Expression Levels Of Signature Genes Research Articles

Crosstalk of cuproptosis-related subtypes, establishment of a prognostic signature, and immune infiltration characteristics in gastric cancer

BackgroundCuproptosis is a novel form of cellular demise that occurs through a unique pathway involving lipoylated proteins in the tricarboxylic acid (TCA) cycle and is closely linked to mitochondrial metabolism. Nevertheless, the comprehensive elucidation of the impact of carcinogenesis-associated genes (CRGs) on prognosis, tumor microenvironment (TME), and therapeutic response in patients with gastric cancer (GC) remains unclear. MethodsIn total, 1374 GC samples were gathered from three Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas database. The samples were then stratified into different subtypes through unsupervised clustering of the 13 CRG profiles. The CRG_score was developed to quantify CRG patterns of individual tumors. Subsequently, we investigated the associations among the various groups and clinicopathological features, immune infiltration features, TME mutation status, and response to immunotherapy. ResultsThe GC samples were divided into two clusters based on their distinct clinicopathological features, prognosis, and immune characteristics. Using LASSO and Cox regression analyses, 9 genes were identified for constructing a prognostic signature related to cuproptosis. The novel signature displayed outstanding durability and prognostic capability for the overall lifespan of individuals. Additionally, the expression levels of signature genes in GC tissues and adjacent normal tissues were tested by qRT-PCR. Moreover, we developed a remarkably dependable nomogram to enhance the practicality of the CRG_score in clinical settings. High tumor mutation burden, increased microsatellite instability-high, immune activation, along with good survival probability and increased immunoreactivity to immune checkpoint inhibitors, were distinguishing features of low CRG_scores. ConclusionsThe findings of this study revealed the possible impacts of CRGs on the TME, clinical and pathological characteristics, and outlook of patients with GC. This signature was strongly linked to the immune response against GC and has the potential to serve as a valuable tool for predicting patient prognosis.

Chemokine- and chemokine receptor-based signature predicts immunotherapy response in female colorectal adenocarcinoma patients

The clinical significance and comprehensive characteristics of chemokines and chemokine receptors in female patients with advanced colorectal adenocarcinoma have not ever been reported. Our study explored the expression profiles of chemokines and chemokine receptors and constructed a chemokine- and chemokine receptor-based signature in female patients with advanced colorectal adenocarcinoma. Four independent cohorts containing 1335 patients were enrolled in our study. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses were performed to construct the signature. CIBERSORT was used to evaluate the landscape of immune cell infiltration. Thirty-two pairs of tissue specimens of female advanced colorectal cancer (CRC) patients and two CRC cell lines were used to validate the signature in vitro. Quantitative real-time PCR and western blotting were performed to validate the mRNA and protein expression levels of signature genes. EdU and colony formation assays were performed to examine proliferative ability. Transwell and wound healing assays were used to evaluate cell invasion and migration capacity. During the signature construction and validation process, we found that the signature was more applicable to female patients with advanced colorectal adenocarcinoma. Hence, the subsequent study mainly focused on the particular subgroup. Enrichment analyses revealed that the signature was closely related to immunity. The landscape of immune cell infiltration presented that the signature was significantly associated with T cells CD8 and neutrophils. Gene set enrichment analysis (GSEA) confirmed that the high-risk group was chiefly enriched in the tumor-promoting related pathways and biological processes, whereas the low-risk group was mainly enriched in anti-tumor immune response pathways and biological processes. The signature was closely correlated with CTLA4, PDL1, PDL2, TMB, MSI, and TIDE, indicating that our signature could serve as a robust biomarker for immunotherapy and chemotherapy response. ROC curves verified that our signature had more robust prognostic power than all immune checkpoints and immunotherapy-related biomarkers. Finally, we used 32 pairs of tissue specimens and 2 CRC cell lines to validate our signature in vitro. We first provided a robust prognostic chemokine- and chemokine receptor-based signature, which could serve as a novel biomarker for immunotherapy and chemotherapy response to guide individualized treatment for female patients with advanced colorectal adenocarcinoma.

Development and validation of a circulating tumor cells-related signature focusing on biochemical recurrence and immunotherapy response in prostate cancer

BackgroundStudies have shown that the circulating tumor cells (CTCs) play a key role for invasion and formation of distant metastases in prostate cancer (PCa). However, few CTCs-related genes (CRGs) have been developed for biochemical recurrence (BCR) prediction and clinical applications of PCa patients. Materials and methodsBioinformatics analysis with public PCa datasets were used to investigate the relationship between the differentially expressed CRGs and BCR. Lasso-COX regression analysis was used to constructed and validated a CRGs-based BCR prediction signature for PCa. Single-cell data were used to validate the expression levels of signature genes in different cell types and then explored the cell-cell communication relationships. Finally, the expression levels of signature genes were verified and the CRGs involved in immunotherapy response were further identified. ResultsThirteen CRGs were differentially expressed and closely associated with BCR in PCa. Then we constructed and validated a BCR prediction signature for PCa patients based on 3 differentially expressed CRGs (EMID1, SPP1 and UBE2C), and the signature was an independent factor to predict BCR for PCa. Single-cell data showed the specific expression patterns of the signature genes, while the SPP1 pathway plays an important role in cell-cell communication. Further analyses suggested UBE2C was highly expressed in BCR group and high expression of UBE2C had a better response for patients who received immunotherapy. Moreover, the expression levels of UBE2C in CTCs were higher than other cells and tissues, indicated that UBE2C may affect the BCR event of PCa patients through CTCs. ConclusionOur findings demonstrated that CRGs were significantly associated with BCR and immunotherapy efficacy in PCa and CRGs may influence the BCR event through CTCs.

Comprehensive Analysis on Prognostic Signature Based on T Cell-Mediated Tumor Killing Related Genes in Gastric Cancer.

Although immunotherapy is a valuable treatment for gastric cancer (GC), identifying the patients who would benefit most from this approach presents a challenge. In this study, GC patients were divided into two subtypes by consensus clustering according to T cell-mediated tumor killing related genes (TTKRGs), and there were significant differences in tumor-infiltrating immune cells, signaling pathways, and gene expression of immunomodulators and inhibitory immune checkpoints between the two subtypes. Then, we developed an individualized signature based on TTKRGs, and its clinical and predictive value in GC patients for chemotherapeutic and immunotherapeutic responses was assessed. We confirmed the expression levels of signature genes in GC tumor tissue using quantitative real-time polymerase chain reaction (qRT-PCR). Additionally, to improve the accuracy of GC prognosis predictions, we established a nomogram. We further identified some compounds as sensitive drugs targeting GC risk groups. The signature showed significant predictive ability across RNA-seq, microarray, and qRT-PCR cohorts, which could assist in predicting survival, immunotherapeutic and chemotherapeutic outcomes in GC patients.

Exploration of prognostic genes and risk signature in breast cancer patients based on RNA binding proteins associated with ferroptosis.

Background: Breast cancer (BRCA) is a life-threatening malignancy in women with an unsatisfactory prognosis. The purpose of this study was to explore the prognostic biomarkers and a risk signature based on ferroptosis-related RNA-binding proteins (FR-RBPs). Methods: FR-RBPs were identified using Spearman correlation analysis. Differentially expressed genes (DEGs) were identified by the "limma" R package. The univariate Cox and multivariate Cox analyses were executed to determine the prognostic genes. The risk signature was constructed and verified with the training set, testing set, and validation set. Mutation analysis, immune checkpoint expression analysis in high- and low-risk groups, and correlation between risk signature and chemotherapeutic agents were conducted using the "maftools" package, "ggplot2" package, and the CellMiner database respectively. The Human Protein Atlas (HPA) database was employed to confirm protein expression trends of prognostic genes in BRCA and normal tissues. The expression of prognostic genes in cell lines was verified by Real-time quantitative polymerase chain reaction (RT-qPCR). Kaplan-meier (KM) plotter database analysis was applied to predict the correlation between the expression levels of signature genes and survival statuses. Results: Five prognostic genes (GSPT2, RNASE1, TIPARP, TSEN54, and SAMD4A) to construct an FR-RBPs-related risk signature were identified and the risk signature was validated by the International Cancer Genome Consortium (ICGC) cohort. Univariate and multivariate Cox regression analysis demonstrated the risk score was a robust independent prognostic factor in overall survival prediction. The Tumor Mutational Burden (TMB) analysis implied that the high- and low-risk groups responded differently to immunotherapy. Drug sensitivity analysis suggested that the risk signature may serve as a chemosensitivity predictor. The results of GSEA suggested that five prognostic genes might be related to DNA replication and the immune-related pathways. RT-qPCR results demonstrated that the expression trends of prognostic genes in cell lines were consistent with the results from public databases. KM plotter database analysis suggested that high expression levels of GSPT2, RNASE1, and SAMD4A contributed to poor prognoses. Conclusion: In conclusion, this study identified the FR-RBPs-related prognostic genes and developed an FR-RBPs-related risk signature for the prognosis of BRCA, which will be of great significance in developing new therapeutic targets and prognostic molecular biomarkers for BRCA.

Comprehensive analyses reveal the role of histone deacetylase genes in prognosis and immune response in low-grade glioma.

Many studies have shown that Histone deacetylases (HDAC) is involved in the occurrence of malignant tumors and regulates the occurrence, proliferation, invasion, and migration of malignant tumors through a variety of signaling pathways. In the present, we explored the role of Histone deacetylases genes in prognosis and immune response in low-grade glioma. Using consensus clustering, we built the new molecular clusters. Using HDAC genes, we constructed and validated the prognostic model in two independent cohort datasets. Patients were divided into high-risk and low-risk groups. Then, we explored the molecular characteristics, clinical characteristics, tumor microenvironment and immune infiltration levels of two clusters and risk groups. Receiver operating characteristic analyses were built for model assessment. We finally detected the expression levels of signature genes between tumor and normal tissues. Low-grade can be separated into two molecular clusters using 11 HDACs genes. Two clusters had different clinical characteristics and prognosis. Nex, we constructed a prognosis model using six HDAC genes (HDAC1, HDAC4, HDAC5, HDAC7, HDAC9, and HDAC10), which was also validated in an independent cohort dataset. Furthermore, multivariate cox regression indicated that the calculated risk score was independently associated with prognosis in low-grade glioma, and risk score can predict the five-year survival probability of low-grade glioma well. High-risk patients can be attributed to multiple complex function and molecular signaling pathways, and the genes alterations of high- and low-risk patients were significantly different. We also found that different survival outcomes of high- and low- risk patients could be involved in the differences of immune filtration level and tumor microenvironment. Subsequently, using signature genes, we identified several small molecular compounds that could be useful for low-grade glioma patients' treatment. Finally, we detected the expression levels of signature genes in tumor tissues. our study uncovers the biology function role of HDAC genes in low-grade glioma. We identified new molecular subtypes and established a prognostic model based on six HDAC genes, which was well applied in two independent cohort data. The regulation of HDAC genes in low-grade glioma involved in multiple molecular function and signaling pathways and immune infiltration levels. Further experiments in vivo and vitro were required to confirm the present findings.

Construction of a Pyroptosis-Related Genes Signature to Improve the Prognostic Prediction and Therapeutic Drugs Selection in Patients with Pancreatic Cancer.

BackgroundEffective prognostic assessment and appropriate drug selection are important for the clinical management of pancreatic cancer (PaC). Here, we aimed to establish a pyroptosis-associated genes (PRGs) signature to predict the prognostic outcomes of PaC and guide clinical drug therapy.MethodsWe identified the differentially expressed PRGs between pancreatic adenocarcinoma (n = 178) and control pancreas samples (n = 171) obtained from different databases, and performed Lasso and Cox regression analysis to create a prognosis signature. Kaplan–Meier (K-M) survival curves and time-dependent receiver operating characteristics were further constructed to assess the utility of the risk model. The International Cancer Genome Consortium (ICGC) PACA-AU cohort (n = 95) was used as a validation dataset to examine the validity of this prognostic model. The correlations of risk score (RS) with clinical features, immune cell infiltration, tumor mutation burden and half-maximal inhibitory concentrations (IC50) of chemotherapeutic drugs were analyzed, and the expression levels of PRGs in cell lines were detected.ResultsA prognostic signature was constructed, which consisted of 4 PRGs (AIM2, IL18, GSMDC and PLCG1). K-M analysis demonstrated a remarkable difference in overall survival (OS) time between low-risk (LR) and high-risk (HR) groups (P < 0.001). The RS contributed to the progression of PaC, and could be a significant independent factor for prognostic prediction. The validation of the ICGC cohort confirmed the effectiveness of the proposed signature. The patients with a HR score in the TCGA cohort had higher tumor mutation burden and more sensitivity to paclitaxel, gemcitabine, 5-fluorouracil and cisplatin than those with a LR score. The differential expression levels of signature genes were verified in vitro.ConclusionThe PRGs signature can be applied for predicting the prognosis of PaC, and may provide useful information for selection of therapeutic drugs.

Development and validation of a hypoxia-stemness-based prognostic signature in pancreatic adenocarcinoma.

Background: Pancreatic adenocarcinoma (PAAD) is one of the most aggressive and fatal gastrointestinal malignancies with high morbidity and mortality worldwide. Accumulating evidence has revealed the clinical significance of the interaction between the hypoxic microenvironment and cancer stemness in pancreatic cancer progression and therapies. This study aims to identify a hypoxia-stemness index-related gene signature for risk stratification and prognosis prediction in PAAD. Methods: The mRNA expression-based stemness index (mRNAsi) data of PAAD samples from The Cancer Genome Atlas (TCGA) database were calculated based on the one-class logistic regression (OCLR) machine learning algorithm. Univariate Cox regression and LASSO regression analyses were then performed to establish a hypoxia-mRNAsi-related gene signature, and its prognostic performance was verified in both the TCGA-PAAD and GSE62452 corhorts by Kaplan-Meier and receiver operating characteristic (ROC) analyses. Additionally, we further validated the expression levels of signature genes using the TCGA, GTEx and HPA databases as well as qPCR experiments. Moreover, we constructed a prognostic nomogram incorporating the eight-gene signature and traditional clinical factors and analyzed the correlations of the risk score with immune infiltrates and immune checkpoint genes. Results: The mRNAsi values of PAAD samples were significantly higher than those of normal samples (p < 0.001), and PAAD patients with high mRNAsi values exhibited worse overall survival (OS). A novel prognostic risk model was successfully constructed based on the eight-gene signature comprising JMJD6, NDST1, ENO3, LDHA, TES, ANKZF1, CITED, and SIAH2, which could accurately predict the 1-, 3-, and 5-year OS of PAAD patients in both the training and external validation datasets. Additionally, the eight-gene signature could distinguish PAAD samples from normal samples and stratify PAAD patients into low- and high-risk groups with distinct OS. The risk score was closely correlated with immune cell infiltration patterns and immune checkpoint molecules. Moreover, calibration analysis showed the excellent predictive ability of the nomogram incorporating the eight-gene signature and traditional clinical factors. Conclusion: We developed a hypoxia-stemness-related prognostic signature that reliably predicts the OS of PAAD. Our findings may aid in the risk stratification and individual treatment of PAAD patients.

Improved radiation expression profiling in blood by sequential application of sensitive and specific gene signatures

Purpose. Combinations of expressed genes can discriminate radiation-exposed from normal control blood samples by machine learning based signatures (with 8 to 20% misclassification rates). These signatures can quantify therapeutically-relevant as well as accidental radiation exposures. The prodromal symptoms of Acute Radiation Syndrome (ARS) overlap those present in Influenza and Dengue Fever infections. Surprisingly, these human radiation signatures misclassified gene expression profiles of virally infected samples as false positive exposures. The present study investigates these and other confounders, and then mitigates their impact on signature accuracy. Methods. This study investigated recall by previous and novel radiation signatures independently derived from multiple Gene Expression Omnibus datasets on common and rare non-malignant blood disorders and blood-borne infections (thromboembolism, S. aureus bacteremia, malaria, sickle cell disease, polycythemia vera, and aplastic anemia). Normalized expression levels of signature genes are used as input to machine learning-based classifiers to predict radiation exposure in other hematological conditions. Results. Except for aplastic anemia, these blood-borne disorders modify the normal baseline expression values of genes present in radiation signatures, leading to false-positive misclassification of radiation exposures in 8 to 54% of individuals. Shared changes, predominantly in DNA damage response and apoptosis-related gene transcripts in radiation and confounding hematological conditions, compromise the utility of these signatures for radiation assessment. These confounding conditions (sickle cell disease, thromboembolism, S. aureus bacteremia, malaria) induce neutrophil extracellular traps, initiated by chromatin decondensation, DNA damage response and fragmentation followed by programmed cell death. Riboviral infections (for example, Influenza or Dengue fever) have been proposed to bind and deplete host RNA binding proteins, inducing R-loops in chromatin. R-loops that collide with incoming replication forks can result in incompletely repaired DNA damage, inducing apoptosis and releasing mature virus. To mitigate the effects of confounders, we evaluated predicted radiation-positive samples with novel gene expression signatures derived from radiation-responsive transcripts encoding secreted blood plasma proteins whose expression levels are unperturbed by these conditions. Conclusions. This approach identifies and eliminates misclassified samples with underlying hematological or infectious conditions, leaving only samples with true radiation exposures. Diagnostic accuracy is significantly improved by selecting genes that maximize both sensitivity and specificity in the appropriate tissue using combinations of the best signatures for each of these classes of signatures.

A Ferroptosis-Related Gene Signature for Predicting the Prognosis and Drug Sensitivity of Head and Neck Squamous Cell Carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers worldwide and has a high mortality. Ferroptosis, an iron-dependent form of programmed cell death, plays a crucial role in tumor suppression and chemotherapy resistance in cancer. However, the prognostic and clinical values of ferroptosis-related genes (FRGs) in HNSCC remain to be further explored. In the current study, we constructed a ferroptosis-related prognostic model based on the Cancer Genome Atlas database and then explored its prognostic and clinical values in HNSCC via a series of bioinformatics analyses. As a result, we built a four-gene prognostic signature, including FTH1, BNIP3, TRIB3, and SLC2A3. Survival analysis showed that the high-risk group presented significantly poorer overall survival than the low-risk group. Moreover, the ferroptosis-related signature was found to be an independent prognostic predictor with high accuracy in survival prediction for HNSCC. According to immunity analyses, we found that the low-risk group had higher anti-tumor immune infiltration cells and higher expression of immune checkpoint molecules and meanwhile corelated more closely with some anti-tumor immune functions. Meanwhile, all the above results were validated in the independent HSNCC cohort GSE65858. Besides, the signature was found to be remarkably correlated with sensitivity of common chemotherapy drugs for HNSCC patients and the expression levels of signature genes were also significantly associated with drug sensitivity to cancer cells. Overall, we built an effective ferroptosis-related prognostic signature, which could predict the prognosis and help clinicians to perform individualized treatment strategy for HNSCC patients.

Identification and external validation of a prognostic signature associated with DNA repair genes in gastric cancer

The aim of this study was to construct and validate a DNA repair-related gene signature for evaluating the overall survival (OS) of patients with gastric cancer (GC). Differentially expressed DNA repair genes between GC and normal gastric tissue samples obtained from the TCGA database were identified. Univariate Cox analysis was used to screen survival-related genes and multivariate Cox analysis was applied to construct a DNA repair-related gene signature. An integrated bioinformatics approach was performed to evaluate its diagnostic and prognostic value. The prognostic model and the expression levels of signature genes were validated using an independent external validation cohort. Two genes (CHAF1A, RMI1) were identified to establish the prognostic signature and patients ware stratified into high- and low-risk groups. Patients in high-risk group presented significant shorter survival time than patients in the low-risk group in both cohorts, which were verified by the ROC curves. Multivariate analysis showed that the prognostic signature was an independent predictor for patients with GC after adjustment for other known clinical parameters. A nomogram incorporating the signature and known clinical factors yielded better performance and net benefits in calibration plot and decision curve analyses. Further, the logistic regression classifier based on the two genes presented an excellent diagnostic power in differentiating early HCC and normal tissues with AUCs higher than 0.9. Moreover, Gene Set Enrichment Analysis revealed that diverse cancer-related pathways significantly clustered in the high-risk and low-risk groups. Immune cell infiltration analysis revealed that CHAF1A and RMI1 were correlated with several types of immune cell subtypes. A prognostic signature using CHAF1A and RMI1 was developed that effectively predicted different OS rates among patients with GC. This risk model provides new clinical evidence for the diagnostic accuracy and survival prediction of GC.

Stable gene expression for normalisation and single-sample scoring.

Gene expression signatures have been critical in defining the molecular phenotypes of cells, tissues, and patient samples. Their most notable and widespread clinical application is stratification of breast cancer patients into molecular (PAM50) subtypes. The cost and relatively large amounts of fresh starting material required for whole-transcriptome sequencing has limited clinical application of thousands of existing gene signatures captured in repositories such as the Molecular Signature Database. We identified genes with stable expression across a range of abundances, and with a preserved relative ordering across thousands of samples, allowing signature scoring and supporting general data normalisation for transcriptomic data. Our new method, stingscore, quantifies and summarises relative expression levels of signature genes from individual samples through the inclusion of these ‘stably-expressed genes’. We show that our list of stable genes has better stability across cancer and normal tissue data than previously proposed gene sets. Additionally, we show that signature scores computed from targeted transcript measurements using stingscore can predict docetaxel response in breast cancer patients. This new approach to gene expression signature analysis will facilitate the development of panel-type tests for gene expression signatures, thus supporting clinical translation of the powerful insights gained from cancer transcriptomic studies.

Validation of a prognostic multi-gene signature in high-risk neuroblastoma using the high throughput digital NanoString nCounter™ system

Microarray-based molecular signatures have not been widely integrated into neuroblastoma diagnostic classification systems due to the complexities of the assay and requirement for high-quality RNA. New digital technologies that accurately quantify gene expression using RNA isolated from formalin-fixed paraffin embedded (FFPE) tissues are now available. In this study, we describe the first use of a high-throughput digital system to assay the expression of genes in an “ultra-high risk” microarray classifier in FFPE high-risk neuroblastoma tumors. Customized probes corresponding to the 42 genes in a published multi-gene neuroblastoma signature were hybridized to RNA isolated from 107 FFPE high-risk neuroblastoma samples using the NanoString nCounter™ Analysis System. For classification of each patient, the Pearson's correlation coefficient was calculated between the standardized nCounter™ data and the molecular signature from the microarray data. We demonstrate that the nCounter™ 42-gene panel sub-stratified the high-risk cohort into two subsets with statistically significantly different overall survival (p = 0.0027) and event-free survival (p = 0.028). In contrast, none of the established prognostic risk markers (age, stage, tumor histology, MYCN status, and ploidy) were significantly associated with survival. We conclude that the nCounter™ System can reproducibly quantify expression levels of signature genes in FFPE tumor samples. Validation of this microarray signature in our high-risk patient cohort using a completely different technology emphasizes the prognostic relevance of this classifier. Prospective studies testing the prognostic value of molecular signatures in high-risk neuroblastoma patients using FFPE tumor samples and the nCounter™ System are warranted.