Cancer Gene Expression Research Articles

PPIH is a novel diagnostic biomarker associated with immune infiltration in cholangiocarcinoma

BackgroundCholangiocarcinoma (CHOL) is the second most common primary liver malignancy, characterized by high aggressiveness and heterogeneity. It is typically diagnosed at an advanced stage, leading to a poor prognosis. Although Peptidyl Proline Isomerase H (PPIH) has been implicated in various tumors, its role in CHOL remains unexplored. This study aims to investigate the diagnostic value and potential function of PPIH in CHOL.MethodsWe analyzed the expression levels, prognostic significance, and diagnostic efficiency of PPIH in CHOL using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, coupled with gene enrichment analyses. The CIBERSORT database was employed to assess the correlation between PPIH expression and immune cell infiltration in CHOL. Additionally, immunohistochemical experiments were conducted to validate PPIH expression levels in CHOL tissues and to explore its correlation with TP53 gene mutations.ResultsOur findings indicate that overexpression of PPIH mRNA in CHOL is associated with poor prognosis, with increased PPIH protein levels observed in CHOL tissues. Furthermore, PPIH expression showed a positive correlation with TP53 mutations. PPIH demonstrated strong diagnostic value for CHOL. Moreover, PPIH may influence tumor progression through its involvement in cell cycle regulation and spliceosome pathways, and is associated with immune cell infiltration levels.ConclusionThe results of this study suggest that PPIH is a potential novel biomarker with significant diagnostic value for patients with CHOL. PPIH may also play a role in modulating the immune microenvironment, contributing to poor prognosis.

Comprehensive landscape and oncogenic role of extrachromosomal circular DNA in malignant biliary strictures

BackgroundExtrachromosomal circular DNA (eccDNA) is crucial for carcinogenesis and bile has direct contact with malignant biliary strictures, yet eccDNA features in bile and its function in malignant biliary strictures remain underexplored.ResultsWe observed the widespread presence of eccDNA in bile and systematically profiled the landscape of bile cell-free eccDNA (bcf-eccDNA). For functional exploration, a simple and efficient workflow was designed to synthesize large eccDNA particularly containing multiple regions. Compared with the noncancer group, bcf-eccDNAs in the cancer group had different origins and larger sizes with six characteristic peaks. These peaks were also identified in the validation cohort (100%). There were more bcf-eccDNA carrying LINC00598 or CELF2 in malignant biliary strictures, showing potential diagnostic performance in training and validation cohorts (all AUCs > 0.9). Bcf-eccDNAs carried cancer-related mutations, which could guide treatment. EccDNA carrying miR-106a/363 cluster or miR-374b/421 cluster were proven to regulate cancer gene expression, accelerate tumor proliferation, and inhibit tumor apoptosis.ConclusionsThis study profiles a comprehensive bcf-eccDNA landscape in patients with biliary strictures and offers valuable insights into eccDNA's role in bile liquid biopsy and carcinogenesis.

GULP1 as a Novel Diagnostic and Predictive Biomarker in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is characterized by high recurrence and mortality, necessitating the identification of reliable biomarkers. In this study, we aimed to identify the predictive gene signatures for HCC recurrence and evaluate the efficiency of GULP PTB domain-containing engulfment adaptor 1 (GULP1) as a predictive and diagnostic marker and therapeutic target for HCC. We analyzed genomic datasets from The Cancer Genome Atlas and Gene Expression Omnibus databases via least absolute shrinkage and selection operator Cox regression and 10-fold cross-validation, leading to the development of a 15-gene risk score model, which was validated using three independent datasets. Serum GULP1 and α-fetoprotein levels were assessed to determine the diagnostic accuracy of the model. Using clinical cohorts and patient sera, GULP1 roles were examined, and functional assays in vitro and in vivo were used to evaluate its effects on cell growth, epithelial-mesenchymal transition (EMT), ADP-ribosylation factor 6 activation, and β-catenin signaling. Our newly developed risk-score model accurately predicted recurrent HCC in all datasets. Among the 15 genes in the risk score model, GULP1 was overexpressed in patients with HCC and independently predicted HCC recurrence. Its expression modulation influenced cell growth and EMT, with observed effects on ADP-ribosylation factor 6 activation and β-catenin signaling pathways. GULP1 is a crucial biomarker for HCC, serving as a non-invasive diagnostic and predictive tool. It also plays key roles in HCC progression. Our findings highlight the potential use of GULP1 in treatment strategies targeting EMT and HCC recurrence to improve the personalized care and patient outcomes.

An anoikis-related signature predicts prognosis and immunotherapy response in gastrointestinal cancers

BackgroundGastrointestinal (GI) cancers have high incidence rates and mortality rates. Anoikis is a special type of cell apoptosis, and anoikis resistance has been reported to be associated with tumor malignancy. We aimed to explore the roles of anoikis-related genes (ARGs) in the GI cancer prognosis.MethodsWe extracted RNA sequencing and clinical data from The Cancer Genome Atlas and Gene Expression Omnibu databases for patients with esophageal cancer, gastric cancer, colon cancer and rectal cancer and identified ARGs from GeneCards and Harmonizome. Anoikis-related patterns were identified via unsupervised clustering analysis. We constructed a prognostic signature (Anoscore) based on prognostic ARGs through univariate, LASSO, and multivariate Cox regression analyses. The model was validated and evaluated using Kaplan–Meier analysis, receiver operating characteristic curves, univariate Cox regression analysis, multivariate Cox regression analysis, column charts, and calibration curves. We also performed a single-cell sequencing analysis of candidate genes via TISCH2. A correlation analysis between the Anoscore, the tumor microenvironment and drug sensitivity was conducted in GI cancers. The expression and function of some candidate genes were validated in vitro.ResultsIn terms of prognostic ARGs, two anoikis-related patterns, ARG clusters A and B, were identified. ARG cluster B had a worse prognosis than did ARG cluster A. Subsequently, the Anoscore was developed as an independent prognostic factor. It demonstrated the robust predictive capability for the prognosis of patients with GI cancers. Notably, patients with high Anoscores exhibited poor outcomes. In addition, we established a nomogram (Ano-nomogram) based on the Anoscore and clinicopathological factors of patients to predict the 3-year and 5-year survival probabilities. Moreover, patients with high Anoscores had higher levels of immune cell infiltration and higher immune checkpoint expression. The drug sensitivity analysis revealed that patients with high or low Anoscores were sensitive to different chemotherapies and targeted drugs. S100A11 and TLR3, representative candidate genes, exhibited different expression patterns and biological functions.ConclusionThis study highlighted the significant potential of the Anoscore in predicting prognosis and guiding the selection of personalized therapeutic regimens for patients with GI cancers.

NOS2 as a prognostic biomarker for early-onset colorectal cancer based on public data and clinical validation analysis

Early-onset colorectal cancer (EOCRC) was characterized by strong aggressiveness and high malignancy. The aim of this study was to screen suitable biomarkers for patients with EOCRC. EOCRC from The Cancer Genome Atlas Program (TCGA) database and Gene Expression Mapping (GEO) database were used to screen biomarkers for prognosis and treatment guidance. Clinical samples were used to verify the expression situation of these candidate biomarkers. The results showed the immune-related gene nitric oxide synthase 2 (NOS2) was independently associated with the poor prognosis of EOCRC patients in both TGCA and GEO database. The Immune Dysfunction and Exclusion (TIDE) analysis showed that multiple immunotherapy signatures, such as TIDE, Exclusion, and CAF, were difference among EOCRC patients with different risk scores, and significantly correlated with the expression of NOS2. Sensitivity analysis of chemotherapy drugs showed that NOS2 was significantly correlated with several chemotherapy drugs, such as MG.132_1862, BMS.754807_2171, and GEN.317_1926. Clinical validation analysis showed that the expression of NOS2 and its related genes CXCL1 and CXCL2 were significantly decreased in EOCRC patients. The results suggested that NOS2 can be used as a potential biomarker for EOCRC, which can be used for prognosis and guidance of immunotherapy and chemotherapy.

Machine learning-random forest model was used to construct gene signature associated with cuproptosis to predict the prognosis of gastric cancer

Gastric cancer (GC) is one of the most common tumors; one of the reasons for its poor prognosis is that GC cells can resist normal cell death process and therefore develop distant metastasis. Cuproptosis is a novel type of cell death and a limited number of studies have been conducted on the relationship between cuproptosis-related genes (CRGs) in GC. The purpose of the present study was to establish a prognostic model of CRGs and provide directions for the diagnosis and treatment of GC. Transcriptome and clinical data of patients with GC were collected from The Cancer Genome Atlas and Gene Expression Omnibus datasets. Single sample gene set enrichment analysis (GSEA) and the randomized forest method were used to establish the prognostic model. Kaplan-Meier survival curve, receiver operating characteristics diagram and a nomogram were used to evaluate the reliability of the model. GSEA and gene set variation analysis (GSVA) were used to examine enrichment pathways between high and low risk groups. Finally, immunohistochemical analysis was used to examine ephrin 4 (EFNA4) expression in GC samples and determine the prognosis of patients with GC based on the expression pattern of EFNA4. A group of 7 predictive models (RTKN2, INO80B, EFNA4, ELF2, MUSTN, KRTAP4, and ARHGEF40) was established which were correlated with CRGs. This model can be used as an independent prognostic factor to predict the prognosis of patients with GC. GSEA and GSVA results indicated that high risk patients with GC were mainly associated with the enrichment of ANGIOGENESIS and TGF_BETA_SIGNALING pathways. Finally, EFNA4 expression in GC was significantly higher than that in normal tissues, and patients with GC and high EFNA4 expression exhibited improved prognosis. In conclusion, the prognosis model based on CRGs could be used as the basis for predicting the potential prognosis of patients with GC and provide new insights for the treatment of GC.

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.

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.

VEGFA Gene Expression in Breast Cancer Is Associated With Worse Prognosis, but Better Response to Chemotherapy and Immunotherapy.

Vascular endothelial growth factor-A (VEGFA) is a key inducer of angiogenesis, responsible for generating new blood vessels in the tumor microenvironment (TME) and facilitating metastasis. Notably, Avastin, which targets VEGFA, failed to demonstrate any significant benefit in clinical trials for breast cancer (BC). This study aimed to investigate the clinical relevance of VEGFA gene expression in BC. A total of 7,336 BC patients across eight independent cohorts: ISPY2 (GSE173839), Sweden Cancerome Analysis Network-Breast (SCAN-B) (GSE96058), Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), GSE25066, GSE163882, GSE34138, GSE20194, and The Cancer Genome Atlas (TCGA), were analyzed. The calculated median VEGFA expression level was used to stratify these cohorts into high and low groups. High VEGFA was associated with worse disease-free, disease-specific, and overall survival in the METABRIC cohort, with findings supported by the SCAN-B cohort, which also showed worse overall survival (all P < 0.02). High VEGFA expression was seen in triple-negative breast cancer (TNBC) but not in BC with lymph node metastasis. Additionally, there was a significant correlation between high VEGFA expression and higher silent and non-silent mutations, single-nucleotide variant (SNV) neoantigens, homologous recombination defect, intratumoral heterogeneity, in the TCGA cohort. In the TCGA, METABRIC, and SCAN-B cohorts, high VEGFA BC was also associated with higher cell proliferation: higher Ki67 gene expression, higher Nottingham histological grade, and consistent enrichment of all the Hallmark cell proliferation-related gene sets. Unexpectedly, the angiogenesis gene set was not enriched in any of the cohorts and showed no association with infiltrations of lymphatic or blood vascular endothelial cells besides pericytes. High VEGFA BC had significantly less infiltration of anti-cancer immune cells but higher infiltration of pro-cancer immune cells in TCGA, METABRIC, and SCAN-B cohorts. Interestingly, BC, which had a pathological complete response (pCR) after anthracycline- and taxane-based neoadjuvant therapy, was associated with significantly heightened VEGFA expression in both estrogen receptor (ER)+/human epidermal growth factor receptor 2 (HER2)- and TNBC subtypes in the GSE25066 cohort and after immunotherapy in ER+/ HER2- subtype, but not TNBC in the ISPY2 cohort. Our research indicates that high VEGFA BC confers high cell proliferation, reduced immune cell infiltration, and poorer survival, but allows better response to anthracycline- and taxane-based chemotherapy, and immunotherapy.

Signature of collagen alpha-1(x) gene expression in human cancers and their therapeutic implications.

Cancers are a class of disorders that entail uncontrollably unwanted cell development with dissemination. One in six fatalities globally is attributed to cancer, a global health issue. The analysis of the entire DNA sequence and how it expresses itself in tumor cells is known as cancer genomics. The development of novel cancer treatments has been facilitated because of the genomics method. COL10A1 gene, a short chain collagen, and an interstitial matrix component, acts as a predictive biomarker for cancer prognosis. Recognizing the fundamental consequences of mutations in the COL10A1 gene and its expression in cancer is crucial. Analyzing the COL10A1 gene expression with a data set and gene expression patterns shows the level of display of the tumor. Examining the therapeutic techniques of COL10A1 gene expression leads to early detection, screening, radiation therapy, and advanced developments. This review highlights the value of the COL10A1 gene in breast, gastric, pancreatic, lung, and colorectal cancers, emphasizing its role in gene expression patterns and therapeutic techniques.

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.

Serial filter-wrapper feature selection method with elite guided mutation strategy on cancer gene expression data

sNowadays, many researchers utilize cancer gene expression data to solve the problem of cancer subtype diagnosis, but cancer gene expression data are often high-dimensional, multi-sample, and multi-classified, so a hybrid serial filter-wrapper feature selection (FS) method based on elite guided mutation strategy for cancer gene expression data is proposed. It is divided into a preliminary screening phase and a combined modeling phase. In the preliminary screening stage, the threshold values of seven filter methods are determined by the leave-one cross-validation method, and the features selected by these seven filter methods are combined to form two subsets by using the thoughts of ‘‘And’’ and ‘‘Or’’ in the logical operation. The union subset of two subsets is used in the equilibrium optimizer (EO) in the subsequent combination model stage as the reserved subset in the preliminary screening stage. The resulting hybrid framework is connected by a parallel filter method designed in the first stage with an improved EO in the second stage, which is named as SFEMEO. In order to prove the effectiveness and generalization of the proposed SFEMEO, it is compared with other 9 basic algorithms on 10 UCI data sets. It is found that the classification accuracy of the SFEMEO is improved by 0.56% ~ 20.19%, and the optimal fitness is also greatly improved. After comparing SFEMEO with other nine intelligent optimization algorithms on ten cancer gene expression data sets, it can be found that compared with most algorithms, the accuracy rate is improved by 3.73% ~ 18.13%, and the optimal fitness is relatively superior. At the same time, Wilcoxon rank sum test was used to evaluate the results of intelligent optimization algorithms such as SFEMEO, which proved the effectiveness of the proposed hybrid framework and its superiority in solving the FS problem of high-dimensional cancer gene expression data.

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.

Potential biomarkers for predicting the risk of thyroid cancer in immunosenescence: a population-based and externally validated multi omics study.

Genome-wide association studies (GWAS) have pinpointed several risk loci linked to thyroid cancer; however, the discovery of new plasma proteins implicated in immunosenescence continues to pose significant challenges. This study aims to uncover novel plasma proteins tied to aging, potentially contributing to thyroid cancer, utilizing diverse investigative methodologies. In this research, we utilized an integrative omics approach to identify novel plasma proteins associated with immunosenescence in relation to the risk of thyroid cancer. Additionally, we performed meta-analyses to pinpoint loci and genes affected by pleiotropic effects. Finally, complementary results were obtained from an independent cohort analyzed at Chongqing Medical University Yongchuan Hospital and Bulk-RNA seq from GEO database. Causal analysis suggests that DNA methylation age acceleration as measured by the Hannum method increases the risk of thyroid cancer (OR: 1.126, 95% CI: 1.002-1.265, P=0.046). Subsequently, we conducted a meta-analysis on the relationship between Hannum DNA methylation age and thyroid cancer risk, which identified 138 potential risk loci through FUMA. Additionally, proteomics and transcriptomics collectively identified 6 potential targets related to immunosenescence and thyroid cancer. Subsequently, Bulk-seq results indicated differential expression of GFRA2 and LILRA2 genes in thyroid cancer. Finally, analyses from an independent cohort at the Second Affiliated Hospital of Chongqing Medical University also demonstrated high expression of LILRA2 in thyroid cancer patients. This study identified novel plasma proteins associated with immunosenescence that may be linked to thyroid cancer development. These findings enhance our understanding of the immunosenescence-thyroid cancer link and support future diagnostic and therapeutic developments.

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.

Hub Detection in Gaussian Graphical Models

Graphical models are popular tools for exploring relationships among a set of variables. The Gaussian graphical model (GGM) is an important class of graphical models, where the conditional dependence among variables is represented by nodes and edges in a graph. In many real applications, we are interested in detecting hubs in graphical models, which refer to nodes with a significant higher degree of connectivity compared to non-hub nodes. A typical strategy for hub detection consists of estimating the graphical model, and then using the estimated graph to identify hubs. Despite its simplicity, the success of this strategy relies on the accuracy of the estimated graph. In this paper, we directly target on the estimation of hubs, without the need of estimating the graph. We establish a novel connection between the presence of hubs in a graphical model, and the spectral decomposition of the underlying covariance matrix. Based on this connection, we propose the method of inverse principal components for hub detection (IPC-HD). Both consistency and convergence rates are established for IPC-HD. Our simulation study demonstrates the superior performance and fast computation of the proposed method compared to existing methods in the literature in terms of hub detection. Our application to a prostate cancer gene expression dataset detects several hub genes with close connections to tumor development.