4-gene Signature Research Articles

IRE1α-XBP1 safeguards hematopoietic stem and progenitor cells by restricting pro-leukemogenic gene programs.

Hematopoietic stem cells must mitigate myriad stressors throughout their lifetime to ensure normal blood cell generation. Here, we uncover unfolded protein response stress sensor inositol-requiring enzyme-1α (IRE1α) signaling in hematopoietic stem and progenitor cells (HSPCs) as a safeguard against myeloid leukemogenesis. Activated in part by an NADPH oxidase-2 mechanism, IRE1α-induced X-box binding protein-1 (XBP1) mediated repression of pro-leukemogenic programs exemplified by the Wnt-β-catenin pathway. Transcriptome analysis and genome-wide mapping of XBP1 targets in HSPCs identified an '18-gene signature' of XBP1-repressed β-catenin targets that were highly expressed in acute myeloid leukemia (AML) cases with worse prognosis. Accordingly, IRE1α deficiency cooperated with a myeloproliferative oncogene in HSPCs to cause a lethal AML in mice, while genetic induction of XBP1 suppressed the leukemia stem cell program and activity of patient-derived AML cells. Thus, IRE1α-XBP1 signaling safeguards the integrity of the blood system by restricting pro-leukemogenic programs in HSPCs.

Identification of a Novel Immune-Gene Signature with Prognostic Value in Patients with Head and Neck Cancer: A Pilot Study.

The tumor microenvironment has a significant input on prognosis and also for predicting clinical outcomes in various types of cancers. However, tumor tissue is not always available, thus, rendering peripheral blood a preferable alternative in the search for prognostic and predictive gene signatures. Head and neck squamous cell carcinoma (HNSCC) constitutes a quite heterogeneous disease characterized by poor prognosis. Therefore, the discovery of novel therapeutics based on prognostic gene signatures for effective disease governance is of paramount importance. In this study, we report for the first time an immune-gene signature identified in the peripheral blood of HNSCC patients comprising five genes (CLEC4C, IL23A, LCK, LY9, and CD19) which were more than threefold downregulated as compared to healthy individuals and were associated with poor prognosis. By performing analyses of HNSCC tumor samples from The Cancer Genome Atlas (TCGA) database, we discovered that decreased expression of these genes, both as single genes and as a 5-gene signature (5-GS), was significantly correlated with worse overall survival (OS). Our data show that the levels of expression of the 5-GS represent an immune profile predicting OS in patients with HNSCC.

The value of a metabolic and immune-related gene signature and adjuvant therapeutic response in pancreatic cancer

BackgroundPancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy characterized by a dismal prognosis. Treatment outcomes exhibit substantial variability across patients, underscoring the urgent need for robust predictive models to effectively estimate survival probabilities and therapeutic responses in PDAC.MethodsMetabolic and immune-related genes exhibiting differential expression were identified using the TCGA-PDAC and GTEx datasets. A genetic prognostic model was developed via univariable Cox regression analysis on a training cohort. Predictive accuracy was assessed using Kaplan-Meier (K-M) curves, calibration plots, and ROC curves. Additional analyses, including GSAE and immune cell infiltration studies, were conducted to explore relevant biological mechanisms and predict therapeutic efficacy.ResultsAn 8-gene prognostic model (AK2, CXCL11, TYK2, ANGPT4, IL20RA, MET, ENPP6, and CA12) was established. Three genes (AK2, ENPP6, and CA12) were associated with metabolism, while the others were immune-related. Most genes correlated with poor prognosis. Validation in TCGA-PDAC and GSE57495 datasets demonstrated robust performance, with AUC values for 1-, 3-, and 5-year OS exceeding 0.7. The model also effectively predicted responses to adjuvant therapy.ConclusionThis 8-gene signature enhances prognostic accuracy and therapeutic decision-making in PDAC, offering valuable insights for clinical applications and personalized treatment strategies.

Convergent and divergent immune aberrations in COVID-19, post-COVID-19-interstitial lung disease, and idiopathic pulmonary fibrosis.

We aimed to study transcriptional and phenotypic changes in circulating immune cells associated with increased risk of mortality in COVID-19, resolution of pulmonary fibrosis in post-COVID-19-interstitial lung disease (ILD), and persistence of idiopathic pulmonary fibrosis (IPF). Whole blood and peripheral blood mononuclear cells (PBMCs) were obtained from 227 subjects with COVID-19, post-COVID-19 interstitial lung disease (ILD), IPF, and controls. We measured a 50-gene signature (nCounter, Nanostring) previously found to be predictive of IPF and COVID-19 mortality along with plasma levels of several biomarkers by Luminex. In addition, we performed single-cell RNA sequencing (scRNA-seq) in PBMCs (10x Genomics) to determine the cellular source of the 50-gene signature. We identified the presence of three genomic risk profiles in COVID-19 based on the 50-gene signature associated with low-, intermediate-, or high-risk of mortality and with significant differences in proinflammatory and profibrotic cytokines. Patients with COVID-19 in the high-risk group had increased expression of seven genes in CD14+HLA-DRlowCD163+ monocytic-myeloid-derived suppressive cells (7Gene-M-MDSCs) and decreased expression of 43 genes in CD4 and CD8 T cell subsets. The loss of 7Gene-M-MDSCs and increased expression of these 43 genes in T cells was seen in survivors with post-COVID-19-ILD. On the contrary, patients with IPF had low expression of the 43 genes in CD4 and CD8 T cells. Collectively, we showed that a 50-gene, high-risk profile, predictive of IPF and COVID-19 mortality is characterized by a genomic imbalance in monocyte and T-cell subsets. This imbalance reverses in survivors with post-COVID-19-ILD highlighting genomic differences between post-COVID-19-ILD and IPF.NEW & NOTEWORTHY Changes in the 50-gene signature, reflective of increase in CD14+HLA-DRlowCD163+ monocytes and decrease in CD4 and CD8 T cells, are associated with increased mortality in COVID-19. A reversal of this pattern can be seen in post-COVID-19-ILD, whereas its persistence can be seen in IPF. Modulating the imbalance between HLA-DRlow monocytes and T cell subsets should be investigated as a potential strategy to treat pulmonary fibrosis associated with severe COVID-19 and progressive IPF.

Single-cell RNA-sequencing and spatial transcriptomic analysis reveal a distinct population of APOE- cells yielding pathological lymph node metastasis in papillary thyroid cancer.

Thyroid cancer is one of the most common endocrine tumors worldwide, especially among women and the metastatic mechanism of papillary thyroid carcinoma remains poorly understood. Thyroid cancer tissue samples were obtained for single-cell RNA-sequencing and spatial transcriptomics, aiming to intratumoral and antimetastatic heterogeneity of advanced PTC. The functions of APOE in PTC cell proliferation and invasion were confirmed through in vivo and in vitro assays. Pseudotime analysis and CellChat were performed to explore the the molecular mechanisms of the APOE in PTC progression. We identified a subpopulation of tumor cells with lower expression levels of APOE, associated with advanced stages of PTC and cervical metastasis. APOE overexpression significantly reduced tumor cell proliferation and invasion, both in vitro and in vivo, by activating the ABCA1-LXR axis. APOE- tumor cells may promote tumor growth by interacting with dendritic cells and CD4+ T cells via CD99- rather than CD6-regulated signaling. We established a machine learning-based scRNA-seq data, 13-gene signature predictive of lymph node metastasis. We identified a distinct APOE- tumor cell population associated with cervical metastasis and poor prognosis. Our results and models have potential clinical, prognostic, and therapeutic implications for advanced PTC. A subpopulation of tumor cells with lower expression levels of APOE was strongly associated with more advanced stages and metastasis of PTC. APOE-negative (APOE-) cellsoverall exhibited weaker interactions with immune cells. A machine-learning bioinformatics model based on scRNA-seq data of in-situ thyroid cancer tissue was established to predict lymph node metastasis.

Identification of a novel immune checkpoint-related gene signature predicts prognosis and immunotherapy in breast cancer and experiment verification

Breast cancer (BRCA) is one of the pivotal causes of female death worldwide. And the morbidity and mortality of breast cancer have increased rapidly. Immune checkpoints are important to maintain immune tolerance and are regarded as important therapeutic targets. However, research for BRCA were limited to single immune checkpoint-related gene (ICG) and few studies have systematically explored expression profile of Immune checkpoint-related genes or attempted to construct a prognostic gene risk model based on immune checkpoint-related genes. We identified immune checkpoint-related differentially expressed genes (DEGs) in BRCA and normal breast tissues from TCGA database. A 7-gene signature was created by utilizing the univariate Cox regression model with least absolute shrinkage and selection operator (LASSO) Cox regression method. In addition, we conducted a nomogram to predict the prognostic significance. This tool enables quantitative prediction of patient prognosis, serving as a valuable reference for clinical decision-making, thereby improving patient outcomes. Relationships between our risk model and clinical indicators, TME (Tumor Microenvironment), immune cell infiltration, immune response and drug susceptibility were investigated. A set of in vitro cell assays was conducted to decipher the relationship between MAP2K6 and proliferation, invasion, migration, colony formation and apoptosis rate of breast cancer cells. As a result, we established a prognostic model composed of seven ICGs in BRCA. Based on the median risk score, BRCA patients were equally assigned into two groups of high- and low-risk. High-risk BRCA patients have poorer OS (overall survival) than low-risk patients. In addition, there were remarkable differences between these two groups in clinicopathological features, TME, immune cell infiltration, immune response and drug susceptibility. The results of GO and KEGG analyses indicated that DEGs between the high- and low-risk groups were involved in immune-related biological processes and pathways. GSEA analysis also showed that a number of immune-related pathways were notably enriched in the low-risk group. Finally, results of cell-based assays indicated that MAP2K6 may play a pivotal role in the initiation and progression of breast cancer as a tumor suppressor gene. In conclusion, we created a novel ICG signature that has the potential to predict the survival and drug sensitivity of BRCA patients. Furthermore, this study indicated that MAP2K6 may serve as a novel target for BRCA therapy.

Cross-talk between cuproptosis and ferroptosis to identify immune landscape in cervical cancer for mRNA vaccines development

Messenger RNA (mRNA)-based vaccines present a promising avenue for cancer immunotherapy; however, their application in cervical cancer remains unexplored. This study investigated the interplay between the regulated cell death pathways of cuproptosis and ferroptosis to advance the development of mRNA vaccines for cervical cancer. We identified key cuproptosis-related and ferroptosis-related genes (CFRGs) from public mRNA profiles and determined their prognostic significance, mutation frequencies, and effect on the immune landscape. Our analysis revealed two distinct subtypes of cervical cancer associated with CFRGs, with differences in prognosis and immune characteristics. Using LASSO, XGBoost, and SVM–RFE methods, we established a 4-gene prognostic signature (TSC22D3, SQLE, ZNF419, and TFRC) to stratify patients based on their risk and determine its correlation with immune microenvironment, mutation profiles, and treatment responses. RT-qPCR validation confirmed the differential expression of these genes in clinical samples. Our findings identify TSC22D3, SQLE, ZNF419, and TFRC as candidate targets for mRNA vaccine development and offer a potential prognostic tool for personalized cervical cancer treatment.

Development of a prognostic model based on lysosome-related genes for ovarian cancer: insights into tumor microenvironment, mutation patterns, and personalized treatment strategies

BackgroundOvarian cancer (OC) is often associated with an unfavorable prognosis. Given the crucial involvement of lysosomes in tumor advancement, lysosome-related genes (LRGs) hold promise as potential therapeutic targets.MethodsTo identify differentially expressed lysosome-related genes (DE-LRGs), we performed a matching analysis between differentially expressed genes (DEGs) in OC and the pool of LRGs. Genes with prognostic significance were analyzed using multiple regression analyses to construct a prognostic risk signature. The model's efficacy was validated through survival analysis in various cohorts. We further explored the model's correlation with clinical attributes, tumor microenvironment (TME), mutational patterns, and drug sensitivity. The quantitative real-time polymerase chain reaction (qRT-PCR) validated gene expression in OC cells.ResultsA 10-gene prognostic risk signature was established. Survival analysis confirmed its predictive accuracy across cohorts. The signature served as an independent prognostic element for OC. The high-risk and low-risk groups demonstrated notable disparities in terms of immune infiltration patterns, mutational characteristics, and sensitivity to therapeutic agents. The qRT-PCR results corroborated and validated the findings obtained from the bioinformatic analyses.ConclusionsWe devised a 10-LRG prognostic model linked to TME, offering insights for tailored OC treatments.

Construction of a novel copper-induced-cell-death-related gene signature for prognosis in colon cancer, with focus on KIF7

BackgroundColon cancer (CC) is a leading cause of cancer-related mortality worldwide. Accurate prognostic markers are essential for patient risk stratification and personalized treatment. Copper-induced cell-death-related genes (CRG) have emerged as potential players in cancer prognosis, yet their role in CC remains unclear.MethodsThis study aimed to comprehensively evaluate the expression of CRG and their roles in CC using gene expression and clinical data from TCGA and GEO databases. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses identified prognostic genes, leading to the construction of a CRG prognostic signature. The signature’s predictive accuracy was validated using Kaplan-Meier survival curves, Receiver Operating Characteristic (ROC) curves, and a nomogram model. Additionally, we conducted experiments including immunofluorescence staining and cellular assays to validate the key genes’ biological functions.ResultsA 12-gene CRG signature was significantly associated with overall survival in CC patients. The high-risk group, as classified by median risk score, exhibited significantly shorter survival times compared to the low-risk group. The signature’s predictive accuracy was further confirmed with Area Under the Curve (AUC) scores exceeding 0.75 in TCGA and GSE17536 cohorts. Notably, the risk score was significantly correlated with immune checkpoints, chemotherapy sensitivity, and tumor microenvironment. Furthermore, the risk score showed a strong association with immunotherapy response in patients from GSE78220 and GSE39688 cohorts. Bioinformatics analysis of KIF7, a key gene within the signature, revealed its upregulation in CC and significant associations with tumor mutation burden, microsatellite instability, and immune cell infiltration across various cancers. Experiments confirmed that KIF7 was upregulated in CC and its knockdown reduced cell proliferation, migration, and invasion.ConclusionThe CRG prognostic signature can effectively predict overall survival, immune microenvironment and chemotherapy response in CC. KIF7, as a potential prognostic marker, has significant potential for the prediction and treatment of CC.

Uncovering key genes and molecular mechanisms of dendritic cell dysfunction in Esophageal Cancer: implications for Novel Diagnostic and therapeutic strategies

Dendritic cells play a crucial role in initiating and regulating immune responses, and their dysfunction is strongly associated with esophageal cancer. In this study, we aimed to develop a predictive signature and identify key genes linked to dendritic cell dysfunction in esophageal cancer. Through bioinformatics analysis of gene expression data from the TCGA database, we identified a set of 603 genes significantly associated with dendritic cell function. Further analysis using Cox regression and LASSO regression revealed six genes (GDF15, GPT, KRTAP5-5, MMP12, SLC5A1, and C5orf52) that were strongly correlated with overall survival. The prognostic signature constructed from these genes demonstrated that patients in the high-risk group had poorer survival outcomes compared to those in the low-risk group. Immune infiltration analysis indicated a higher abundance of macrophages in the high-risk group, and correlation studies showed a strong positive association between the risk score and the expression of immune checkpoint markers PD1 and PD-L1. Drug sensitivity analysis suggested that Metformin, Gefitinib, and Lapatinib may be more effective in the low-risk group, while Pyrimethamine, Axitinib, and Rapamycin may be more beneficial for high-risk patients. In summary, we identified a 6-gene signature related to dendritic cell dysfunction that can predict prognosis in esophageal cancer, offering valuable insights for personalized therapeutic strategies.

Identification of a distinctive immunogenomic gene signature in stage-matched colorectal cancer

BackgroundColorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Despite advances in diagnosis and treatment, including surgery, chemotherapy, and immunotherapy, accurate clinical markers are still lacking. The development of prognostic and predictive indicators, particularly in the context of personalized medicine, could significantly improve CRC patient management.MethodIn this retrospective study, we used FFPE blocks of tissue samples from CRC patients at Augusta University (AU) to quantify a custom 15-gene panel. To differentiate the tumor and adjacent normal regions (NAT), H&E staining was utilized. For the quantification of transcripts, we used the NanoString nCounter platform. Kaplan–Meier and Log-rank tests were used to perform survival analyses. Several independent datasets were explored to validate the gene signature. Orthogonal analyses included single-cell profiling, differential gene expression, immune cell deconvolution, neoantigen prediction, and biological pathway assessment.ResultsA 3-gene signature (GTF3A, PKM, and VEGFA) was found to be associated with overall survival in the AU cohort (HR = 2.26, 95% CI 1.05–4.84, p = 0.02, 93 patients), TCGA cohort (HR = 1.57, 95% CI 1.05–2.35, p < 0.02, 435 patients) and four other GEO datasets. Independent single-cell analysis identified relatively higher expression of the 3-gene signature in the tumor region. Differential analysis revealed dysregulated tissue inflammation, immune dysfunction, and neoantigen load of cell cycle processes among high-risk patients compared to low-risk patients.ConclusionWe developed a 3-gene signature with the potential for prognostic and predictive clinical assessment of CRC patients. This gene-based stratification offers a cost-effective approach to personalized cancer management. Further research using similar methods could identify therapy-specific gene signatures to strengthen the development of personalized medicine for CRC patients.

Loss of Glutathione-S-Transferase Theta 2 (GSTT2) Modulates the Tumor Microenvironment and Response to BCG Immunotherapy in a Murine Orthotopic Model of Bladder Cancer.

Loss of the glutathione-S-transferases Theta 2 (Gstt2) expression is associated with an improved response to intravesical Mycobacterium bovis, Bacillus Calmette-Guérin (BCG) immunotherapy for non-muscle-invasive bladder cancer (NMIBC) patients who receive fewer BCG instillations. To delineate the cause, Gstt2 knockout (KO) and wildtype (WT) C57Bl/6J mice were implanted with tumors before treatment with BCG or saline. RNA was analyzed via single-cell RNA sequencing (scRNA-seq) and real-time polymerase chain reaction (RT-PCR). BCG induced PD-L1 expression in WT mice bladders, while pro-inflammatory TNF-α was upregulated in KO bladders. ScRNA-seq analysis showed that Gstt2 WT mice bladders had a higher proportion of matrix remodeling fibroblasts, M2 macrophages, and neuronal cells. In KO mice, distinct tumor cell types, activated fibroblasts, and M1 macrophages were enriched in the bladders. In WT bladders, the genes expressed supported tumorigenesis and immunosuppressive PD-L1 expression. In contrast, Gstt2 KO bladders expressed genes involved in inflammation, immune activation, and tumor suppression. An 11-gene signature (Hmga2, Peak 1, Kras, Slc2a1, Ankfn1, Ahnak, Cmss1, Fmo5, Gphn, Plec, Gstt2), derived from the scRNA-seq analysis predicted response in NMIBC patients (The Cancer Genome Atlas (TCGA) database). In conclusion, our results indicate that patients with WT Gstt2 may benefit from anti-PD-L1 checkpoint inhibition therapy.

Diverse regulated cell death patterns and immune traits in kidney allograft with fibrosis: a prediction of renal allograft failure based on machine learning, single-nucleus RNA sequencing and molecular docking

Objectives: Post-transplant allograft fibrosis remains a challenge in prolonging allograft survival. Regulated cell death has been widely implicated in various kidney diseases, including renal fibrosis. However, the role of different regulated cell death (RCD) pathways in post-transplant allograft fibrosis remains unclear. Methods and Results: Microarray transcriptome profiling and single-nuclei sequencing data of post-transplant fibrotic and normal grafts were obtained and used to identify RCD-related differentially expressed genes. The enrichment activity of nine RCD modalities in tissue and cells was examined using single-sample gene set enrichment analysis, and their relations with immune infiltration in renal allograft samples were also assessed. Parenchymal and non-parenchymal cells displayed heterogeneity in RCD activation. Additionally, cell–cell communication analysis was also conducted in fibrotic samples. Subsequently, weighted gene co-expression network analysis and seven machine learning algorithms were employed to identify RCD-related hub genes for renal fibrosis. A 9-gene signature, termed RCD risk score (RCDI), was constructed using the least absolute shrinkage and selection operator and multivariate Cox regression algorithms. This signature showed robust accuracy in predicting 1-, 2-, and 3-year allograft survival status (area under the curve for 1-, 2-, and 3-year were 0.900, 0.877, 0.858, respectively). Immune infiltration analysis showed a strong correlation with RCDI and the nine model genes. Finally, molecular docking simulation suggested rapamycin, tacrolimus and mycophenolate mofetil exhibit strong interactions with core RCD-related receptors. Conclusions: In summary, this study explored the activation of nine RCD pathways and their relationships with immune traits, identified potential RCD-related hub genes associated with renal fibrosis, and highlighted potential therapeutic targets for renal allograft fibrosis.

Blood transcriptomics reveal systemic eosinophilic and neutrophilic inflammation patterns in patients with nasal polyps.

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a chronic sinonasal disease characterized by heterogeneous inflammation. However, the presence of systemic inflammation heterogeneity in CRSwNP patients remains unknown. This study aims to profile transcriptomic alterations in the blood of CRSwNP patients and characterize the CRSwNP heterogeneity based on blood transcriptomic biomarkers. Patients with CRSwNP were prospectively recruited from three hospitals and chronologically divided into exploratory (n=123) and independent validation (n=46) cohorts. Transcriptomic profiles were generated by whole blood mRNA sequencing and subjected to patient clustering, differential expression, and pathway analysis. Differences in immune pattern and clinicopathologic features between clusters were assessed. A transcriptomic signature was defined and applied to an independent cohort to validate the findings. CRSwNP patients showed diverse blood transcriptomic profiles versus healthy controls, or when stratified by tissue and blood eosinophils and asthma comorbidity. Transcriptome-wide correlation analysis revealed a transcriptional signature associated with blood eosinophil levels, consisting of nine T2-related genes (CLC, SIGLEC8, ALOX15, IL5RA, PTGDR2, CCL23, CCR3, EPX and IL1RL1). Three distinct clusters with differing systemic eosinophilic and neutrophilic inflammation patterns and asthma comorbidity were identified based on transcriptomic profiling of T2 and T1/3-related blood biomarkers. A 36-gene signature was developed by machine learning and accurately predicted the three CRSwNP subtypes. Validation on an independent cohort confirmed the prediction robustness. There is heterogeneous systemic inflammation associated with eosinophilic and neutrophilic patterns in patients with CRSwNP. Endotyping based on blood transcriptomic biomarkers might lead to more personalized treatment strategies for CRSwNP in the future.

Randomized trial investigating the utility of a liver tissue transcriptional biomarker in identifying adult liver transplant recipients not requiring maintenance immunosuppression.

The maintenance of stable allograft status in the absence of immunosuppression, known as operational tolerance, can be achieved in a small proportion of liver transplant recipients, but we lack reliable tools to predict its spontaneous development. We conducted a prospective, multi-center, biomarker-strategy design, immunosuppression withdrawal clinical trial to determine the utility of a predictive biomarker of operational tolerance. The biomarker test, originally identified in a patient cohort with high operational tolerance prevalence, consisted of a 5-gene transcriptional signature measured in liver tissue collected before initiating immunosuppression weaning. 116 adult stable liver transplant recipients were randomized 1:1 to either Arm A (immunosuppression withdrawal regardless of biomarker status) or Arm B (immunosuppression withdrawal in biomarker-positive recipients). Immunosuppression withdrawal was initiated in 82 participants, rejection occurred in 54 (67.5%), successful discontinuation of immunosuppression was achieved in 22 (27.5%), but only 13 (16.3%) met operational tolerance histological criteria (10 in Arm A, 3 in Arm B). The biomarker test did not yield useful information in selecting patients able to successfully discontinue immunosuppression. Operational tolerance was associated with time post-transplantation, recipient age, presence of circulating exhausted CD8+ T cells, and a reduced number of immune synapses within the graft. TRIAL REGISTRATION: ISRCTN 47808000 and EudraCT 2014-004557-14.

Decoding the Ferroptosis-Related Gene Signatures and Immune Infiltration Patterns in Ovarian Cancer: Bioinformatic Prediction Integrated with Experimental Validation.

Ovarian cancer is a type of gynecological cancer with extremely high fatality rate. Ferroptosis, an iron-dependent regulated cell death, inhibits the immune infiltration of tumor cells. Therefore, it is worthwhile to explore the effects of ferroptosis-related gene signatures and immune infiltration patterns on the clinical prognosis of ovarian cancer. In this study, we used the mRNA expression matrix and related medical information of those who suffer from ovarian cancer in the TCGA database. After that, we established a ferroptosis-related gene signature based on LASSO Cox regression model, and employed several specific enrichment analyses to explore the bioinformatics functions of differentially expressed genes (DEGs). Additionally, we analyzed the link between ferroptosis and immune cells by single-sample gene set enrichment analysis (ssGSEA) to create a heatmap of gene-immune cell correlation. We then examined the expression of immune checkpoints and verified the gene expression in ovarian cancer tissues by qPCR assays. Finally, we induced ferroptosis in ovarian cancer cells using drugs and analyzed their migration, invasion and gene expression. According to LASSO Cox regression analysis, 9 prognostic DEGs were in association with overall survival (OS), which was utilized to construct a 9-gene signature for patients. Patients were divided into two groups, in which high-risk group's OS was markedly shorter than that of low-risk group (Log-rank p<0.001). KEGG enrichment analysis showed that these DEGs were linked to human cytomegalovirus (HCMV) infection. The ssGSEA analysis revealed significant differences in immune cell type and expression between ALOX12 and GLRX5 groups (p<0.05). Heatmap showed high correlation of prognostic genes with various immune cells. qPCR assay confirmed the 9 gene expression signature in ovarian cancer tissues. The ovarian cancer cell invasion and migration were significantly inhibited after induction of ferroptosis. We decoded the ferroptosis-related gene signatures and immune infiltration patterns that can be used to predict the prognosis of ovarian cancer patients.

Multi-omics decipher the immune microenvironment and unveil therapeutic strategies for postoperative ovarian cancer patients.

Ovarian cancer (OC) is a highly aggressive and often fatal disease that frequently goes undetected until it has already metastasized. The classic treatment for OC involves surgery followed by chemotherapy. However, despite the effectiveness of surgery, relapse is still a common occurrence. Unfortunately, there is currently no ideal predictive model for the progression and drug sensitivity of postoperative OC patients. Cell death patterns play an important role in tumor progression. So we aimed to investigate their potential to be used as indicators of postoperative OC prognosis and drug sensitivity. A total of 12 programmed cell death (PCD) patterns were employed to construct novel classification and prognosis model. Bulk transcriptome, genomics, and clinical information were collected from The Cancer Genome Atlas (TCGA) Program-OV, GSE9891, GSE26712, GSE49997 and GSE63885. In addition, single-cell transcriptome data GSE210347 were procured from the Gene Expression Omnibus (GEO) database for subsequent analysis. In this study, a novel PCD classification has been employed to phenotype postoperative OC patients, revealing that patients in cluster 1 exhibited heightened sensitivity to immune-based therapies combined with high expression of chemokines, interleukins, interferons, and checkpoints. Meanwhile, a programmed cell death index (PCDI) was established using an 8-gene signature with the help of a machine learning algorithm. The patients with high-PCDI had a worse prognosis after surgery in OC. In addition, we also found that patients with low PCDI patients may exhibit sensitivity to immunotherapy, while those with high PCDI patients may display increased responsiveness to tyrosine kinase inhibitors. This study provides a novel PCD model and nomogram that can effectively predict the clinical prognosis and drug sensitivity of OC patients post-surgery.

Integrated multi-omics and machine learning reveal a gefitinib resistance signature for prognosis and treatment response in lung adenocarcinoma.

Gefitinib resistance (GR) presents a significant challenge in treating lung adenocarcinoma (LUAD), highlighting the need for alternative therapies. This study explores the genetic basis of GR to improve prediction, prevention, and treatment strategies. We utilized public databases to obtain GR gene sets, single-cell data, and transcriptome data, applying univariate and multivariate regression analyses alongside machine learning to identify key genes and develop a predictive signature. The signature's performance was evaluated using survival analysis and time-dependent ROC curves on internal and external datasets. Enrichment and tumor immune microenvironment analyses were conducted to understand the mechanistic roles of the signature genes in GR. Our analysis identified a robust 22-gene signature with strong predictive performance across validation datasets. This signature was significantly associated with chromosomal processes, DNA replication, immune cell infiltration, and various immune scores based on enrichment and tumor microenvironment analyses. Importantly, the signature also showed potential in predicting the efficacy of immunotherapy in LUAD patients. Moreover, we identified alternative agents to gefitinib that could offer improved therapeutic outcomes for high-risk and low-risk patient groups, thereby guiding treatment strategies for gefitinib-resistant patients. In conclusion, the 22-gene signature not only predicts prognosis and immunotherapy efficacy in gefitinib-resistant LUAD patients but also provides novel insights into non-immunotherapy treatment options.

Constructing and identifying an eighteen-gene tumor microenvironment prognostic model for non-small cell lung cancer

BackgroundThe tumor microenvironment (TME) plays a crucial role in tumorigenesis and tumor progression. This study aimed to identify novel TME-related biomarkers and develop a prognostic model for patients with non-small-cell lung cancer (NSCLC).MethodsAfter downloading and preprocessing data from The Cancer Genome Atlas (TCGA) data portal and Gene Expression Omnibus (GEO) datasets, we classified the molecular subtypes using the “NMF” R package. We performed survival analysis and quantified immune scores between clusters. A Cox proportional hazards model was then constructed, and its formula was produced. We assessed model performance and clinical utility. A prediction nomogram was also constructed and validated. Additionally, we explored the potential regulatory mechanisms of our TME gene signature using Gene Set Enrichment Analysis (GSEA).ResultsFrom data processing and univariate Cox regression analysis, 57 TME-related prognostic genes were identified, and two significantly distinct clusters were established. Using Cox regression and Lasso regression, an 18-gene TME-related prognostic model was developed. Patients were stratified into high- and low-risk groups based on the risk score, with survival analysis showing that the low-risk group had significantly better outcomes than the high-risk group (P < 0.01). ROC curve analysis demonstrated strong predictive performance, with 1-year, 3-year, and 5-year AUC values ranging from 0.654 to 0.702 across different cohorts. The model accurately predicted survival outcomes across subgroups with varying clinical features, and its predictive accuracy was validated through a nomogram.ConclusionsWe developed a prognostic model based on TME-related genes in NSCLC. Our 18-gene TME signature can effectively predict the prognosis of NSCLC with high accuracy.

Identification and validation of a novel defined stress granule-related gene signature for predicting the prognosis of ovarian cancer via bioinformatics analysis.

Ovarian cancer (OC) is a malignant gynecological cancer with an extremely poor prognosis. Stress granules (SGs) are non-membrane organelles that respond to stressors; however, the correlation between SG-related genes and the prognosis of OC remains unclear. This systematic analysis aimed to determine the expression levels of SG-related genes between high- and low-risk groups of patients with OC and to explore the prognostic value of these genes. RNA-sequencing data and clinical information from GSE18520 and GSE14407 in the Gene Expression Omnibus (GEO) and ovarian plasmacytoma adenocarcinoma in The Cancer Genome Atlas (TCGA) were downloaded. SG-related genes were obtained from GeneCards, the Molecular Signatures Database, and the literature. First, 13 SG-related genes were identified in the prognostic model using least absolute shrinkage and selection operator (LASSO) Cox regression. The prognostic value of each SG-related gene for survival and its relationship with clinical characteristics were evaluated. Next, we performed a functional enrichment analysis of SG-related genes. The protein-protein interactions (PPI) of SG-related genes were visualized using Cytoscape with STRING. According to the median risk score from the LASSO Cox regression, a 13-gene signature was created. All patients with OC in TCGA cohort and GEO datasets were classified into high- and low-risk groups. Five SG-related genes were differentially expressed between the high- and low-risk OC groups in the GEO datasets. The 13 SG-related genes were related to several important oncogenic pathways (TNF-α signaling, PI3K-AKT-mTOR signaling, and WNT-β-catenin signaling) and several cellular components (cytoplasmic stress granule, cytoplasmic ribonucleoprotein granule, and ribonucleoprotein granule). The PPI network identified 11 hub genes with the strongest interactions with ELAVL1. These findings indicate that SG-related genes (DNAJA1, ELAVL1, FBL, GRB7, MOV10, PABPC3, PCBP2, PFN1, RFC4, SYNCRIP, USP10, ZFP36, and ZFP36L1) can be used to predict OC prognosis.