Genome Gene Expression Research Articles

Identification of SLC22A17 DNA methylation hotspot as a potential biomarker in cutaneous melanoma

BackgroundCancer onset and progression are driven by genetic and epigenetic alterations leading to oncogene activation and the silencing of tumor suppressor genes. Among epigenetic mechanisms, DNA methylation (methDNA) is gaining growing interest in cancer. Promoter hypomethylation is associated with oncogene activation while intragenic methDNA can be involved in transcriptional elongation, alternative spicing, and the activation of cryptic start sites. Several genes involved in the modulation of the tumor microenvironment are regulated by methDNA, including the Solute Carrier Family 22 Member 17 (SLC22A17), which is involved in iron trafficking and extracellular matrix remodeling cooperating with the Gelatinase-Associated Lipocalin (NGAL) ligand. However, the exact role of intragenic methDNA in cancer has not been fully investigated. Therefore, the aim of the present study is to explore the role of methDNA in the regulation of SLC22A17 in cutaneous melanoma (CM), used as a tumor model.MethodsCorrelation and differential analyses between SLC22A17 expression and methDNA were performed using the data contained in The Cancer Genome Atlas and Gene Expression Omnibus databases. Functional studies on melanoma cell lines treated with 5-Azacytidine (5-Aza) were conducted to assess the correlation between methDNA and SLC22A17 expression. A validation study on the diagnostic potential of the in silico-identified SLC22A17 methDNA hotspot was finally performed by analyzing tissue samples obtained from CM patients and healthy controls.ResultsThe computational analyses revealed that SLC22A17 was significantly downregulated in CM, and its expression was related to promoter hypomethylation and intragenic hypermethylation. Moreover, SLC22A17 overexpression and hypermethylation of two intragenic methDNA hotspots were associated with a better clinical outcome in CM patients. The correlation between SLC22A17 methDNA and expression was confirmed in 5-Aza-treated cells. In agreement with in silico analyses, the SLC22A17 promoter methylation hotspot showed higher methDNA levels in CM samples compared to nevi. In addition, the methDNA levels of this hotspot were positively correlated with advanced CM.ConclusionsThe SLC22A17 methDNA hotspot could represent a promising biomarker for CM, highlighting the regulatory role of methDNA on SLC22A17 expression. These results pave the way for the identification of novel epigenetic biomarkers and therapeutic targets for the management of CM patients.

ERBB2 is a potential diagnostic and prognostic biomarker in renal clear cell carcinoma

Renal clear cell carcinoma (ccRCC) is a common parenchymal tumor of the kidney, and the discovery of biomarkers for early and effective diagnosis of ccRCC can improve the early diagnosis of patients and thus improve long-term survival. Erb-b2 receptor tyrosine kinase 2 (ERBB2) mediates the processes of cell proliferation, differentiation, and apoptosis inhibition. The purpose of this study was to investigate the diagnostic and prognostic role of ERBB2 in ccRCC. We analyzed the expression levels of ERBB2 in various cancers from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. RNA-seq data were analyzed using R packages to identify differentially expressed genes between the high and low ERBB2 expression groups in the TCGA-KIRC dataset. Spearman correlation analysis was performed to determine the correlation between ERBB2 expression and immune cell infiltration, immune checkpoint expression, and PTEN expression. DNA methylation changes and genetic alterations in ERBB2 were assessed using the MethSurv and cBioPortal databases. Logistic regression analysis was performed to determine the correlation between ERBB2 expression and the clinicopathological characteristics of ccRCC patients. The diagnostic and prognostic value of ERBB2 was assessed using Kaplan‒Meier (K‒M) survival curves, diagnostic ROC curves, time-dependent ROC curves, nomogram models, and Cox regression models. The expression level of ERBB2 is lower in tumor tissues of ccRCC patients than in the corresponding control tissues. Differentially expressed genes associated with ERBB2 were significantly enriched in the pathways “BMP2WNT4FOXO1 pathway in primary endometrial stromal cell differentiation” and “AMAN pathway”. In ccRCC tissues, ERBB2 expression levels were associated with immune cell infiltration, immune checkpoints, and PTEN. The DNA methylation status of 10 CpG islands in the ERBB2 gene was associated with the prognosis of ccRCC. ERBB2 expression levels in ccRCC tissues were associated with race, sex, T stage, M stage, histological grade, and pathological stage. Cox regression analysis showed that ERBB2 was a potential independent predictor of overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in ccRCC patients. ROC curve analysis showed that the expression level of ERBB2 could accurately distinguish between ccRCC tissue and adjacent normal renal tissue. Our study showed that ERBB2 expression in ccRCC tissues can be of clinical importance as a potential diagnostic and prognostic biomarker.

Construction and experimental verification of a novel nine-glycosylation-related gene prognostic risk model for clear cell renal carcinoma

ObjectivePatients with clear cell renal carcinoma (ccRCC) typically have poor prognosis. Glycosylation modification plays an important role in ccRCC. This study aimed to develop a novel signature for predicting ccRCC prognosis based on glycosylation-related genes (GRGs). MethodsDifferentially expressed GRGs (DE_GRGs) were identified using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus and used for constructing the risk model. The function of key genes was validated. ResultsTwenty-two DE_GRGs were intersected between GSE53757 and TCGA. Patients with ccRCC were divided into two clusters based on the expression profile of these DE_GRGs. Significant differences in the infiltration of 10 immune cell types were observed between two subclusters. Subsequently, the prognostic signatures of nine DE_GRGs (CHST9, COLGALT1, FUT3, FUT6, HS3ST2, POMGNT2, ST8SIA4, UGT3A1, and UGT8) were established. Patients in the high-risk group showed a poorer prognosis relative to the low-risk group. According to univariate and multivariate Cox regression analyses, the risk score, stage, and grade could be independent prognostic factors. A nomogram incorporating information on gender, age, risk group, TNM stage, and clinical stage showed accurate prediction in the survival probability. Except for CHST9, HS3ST2, and ST8SIA4, expression patterns of the remaining 6 DE_GRGs in Caki-1and 786-O cells were confirmed by quantitative real-time PCR. FUT6 overexpression resulted in the inhibition of proliferation, migration, and invasion of ccRCC cells. ConclusionThis study established a nine-DE_GRG-based prognostic signature, which independently predicted ccRCC prognosis. This finding emphasizes that GRGs are stratification factors for the precise prognosis of ccRCC.

A Risk Model Developed based on Homologous Recombination Deficiency Genes for Evaluating the Drug Sensitivity and Prognostic Prediction of Lung Adenocarcinoma.

This study was designed to construct a risk model based on homologous recombination deficiency (HRD) to evaluate the prognosis and drug sensitivity for patients with lung adenocarcinoma (LUAD). LUAD is a subtype of lung cancer with unfavorable overall survival (OS) and prognosis. HRD has been widely studied in various tumors, but its role in LUAD has not been fully understood. We aimed to construct an HRD-related risk model for predicting the prognosis and drug sensitivity of patients with LUAD. Gene expression data of the LUAD samples were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We extracted HRD genes from previous literature and performed univariate COX analysis to select those closely associated with LUAD prognosis. ConsensusClusterPlus was employed to stratify the samples in the TCGA-LUAD cohort into different subtypes. A RiskScore model was established applying random forest method. Furthermore, immunotherapy response and drug sensitivity were predicted using Tumor Immune Dysfunction and Exclusion (TIDE) software and pRRophytic R package, respectively. Finally, the clinical features between High- and Low- RiskScore groups were compared. A total of 16 HRD genes relevant to LUAD prognosis were selected and used to classify 3 LUAD clusters (C1, C2, and C3). Specifically, C1, with a lower TIDE score displayed higher immune infiltration and immunotherapy benefit and the optimal OS, while C2 was closely correlated with tumor-relevant pathways and had the worst OS. Finally, 4 HRD genes (RAD51AP1, BRCA1, H2AFX, and FANCL) were determined to develop a RiskScore signature. It was found that a higher RiskScore was related to more advanced stages, worse OS, and tumor development pathways. Additionally, the High-RiskScore group with a higher TIDE score was sensitive to 44 traditional chemotherapy drugs. A nomogram combined with RiskScore exhibited an accurate survival prediction ability. The HRD-based RiskScore played a crucial role in LUAD development, showing a strong potential to serve as a prognostic indicator for LUAD. Our findings contributed to the diagnosis of LUAD and its treatment.

The role of SEC14L4 in esophageal squamous cell cancer: insights into clinical relevance and molecular pathways.

Esophageal squamous cell cancer (ESCC) is the most common type of esophageal cancer. This study aimed to elucidate the role of Saccharomyces cerevisiae-like 4 (SEC14L4) in ESCC. To elucidate the role of SEC14L4 in ESCC, this study analyzed the clinical data, gene sequencing data, and other relevant data retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information. The methodology involved several analytical approaches, including nomogram model analysis, co-expression analysis, gene set enrichment and variation analysis, weighted correlation network analysis, drug susceptibility analysis, and single-cell analysis. These methods were employed to evaluate the significance of SEC14L4 in ESCC. The expression of SEC14L4 was evaluated via quantitative real-time polymerase chain reaction (qRT-PCR). SEC14L4 expression (P<0.001) was significantly elevated in those with ESCC, especially in patients with locally advanced disease (P=0.005), and indicated a poor prognosis (P=0.045). Findings from the nomogram model analysis identified the contribution of clinical indicators to survival prediction with good efficacy. Subsequently, the single-nucleotide polymorphisms and co-expressed genes of SEC14L4 were identified. Furthermore, pathways associated with SEC14L4, including DNA metabolic process, transcription factor binding, apoptosis, and others, were examined. Notably, SEC14L4 expression was predominantly observed in monocytes. Drug sensitivity analysis indicated the association of SEC14L4 expression with sensitivity of ESCC to the common chemotherapy drugs AICAR, BMS.708163, GNF.2, Nutlin.3a, PD.0325901, and RDEA119. Verification of the high expression of SEC14L4 in KYSE520 and KYSE150 was conducted, thereby confirming the study's findings. High expression of SEC14L4 is associated with poorer clinical outcomes, highlighting its potential as a therapeutic target and suggesting its involvement in the molecular mechanisms underlying ESCC.

HDAC1: a promising target for cancer treatment: insights from a thorough analysis of tumor functions.

Many significant findings from recent studies have revealed the significance of histone deacetylase 1 (HDAC1) in the development of tumors and its strong association with tumor prognosis; these studies have mainly focused on one single cancer such as in lung cancer, breast cancer, and hepatocellular carcinoma (HCC). To date, there has been no comprehensive analysis and pan-analysis conducted from the overall perspective of cancer across all types. Hence, we analyzed public databases, conducted tube formation assay, and immunohistochemistry (IHC) staining of HDAC1 on six kinds of clinical samples to explore the prognostic and oncogenic effects of HDAC1 on 33 tumors for the first time. There currently remains a lack of efficient testing methods, therapies, and diagnostic and prognostic markers of tumor formation and development in different tumors. Our initial objective was to investigate the possible cancer-causing functions of HDAC1 in 33 different types of tumors by utilizing The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and many different online websites, such as Tumor IMmune Estimation Resource 2 (TIMER2), Gene Expression Profiling Interactive Analysis 2 (GEPIA2), Genotype Tissue Expression (GTEx) database, Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset, and University of ALabama at Brimingham CANcer data analysis portal (UALCAN) tool, and so on. We even used small interfering RNA (siRNA) to knock down HDAC2 in HCC cell lines. IHC of HDAC1 was performed. HDAC1 exhibited high expression in numerous tumors, and strong correlations were observed between the messenger RNA (mRNA) levels of HDAC1 and the prognosis of individuals diagnosed with tumors. Human umbilical vein endothelial cells (HUVECs) tube formation and migration were significantly inhibited by conditioned media from HCC cells treated with siRNA of HDAC1. Several types of cancer have been found to exhibit elevated levels of phosphorylation at S421. Furthermore, as in bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), and kidney renal papillary cell carcinoma (KIRP), HDAC1 expression was found to be correlated with inflammatory cell infiltration. The levels of HDAC1 are expected to adapt to clinical adjuvant targeted therapy in most types of solid cancer.

Two-step consensus clustering approach to immune cell infiltration: An integrated exploration and validation of prognostic and immune implications in sarcomas

To conduct a comprehensive investigation of the sarcoma immune cell infiltration (ImmCI) patterns and tumoral microenvironment (TME). We utilized transcriptomic, clinical, and mutation data of sarcoma patients (training cohort) obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) server. Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithms were applied to decipher the immune cell infiltration landscape and TME profiles of sarcomas. An unsupervised clustering method was utilized for classifying ImmCI clusters (initial clustering) and ImmCI-based differentially expressed gene-driven clusters (secondary clustering). Mortality rates and immune checkpoint gene levels was analyzed among the identified clusters. We calculated the ImmCI score through principal component analysis. The tumor immune dysfunction evaluation (TIDE) score was also employed to quantify immunotherapy efficacy between two ImmCI score groups. We further validated the biomarkers for ImmCI and gene-driven clusters via experimental verification and the accuracy of the ImmCI score in predicting survival outcomes and immunotherapy efficacy by external validation cohorts (testing cohort). We demonstrated that ImmCI cluster A and gene-driven cluster A, were beneficial prognostic biomarkers and indicators of immune checkpoint blockade response in sarcomas via in-silico and laboratory experiments. Additionally, the ImmCI score exhibited independent prognostic significance and was predictive of immunotherapy response. Our research underscores the clinical significance of ImmCI scores in identifying sarcoma patients likely to respond to immunotherapy.

Compiling molecular evidence from a tetraploid rose genome into a near-saturated map for the identification of pigment-related genes

With their high economic value and cultural significance, modern roses are one of the most important ornamental plants. Because of their complicated genetic background and tetraploid nature, the creation of high-density genetic maps of roses has been a challenge that has slowed the pace of molecular breeding for modern roses. The current construction of tetraploid genetic maps based on existing diploid rose genomes could lead to inaccurate marker information and genotyping results. Therefore, we generated the first high-quality tetraploid genome of Rosa chinensis ‘Yunzheng Xiawei.’ Utilizing Illumina, PacBio, and Hi-C sequencing technologies, we assembled a genome of 858.59 Mb with 14 pseudo-chromosomes. Mode of inheritance analysis using PolyOrigin indicated that modern roses show both quadrivalent and bivalent pairing. Based on this reference genome, high-density genetic maps were constructed using MSTmap with nearly saturated markers. Quantitative trait locus (QTL) analysis was conducted using WinQTLCart and R/qtl for flavonoids and carotenoids, and 11 QTL clusters were identified. By combining the genome annotation, phylogenetic analyses, and gene expression analyses, we were able to identify several key genes related to flavonoid and carotenoid biosynthesis. This study provides the basis for further genetic analyses of highly heterozygous tetraploid roses and could facilitate the progress of marker-assisted selection in modern roses.

Examining the influence of tumor-infiltrating macrophages on breast cancer outcomes and identifying relevant genes for diagnostic purposes

ObjectiveThe purpose of this research was to investigate how different types of immune cells impact the outlook of individuals with breast cancer, as well as identify the essential genes associated with immune cell subtype enrichment.MethodsThe Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database were used to obtain global transcriptome sequencing data sets of breast tissue. The study utilized the CIBERSORT algorithm to determine the presence of 22 different types of immune cells in both breast cancer tissue and normal breast tissue.Immune cell infiltration content was utilized to conduct univariate COX analysis in order to identify risk factors linked to breast cancer prognosis.ResultsUnivariate COX analysis indicates that Macrophages M1 and B cells naive are beneficial factors for the outlook of individuals with breast cancer (P < 0.05), while Macrophages M2 and Monocytes are detrimental factors for the prognosis of breast cancer patients (P < 0.05). The high infiltration group of macrophage M2 had a poorer prognosis compared to the low infiltration group (P < 0.001); Conversely, the high infiltration group of macrophage M1 had a better prognosis than the low infiltration group (P = 0.002).ConclusionThe study provided an overview of immune cell infiltration in breast cancer tissues, identifying macrophage M1 and macrophage M2 as potential factors in breast cancer development and progression. Additionally, genes associated with macrophage phenotype were analyzed, offering insights into macrophage polarization mechanisms.

Identification of key ferroptosis genes and subtypes in kidney renal clear cell carcinoma

Tumour immunity is highly important for the occurrence and development of tumours, and many cancers are resistant to ferroptosis. This study aims to explore the relationship between ferroptosis-related genes (FRGs) and the immunological characteristics of kidney renal clear cell carcinoma (KIRC). We obtained RNA-seq profiles and clinical data of KIRC patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and identified CD44 and GLRX5 as the key FRGs involved in KIRC immune infiltration through Spearman’s correlation analysis. Based on the expression of CD44 and GLRX5, the consensus clustering algorithm was used to classify the TCGA-KIRC samples into two clusters. A nomogram was constructed to evaluate the prognosis of KIRC patients. ESTIMATE, CIBERSORT, and single-sample gene set enrichment analysis (ssGSEA) were performed to evaluate immune infiltration between the two clusters. A weighted gene co-expression network analysis (WGCNA) was used to identify the most relevant genes to the clusters and immunity. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. The external dataset GSE53757 was used to validate the immunological features between the two clusters. Cluster 2 patients had more active immune infiltration and might be more sensitive to immunotherapy; Cluster 2 patients also had a worse prognosis and might be at a more advanced stage of KIRC. We identified key ferroptosis-related genes and subgroups involved in the immune infiltration of KIRC, which is highly important for exploring the molecular mechanisms and treatments of KIRC.

Long non-coding RNA FAM87A is associated with overall survival and promotes cell migration and invasion in gastric cancer.

The role of long non-coding RNAs (lncRNAs) in the invasion and metastasis of gastric cancer remains largely unclear. Integrating transcriptome data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, differentially expressed genes were identified in gastric cancer. Using the Catalogue of Somatic Mutations in Cancer (COSMIC) database-curated gene set, lncRNAs associated with invasion and metastasis were identified. The Cox analyses were performed to identify prognostic lncRNAs. The competing endogenous RNA (ceRNA) regulation network was constructed to identify hub lncRNAs in gastric cancer. Functional and pathway analyses were used to investigate the function of identified lncRNAs. RT-qPCR and Transwell assays were used to investigate the expression in gastric cancer tissues and functions in gastric cancer cell lines. Based on GEO and TCGA databases, 111 differentially expressed lncRNAs were identified between gastric cancer and normal samples. A total of 43 lncRNAs were significantly correlated with hallmark genes of cancer invasion and metastasis. Among them, as a hub lncRNA in the invasion-related ceRNA regulation network, FAM87A showed potential regulation on MAPK signaling and transforming growth factor (TGF) signaling cascade, such as TGFB2, TGFBR1, and TGFBR2. Furthermore, FAM87A also showed a significant correlation with cell adhesion molecules, such as Integrin alpha 6 (ITGA6) and Contactin-1 (CNTN1). RT-qPCR experiments showed that FAM87A expression was upregulated in gastric cancer tissues compared to normal samples (n = 30). Transwell assays showed that FAM87A knockdown inhibited the migration and invasion abilities of gastric cancer cells in vitro. Notably, clinical data analysis showed that lncRNA FAM87A could be an independent factor for the overall survival of patients with gastric cancer. LncRNA FAM87A may play a pivotal role in regulating migration and invasion of gastric cancer cells. FAM87A could be a potential biomarker for the overall survival of patients with gastric cancer.

SUN2 mediates calcium-triggered nuclear actin polymerization to cluster active RNA polymerase II

The nucleoskeleton is essential for nuclear architecture as well as genome integrity and gene expression. In addition to lamins, titin or spectrins, dynamic actin filament polymerization has emerged as a potential intranuclear structural element but its functions are less well explored. Here we found that calcium elevations trigger rapid nuclear actin assembly requiring the nuclear membrane protein SUN2 independently of its function as a component of the LINC complex. Instead, SUN2 colocalized and associated with the formin and actin nucleator INF2 in the nuclear envelope in a calcium-regulated manner. Moreover, SUN2 is required for active RNA polymerase II (RNA Pol II) clustering in response to calcium elevations. Thus, our data uncover a SUN2-formin module linking the nuclear envelope to intranuclear actin assembly to promote signal-dependent spatial reorganization of active RNA Pol II.

Oxidative stress gene signature construction to identify subtypes and prognosis of patients with lung adenocarcinoma

BackgroundAlthough oxidative stress and malignancies are intimately connected, it is unknown how lung adenocarcinoma (LUAD) is affected by oxidative stress response-related genes (OSRGs).Our goal in this work was to create a genetic signature based on OSRGs that might both predict prognosis and hint to potential treatment options for LUAD. MethodsClinicopathological and transcriptome information on LUAD patients was obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A model for predicting risk was created using LASSO regression. The TCGA, GSE72094, and GSE41271 cohorts all demonstrated the risk model's prediction ability. Immune cell infiltration was measured using the CIBERSORT method, and the TIDE platform was implemented to evaluate the therapeutic efficacy of immune checkpoint inhibition (ICI). Chemotherapy sensitivity was predicted using drug activity data by the Genomics of Drug Sensitivity. An investigation into gene expression was conducted using qRT-PCR. CCK-8 and transwell assays were employed to look into how DKK1 affected the migration and proliferation of LUAD cells. ResultsA gene signature consisting of ANLN, FAM83A, DKK1, LOXL2, RHOV, IGFBP1, CCR2, GNG7, and C11orf16 was efficiently determined and used to calculate a patient-specific risk score, this functioned as a stand-alone biomarker for prediction. Correlations were found between risk scores and immune cell infiltration frequency, ICI therapy response rate, estimated chemotherapeutic drug susceptibility and autophagy-related genes.Furthermore, DKK1 knockdown reduced the ability of LUAD cells to multiply and migrate. ConclusionOur thorough transcriptome study of OSRGs generated a biological framework effective in forecasting outcome and responsiveness to therapy in LUAD patients.

Survival prediction and analysis of drug-resistance genes in HER2-positive breast cancer

Despite the approval of several therapeutic agents for HER2-positive breast cancer, drug resistance remains a significant challenge, hindering the patient's prognosis. Thus, our study aimed to establish a risk model to predict the prognosis of patients and identify key genes regulating drug resistance in HER2-positive breast cancer. Utilizing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), a predictive model was constructed based on 5 drug resistance-related genes, which demonstrated a notable capacity to indicate the survival rates of patients. Besides, through eccDNA and transcriptome sequencing of drug-sensitive and resistant cancer cells, 3 significant DEGs were identified: MED1, MED24, and NMD3. Among them, MED1 showed the most significant elevation in drug-resistance cells, highlighting its crucial role in mediating drug resistance. MED1 may serve as a valuable target for alleviating drug resistance in HER2-positive breast cancer.

Identification and Validation of an Invasion-Related Disease-Free Survival Prognostic Model for Tongue Squamous Cell Carcinoma

Introduction: Tongue squamous cell carcinoma (TSCC) is a common malignant tumour type with aggressive invasion and a poor prognosis. To date, invasion-related gene expression signatures for the prognostic stratification of TSCC patients are unavailable in clinical practice. This study aimed to assess the impact of invasion-related genes on the prognosis of TSCC patients. Methods: We obtained mRNA profiles and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (TCGA-TSCC and GSE41116, respectively). The TSCC samples from the TCGA-TSCC cohort were randomly divided into TCGA training and TCGA test datasets at a 7:3 ratio. Next, a disease-free survival (DFS) prognostic risk model was established on the basis of univariate and stepwise multivariate Cox regression analyses of the TCGA training cohort. Moreover, prognostic genes were screened. The model was subsequently evaluated and validated using the TCGA test and GSE41116 datasets. In addition, the prognostic genes were validated in the human TSCC cell line UM1 and the human oral keratinocyte (HOK) cell line using quantitative real-time polymerase chain reaction (qRT-PCR) analysis. Results: A total of 70 candidate genes related to invasion were identified in the TCGA-TSCC cohort. DFS data were subsequently constructed, and 6 prognostic genes, HMGN2, MYL12B, ACTB, PPP1CA, PSMB9, and IFITM3, were identified. The TSCC samples were divided into high- and low-risk groups in the TCGA training, TCGA test, and GSE41116 cohorts, respectively. In particular, patients with TSCC in the low-risk group had longer DFS than those in the high-risk group. Furthermore, qRT-PCR analysis confirmed that the expression levels of the 6 prognostic genes were significantly greater in the TSCC cell line UM1 than in the HOK cell line. Conclusion: This study identified new invasion-related target genes related to poor prognosis in TSCC patients, providing new insights into the underlying mechanisms of TSCC invasion.

Transcriptome responses to benzo[a]pyrene in liver slices of sub-arctic fish species

Due to the expanding oil-related activities, the arctic and sub-arctic marine environments are increasingly vulnerable to oil-related pollution such as accidental oil spills. These cold-water ecosystems harbor many fish species that are both ecologically and economically important such as the pelagic polar cod (Boreogadus saida), capelin (Mallotus villosus), and benthic long rough dab (Hippoglossoides platessoides). The latter two are much less studied and it is crucial to characterize their responses to oil-related contaminants and develop molecular biomarkers and genomic resources for future monitoring. In this study, liver slice preparation and culture methods were used to characterize the transcriptome responses (using RNA-seq) in capelin and long rough dab to exposures of the polycyclic aromatic hydrocarbon (PAH) compound benzo[a]pyrene (BaP). The liver slice culture and exposure experiments were performed onboard a research vessel in the Barents Sea. Strong up-regulation of genes involved in biotransformation, particularly the aryl hydrocarbon receptor signaling pathway was observed in both species. A comparison of the number of differentially expressed genes (DEGs) with previously published polar cod exposures indicates that the latter responded more strongly (higher number of genes), suggesting higher uptake and bioconcentration of BaP in the fatty liver tissue, although other factors such as differences in clearance rate could potentially affect the responses. This study provides new genomic resources and gene expression biomarkers in capelin and long rough dab, enhancing our understanding of their response mechanism to oil-related contaminants.

Anoikis-related genes in breast cancer patients: reliable biomarker of prognosis

BackgroundBreast cancer (BC) is the most common cancer in women, and its progression is closely related to the phenomenon of anoikis. Anoikis, the specific programmed death resulting from a lack of contact between cells and the extracellular matrix, has recently been recognized as playing a critical role in tumor initiation, maintenance, and treatment. The ability of cancer cells to resist anoikis leads to cancer progression and metastatic colonization. However, the impact of anoikis on the prognosis of BC patients remains unclear.MethodThis study utilized data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to collect transcriptome and clinical data of BC patients. Anoikis-related genes (ARGs) were classified into subtypes A and B through consensus clustering. Subsequently, survival prognosis analysis, immune cell infiltration analysis, and functional enrichment analysis were performed for both subtypes. Using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a set of 10 ARGs related to prognosis was identified. Immune cell infiltration and tumor microenvironment analyses were conducted on these 10 ARGs to develop a prognostic model. Furthermore, single-cell data analysis and real-time polymerase chain reaction (RT-PCR) analysis were employed to study the expression of the 10 identified prognostic ARGs in BC cells.ResultsOne hundred thirty-five ARGs were identified as differentially expressed genes in the TCGA and GEO databases, with 42 of them associated with the survival prognosis of BC patients. Analyses involving Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) revealed distinct expression patterns of ARGs between types A and B. Patients in type A exhibited worse survival prognosis and lower immune cell infiltration compared to type B. Subsequent analyses identified 10 key ARGs (YAP1, PIK3R1, BAK1, PHLDA2, EDA2R, LAMB3, CD24, SLC2A1, CDC25C, and SLC39A6) relevant to BC prognosis. Kaplan–Meier analysis indicated that high-risk patients based on these ARGs had a poorer BC prognosis. Additionally, Cox regression analysis established gender, age, T (tumor), N (nodes), and risk score as predictive factors in a nomogram model for BC. The model demonstrated diagnostic value for BC patients at 1, 3, and 5 years. Decision curve analysis (DCA) verified the risk score as a reliable predictor of BC patient survival rates. Moreover, RT-PCR results confirmed differential expressions of YAP1, PIK3R1, BAK1, PHLDA2, CD24, SLC2A1, and CDC25C in BC cells, with SLC39A6, EDA2R, and LAMB3 showing low expression levels.ConclusionARGs markers can be used as BC biomarkers for risk stratification and survival prediction in BC patients. Besides, ARGs can be used as stratification factors for individualized and precise treatment of BC patients.

Elucidating the molecular markers and biological pathways associated with extrahepatic cholangiocarcinoma: a transcriptome sequencing study.

Cholangiocarcinoma is a malignancy with high aggressiveness, and extrahepatic cholangiocarcinoma (ECCA) represents the predominant subtype. However, the molecular architecture and underlying pathogenic mechanisms of ECCA remain poorly understood. The objective of this study is to elucidate the molecular markers and biological pathways associated with ECCA. In order to identify the factors influencing ECCA, we conducted transcriptome sequencing on a cohort of 8 surgically resected ECCA specimens. To validate our findings, we integrated data from The Cancer Genome Atlas and Gene Expression Omnibus (GEO) databases using batch integration analysis. Finally, we confirmed our results using clinical samples. The findings of this study reveal that through the analysis of sequencing data, we have successfully identified the genes that are differentially expressed and have a significant role in the development of ECCA. Utilizing the Weighted Gene Co-expression Network Analysis approach, we have integrated these identified gene modules with the GEO dataset, leading to the identification of four key genes (PTGDS, ITIH2, LSAMP, HBB) that are strongly associated with the progression-free survival of ECCA. We screened a key gene LSAMP from four genes using immunohistochemistry. The gene primarily participate in crucial biological processes such as the ECCA cell cycle and DNA replication. The qRT-PCR reaction and Western Blot conducted on the tissues provided confirmation of the expression levels of the gene, which exhibited consistency with the outcomes of our analysis. Our study has successfully identified potential biomarkers LSAMP for ECCA, which can serve as valuable tools for early detection and targeted therapeutic interventions in clinical settings.

Characteristics of Oxidative Phosphorylation-Related Subtypes and Construction of a Prognostic Signature in Ovarian Cancer.

Ovarian cancer is associated with a high mortality rate. Oxidative Phosphorylation (OXPHOS) is an active metabolic pathway in cancer; nevertheless, its role in ovarian cancer continues to be ambiguous. Therefore, the objective of this study was to identify the prognostic value of OXPHOS-related genes and the immune landscape in ovarian cancer. We obtained public ovarian cancer-related datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and recognized OXPHOS-related genes from the GeneCards database and literature. Cox regression analyses were conducted to identify prognostic OXPHOS-related genes and develop a prognostic nomogram based on the OXPHOS score and clinicopathological features of patients. Functional enrichment analyses were employed to identify related processes. A 12-gene signature was identified to classify the ovarian cancer patients into high- and low-risk groups. The Immunophenoscore (IPS) was higher in the OXPHOS score-high group than in the OXPHOS score-low group, suggesting a better response to immune checkpoint inhibitors. Functional enrichment analyses unveiled that OXPHOS-related genes were considerably abundant in a series of immune processes. The calibration curves of the constructed prognostic nomograms at 1, 2, and 3 years exhibited strong concordance between the anticipated and observed survival probabilities of ovarian cancer patients. We have constructed a prognostic model containing 12 OXPHOS-related genes and demonstrated its strong predictive value in ovarian cancer patients. OXPHOS has been found to be closely linked to immune infiltration and the reaction to immunotherapy, which may contribute to improving individualized treatment and prognostic evaluation in ovarian cancer.

A Circadian Rhythm-related Signature to Predict Prognosis, Immunei Infiltration, and Drug Response in Breast Cancer.

Circadian rhythm genes (CRRGs) play essential roles in cancer occurrence and development. However, the prognostic significance of CRRGs in breast cancer (BC) has not been fully elucidated. Our study aimed to develop a prognostic gene signature based on CRRGs that can accurately and stably predict the prognosis of BC. The transcriptome data and clinical information for BC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A consensus unsupervised clustering analysis was carried out to investigate the roles of CRRGs in BC. A CRRGs-related prognostic risk model was established by using logistic least absolute shrinkage and selection operator (LASSO) Cox regression and univariate Cox regression analyses. Kaplan-Meier (KM) curves analysis, time-dependent receptor operation characteristics (ROC) curves analysis, and nomogram were plotted to evaluate the predictive efficacy of the model. The relevance of risk score to the immune cell infiltration, tumor burden mutation (TMB), and therapeutic response was assessed. A risk model comprising six CRRGs (SLC44A4, SLC16A6, TPRG1, FABP7, GLYATL2, and FDCSP) was constructed and validated, demonstrating a good predictor of BC. The low-risk group displayed a higher number of immune activities and immune checkpoint expression and a lower burden of tumor mutation. Additionally, drug sensitivity analysis demonstrated the prognostic signature may serve as a potential chemosensitivity predictor. We established 6 CRRGs-related risk signatures for the prognosis of BC, which is of great value in predicting the prognosis of patients with BC and guiding the treatment for BC.