4-gene Signature Research Articles

Integrated clinical and prognostic analyses of mTOR/Hippo pathway core genes in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most aggressive and dismal cancers globally. Emerging evidence has established that mTOR and Hippo pathways are oncogenic drivers of HCC. However, the prognostic value of these pathways in HCC remains unclear. In this study, we aimed to develop a gene signature utilizing the mTOR/Hippo genes for HCC prognostication. A multiple stage strategy was employed to screen, and a 12-gene signature based on mTOR/Hippo pathways was constructed to predict the prognosis of HCC patients. The risk scores calculated by the signature were inversely correlated with patient prognosis. Validation of the signature in independent cohort confirmed its predictive power. Further analysis revealed molecular differences between high and low-risk groups at genomic, transcriptomic, and protein-interactive levels. Moreover, immune infiltration analysis revealed an immunosuppressive state in the high-risk group. Finally, the gene signature could predict the sensitivity to current chemotherapeutic drugs. This study demonstrated that combinatorial mTOR/Hippo gene signature was a robust and independent prognostic tool for survival prediction of HCC. Our findings not only provide novel insights for the molecular understandings of mTOR/Hippo pathways in HCC, but also have important clinical implications for guiding therapeutic strategies.

Immunogenic Cell Death-related Signature Evaluates the Tumor Microenvironment and Predicts the Prognosis in Diffuse Large B-Cell Lymphoma.

Current literatures suggest a growing body of evidence highlighting the pivotal role of Immunogenic Cell Death (ICD) in multiple tumor types. Nevertheless, the potential and mechanisms of ICD in diffuse large B-cell lymphoma (DLBCL) remain inadequately studied. To address this gap, our current study aims to examine the impact of ICD on DLBCL and identify a corresponding gene signature in DLBC. Using the expression profiles of ICD-associated genes, the gene expression omnibus (GEO) samples were segregated into ICD-high and ICD-lowsubtypes utilizing non-negative matrix factorization clustering. Next, univariate and LASSO Cox regression analyses were employed to establish the ICD-related gene signature. Subsequently, the CIBERSORT tool, ssGSEA, and ESTIMATE algorithm were utilized to examine the association between the signature and tumor immune microenvironment of DLBC. Finally, the oncoPredict algorithm was implemented to evaluate the drug sensitivity prediction of DLBCL patients. These findings suggest that the immune microenvironment of the ICD-high group with a poor prognosis was significantly suppressed. An 8-gene ICD-related signature was identified and validated to prognosticate and evaluate the tumor immune microenvironment in DLBCL. Similarly, the high-risk group exhibited a worse prognosis compared to the low-risk group, and the immune function was considerably suppressed. Moreover, the results of oncoPredict algorithm indicated that patients in the high-risk group exhibited higher sensitivity to Cisplatin, Cytarabine, Epirubicin, Oxaliplatin, and Vincristine with low IC50. In conclusion, the present study provides novel insights into the role of ICD in DLBCL by identifying a new biomarker for the disease and may have implications for the development of immune-targeted therapies for the tumor.

Identification of prognostic models for glycosylation-related subtypes and tumor microenvironment infiltration characteristics in clear cell renal cell cancer

One of the most fatal forms of cancer of the urinary system, renal cell carcinoma (RCC), significantly negatively impacts human health. Recent research reveals that abnormal glycosylation contributes to the growth and spread of tumors. However, there is no information on the function of genes related to glycosylation in RCC. In this study, we created a technique that can be used to guide the choice of immunotherapy and chemotherapy regimens for RCC patients while predicting their survival prognosis. The Cancer Genome Atlas (TCGA) provided us with patient information, while the GeneCards database allowed us to collect genes involved in glycosylation. GSE29609 was used as external validation to assess the accuracy of prognostic models. The "ConsensusClusterPlus" program created molecular subtypes based on genes relevant to glycosylation discovered using differential expression analysis and univariate Cox analysis. We examined immune cell infiltration as measured by estimate, CIBERSORT, TIMER, and ssGSEA algorithms, Tumor Immune Dysfunction and Exclusion (TIDE) and exclusion of tumour stemness indices (TSIs) based on glycosylation-related molecular subtypes and risk profiles. Stratification, somatic mutation, nomogram creation, and chemotherapy response prediction were carried out based on risk factors. We built and verified 16 gene signatures associated with the prognosis of ccRCC patients, which are independent prognostic variables, and identified glycosylation-related genes by bioinformatics research. Cluster 2 is associated with lower human leukocyte antigen expression, worse overall survival, higher immunological checkpoints, and higher immune escape scores. In addition, cluster 2 had significantly better angiogenic activity, mesenchymal EMT, and stem ability scores. Higher immune checkpoint genes and human leukocyte antigens are associated with lower overall survival and a higher risk score. Higher estimated and immune scores, lesser tumor purity, lower mesenchymal EMT, and higher stem scores were all characteristics of the high-risk group. High amounts of tumor-infiltrating lymphocytes, a high mutation load, and a high copy number alteration frequency were present in the high-risk group.Discussion.According to our research, the 16-gene prognostic signature may be helpful in predicting prognosis and developing individualized treatments for patients with renal clear cell carcinoma, which may result in new personalized management options for these patients.

297 (PB-113) Poster - Insights into HER2-Low Status and the Breast Cancer Immune Microenvironment: A 20-Gene Signature Study

297 (PB-113) Poster - Insights into HER2-Low Status and the Breast Cancer Immune Microenvironment: A 20-Gene Signature Study

Elucidating Hedgehog pathway's role in HNSCC progression: insights from a 6-gene signature

With the emergence of targeted inhibition strategies for Hedgehog signaling in cancer, multiple Hedgehog signaling pathway-related biomarkers have become the focus of research. SsGSEA algorithm was employed to analyze the Hedgehog pathway scores of samples in TCGA-HNSC dataset and divide them into two groups. Weighted co-expression network analysis was performed to identify modules strongly associated with the Hedgehog pathway. Differentially up-regulated genes in tumor samples in comparison to the normal ones were screened by Limma, in which genes belonging to modules strongly related to Hedgehog pathway were further filtered by LASSO reduction and multivariate Cox regression analysis to develop a model. ESTIMATE and CIBERSORT were served to characterize the tumor microenvironment (TME). TIDE assessed immunotherapy response. Hedgehog pathway activity was significantly higher in head and neck squamous cell carcinoma (HNSCC) tissues than in normal tissues and was correlated with HNSCC survival, glycan, cofactors and vitamins, drug metabolism, and matrix scores. Six genes (SLC2A3, EFNB2, OAF, COX4I2, MT2A and TXNRD1) were captured to form a Hedgehog associated 6-gene signature, and the resulting risk score was an independent indicator of HNSCC prognosis. It was significantly positively correlated with stromal score, metabolism, angiogenesis and inflammatory response. Patients in low-risk group with a low TIDE score had higher immunotherapy sensitivity relative to those in high-risk group. This study revealed novel findings of the Hedgehog pathway in HNSCC progression and opened up a Hedgehog pathology-related signature to help identify risk factors contributing to HNSCC progression and help predict immunotherapy outcomes.

A serum-induced gene signature in hepatocytes is associated with pediatric nonalcoholic fatty liver disease.

Pediatric nonalcoholic fatty liver disease (NAFLD) is a growing problem, but its underlying mechanisms are poorly understood. We used transcriptomic reporter cell assays to investigate differences in transcriptional signatures induced in hepatocyte reporter cells by the sera of children with and without NAFLD. We studied serum samples from 45 children with NAFLD and 28 children without NAFLD. The sera were used to induce gene expression in cultured HepaRG cells and RNA-sequencing was used to determine gene expression. Computational techniques were used to compare gene expression patterns. Sera from children with NAFLD induced the expression of 195 genes that were significantly differentially expressed in hepatocytes compared to controls with obesity. NAFLD was associated with increased expression of genes promoting inflammation, collagen synthesis, and extracellular matrix remodeling. Additionally, there was lower expression of genes involved in endobiotic and xenobiotic metabolism, and downregulation of peroxisome function, oxidative phosphorylation, and xenobiotic, bile acid, and fatty acid metabolism. A 13-gene signature, including upregulation of TREM1 and MMP1 and downregulation of CYP2C9, was consistently associated with all diagnostic categories of pediatric NAFLD. The extracellular milieu of sera from children with NAFLD induced specific gene profiles distinguishable by a hepatocyte reporter system. Circulating factors may contribute to inflammation and extracellular matrix remodeling and impair xenobiotic and endobiotic metabolism in pediatric NAFLD.

Identification of a 9-gene signature to enhance biochemical recurrence prediction in primary prostate cancer: A benchmarking study using ten machine learning methods and twelve patient cohorts

Prostate cancer (PCa) is a prevalent malignancy among men worldwide, and biochemical recurrence (BCR) after radical prostatectomy (RP) is a critical turning point commonly used to guide the development of treatment strategies for primary PCa. However, the clinical parameters currently in use are inadequate for precise risk stratification and informing treatment choice. To address this issue, we conducted a study that collected transcriptomic data and clinical information from 1662 primary PCa patients across 12 multicenter cohorts globally. We leveraged 101 algorithm combinations that consisted of 10 machine learning methods to develop and validate a 9-gene signature, named BCR SCR, for predicting the risk of BCR after RP. Our results demonstrated that BCR SCR generally outperformed 102 published prognostic signatures. We further established the clinical significance of these nine genes in PCa progression at the protein level through immunohistochemistry on Tissue Microarray (TMA). Moreover, our data showed that patients with higher BCR SCR tended to have higher rates of BCR and distant metastasis after radical radiotherapy. Through drug target prediction analysis, we identified nine potential therapeutic agents for patients with high BCR SCR. In conclusion, the newly developed BCR SCR has significant translational potential in accurately stratifying the risk of patients who undergo RP, monitoring treatment courses, and developing new therapies for the disease.

A hypoxia biomarker does not predict benefit from giving chemotherapy with radiotherapy in the BC2001 randomised controlled trial

BC2001 showed combining chemotherapy (5-FU+mitomycin-C) with radiotherapy improves loco-regional disease-free survival in patients with muscle-invasive bladder cancer (MIBC). We previously showed a 24-gene hypoxia-associated signature predicted benefit from hypoxia-modifying radiosensitisation in BCON and hypothesised that only patients with low hypoxia scores (HSs) would benefit from chemotherapy in BC2001. BC2001 allowed conventional (64Gy/32 fractions) or hypofractionated (55Gy/20 fractions) radiotherapy. An exploratory analysis tested an additional hypothesis that hypofractionation reduces reoxygenation and would be detrimental for patients with hypoxic tumours. RNA was extracted from pre-treatment biopsies (298 BC2001 patients), transcriptomic data generated (Affymetrix Clariom-S arrays), HSs calculated (median expression of 24-signature genes) and patients stratified as hypoxia-high or -low (cut-off: cohort median). invasive loco-regional control (ILRC); secondary overall survival. Hypoxia affected overall survival (HR=1.30; 95% CI 0.99-1.70; p=0.062): more uncertainty for ILRC (HR=1.29; 95% CI 0.82-2.03; p=0.264). Benefit from chemotherapy was similar for patients with high or low HSs, with no interaction between HS and treatment arm. High HS associated with poor ILRC following hypofractionated (n=90, HR 1.69; 95% CI 0.99-2.89 p=0.057) but not conventional (n=207, HR 0.70; 95% CI 0.28-1.80, p=0.461) radiotherapy. The finding was confirmed in an independent cohort (BCON) where hypoxia associated with a poor prognosis for patients receiving hypofractionated (n=51; HR 14.2; 95% CI 1.7-119; p=0.015) but not conventional (n=24, HR 1.04; 95% CI 0.07-15.5, p=0.978) radiotherapy. Tumour hypoxia status does not affect benefit from BC2001 chemotherapy. Hypoxia appears to affect fractionation sensitivity. Use of HSs to personalise treatment needs testing in a biomarker-stratified trial. Cancer Research UK, NIHR, MRC.

A novel peroxisome-related gene signature predicts clinical prognosis and is associated with immune microenvironment in low-grade glioma.

Low-grade glioma (LGG), a common primary tumor, mainly originates from astrocytes and oligodendrocytes. Increasing evidence has shown that peroxisomes function in the regulation of tumorigenesis and development of cancer. However, the prognostic value of peroxisome-related genes (PRGs) in LGG has not been reported. Therefore, it is necessary to construct a prognostic risk model for LGG patients based on the expression profiles of peroxisome-related genes. Our study mainly concentrated on developing a peroxisome-related gene signature for overall survival (OS) prediction in LGG patients. First, according to these peroxisome-related genes, all LGG patients from The Cancer Genome Atlas (TCGA) database could be divided into two subtypes. Univariate Cox regression analysis was used to find prognostic peroxisome-related genes in TCGA_LGG dataset, and least absolute shrinkage and selection operator Cox regression analysis was employed to establish a 14-gene signature. The risk score based on the signature was positively associated with unfavorable prognosis. Then, multivariate Cox regression incorporating additional clinical characteristics showed that the 14-gene signature was an independent predictor of LGG. Time-dependent ROC curves revealed good performance of this prognostic signature in LGG patients. The performance about predicting OS of LGG was validated using the GSE107850 dataset derived from the Gene Expression Omnibus (GEO) database. Furethermore, we constructed a nomogram model based on the gene signature and age, which showed a better prognostic power. Gene ontology (GO) and Kyoto Encylopedia of Genes and Genomes (KEGG) analyses showed that neuroactive ligand-receptor interaction and phagosome were enriched and that the immune status was decreased in the high-risk group. Finally, cell counting kit-8 (CCK8) were used to detect cell proliferation of U251 and A172 cells. Inhibition of ATAD1 (ATPase family AAA domain-containing 1) and ACBD5 (Acyl-CoA binding-domain-containing-5) expression led to significant inhibition of U251 and A172 cell proliferation. Flow cytometry detection showed that ATAD1 and ACBD5 could induce apoptosis of U251 and A172 cells. Therefore, through bioinformatics methods and cell experiments, our study developed a new peroxisome-related gene signature that migh t help improve personalized OS prediction in LGG patients.

Gene Expression Profiling in Pediatric Appendicitis

Appendicitis is the most common indication for urgent surgery in the pediatric population, presenting across a range of severity and with variable complications. Differentiating simple appendicitis (SA) and perforated appendicitis (PA) on presentation may help direct further diagnostic workup and appropriate therapy selection, including antibiotic choice and timing of surgery. To provide a mechanistic understanding of the differences in disease severity of appendicitis with the objective of developing improved diagnostics and treatments, specifically for the pediatric population. The Gene Expression Profiling of Pediatric Appendicitis (GEPPA) study was a single-center prospective exploratory diagnostic study with transcriptomic profiling of peripheral blood collected from a cohort of children aged 5 to 17 years with abdominal pain and suspected appendicitis between November 2016 and April 2017 at the Alberta Children's Hospital in Calgary, Alberta, Canada, with data analysis reported in August 2023. There was no patient follow-up in this study. SA, PA, or nonappendicitis abdominal pain. Blood transcriptomics was used to develop a hypothesis of underlying mechanistic differences between SA and PA to build mechanistic hypotheses and blood-based diagnostics. Seventy-one children (mean [SD] age, 11.8 [3.0] years; 48 [67.6%] male) presenting to the emergency department with abdominal pain and suspected appendicitis were investigated using whole-blood transcriptomics. A central role for immune system pathways was revealed in PA, including a dampening of major innate interferon responses. Gene expression changes in patients with PA were consistent with downregulation of immune response and inflammation pathways and shared similarities with gene expression signatures derived from patients with sepsis, including the most severe sepsis endotypes. Despite the challenges in identifying early biomarkers of severe appendicitis, a 4-gene signature that was predictive of PA compared to SA, with an accuracy of 85.7% (95% CI, 72.8-94.1) was identified. This study found that PA was complicated by a dysregulated immune response. This finding should inform improved diagnostics of severity, early management strategies, and prevention of further postsurgical complications.

Construction of a New Ferroptosis-related Prognosis Model for Survival Prediction in Colorectal Cancer.

This study was designed to develop a ferroptosis-related gene signature for guiding the prognostic prediction in colorectal cancer (CRC) and to explore the potential in the molecular functions of the gene signature. Ferroptosis is mainly characterized by lipid peroxide accumulation on the cell membranes in an iron-dependent manner, resulting in cellular oxidative stress, metabolic disorders, and, ultimately, cell death. This study aimed to develop a prognostic ferroptosis signature in CRC and explore its potential molecular function. The present work was designed to devise a ferroptosis signature for CRC prognosis and explore its potential molecular function. Single-cell RNA sequencing data GSE161277 and transcriptome sequencing data GSE17537 and TCGA-CRC from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases were downloaded, respectively. Quality control, dimension reduction, clustering, and clustering of single-cell RNA sequencing (scRNA- seq) data were performed using the Seurat package. A total of 259 ferroptosis-correlated genes from the FerrDb database were acquired. The single sample gene set enrichment analysis (ssGSEA) was performed to calculate the scores of genes related to ferroptosis. ESTIMATE was used to calculate immune infiltration. Independent prognostic factors were determined by performing Weighted Gene Co-Expression Network Analysis (WGCNA), univariate and Cox analyses, and Lasso analyses were used to search for independent prognostic factors. From the scRNA-seq (GSE161277) dataset, 22 cell clusters were initially identified, and according to immune cell markers, only 8 types of cells (Follicular B, central memory T cell, Epithelial, Natural killer T cell, Plasma B, M1 macrophage, Fibroblasts, and Mast cell) were finally determined to be related to CRC prognosis. The results of the scRNA-seq analysis showed that the score of ferroptosis-related genes was higher in tumour tissues and in 8 types of cells in tumour samples. In the TCGA dataset, CRC samples were divided into ferroptosis-related high scores, ferroptosis-related median scores, and ferroptosis-related low scores. Immune cell analysis revealed that ferroptosis- related high scores had the highest abundance of immune cells. An 11-gene signature was developed by WGCNA, univariate Cox, and Lasso Cox regression. The prediction ability of the signature was successfully validated in the GSE17537 dataset. A comprehensive nomogram combining the 11 signature genes and clinical parameters could effectively predict the overall survival of CRC patients. The present molecular signature established based on the 11 ferroptosis-related genes performed well in assessing CRC prognosis. The present discoveries could inspire further research on ferroptosis, providing a new direction for CRC management.

Prognostic implication of novel immune-related signature in breast cancer.

Checkpoint inhibitor therapy has become increasingly important and has been endorsed as a treatment regimen in breast cancer. But benefits were limited to a small proportion of patients. We aimed to develop an improved signature on the basis of immune genes for detection of potential benefit from immunotherapy. Gene expression data of patients with breast cancer initially extracted from The Cancer Genome Atlas were analyzed. Ten genes were selected from the interaction of differentially expressed genes as well as immune-related genes to develop a survival signature. We compared the high-risk and low-risk groups by gene set enrichment analysis, immune infiltration, checkpoint molecule expression and immunophenoscore. Ten genes were extracted from interactions of differentially expressed and immune-related genes. The immune risk score was determined on the basis of the Cox regression coefficient of hub genes and validated with the GSE96058 dataset. Immune cell infiltrates, including CD8 + T cells, plasma cells, follicular helper T cells, CD4 + memory T cells, M1 macrophages, regulatory T cells and resting NK cells, were more highly infiltrated in the high-risk group as compared to the low-risk group. Checkpoint molecules, including CTLA-4, PD-L1, TIM-3, VISTA, ICOS, PD-1, and PD-L2, were expressed at markedly lower levels in the high-risk group as compared to the low-risk group. Immunophenoscores, as a surrogate of response to immune checkpoint therapy, was observed significant lower in the high-risk group. The 10-gene prognostic signature could identify patients' survival and was correlated with the biomarkers of immune checkpoint inhibitor therapy, which may guide precise therapeutic decisions in clinical practice.

Identification and validation of a new prognostic signature based on cancer-associated fibroblast-driven genes in breast cancer.

Breast cancer (BC), a leading malignant disease, affects women all over the world. Cancer associated fibroblasts (CAFs) stimulate epithelial-mesenchymal transition, and induce chemoresistance and immunosuppression. To establish a CAFs-associated prognostic signature to improve BC patient outcome estimation. We retrieved the transcript profile and clinical data of 1072 BC samples from The Cancer Genome Atlas (TCGA) databases, and 3661 BC samples from the The Gene Expression Omnibus. CAFs and immune cell infiltrations were quantified using CIBERSORT algorithm. CAF-associated gene identification was done by weighted gene co-expression network analysis. A CAF risk signature was established via univariate, least absolute shrinkage and selection operator regression, and multivariate Cox regression analyses. The receiver operating characteristic (ROC) and Kaplan-Meier curves were employed to evaluate the predictability of the model. Subsequently, a nomogram was developed with the risk score and patient clinical signature. Using Spearman's correlations analysis, the relationship between CAF risk score and gene set enrichment scores were examined. Patient samples were collected to validate gene expression by quantitative real-time polymerase chain reaction (qRT-PCR). Employing an 8-gene (IL18, MYD88, GLIPR1, TNN, BHLHE41, DNAJB5, FKBP14, and XG) signature, we attempted to estimate BC patient prognosis. Based on our analysis, high-risk patients exhibited worse outcomes than low-risk patients. Multivariate analysis revealed the risk score as an independent indicator of BC patient prognosis. ROC analysis exhibited satisfactory nomogram predictability. The area under the curve showed 0.805 at 3 years, and 0.801 at 5 years in the TCGA cohort. We also demonstrated that a reduced CAF risk score was strongly associated with enhanced chemotherapeutic outcomes. CAF risk score was significantly correlated with most hallmark gene sets. Finally, the prognostic signature were further validated by qRT-PCR. We introduced a newly-discovered CAFs-associated gene signature, which can be employed to estimate BC patient outcomes conveniently and accurately.

Anoikis-related signature predicts prognosis and characterizes immune landscape of ovarian cancer.

Ovarian cancer (OV) is the most lethal gynecological malignancy worldwide, with high recurrence rates. Anoikis, a newly-acknowledged form of programmed cell death, plays an essential role in cancer progression, though studies focused on prognostic patterns of anoikis in OV are still lacking. We filtered 32 potential anoikis-related genes (ARGs) among the 6406 differentially expressed genes (DEGs) between the 180 normal controls and 376 TCGA-OV samples. Through the LASSO-Cox analysis, a 2-gene prognostic signature, namely AKT2, and DAPK1, was finally distinguished. We then demonstrated the promising prognostic value of the signature through the K-M survival analysis and time-dependent ROC curves (p-value < 0.05). Moreover, based on the signature and clinical features, we constructed and validated a nomogram model for 1-year, 3-year, and 5-year overall survival, with reliable prognostic values in both TCGA-OV training cohort (p-value < 0.001) and ICGC-OV validation cohort (p-value = 0.030). We evaluated the tumor immune landscape through the CIBERSORT algorithm, which indicated the upregulation of resting Myeloid Dendritic Cells (DCs), memory B cells, and naïve B cells and high expression of key immune checkpoint molecules (CD274 and PDCD1LG2) in the high-risk group. Interestingly, the high-risk group exhibited better sensitivity toward immunotherapy and less sensitivity toward chemotherapies, including Cisplatin and Bleomycin. Especially, based on the IHC of tissue microarrays among 125 OV patients at our institution, we reported that aberrant upregulation of DAPK1 was related to poor prognosis. Conclusively, the anoikis-related signature was a promising tool to evaluate prognosis and predict therapy responses, thus assisting decision-making in the realm of OV precision medicine.

Development of a novel prognostic signature based on single-cell combined bulk RNA analysis in breast cancer.

Breast cancer (BC), a malignant tumor, is a significant cause of death and disability among women globally. Recent research indicates that copy number variation plays a crucial role in tumor development. In this study, we employed the Single-Cell Variational Aneuploidy Analysis (SCEVAN) algorithm to differentiate between malignant and non-malignant cells, aiming to identify genetic signatures with prognostic relevance for predicting patient survival. We analyzed gene expression profiles and associated clinical data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Using the SCEVAN algorithm, we distinguished malignant from non-malignant cells and investigated cellular interactions within the tumor microenvironment (TME). We categorized TCGA samples based on differentially expressed genes (DEGs) between these cell types. Subsequent Kyoto Encyclopedia of Genes and Genomes pathway analysis was conducted. Additionally, we developed polygenic models for the DEGs using least absolute shrinkage and selection operator-penalized Cox regression analysis. To assess the prognostic accuracy of these characteristics, we generated Kaplan-Meier and receiver operating characteristic curves from training and validation datasets. We also monitored the expression variations of prognostic genes across the pseudotime of malignant cells. Patients were divided into high-risk and low-risk groups based on median risk scores to compare their TME and identify potential therapeutic agents. Lastly, polymerase chain reaction was used to validate seven pivotal genes. The SCEVAN algorithm identified distinct malignant and non-malignant cells in GSE180286. Cellchat analysis revealed significantly increased cellular communication, particularly between fibroblasts, endothelial cells and malignant cells. The DEGs were predominantly involved in immune-related pathways. TCGA samples were classified into clusters A and B based on these genes. Cluster A, enriched in immune pathways, was associated with poorer prognosis, whereas cluster B, predominantly involved in circadian rhythm pathways, showed better outcomes. We constructed a 14-gene prognostic signature, validated in a 1:1 internal TCGA cohort and external GEO datasets (GSE42568 and GSE146558). Kaplan-Meier analysis confirmed the prognostic signature's accuracy (p < 0.001). Receiver operating characteristic curve analysis demonstrated the predictive reliability of these prognostic features. Single-cell pseudotime analysis with monocle2 highlighted the distinct expression trends of these genes in malignant cells, underscoring the intratumoral heterogeneity. Furthermore, we explored the differences in TME between high- and low-risk groups and identified 16 significantly correlated drugs. Our findings suggest that the 14-gene prognostic signature could serve as a novel biomarker for forecasting the prognosis of BC patients. Additionally, the immune cells and pathways in different risk groups indicate that immunotherapy may be a crucial component of treatment strategies for BC patients.

Exploring diverse programmed cell-death patterns to develop a novel gene signature for predicting the prognosis of lung adenocarcinoma patients.

Programmed cell death (PCD) plays a critical role in tumor progression and malignancy, and exploring its relationship with lung adenocarcinoma (LUAD)'s survival outcomes is important for personalized diagnosis and treatment. This study aimed to identify survival-related genes and construct an effective prognostic indicator for LUAD based on 12 forms of PCD. A total of 1,933 candidate genes related to PCD were collected from published studies and public data center. A prognostic gene signature, called the cell death index (CDI), was established based on RNA-Seq and immunohistochemistry (IHC). IHC staining on tissue microarray was applied for the validation of protein level. Moreover, GSE42127, GSE72094 were used as validation datasets. The CDI based on expression level of nine genes (CCNB2, HMGA1, CACNA2D2, BUB1B, BTG2, KIF14, PTGDS, SERPINB5, BRCA1) was highly predictive for overall survival (OS) of LUAD in our cohort [36-month area under the curve (AUC): 0.750, 60-month AUC: 0.809]. The CDI was further validated in independent cohorts (GSE72094, 36-month AUC: 0.717, 60-month AUC: 0.737; GSE42127, 12-month AUC: 0.829, 60-month AUC: 0.663). And the CDI was found to be an independent prognostic factor after adjusting for other clinical characteristics. Furthermore, the high-CDI group was associated with upregulated tumor immune infiltration compared to the low-CDI group. This study identified a 9-gene signature (CDI) based on PCD-related genes that accurately predicted the prognosis of LUAD patients. The CDI could serve as a valuable prognostic indicator and guide personalized therapeutic strategies for LUAD.

A 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine.

Ovarian cancer is a major cause of cancer deaths among women. Early diagnosis and precision/personalized medicine are essential to reduce mortality and morbidity of ovarian cancer, as with new molecular targets to accelerate drug discovery. We report here an integrated systems biology and machine learning (ML) approach based on the differential coexpression analysis to identify candidate systems biomarkers (i.e., gene modules) for serous ovarian cancer. Accordingly, four independent transcriptome datasets were statistically analyzed independently and common differentially expressed genes (DEGs) were identified. Using these DEGs, coexpressed gene pairs were unraveled. Subsequently, differential coexpression networks between the coexpressed gene pairs were reconstructed so as to identify the differentially coexpressed gene modules. Based on the established criteria, "SOV-module" was identified as being significant, consisting of 19 genes. Using independent datasets, the diagnostic capacity of the SOV-module was evaluated using principal component analysis (PCA) and ML techniques. PCA showed a sensitivity and specificity of 96.7% and 100%, respectively, and ML analysis showed an accuracy of up to 100% in distinguishing phenotypes in the present study sample. The prognostic capacity of the SOV-module was evaluated using survival and ML analyses. We found that the SOV-module's performance for prognostics was significant (p-value = 1.36 × 10-4) with an accuracy of 63% in discriminating between survival and death using ML techniques. In summary, the reported genomic systems biomarker candidate offers promise for personalized medicine in diagnosis and prognosis of serous ovarian cancer and warrants further experimental and translational clinical studies.

A gene expression-based classifier for HER2-low breast cancer

In clinical trials evaluating antibody-conjugated drugs (ADCs), HER2-low breast cancer is defined through protein immunohistochemistry scoring (IHC) 1+ or 2+ without gene amplification. However, in daily practice, the accuracy of IHC is compromised by inter-observer variability. Herein, we aimed to identify HER2-low breast cancer primary tumors by leveraging gene expression profiling. A discovery approach was applied to gene expression profile of institutional INT1 (n = 125) and INT2 (n = 84) datasets. We identified differentially expressed genes (DEGs) in each specific HER2 IHC category 0, 1+, 2+ and 3+. Principal Component Analysis was used to generate a HER2-low signature whose performance was evaluated in the independent INT3 (n = 95), and in the publicly available TCGA and GSE81538 datasets. The association between the HER2-low signature and HER2 IHC categories was evaluated by Kruskal–Wallis test with post hoc pair-wise comparisons. The HER2-low signature discriminatory capability was assessed by estimating the area under the receiver operating characteristic curve (AUC). Gene Ontology and KEGG analyses were performed to evaluate the HER2-low signature genes functional enrichment. A HER2-low signature was computed based on HER2 IHC category-specific DEGs. The twenty genes included in the signature were significantly enriched with lipid and steroid metabolism pathways, peptidase regulation, and humoral immune response. The HER2-low signature values showed a bell-shaped distribution across IHC categories (low values in 0 and 3+; high values in 1+ and 2+), effectively distinguishing HER2-low from 0 (p < 0.001) to 3+ (p < 0.001). Notably, the signature values were higher in tumors scored with 1+ as compared to 0. The HER2-low signature association with IHC categories and its bell-shaped distribution was confirmed in the independent INT3, TCGA and GSE81538 datasets. In the combined INT1 and INT3 datasets, the HER2-low signature achieved an AUC value of 0.74 (95% confidence interval, CI 0.67–0.81) in distinguishing HER2-low vs. the other categories, outperforming the individual ERBB2 mRNA AUC value of 0.52 (95% CI 0.43–0.60). These results represent a proof-of-concept for an observer-independent gene-expression-based classifier of HER2-low status. The herein identified 20-gene signature shows promise in distinguishing between HER2 0 and HER2-low expressing tumors, including those scored as 1+ at IHC, and in developing a selection approach for ADCs candidates.

Identification and validation of a novel 9-gene signature of non-specific classification to predict prognosis in glioma patients.

This study aimed to identify and validate a 9-gene signature for predicting overall survival (OS) in glioma patients. Analysis of multiple gene expression datasets led to the identification of 135 candidate genes associated with OS in glioma patients. Further analysis revealed that IGFBP2, PBK, NRXN3, TGIF1, DNAJA4, and LGALS3BP were identified as risk factors for OS, while ENAH, PPP2R2C, and SPHKAP were found to be protective factors. Multifaceted validation using different databases confirmed their differential expression patterns in glioma tissues compared to normal brain tissue. By utilizing LASSO regression and multivariate Cox regression analysis, a risk score was developed based on the expression levels of the 9 crucial genes. The risk score showed a significant correlation with OS in both training and validation cohorts and yielded superior predictive accuracy compared to individual gene expression. Moreover, a predictive nomogram incorporating the risk score, WHO grade, age, IDH mutation, and 1p/19q co-deletion was constructed and validated, which exhibited high predictive capabilities for survival rates at different time points. Enrichment analysis revealed the involvement of extracellular matrix-related pathways and immune system signaling in glioma prognosis. Furthermore, the risk score showed a strong correlation with immune cell infiltration and immune checkpoint expression, suggesting its potential role in the tumor immune microenvironment. In conclusion, our study provides a robust 9-gene signature and a predictive nomogram for evaluating the prognosis of glioma patients, offering valuable insights into personalized treatment strategies.

Construction of disulfidptosis-based immune response prediction model with artificial intelligence and validation of the pivotal grouping oncogene c-MET in regulating T cell exhaustion.

Given the lack of research on disulfidptosis, our study aimed to dissect its role in pan-cancer and explore the crosstalk between disulfidptosis and cancer immunity. Based on TCGA, ICGC, CGGA, GSE30219, GSE31210, GSE37745, GSE50081, GSE22138, GSE41613, univariate Cox regression, LASSO regression, and multivariate Cox regression were used to construct the rough gene signature based on disulfidptosis for each type of cancer. SsGSEA and Cibersort, followed by correlation analysis, were harnessed to explore the linkage between disulfidptosis and cancer immunity. Weighted correlation network analysis (WGCNA) and Machine learning were utilized to make a refined prognosis model for pan-cancer. In particular, a customized, enhanced prognosis model was made for glioma. The siRNA transfection, FACS, ELISA, etc., were employed to validate the function of c-MET. The expression comparison of the disulfidptosis-related genes (DRGs) between tumor and nontumor tissues implied a significant difference in most cancers. The correlation between disulfidptosis and immune cell infiltration, including T cell exhaustion (Tex), was evident, especially in glioma. The 7-gene signature was constructed as the rough model for the glioma prognosis. A pan-cancer suitable DSP clustering was made and validated to predict the prognosis. Furthermore, two DSP groups were defined by machine learning to predict the survival and immune therapy response in glioma, which was validated in CGGA. PD-L1 and other immune pathways were highly enriched in the core blue gene module from WGCNA. Among them, c-MET was validated as a tumor driver gene and JAK3-STAT3-PD-L1/PD1 regulator in glioma and T cells. Specifically, the down-regulation of c-MET decreased the proportion of PD1+ CD8+ T cells. To summarize, we dissected the roles of DRGs in the prognosis and their relationship with immunity in pan-cancer. A general prognosis model based on machine learning was constructed for pan-cancer and validated by external datasets with a consistent result. In particular, a survival-predicting model was made specifically for patients with glioma to predict its survival and immune response to ICIs. C-MET was screened and validated for its tumor driver gene and immune regulation function (inducing t-cell exhaustion) in glioma.