experiments, the role of KIIN17 was explored in PI3K/AKT/mTOR activity. KIN17 inhibition was shown to suppress the migration and invasion of pancreatic cancer cells through PI3K/AKT/mTOR‑mediated autophagy. Furthermore, combined with mTOR inhibition, dual inhibition could enhance autophagy, leading to anti‑migratory and anti‑invasion effects in pancreatic cancer. In conclusion, the present study indicated that KIN17 may have a role in carcinogenesis and could serve as a prognostic biomarker of pancreatic cancer, owing to its high expression. In addition, KIN17 may be considered a potential therapeutic target with its knockdown having an inhibitory effect on pancreatic cancer.

Development of a novel risk signature revealing prognostic and tumor microenvironmental features in breast cancer.

This study aimed to devise a breast cancer (BC) risk signature for based on pyrimidine metabolism-related genes (PMRGs) to evaluate its prognostic value and association with drug sensitivity. Transcriptomic and clinical data were retrieved from The Cancer Genome Atlas database and Gene Expression Omnibus repository. Pyrimidine metabolism-associated genes were identified from the Molecular Signatures Database collection. A risk signature was constructed through Cox regression and Lasso regression methods. Further, the relationship between the PMRG-derived risk feature and clinicopathological characteristics, gene expression patterns, somatic mutations, drug susceptibility, and tumor immune microenvironment was thoroughly investigated, culminating in the development of a nomogram. PMRGs displayed differential expression and diverse somatic mutations in BC. Univariate Cox analysis identified 36 genes significantly associated with BC prognosis, leading to the categorization of 2 BC molecular subtypes with discernible differences in prognosis. Using Lasso Cox regression, a risk signature composed of 16 PMRGs was established, wherein high-risk scores were indicative of poor prognosis. The PMRG-derived risk feature was also related to chemotherapy regimens and showed significant correlations with sensitivity to multiple drugs. Furthermore, distinct tumor immune microenvironment properties, gene expression profiles, and somatic mutation patterns were evident across varying risk scores. Ultimately, a nomogram was constructed incorporating the PMRGs-based risk signature alongside stage, and chemotherapy status, demonstrating excellent performance in prognosis prediction. We successfully developed a PMRG-based BC risk signature that effectively combines with clinicopathological attributes for accurate prognosis assessment in BC.

Prognostic models of immune-related cell death and stress unveil mechanisms driving macrophage phenotypic evolution in colorectal cancer

BackgroundTumor microenvironment (TME), particularly immune cell infiltration, programmed cell death (PCD) and stress, has increasingly become a focal point in colorectal cancer (CRC) treatment. Uncovering the intricate crosstalk between these factors can enhance our understanding of CRC, guide therapeutic strategies, and improve patient prognosis.MethodsWe constructed an immune-related cell death and stress (ICDS) prognostic model utilizing machine learning methodologies. Furthermore, we performed enrichment analyses and deconvolution algorithms to elucidate the complex interactions between immune cell infiltration and the processes of PCD and stress within a substantial array of transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus data base (GEO) related to CRC. Single-cell sequencing and biochemical experiments were used to validate the interaction between the model genes and programmed cell death in tumor cells.ResultsThe ICDS prognostic model exhibited robust predictive performance in seven independent cohorts, revealing an inverse correlation between model scores and patient prognosis. Meanwhile, the ICDS index was positively correlated with clinical stage. Model analysis indicated that patient subgroups with low ICDS index exhibited heightened immune activation features and elevated activity in PCD and stress pathways. Single-cell analysis further revealed that macrophages were the central drivers of immune characteristics underlying prognostic differences within the ICDS prognostic model. Pseudotime analysis and cellular experiments indicated that the model gene GAL3ST4 promotes the transition of macrophages toward an M2 pro-tumor phenotype. Furthermore, cell communication analysis and experimental validation revealed that the cuproptosis in tumor cells suppress GAL3ST4 expression, thereby inhibiting M2-like macrophage polarization.ConclusionIn summary, we constructed the ICDS prognostic model and uncovered the mechanism by which tumor cells downregulate GAL3ST4 expression via cuproptosis to inhibit M2-like macrophage polarization, providing new targets and biomarkers for CRC treatment and prognosis evaluation.

Integrated Genomics Reveal Potential Resistance Mechanisms of PANoptosis-Associated Genes in Acute Myeloid Leukemia.

Acute myeloid leukemia (AML) is marked by the proliferation of abnormal myeloid progenitor cells in the bone marrow and blood, leading to low cure rates despite new drug approvals from 2017 to 2018. Current therapies often fail due to the emergence of drug resistance mechanisms, such as those involving anti-apoptotic pathways and immune evasion, highlighting an urgent need for novel approaches to overcome these limitations. Programmed cell death (PCD) is crucial for tissue homeostasis, with PANoptosis-a form of PCD integrating pyroptosis, apoptosis, and necroptosis-recently identified. This process, regulated by the PANoptosome complex, could be key to overcoming AML drug resistance. Targeting multiple PCD pathways simultaneously may prove more effective than single-target therapies. Research suggests that disrupting anti-apoptotic mechanisms, such as those involving Bcl-2, can enhance drug sensitivity in AML. This study hypothesizes that PANoptosis-associated resistance genes (PARGs) play a critical role in AML drug resistance by modulating immune responses and offers a multi-faceted approach to tackle this challenge. Using RNA sequencing data from the Cancer Genome Atlas and Gene Expression Omnibus databases, we performed differential expression analysis to identify significantly dysregulated PARGs in AML. Regression analysis identified prognostic PARGs, bridging a key gap in understanding how these genes contribute to treatment resistance. We then verified their expression in AML cell lines and cell samples treated with cytarabine using RT-qPCR. Hierarchical clustering revealed distinct PARG expression patterns, and functional enrichment analysis highlighted their involvement in immune-related pathways. The combination of bioinformatics and experimental validation underscores how these genes may mediate immune modulation in drug resistance, providing a robust framework for further study. Our findings suggest that PARGs contribute to AML resistance by modulating immune responses and provide potential targets for therapeutic intervention. This study highlights the potential of targeting PARGs to improve treatment outcomes in AML. By analyzing the expression changes of these genes in response to standard clinical treatments, we provide a framework for developing multi-target therapeutic strategies that simultaneously disrupt multiple programmed cell death pathways. Such an approach directly addresses the limitations of current treatments by offering a method to enhance drug sensitivity and mitigate resistance, potentially improving survival rates. Our findings underscore the importance of a comprehensive understanding of PCD mechanisms and pave the way for innovative treatments that could significantly impact AML management.

Unlocking the future: Mitochondrial genes and neural networks in predicting ovarian cancer prognosis and immunotherapy response

BACKGROUND Mitochondrial genes are involved in tumor metabolism in ovarian cancer (OC) and affect immune cell infiltration and treatment responses. AIM To predict prognosis and immunotherapy response in patients diagnosed with OC using mitochondrial genes and neural networks. METHODS Prognosis, immunotherapy efficacy, and next-generation sequencing data of patients with OC were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus. Mitochondrial genes were sourced from the MitoCarta3.0 database. The discovery cohort for model construction was created from 70% of the patients, whereas the remaining 30% constituted the validation cohort. Using the expression of mitochondrial genes as the predictor variable and based on neural network algorithm, the overall survival time and immunotherapy efficacy (complete or partial response) of patients were predicted. RESULTS In total, 375 patients with OC were included to construct the prognostic model, and 26 patients were included to construct the immune efficacy model. The average area under the receiver operating characteristic curve of the prognostic model was 0.7268 [95% confidence interval (CI): 0.7258-0.7278] in the discovery cohort and 0.6475 (95%CI: 0.6466-0.6484) in the validation cohort. The average area under the receiver operating characteristic curve of the immunotherapy efficacy model was 0.9444 (95%CI: 0.8333-1.0000) in the discovery cohort and 0.9167 (95%CI: 0.6667-1.0000) in the validation cohort. CONCLUSION The application of mitochondrial genes and neural networks has the potential to predict prognosis and immunotherapy response in patients with OC, providing valuable insights into personalized treatment strategies.

Single-cell sequencing uncovers the mechanistic role of DAPK1 in glioma and its diagnostic and prognostic implications

BackgroundWe conducted an investigation into the characteristics of single-cell differentiation data in gliomas, with a focus on developing DAPK1-based prognostic markers to predict patient outcomes. Dysregulated expression of DAPK1 has been associated with the invasive behavior of various malignancies, including gliomas. However, the precise role and underlying mechanisms of DAPK1 in gliomas remain inadequately understood.MethodsWe performed analyses on RNA-seq and microarray datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), in addition to single-cell RNA sequencing (scRNA-seq) data from glioma patients available in GEO. Utilizing the Seurat R package, we identified gene clusters associated with survival from the scRNA-seq data. Prognostic models were developed using LASSO and stepwise regression algorithms. Furthermore, we assessed the predictive potential of these genes within the immune microenvironment and their relevance in immunotherapy contexts.ResultsOur scRNA-seq data analysis revealed 32 distinct cell clusters corresponding to 10 cell types. Through dimensionality reduction and clustering, we identified three glial cell subpopulations based on their differentiation trajectories. DAPK1, serving as a marker gene for the terminal subpopulation, exhibited an association with poor prognosis.ConclusionsDAPK1-based prognostic models show promise for accurately predicting outcomes in glioblastoma and glioma. An in-depth examination of DAPK1’s specific mechanisms in glioblastoma could elucidate its role in immunotherapy response. Targeting the DAPK1 gene may offer therapeutic benefits for glioma patients.

Droplet-based high-throughput 3D genome structure mapping of single cells with simultaneous transcriptomics

Single-cell three-dimensional (3D) genome techniques have advanced our understanding of cell-type-specific chromatin structures in complex tissues, yet current methodologies are limited in cell throughput. Here we introduce a high-throughput single-cell Hi-C (dscHi-C) approach and its transcriptome co-assay (dscHi-C-multiome) using droplet microfluidics. Using dscHi-C, we investigate chromatin structural changes during mouse brain aging by profiling 32,777 single cells across three developmental stages (3 months, 12 months, and 23 months), yielding a median of 78,220 unique contacts. Our results show that genes with significant structural changes are enriched in pathways related to metabolic process and morphology change in neurons, and innate immune response in glial cells, highlighting the role of 3D genome organization in physiological brain aging. Furthermore, our multi-omics joint assay, dscHi-C-multiome, enables precise cell type identification in the adult mouse brain and uncovers the intricate relationship between genome architecture and gene expression. Collectively, we developed the sensitive, high-throughput dscHi-C and its multi-omics derivative, dscHi-C-multiome, demonstrating their potential for large-scale cell atlas studies in development and disease.

Tertiary lymphoid structure signatures are associated with survival and immunotherapy response in lung adenocarcinoma

The presence of tertiary lymphoid structures (TLSs) has been correlated with improved prognosis and clinical outcomes in response to immunotherapy in certain solid tumors. However, the precise role of TLSs in lung adenocarcinoma (LUAD) remains unclear. Four datasets of LUAD were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The TLSs model was constructed using a multivariate Cox proportional hazards model. GO and KEGG analyses were performed to explore the biological process associated with the TLSs model. The ESTIMATE and CIBERSORT algorithms were employed to quantify immune infiltration status. TLSs signature genes (TSGs) were identified, including chemokine signature genes, TFH cell markers, TH1 cell and B cell markers, and a plasma cell marker. Diagnostic evaluations identified key genes with high diagnostic value, particularly among chemokine signature genes and TFH cells markers. Furthermore, high expression of CCL20 and IL1R2 was correlated with poorer outcomes, while other TSGs indicated more favorable prognoses. A novel TLSs score model was constructed, integrating 4 TSGs (SGPP2, MS4A1, IL1R2 and CCL20), which accurately predicted patient survival and was independently associated with prognosis. Additionally, the TLSs score served as a robust indicator for LUAD survival prediction, outperforming traditional staging systems. Comprehensive analyses of enriched pathways and immune cell infiltration patterns revealed that this score involved in metabolic processes and immune cell regulation. Furthermore, the TLSs score showed potential as an indicator of response to immunotherapy, with higher scores associated with reduced expression of immune checkpoint genes and poorer response rates. The TLSs score may serve as a predictor of prognosis and immunotherapeutic response in LUAD. These findings may offer new insights on the study of malignancy and personalized immunotherapy for LUAD patients.

Three-dimensional genome architecture in intrahepatic cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (ICC) is a common primary hepatic tumors with a 5-year survival rate of less than 20%. Therefore, it is crucial to elucidate the molecular mechanisms of ICC. Recently, the advance of high-throughput chromosome conformation capture (Hi-C) technology help us look insight into the three-dimensional (3D) genome structure variation during tumorigenesis. However, its function in ICC pathogenesis remained unclear. Hi-C and RNA-sequencing were applied to analyze 3D genome structures and gene expression in ICC and adjacent noncancerous hepatic tissue (ANHT). Furthermore, the dysregulated genes due to 3D genome changes were validated via quantitative real-time PCR and immunohistochemistry. Primarily, the intrachromosomal interactions of chr1, chr2, chr3, and chr11 and the interchromosomal interactions of chr1-chr10, chr13-chr21, chr16-chr19, and chr19-chr22 were also significantly distinct between ANHT and ICC, which may potentially contribute to the activation of cell migration and invasion via the upregulation of WNT10A, EpCAM, S100A3/A6, and MAPK12. Interestingly, 56 compartment regions from 23 chromosomes underwent A to B or B to A transitions during ICC oncogenesis, which attenuated the complement pathway through the downregulation of C8A/C8B, F7, F10, and F13B. Notably, topologically associated domain (TAD) rearrangements were identified in the region containing HOPX (chr4: 57,514,154-57,522,688) and ACVR1 (chr2:158,592,958-158,732,374) in ICC, which may contribute to the hijacking of remote enhancers that were previously outside the TAD and increased expression of HOPX and ACVR1. This study reveals relationship between 3D genome structural variations and gene dysregulation during ICC tumorigenesis, indicating the molecular mechanisms and potential biomarkers.

High Frequency of PIK3R1 Alterations in Ovarian Cancers: Clinicopathological and Molecular Associations.

The phosphoinositide 3-kinase (PI3K) pathway is activated in multiple cancers. However, the significance of PIK3R1 encoding the PI3K regulatory subunit, an inhibitor of the PI3K catalytic subunit encoded by PIK3CA, in ovarian cancer development is largely unknown. Here, we investigated PIK3R1 genomic alterations and gene expression by direct sequencing and qPCR methods in 197 ovarian cancers. The results were correlated with clinicopathological and molecular variables and patient outcomes. In addition to mutations (3.5%) and allelic losses (28.4%), we observed a very high frequency of decreased PIK3R1 mRNA levels in ovarian carcinomas (95.8%). Tumors with PIK3R1 mutations mostly represented low-stage cancers of endometrioid and clear-cell type. Tumors with PIK3R1 deletion and underexpression shared similar phenotypes of high-grade carcinomas (p = 0.003 and p = 0.025, respectively). Allelic loss was also associated with advanced stages (p = 0.003) and high-grade serous histotypes (p = 0.004). The PIK3R1 copy number correlated with mRNA levels (p = 0.009). PIK3R1 mutations coexisted with PTEN mutations (p = 0.041), whereas PIK3R1 deletion and underexpression were linked to PIK3CA amplification (p = 0.038 and p = 0.033, respectively). Low PIK3R1 expression diminished the probability of a complete response (OR 0.07, p = 0.03) in patients treated with platinum-based regimens. PIK3R1 alterations may contribute to the development of ovarian cancers with different malignant potential and molecular changes. The high frequency of PIK3R1 aberrations suggests their importance in PI3K pathway deregulation, and they may potentially serve as an alternative to PIK3CA markers for therapy with these pathway inhibitors.

Revisiting ABC Transporters and Their Clinical Significance in Glioblastoma.

Background: The multiple drug-resistant phenomenon has long since plagued the effectiveness of various chemotherapies used in the treatment of patients with glioblastoma (GBM), which is still incurable to this day. ATP-binding cassette (ABC) transporters function as drug transporters and have been touted to be the main culprits in developing resistance to xenobiotic drugs in GBM. Methods: This review systematically analyzed the efficacy of ABC transporters against various anticancer drugs from 16 studies identified from five databases (PubMed, Medline, Embase, Scopus, and ScienceDirect). Results: Inhibition of ABC transporters, especially ABCB1, improved drug efficacies. Staple GBM phenotypes, such as GBM stem cells and increased activation of the PI3K/Akt/NF-κB pathway, have been implicated in the expression of several ABC transporters. Using the datasets in The Cancer Genome Atlas and Gene Expression Omnibus, we found upregulated ABC transporters that either negatively impacted survival in univariate analyses (ABCA1, ABCA13, ABCB9, ABCD4) or were independent negative prognosis factors for patients with GBM (ABCA13, ABCB9). Our multivariate analysis further demonstrated three ABC transporters, ABCA13 (Hazard Ratio (HR) = 1.31, p = 0.017), ABCB9 (HR = 1.26, p = 0.03), and ABCB5 (HR = 0.77, p = 0.016), with the administration of alkylating agents (HR = 0.41, p < 0.001), were independent negative prognosis factors for patients with GBM. Conclusions: These findings reinforce the important role played by ABC transporters, particularly by ABCA13, ABCB9, and ABCB1, which could be potential targets that warrant further evaluations for alternate strategies to augment the effects of existing alkylating agents and xenobiotic drugs.

Spatially resolved transcriptomics reveals gene expression characteristics in uveal melanoma

PurposeUveal melanoma (UM) is the most common intraocular malignancy in adults. Previous studies have examined the intra-tumoral heterogeneity. However, the spatial distribution of tumor cells within the tumor microenvironment and its relationship with tumor progression still remains largely unclear. Our study aimed to analyze the correlation between cell distribution patterns and the prognosis of UM.MethodsIn this paper, we performed spatial transcriptomics (ST) sequencing on two UM samples to describe the different cellular distribution patterns. Gene Ontology (GO) and Kyoto Encyclopedia of Genes, Genomes (KEGG) functional enrichment analysis, and protein–protein interaction (PPI) network were performed to define the biological function of each cluster. Differentially expressed genes (DEGs) and survival analysis based on datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database further confirmed the correlation between cellular distribution and clinical prognosis.ResultsWe found two different patterns of tumor cell distribution. The focal tumor cells have a distinct ribosome synthesis and rRNA pathway. In contrast, the subpopulation tented to distribute diffusely was related to fatty acids metabolism profile, presumably supporting tumor growth by providing energy. The scattered tumor cell cluster was associated with malignant biological behaviors and was involved in extensive cellular interactions, including COLLAGEN. Moreover, pseudo-time analysis showed that migration started from the basal region through cell differentiation. According to the TCGA and GEO database, genes expressed characteristically in the scattered tumor cell cluster were related to poor prognosis.ConclusionsOur study drew the ST maps for UM for the first time. These findings revealed the distribution patterns of tumor cells associated with different biological functions and pointed towards specific tumor subpopulations with higher invasiveness as potential therapeutic targets. Together, our study displayed an overview of UM transcriptome and explored the intra-tumoral heterogeneity of UM at the spatial level.

Integrating single-cell RNA-Seq and bulk RNA-Seq data to explore the key role of fatty acid metabolism in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a predominant cause of cancer-related mortality globally, noted for its propensity towards late-stage diagnosis and scarcity of effective treatment modalities. The process of metabolic reprogramming, with a specific emphasis on lipid metabolism, is instrumental in the progression of HCC. Nevertheless, the precise mechanisms through which lipid metabolism impacts HCC and its viability as a therapeutic target have yet to be fully elucidated. In the current investigation, single-cell RNA sequencing in conjunction with weighted gene co-expression network analysis (WGCNA) was utilized to delineate lipid metabolism-related genes correlated with the prognostic outcomes of hepatocellular carcinoma (HCC). Data procurement encompassed transcriptomic and clinical datasets from HCC patients, sourced from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories. Subsequent to this, consensus clustering analysis was implemented to stratify patients into distinct subgroups, contingent upon the expression patterns of lipid metabolism genes. Further analytical procedures involved functional enrichment analysis, evaluation of immune infiltration, and examination of the mutation landscape.PTGES3 was identified as a pivotal gene associated with lipid metabolism. Subsequent to its identification, cellular communication analysis was employed to assess the immunological attributes of PTGES3 within the tumor microenvironment. The functional role of PTGES3 was further corroborated through molecular docking simulations and in vitro experimental assays. We identified 27 genes associated with lipid metabolism, 18 of which exhibited significant correlation with overall survival in HCC patients. PTGES3 emerged as a central gene, demonstrating a robust association with immune cell infiltration and unfavorable prognosis. Cellular communication analysis revealed that PTGES3 exhibits the highest communication intensity with T cells, modulating the tumor microenvironment by potentiating the FN1/CD44 + MDK/NCL signaling pathway. Elevated expression of PTGES3 was linked to immunosuppressive cascades, diminished responsiveness to immunotherapy, and inferior overall survival outcomes. Molecular docking analysis indicated that etoposide, methotrexate, and doxorubicin could effectively bind to PTGES3. In vitro experiments confirmed that PTGES3 knockdown significantly impaired the proliferation, invasion, and migration of HCC cells. This study highlights the pivotal role of lipid metabolism in HCC progression and identifies PTGES3 as a potential prognostic biomarker and therapeutic target. These findings offer new insights into the development of targeted therapies for HCC, particularly in patients with high PTGES3 expression.

Integrating single-cell RNA-seq and bulk RNA-seq to explore prognostic value and immune landscapes of methionine metabolism-related signature in breast cancer.

Neoadjuvant, endocrine, and targeted therapies have significantly improved the prognosis of breast cancer (BC). However, due to the high heterogeneity of cancer, some patients cannot benefit from existing treatments. Increasing evidence suggests that amino acids and their metabolites can alter the tumor malignant behavior through reshaping tumor microenvironment and regulation of immune cell function. Breast cancer cell lines have been identified as methionine-dependent, and methionine restriction has been proposed as a potential cancer treatment strategy. We integrated transcriptomic and single-cell RNA sequencing (ScRNA-seq) analyses based on The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) datasets. Then we applied weighted gene co-expression network analysis (WGCNA) and Cox regression to evaluate methionine metabolism-related genes (MRGs) in BC, constructing and validating a prognostic model for BC patients. Immune landscapes and immunotherapy were further explored. Finally, in vitro experiments were conducted to assess the expression and function of key genes APOC1. In this study, we established and validated a prognostic signature based on eight methionine-related genes to predict overall survival (OS) in BC patients. Patients were further stratified into high-risk and low-risk groups according to prognostic risk score. Further analysis revealed significant differences between two groups in terms of pathway alterations, immune microenvironment characteristics, and immune checkpoint expression. Our study shed light on the relationship between methionine metabolism and immune infiltration in BC. APOC1, a key gene in the prognostic signature, was found to be upregulated in BC and closely associated with immune cell infiltration. Notably, APOC1 was primarily expressed in macrophages. Subsequent in vitro experiments demonstrated that silencing APOC1 reduced the generation of tumor-associated macrophages (TAMs) with an M2 phenotype while significantly decreasing the proliferation, invasion, and migration of MDA-MB-231 and MDA-MB-468 breast cancer cell lines. We established a prognostic risk score consisting of genes associated with methionine metabolism, which helps predict prognosis and response to treatment in BC. The function of APOC1 in regulating macrophage polarization was explored.

The role of lnc‑MAPKAPK5‑AS1 in immune cell infiltration in hepatocellular carcinoma: Bioinformatics analysis and validation.

The oncogenic and tumor suppressor roles of lnc-MAPKAPK5-AS1 in multiple cancers suggest its complexity in modulating cancer progression. The expression and promoter methylation level of lnc-MAPKAPK5-AS1 in hepatocellular carcinoma (HCC) was investigated through data mining from The Cancer Genome Atlas and Gene Expression Omnibus and its significance in prognosis and immunity was explored. lnc-MAPKAPK5-AS1 was co-expressed with its protein-coding gene MAPKAPK5 in HCC and exhibited upregulation in HCC tissues as a result of hypomethylation of its promoter region. High expression of lnc-MAPKAPK5-AS1 was associated with poor prognosis. Enrichment analysis revealed that lnc-MAPKAPK5-AS1 is involved in immune and metabolic-related pathways. Changes in the expression of lnc-MAPKAPK5-AS1 affected plasma cells, T cells CD4+ memory resting, NK cells, macrophages M0/M1, and mast cells resting in the tumor microenvironment. lnc-MAPKAPK5-AS1 was found to correlate with multiple immune checkpoints. Analysis of the Sangerbox database revealed positive relationships between expression of lnc-MAPKAPK5-AS1, tumor mutational burden and microsatellite instability, which suggested that immunotherapy may be effective in tumors with high expression of lnc-MAPKAPK5-AS1. The expression of lnc-MAPKAPK5-AS1 was verified to indicate sensitivity to 16 common targeted drugs. Immunohistochemistry confirmed the expression of MAPKAPK5 protein in HCC and its prognostic significance. Weighted gene co-expression network analysis was applied to identify hub genes related to both immunoreactive score and gene expression. These results revealed that lnc-MAPKAPK5-AS1 may be involved in the occurrence and development of HCC as an oncogene and may represent a potential therapeutic target through modulating the substance metabolism and immune response.

Identification of critical biomarkers and immune landscape patterns in glioma based on multi-database

PurposeGlioma is the most prevalent tumor of the central nervous system. The poor clinical outcomes and limited therapeutic efficacy underscore the urgent need for early diagnosis and an optimized prognostic approach for glioma. Therefore, the aim of this study was to identify sensitive biomarkers for glioma.Patients and methodsDifferentially expressed genes (DEGs) of glioma were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The potential biomarkers were identified using weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression. The prognostic ability of the potential biomarkers was evaluated by Cox regression and survival curve. CellMiner was used to access the correlation between the expression of potential biomarkers and anticancer drug sensitivity. We then explored the association of potential biomarkers and tumor immune infiltration by single-sample GSEA (ssGSEA) and CIBERSORT. Immune staining in glioma patient samples and cell experiments of potential biomarkers further verified their expression and function.ResultsUltimately, we identified three potential biomarkers: SLC8A2, ATP2B3, and SRCIN1. These 3 genes were found significantly correlated with clinicopathological features (age, WHO grade, IDH mutation status, 1p19q codeletion status). Furthermore, the overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS) were found to be positively correlated with high expression of these 3 potential biomarkers. Besides, there was a substantial relationship between the sensitivity of anticancer drugs and these biomarkers expression. More importantly, the negative association between the 3 genes with most tumor immune cells was also established. Moreover, the decreased expression of the biomarkers was also verified in glioma patient samples. Finally, we confirmed that these 3 genes might promotes glioma proliferation and migration in vitro.ConclusionSLC8A2, ATP2B3, and SRCIN1 were identified as underlying biomarkers for glioma associated with prognosis assessments and personal immunotherapy.Graphical

The lysosome-related characteristics affects the prognosis and tumor microenvironment of lung adenocarcinoma.

The lysosome plays a vitally crucial role in tumor development and is a major participant in the cell death process, involving aberrant functional and structural changes. However, there are few studies on lysosome-associated genes (LAGs) in lung adenocarcinoma (LUAD). Bulk RNA-seq of LUAD was downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The lysosome risk signature was constructed after univariate and least absolute shrinkage and selection operator (Lasso) cox regression analysis of the TCGA training set, and its capability was validated by additional validation sets from GEO. Single cell sequencing (scRNA) was obtained from GEO to analyze the differences of lysosome risk signature at the single-cell level and the differences in the function and pathway. In vitro experiments have validated the function of CTSH in LUAD. The risk signature contained seven key LAGs, and patients were categorized into high- and low-risk groups based on a specific calculation formula. The LAG risk signature, which accurately predicted the prognostic status of LUAD patients, was still regarded as an independent prognostic indicator in multifactorial cox regression analysis. Subsequently, the combination of the signature and key clinical information was used to construct a column-line diagram for clinical assessment, which had a high discriminatory power. Immune infiltration analysis from bulk RNA-seq and scRNA-seq indicated that the low-risk group was immune-activated and had a better benefit in the prediction of immunotherapy. Finally, we validated its role in inhibiting tumor proliferation and metastasis in LUAD cells by knockdown of CTSH. We defined a new biomarker that provided unique insights for individualized survival prediction and immunotherapy recommendations for LUAD patients.