Multigene Model Research Articles

Comparative Analysis of Transcription Factors TWIST2, GATA3, and HES5 in Glioblastoma Multiforme : Evaluating Biomarker Potential and Therapeutic Targets Using In Silico Methods.

Glioblastoma multiforme (GBM) is characterized by substantial heterogeneity and limited therapeutic options. As molecular approaches to central nervous system (CNS) tumors have gained prominence, this study examined the roles of three genes, TWIST2, GATA3, and HES5, known to be involved in oncogenesis, developmental processes, and maintenance of cancer stem cell properties, which have not yet been extensively studied in GBM. This study is the first to present gene expression data for TWIST2, GATA3, and HES5 specifically within the context of GBM patient survival. Gene expression data for TWIST2, GATA3, and HES5 were collected from GBM and normal brain tissues using datasets from The Cancer Genome Atlas (TCGA) via the Genomic Data Commons (GDC) portal and the Genotype-Tissue Expression (GTEx) database. These data were rigorously analyzed using in silico methods. All three genes were significantly more expressed in GBM tissues than in normal tissues. TWIST2 and GATA3 were linked to lower survival rates in GBM patients. Interestingly, higher HES5 levels were associated with better survival rates, suggesting a complex role that needs more investigation. This study shows that TWIST2, GATA3, and HES5 could help predict outcomes in GBM patients. Our multigene model offers a better understanding of GBM and points to new treatment options, bringing hope for improved therapies and patient outcomes. This research advances our knowledge of GBM and highlights the potential of molecular diagnostics in oncology.

Cross-talk between oxidative stress and lipid metabolism regulators reveals molecular clusters and immunological characterization in polycystic ovarian syndrome

BackgroundChanges in the oxidative stress and lipid metabolism (OSLM) pathways play important roles in polycystic ovarian syndrome (PCOS) pathogenesis and development. Consequently, a systematic analysis of genes related to OSLM was conducted to identify molecular clusters and explore new biomarkers that are helpful for the diagnostic of PCOS.MethodsGene expression and clinical data from 22 PCOS women and 14 normal women were obtained from the GEO database (GSE34526, GSE95728, and GSE106724). Consensus clustering identified OSLM-related molecular clusters, and WGCNA revealed co-expression patterns. The immune microenvironment was quantitatively assessed utilizing the CIBERSORT algorithm. Multiple machine learning models and connectivity map analyses were subsequently applied to explore potential biomarkers for PCOS, and nomograms were employed to develop a predictive multigene model of PCOS. Finally, the OSLM status of PCOS and the hub genes expression profiles were preliminarily verified using TUNEL, qRT‒PCR, western blot, and IHC assays in a PCOS mouse model.Results19 differential expression genes (DEGs) related to OSLM were identified. Based on 19 DEGs that were strongly influenced by OSLM, PCOS patients were stratified into two distinct clusters, designated Cluster 1 and Cluster 2. Distinct differences in the immune cell proportions existed in normal and two PCOS clusters. The random forest showed the best results, with the least cross-entropy and the utmost AUC (cross-entropy: 0.111 AUC: 0.960). Among the 19 OSLM-related genes, CXCR1, ACP5, CEACAM3, S1PR4, and TCF7 were identified by a Bayesian network and had a good fit with PCOS disease risk by the nomogram (AUC: 0.990 CI: 0.968–1.000). TUNEL assays revealed more severe DNA damage within the ovarian granule cells of PCOS mice than in those of normal mice (P < 0.001). The RNA and protein expression levels of the five hub genes were significantly elevated in PCOS mice, which was consistent with the results of the bioinformatics analyses.ConclusionA novel predictive model was constructed for PCOS patients and five hub genes were identified as potential biomarkers to offer novel insights into clinical diagnostic strategies for PCOS.

Glycolysis-related genes predict prognosis and indicate immune microenvironment features in gastric cancer.

Gastric cancer (GC) is a major contributor to cancer-related mortality. Glycolysis plays a pivotal role in tumor microenvironment (TME) reprogramming. In this research, the functions of glycolysis-associated genes (GRGs) were evaluated to predict the outcome and reveal the characteristics of the immune microenvironment in individuals with stomach cancer. The Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) cohort provided gene expression and clinical data for gastric cancer (GC) patients, which were further authenticated using datasets sourced from the Gene Expression Omnibus (GEO). By referencing the Molecular Signatures Database (MSigDB), a total of 326 GRGs were pinpointed. The various subtypes of GC were outlined through consensus clustering, derived from the expression patterns of these GRGs. Utilizing multivariate Cox regression analysis, a multigene risk score model was formulated. Both the CIBERSORT and ESTIMATE algorithms played a pivotal role in assessing the immune microenvironment. To delve into the biological functions of the key genes, wound healing, transwell invasion, and MTT assays were conducted. Based on the expression patterns of GRGs, patients were categorized into two distinct groups: the metabolic subtype, designated as cluster A, and the immune subtype, labeled as cluster B. Patients belonging to cluster B exhibited a poorer prognosis. A prognostic risk score model, formulated upon the expression levels of six key GRGs - ME1, PLOD2, NUP50, CXCR4, SLC35A3, and SRD35A3 - emerged as a viable tool for predicting patient outcomes. The downregulation of CXCR4 notably diminished the glycolytic capacity of gastric cancer (GC) cells, alongside their migratory, invasive, and proliferative capabilities. Intriguingly, despite the adverse prognostic implications associated with both the immune subtype (cluster B) and the high-risk cohort, these groups exhibited a favorable immune microenvironment coupled with elevated expression of immune checkpoint genes. Our investigations revealed a positive correlation between high CXCR4 expression and low ME1 expression with the infiltration of CD8+ T cells, as well as an enhanced responsiveness to treatment with an anti-PD-1 immune checkpoint inhibitor. In this study, we discovered that the expression profiles of GRGs hold the potential to forecast the prognosis of gastric cancer (GC) patients, thereby possibly aiding in clinical treatment decision-making.

A Plasmodium falciparum genetic cross reveals the contributions of pfcrt and plasmepsin II/III to piperaquine drug resistance.

Piperaquine (PPQ) is widely used in combination with dihydroartemisinin as a first-line treatment against malaria. Multiple genetic drivers of PPQ resistance have been reported, including mutations in the Plasmodium falciparum chloroquine resistance transporter (pfcrt) and increased copies of plasmepsin II/III (pm2/3). We generated a cross between a Cambodia-derived multidrug-resistant KEL1/PLA1 lineage isolate (KH004) and a drug-susceptible Malawian parasite (Mal31). Mal31 harbors a wild-type (3D7-like) pfcrt allele and a single copy of pm2/3, while KH004 has a chloroquine-resistant (Dd2-like) pfcrt allele with an additional G367C substitution and multiple copies of pm2/3. We recovered 104 unique recombinant parasites and examined a targeted set of progeny representing all possible combinations of variants at pfcrt and pm2/3. We performed a detailed analysis of competitive fitness and a range of PPQ susceptibility phenotypes with these progenies, including PPQ survival assay, area under the dose response curve, and a limited point IC50. We find that inheritance of the KH004 pfcrt allele is required for reduced PPQ sensitivity, whereas copy number variation in pm2/3 further decreases susceptibility but does not confer resistance in the absence of additional mutations in pfcrt. A deep investigation of genotype-phenotype relationships demonstrates that progeny clones from experimental crosses can be used to understand the relative contributions of pfcrt, pm2/3, and parasite genetic background to a range of PPQ-related traits. Additionally, we find that the resistance phenotype associated with parasites inheriting the G367C substitution in pfcrt is consistent with previously validated PPQ resistance mutations in this transporter.IMPORTANCEResistance to piperaquine, used in combination with dihydroartemisinin, has emerged in Cambodia and threatens to spread to other malaria-endemic regions. Understanding the causal mutations of drug resistance and their impact on parasite fitness is critical for surveillance and intervention and can also reveal new avenues to limiting the evolution and spread of drug resistance. An experimental genetic cross is a powerful tool for pinpointing the genetic determinants of key drug resistance and fitness phenotypes and has the distinct advantage of quantifying the effects of naturally evolved genetic variation. Our study was strengthened since the full range of copies of KH004 pm2/3 was inherited among the progeny clones, allowing us to directly test the role of the pm2/3 copy number on resistance-related phenotypes in the context of a unique pfcrt allele. Our multigene model suggests an important role for both loci in the evolution of this multidrug-resistant parasite lineage.

Integrated bioinformatics, network pharmacology, molecular docking, and molecular dynamics simulation to explore the potential pharmacological mechanism of Erigeron breviscapus (Vant.) Hand-Mazz regulating ferroptosis for the treatment of Alzheimer's disease

Ferroptosis plays a role in Alzheimer's disease (AD) development. Erigeron breviscapus (Vant.) Hand-Mazz (EBHM) shows promising effects in treating cognitive impairment-related diseases. However, the mechanisms by which EBHM regulates ferroptosis in AD treatment are not fully understood. This study used bioinformatics, network pharmacology, molecular docking, and molecular dynamics simulation to explore how EBHM regulates ferroptosis in AD treatment. The results identified four key genes—HSPA8, GSK3B, CTSB, and YWHAG—that are involved in this regulation, and constructed a multigene diagnostic model for AD. The model demonstrated moderate accuracy (area under the curve [AUC] = 0.636) in distinguishing AD from non-demented (ND) and was further validated with external datasets showing good diagnostic capabilities (AUC values of 0.898, 0.889, 0.746, and 0.712). Additionally, CIBERSORT analysis revealed a significant correlation between immune cell infiltration and these four genes, highlighting their potential role in AD immunity. Molecular docking studies indicated that 3,4,5-tricaffeoylquinic acid (TCQA) had the highest binding affinity to HSPA8, suggesting that TCQA and HSPA8 are key components and core targets in EBHM's regulation of ferroptosis in AD therapy. Molecular dynamics simulations confirmed the stability and strong binding affinity of the TCQA-HSPA8 complex. These findings enhance our understanding of the molecular mechanisms underlying ferroptosis regulation by EBHM for the treatment of AD and may offer new avenues for developing effective AD treatments.

Prognostic and chemotherapeutic implications of a novel four-gene pyroptosis model in head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancers. Chemotherapy remains one dominant therapeutic strategy, while a substantial proportion of patients may develop chemotherapeutic resistance; therefore, it is particularly significant to identify the patients who could achieve maximum benefits from chemotherapy. Presently, four pyroptosis genes are reported to correlate with the chemotherapeutic response or prognosis of HNSCC, while no study has assessed the combinatorial predicting efficacy of these four genes. Hence, this study aims to evaluate the predictive value of a multi-gene pyroptosis model regarding the prognosis and chemotherapeutic responsiveness in HNSCC. By utilizing RNA-sequencing data from The Cancer Genome Atlas database and the Gene Expression Omnibus database, the pyroptosis-related gene score (PRGscore) was computed for each HNSCC sample by performing a Gene Set Variation Analysis (GSVA) based on four genes (Caspase-1, Caspase-3, Gasdermin D, Gasdermin E). The prognostic significance of the PRGscore was assessed through Cox regression and Kaplan-Meier survival analyses. Additionally, chemotherapy sensitivity stratified by high and low PRGscore was examined to determine the potential association between pyroptosis activity and chemosensitivity. Furthermore, chemotherapy sensitivity assays were conducted in HNSCC cell lines in vitro. As a result, our study successfully formulated a PRGscore reflective of pyroptotic activity in HNSCC. Higher PRGscore correlates with worse prognosis. However, patients with higher PRGscore were remarkably more responsive to chemotherapy. In agreement, chemotherapy sensitivity tests on HNSCC cell lines indicated a positive association between overall pyroptosis levels and chemosensitivity to cisplatin and 5-fluorouracil; in addition, patients with higher PRGscore may benefit from the immunotherapy. Overall, our study suggests that HNSCC patients with higher PRGscore, though may have a less favorable prognosis, chemotherapy and immunotherapy may exhibit better benefits in this population.

Abstract PO3-20-05: Phase II Study of Genomically Guided Radiation Dose Personalization in the Management of Triple Negative Breast Cancer

Abstract Background: Our group has previously developed the radiosensitivity index (RSI) using a multigene expression model that is directly proportional to tumor radioresistance (high RSI = increased radioresistance). RSI has been previously validated in two datasets of patients with triple negative breast cancer (TNBC). In this study, we will run a selective dose personalization study in TNBC patients undergoing breast conservation therapy (BCT). Based on patients RSI scores, they will either receive a radiation therapy (RT) boost of 10 Gy to the tumor cavity or not. Given our data in two independent datasets, the current study will reveal the feasibility and benefit of selective genomic dose personalization in TNBC following BCT. Trial Design: The study is designed as a prospective, nonrandomized, phase II trial of genomically guided RT in the management of TNBC undergoing BCT. Patients will be allocated to one of two groups based on their RSI determination from fresh frozen tissue collected by biopsy or at the time of BCT. These groups will be Group A, RSI optimized whole breast radiotherapy alone or with a 10 Gy boost or Group B, RSI not optimized whole breast radiotherapy with a boost of 10 Gy to tumor cavity. Patients will receive standard of care chemotherapy, neoadjuvant or adjuvant. Eligibility: TNBC patients undergoing BCT. Specific Aims: To determine the three-year local control following genomically guided dose personalization in the management of TNBC following BCT. Secondary objectives include determination of overall survival (OS), progression free survival (PFS), and quality of life (QOL) following genomically guided dose personalization. Statistical Methods: The primary hypothesis is the three-year local control rates differ for groups A and B, against the null hypothesis that the two rates are identical. Patients will allocate approximately 78% in group A and 22% in group B and local control rates are expected to be 96% and 75%, respectively. Assuming 80% power and 10% type I error for a log-rank test, 86 patients are needed. An interim analysis will be completed when 4 disease progression events are observed. There will be approximately 43 patients at the time of interim analysis. Patient Accrual: This study is open with 1 patient enrolled at the time of submission. A total of 86 patients will be enrolled. Contact Information: Kamran A. Ahmed MD, Moffitt Cancer Center, email: kamran.ahmed@moffitt.org, Clinical trial information: NCT05115474. Funding: Moffitt and Morton Plant Mease Foundations. Citation Format: Kamran Ahmed, Iman Washington, Matthew Mills, Michelle DeJesus, Youngchul Kim, Ronica Nanda, Javier Torres-Roca, Steven Eschrich, Janis De La Iglesia, John Puskas, Marilin Rosa, Jason Wilson, Paula Lundgren, Negar Golesorkhi, Nazanin Khakpour, Susan Hoover, Marie Lee, John Kiluk, Melissa Mallory, Christine Laronga, Laura Kruper, Brian Czerniecki, Roberto Diaz. Phase II Study of Genomically Guided Radiation Dose Personalization in the Management of Triple Negative Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-20-05.

Novel targets and their functions in the prognosis of uterine corpus endometrial cancer patients.

Aberrant mRNA expression is implicated in uterine corpus endometrial carcinoma (UCEC) oncogenesis and progression. However, effective prognostic biomarkers for UCEC remain limited. We aimed to construct a reliable multi-gene risk model using gene expression profiles. Utilizing TCGA data (543 UCEC samples, 35 controls), we identified 1517 differentially acting genes. Weighted gene co-expression complex analysis (WGCCA), hub gene screening, and risk regression analysis (RRA) were employed to determine prognosis-related genes and construct the risk model. Nomograms visualized risk scores and receiver operator characteristic (ROC) curves assessed model performance. Seven novel prognosis-related hub genes (ANGPT1, ASB2, GAL, GDF7, ONECUT2, SV2B, TRPC6) were identified. The model's concordance index (C index) by multivariate Cox regression analysis was 0.79. ROC curves yielded AUCs of 0.811 (3-year) and 0.79 (5-year), demonstrating the model's efficacy in predicting UCEC survival. Our study proposes a promising seven-biomarker risk model for predicting UCEC prognosis, offering potential clinical utility.

Deciphering age-specific molecular features in cervical cancer and constructing an angio-immune prognostic model.

Cancer incidence is increasingly seen in younger individuals. Molecular distinctions between young and elderly patients at onset are understudied. This study used public databases to explore genomic, transcriptomic, and immune-related features across age groups in cervical cancer. Additionally, it aims to create a prognostic model applicable across diverse age cohorts, enabling precise patient stratification, and personalized therapies. Gene mutations, expression data, and clinicopathological information were obtained from 317 cervical cancer patients. These patients were divided into a young group and an old group based on the median age of onset. The characteristics of differential gene mutation, gene expression, and immune cells analysis were analyzed by R software. Finally, the prognostic model was constructed by univariate Cox, least absolute shrinkage and selection operator, and multivariate Cox regression analyses of angiogenic and immune gene sets. Its validity was further confirmed using an additional 300 cervical squamous cell carcinoma and endocervical adenocarcinoma tissues. Cervical cancer patients at elderly onset age exhibit a significantly higher frequency of NOTCH1 and TP53 driver mutations compared to young patients, along with a notably higher tumor mutational burden. However, there were no significant differences between the 2 groups in terms of genomic instability and age-related mutational signatures. Differential gene expression analysis revealed that the young group significantly upregulated interferon-alpha and gamma responses and exhibited significantly higher activity in multiple metabolic pathways. Immune microenvironment analysis indicated enrichment of dendritic cells and natural killer cells in the young group, while transforming growth factor-β signature was enriched in the elderly group, indicating a higher degree of immune exclusion. A multigene prognostic model based on angiogenesis and T cell immune gene sets showed excellent prognostic performance independent of clinical factors such as age. High-risk groups identified by the model exhibit significant activation of tumor-promoting processes, such as metastasis and angiogenesis. Our study reveals distinct patterns in cancer-driving mechanisms, biological processes, and immune system status between young and elderly patients at onset with cervical cancer. These findings shed light on the age-specific underlying mechanisms of carcinogenesis. Furthermore, an independent molecular prognostic model is constructed to provide valuable references for patient stratification and the development of potential drug targets.

The Construction of a Multi-Gene Risk Model for Colon Cancer Prognosis and Drug Treatments Prediction.

In clinical practice, colon cancer is a prevalent malignant tumor of the digestive system, characterized by a complex and progressive process involving multiple genes and molecular pathways. Historically, research efforts have primarily focused on investigating individual genes; however, our current study aims to explore the collective impact of multiple genes on colon cancer and to identify potential therapeutic targets associated with these genes. For this research, we acquired the gene expression profiles and RNA sequencing data of colon cancer from TCGA. Subsequently, we conducted differential gene expression analysis using R, followed by GO and KEGG pathway enrichment analyses. To construct a protein-protein interaction (PPI) network, we selected survival-related genes using the log-rank test and single-factor Cox regression analysis. Additionally, we performed LASSO regression analysis, immune infiltration analysis, mutation analysis, and cMAP analysis, as well as an investigation into ferroptosis. Our differential expression and survival analyses identified 47 hub genes, and subsequent LASSO regression analysis refined the focus to 23 key genes. These genes are closely linked to cancer metastasis, proliferation, apoptosis, cell cycle regulation, signal transduction, cancer microenvironment, immunotherapy, and neurodevelopment. Overall, the hub genes discovered in our study are pivotal in colon cancer and are anticipated to serve as important biological markers for the diagnosis and treatment of the disease.

Multi-omics analysis for ferroptosis-related genes as prognostic factors in cutaneous melanoma.

Melanoma is highly malignant and heterogeneous. It is essential to develop a specific prognostic model for improving the patients' survival and treatment strategies. Recent studies have shown that ferroptosis results from the overproduction of lipid peroxidation and is an iron-dependent form of programmed cell death. Despite this, ferroptosis-related genes (FRGs) and their clinical significances remain unknown in malignant melanoma. This study aims to assess the role of FRGs in melanoma, with the goal of developing a novel prognostic model that provides new insights into personalized treatment and improvement of therapeutic outcomes for melanoma. We systematically characterized the genetic alterations and mRNA expression of 73 FRGs in The Cancer Genome Atlas (TCGA)-skin cutaneous melanoma (SKCM) dataset in this study. The results were validated with real-time RT-PCR and Western blotting. Subsequently, a multi-gene feature model was constructed using the TCGA-SKCM cohort. Melanoma patients were classified into a high-risk group and a low-risk group based on the feature model. As a final step, correlations between ferroptosis-related signatures and immune features, immunotherapy efficacy, or drug response were analyzed. By analyzing melanoma samples from TCGA-SKCM dataset, FRGs exhibited a high frequency of genetic mutations and copy number variations (CNVs), significantly impacting gene expression. Additionally, compared with normal skin tissue, 30 genes with significantly differential expression were identified in melanoma tissues. A prognostic model related to FRGs, constructed using the LASSO Cox regression method, identified 13 FRGs associated with overall survival prognosis in patients and was validated with external datasets. Finally, functional enrichment and immune response analysis further indicated significant differences in immune cell infiltration, mutation burden, and hypoxia status between the high-risk group and the low-risk group, and the model was effective in predicting responses to immunotherapy and drug sensitivity. This study develops a strong ferroptosis-related prognostic signature model which could put forward new insights into target therapy and immunotherapy for patients with melanoma.

CRISPR Ribonucleoprotein-Mediated Precise Editing of Multiple Genes in Porcine Fibroblasts.

The multi-gene editing porcine cell model can analyze the genetic mechanisms of multiple genes, which is beneficial for accelerating genetic breeding. However, there has been a lack of an effective strategy to simultaneously perform precise multi-gene editing in porcine cells. In this study, we aimed to improve the efficiency of CRISPR RNP-mediated precise gene editing in porcine cells. CRISPR RNP, including Cas9 protein, sgRNA, and ssODN, was used to generate precise nucleotide substitutions by homology-directed repair (HDR) in porcine fetal fibroblasts (PFFs). These components were introduced into PFFs via electroporation, followed by PCR for each target site. To enhance HDR efficacy, small-molecule M3814 and phosphorothioate-modified ssODN were employed. All target DNA samples were sequenced and analyzed, and the efficiencies of different combinations of the CRISPR RNP system in target sites were compared. The results showed that when 2 μM M3814, a small molecule which inhibits NHEJ-mediated repair by blocking DNA-PKs activity, was used, there was no toxicity to PFFs. The CRISPR RNP-mediated HDR efficiency increased 3.62-fold. The combination of CRISPR RNP with 2 μM M3814 and PS-ssODNs achieved an HDR-mediated precision gene modification efficiency of approximately 42.81% in mutated cells, a 6.38-fold increase compared to the control group. Then, we used the optimized CRISPR RNP system to perform simultaneous editing of two and three loci at the INS and RLN3 genes. The results showed that the CRISPR RNP system could simultaneously edit two and three loci. The efficiency of simultaneous editing of two loci was not significantly different from that of single-gene editing compared to the efficiency of single-locus editing. The efficiency of simultaneous precise editing of INS, RLN3 exon 1, and RLN3 exon 2 was 0.29%, 0.24%, and 1.05%, respectively. This study demonstrated that a 2 μM M3814 combination with PS-ssODNs improves the efficacy of CRISPR RNP-mediated precise gene editing and allows for precise editing of up to three genes simultaneously in porcine cells.

Molecular subtype identification and prognosis stratification based on golgi apparatus-related genes in head and neck squamous cell carcinoma

BackgroundAbnormal dynamics of the Golgi apparatus reshape the tumor microenvironment and immune landscape, playing a crucial role in the prognosis and treatment response of cancer. This study aims to investigate the potential role of Golgi apparatus-related genes (GARGs) in the heterogeneity and prognosis of head and neck squamous cell carcinoma (HNSCC).MethodsTranscriptional data and corresponding clinical information of HNSCC were obtained from public databases for differential expression analysis, consensus clustering, survival analysis, immune infiltration analysis, immune therapy response assessment, gene set enrichment analysis, and drug sensitivity analysis. Multiple machine learning algorithms were employed to construct a prognostic model based on GARGs. A nomogram was used to integrate and visualize the multi-gene model with clinical pathological features.ResultsA total of 321 GARGs that were differentially expressed were identified, out of which 69 were associated with the prognosis of HNSCC. Based on these prognostic genes, two molecular subtypes of HNSCC were identified, which showed significant differences in prognosis. Additionally, a risk signature consisting of 28 GARGs was constructed and demonstrated good performance for assessing the prognosis of HNSCC. This signature divided HNSCC into the high-risk and low-risk groups with significant differences in multiple clinicopathological characteristics, including survival outcome, grade, T stage, chemotherapy. Immune response-related pathways were significantly activated in the high-risk group with better prognosis. There were significant differences in chemotherapy drug sensitivity and immune therapy response between the high-risk and low-risk groups, with the low-risk group being more suitable for receiving immunotherapy. Riskscore, age, grade, and radiotherapy were independent prognostic factors for HNSCC and were used to construct a nomogram, which had good clinical applicability.ConclusionsWe successfully identified molecular subtypes and prognostic signature of HNSCC that are derived from GARGs, which can be used for the assessment of HNSCC prognosis and treatment responses.

Development of an obesity-related multi-gene prognostic model incorporating clinical characteristics in luminal breast cancer

Despite adjuvant chemotherapy and endocrine therapy in luminal breast cancer (LBC), relapses are common. Addressing this, we aim to develop a prognostic model to refine adjuvant therapy strategies, particularly for patients at high recurrence risk. Notably, obesity profoundly affects the tumor microenvironment (TME) of LBC. However, it is unclear whether obesity-related biological features can effectively screen high-risk patients. Utilizing weighted gene coexpression network analysis (WGCNA) on RNA sequencing (RNAseq) data, we identified seven obese LBC genes (OLGs) closely associated with patient prognosis. Subsequently, we developed a luminal obesity-gene clinical prognostic index (LOG-CPI), combining a 7-gene signature, TNM staging, and age. Its predictive efficacy was confirmed across validation datasets and a clinical cohort (5-year accuracy= 0.828, 0.760, 0.751, and 0.792, respectively). LOG-CPI emerges as a promising predictor for clinical prognosis and treatment response, helping distinguish molecular and immunological features in LBC patients and guiding clinical practice by identifying varying prognoses.

Selective Organ-Targeting Hafnium Oxide Nanoparticles with Multienzyme-Mimetic Activities Attenuate Radiation-Induced Tissue Damage.

Radioprotective agents hold clinical promises to counteract off-target adverse effects of radiation and benefit radiotherapeutic outcomes, yet the inability to control drug transport in human organs poses a leading limitation. Based upon a validated rank-based multigene signature model, radiosensitivity indices are evaluated of diverse normal organs as a genomic predictor of radiation susceptibility. Selective ORgan-Targeting (SORT) hafnium oxide nanoparticles (HfO2 NPs) are rationally designed via modulated synthesis by α-lactalbumin, homing to top vulnerable organs. HfO2 NPs like Hensify are commonly radioenhancers, but SORT HfO2 NPs exhibit surprising radioprotective effects dictated by unfolded ligands and Hf(0)/Hf(IV) redox couples. Still, the X-ray attenuation patterns allow radiological confirmation in target organs by dual-beam spectral computed tomography. SORT HfO2 NPs present potent antioxidant activities, catalytically scavenge reactive oxygen species, and mimic multienzyme catalytic activities. Consequently, SORT NPs rescue radiation-induced DNA damage in mouse and rabbit models and provide survival benefits upon lethal exposures. In addition to inhibiting radiation-induced mitochondrial apoptosis, SORT NPs impede DNA damage and inflammation by attenuating activated FoxO, Hippo, TNF, and MAPK interactive cascades. A universal methodology is proposed to reverse radioenhancers into radioprotectors. SORT radioprotective agents with image guidance are envisioned as compelling in personalized shielding from radiation deposition.

Identification of Co-diagnostic Genes for Heart Failure and Hepatocellular Carcinoma Through WGCNA and Machine Learning Algorithms.

This research delves into the intricate relationship between hepatocellular carcinoma (HCC) and heart failure (HF) by exploring shared genetic characteristics and molecular processes. Employing advanced methodologies such as differential analysis, weighted correlation network analysis (WGCNA), and algorithms like Random Forest (RF), Least Absolute Shrinkage Selection (LASSO), and XGBoost, we meticulously identified modular differential genes (DEGs) associated with both HF and HCC. Gene Set Variation Analysis (GSVA) and single sample gene set enrichment analysis (ssGSEA) were employed to unveil underlying biological mechanisms. The study revealed 88 core genes shared between HF and HCC, indicating a common mechanism. Enrichment analysis emphasized the roles of immune responses and inflammation in both diseases. Leveraging XGBoost, we crafted a robust multigene diagnostic model (including FCN3, MAP2K1, AP3M2, CDH19) with an area under the curve (AUC)>0.9, showcasing exceptional predictive accuracy. GSVA and ssGSEA analyses unveiled the involvement of immune cells and metabolic pathways in the pathogenesis of HF and HCC. This research uncovers a pivotal interplay between HF and HCC, highlighting shared pathways and key genes, offering promising insights for future clinical treatments and experimental research endeavors.

Prediction of Prognostic Features Based on Neutrophil-Related Genes for Lung Squamous Cell Carcinoma Reveals Immune Landscape and Drug Candidates.

Background: Since to the prognosis of lung squamous cell carcinoma is generally poor, there is an urgent need to innovate new prognostic biomarkers and therapeutic targets to improve patient outcomes. Objectives: Our goal was to develop a novel multi-gene prognostic model linked to neutrophils for predicting lung squamous cell carcinoma prognosis. Methods: We utilized messenger RNA expression profiles and relevant clinical data of lung squamous cell carcinoma patients from the Cancer Genome Atlas database. Through K-means clustering, least absolute shrinkage and selection operator regression, and univariate/multivariate Cox regression analyses, we identified 12 neutrophil-related genes strongly related to patient survival and constructed a prognostic model. We verified the stability of the model in the Cancer Genome Atlas database and gene expression omnibus validation set, demonstrating the robust predictive performance of the model. Results: Immunoinfiltration analysis revealed remarkably elevated levels of infiltration for natural killer cells resting and monocytes in the high-risk group compared to the low-risk group, while macrophages had considerably lower infiltration in the high risk group. Most immune checkpoint genes, including programmed cell death protein 1 and cytotoxic T-lymphocyte-associated antigen 4, exhibited high expression levels in the high risk group. Tumor immune dysfunction and exclusion scores and immunophenoscore results suggested a potential inclination toward immunotherapy in the "RIC" version V2 revised high risk group. Moreover, prediction results from the CellMiner database revealed great correlations between drug sensitivity (e.g., Vinorelbine and PKI-587) and prognostic genes. Conclusion: Overall, our study established a reliable prognostic risk model that possessed significant value in predicting the overall survival of lung squamous cell carcinoma patients and may guide personalized treatment strategies. (Rev Invest Clin. 2024;76(2):116-31).

Construction and validation of a prognostic model for tongue cancer based on three genes signature.

Tongue squamous cell carcinoma (TSCC) has a poor prognosis and destructive characteristics. Reliable biomarkers are urgently required to predict disease outcomes and to guide TSCC treatment. This study aimed to develop a multigene signature and prognostic nomogram that can accurately predict the prognosis of patients with TSCC. We screened differentially expressed genes associated with TSCC using The Cancer Genome Atlas dataset. Based on this, we developed a new multi-mRNA gene signature using univariate Cox regression, Least Absolute Shrinkage and Selection Operator regression, and multivariate Cox regression. We used the concordance index to evaluate the accuracy of this new multigene model. Moreover, we performed receiver operating characteristic and Kaplan-Meier survival analyses to assess the predictive ability of the new multigene model. In addition, we created a prognostic nomogram incorporating clinical and pathological characteristics, with the aim of enhancing the adaptability of this model in practical clinical settings. We successfully developed a new prognostic model based on the expression levels of these 3 mRNAs that can be used to predict the prognosis of patients with TSCC. This prediction model includes 3 genes: KRT33B, CDKN2A, and CA9. In the validation set, the concordance index of this model was 0.851, and the area under the curve was 0.778 and 0.821 in the training and validation sets, respectively. Kaplan-Meier survival analysis showed that regardless of whether it was in the training or validation set, the prognosis of high-risk patients was significantly worse than that of low-risk patients (P < .001). Multivariate Cox regression analysis revealed that this model was an independent prognostic factor for patients with TSCC (P < .001). Our study suggests that this 3-gene signature model has a high level of accuracy and predictive ability, is closely related to the overall survival rate of patients with TSCC, and can independently predict the prognosis of TSCC patients with high accuracy and predictive ability.

Identification of the shared molecular mechanisms between major depressive disorder and COVID-19 from postmortem brain transcriptome analysis

ObjectivesThis study aims to investigate the molecular mechanisms underlying the interaction of major depressive disorder (MDD) and COVID-19, and on this basis, diagnostic biomarkers and potential therapeutic drugs are further explored. MethodsDifferential gene expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify common key genes involved in the pathogenesis of COVID-19 and MDD. Correlations with clinical features were explored. Detailed mechanisms were further investigated through protein interaction networks, GSEA, and immune cell infiltration analysis. Finally, Enrichr's Drug Signature Database and Coremine Medical were used to predict the potential drugs associated with key genes. ResultsThe study identified 18 genes involved in both COVID-19 and MDD. Four key genes (MBP, CYP4B1, ERMN, and SLC26A7) were selected based on clinical relevance. A multi-gene prediction model showed good diagnostic efficiency for the two diseases: AUC of 0.852 for COVID-19 and 0.915 for MDD. GO and GSEA analyses identified specific biological functions and pathways associated with key genes in COVID-19 (axon guidance, metabolism, stress response) and MDD (neuron ensheathment, biosynthesis, glutamatergic neuron differentiation). The key genes also affected immune infiltration. Potential therapeutic drugs, including small molecules and traditional Chinese medicines, targeting these genes were identified. ConclusionThis study provides insights into the complex biological mechanisms underlying COVID-19 and MDD, develops an effective diagnostic model, and predicts potential therapeutic drugs, which may contribute to the prevention and treatment of these two prevalent diseases.

Multigenic prognosis assessment model for nasopharyngeal carcinoma via a modified meta-analysis approach

Abstract Objectives Currently, clinically relevant multigene-based prognostic assessment models for nasopharyngeal carcinoma (NPC) are limited. This paper reports a novel NPC prognosis assessment model based on multiple established NPC-associated biomarkers. Methods We used a modified meta-analysis approach to retrieve eligible studies and analyse the data. Different prognostic biomarkers and hazard ratios (HRs) with 95 % confidence intervals (CIs) of overall survival (OS) data were extracted and tabulated from eligible studies. We then used the formula based on Parmar et al. to determine OS (expressed as HR with 95 % CI). Prognosis assessment risk scores assigned to the logarithm of HR were the basis for interpreting the multigene prognosis assessment model. Finally, we explained the biological significance of this model using a multigenic NPC oncogenesis network system. Results We constructed a multigenic NPC prognosis assessment model consisting of 10 prognostic biomarkers to determine the OS rate in NPC patients. Based on the biomarkers’ expression patterns, the model could determine 1,023 possible OS rates of NPC patients. The risk score derived determines the prognosis status of the NPC patients. The higher the total risk assessment score, the poorer the prognosis. An NPC-associated network involving all ten biomarkers was also derived. Conclusions We provided a novel multigenic NPC prognosis assessment model comprising ten prognostic biomarkers on OS rate in NPC patients. A conceptual molecular-based pathophysiological network of NPC oncogenesis supported the biological relevance of this model.