Identification of potential molecular targets for thyroid cancer using multiple-omics analysis and machine learning

Lung adenocarcinoma (LUAD) exhibits molecular heterogeneity, with mitochondrial damage affecting progression. The relationship between mitochondrial damage and immune infiltration, and Weighted Gene Co-expression Network Analysis (WGCNA)-derived biomarkers for LUAD classification and prognosis, remains unexplored. The objective of our research is to identify gene modules closely related to the clinical stages of LUAD using the WGCNA method. Based on the genes within these modules, we constructed machine learning (ML) models for classification and prognosis prediction, thereby facilitating precise diagnosis and personalized treatment of LUAD. Using GeneCards and The Cancer Genome Atlas (TCGA) databases, we screened differentially expressed mitochondrial damage-related genes in LUAD. Immune cell infiltration patterns were assessed using Single-Sample Gene Set Enrichment Analysis (SSGSEA) method. Functional enrichment analyses were conducted to explore biological functions and signaling pathways. Gene modules related to clinical stages of LUAD were identified by WGCNA. ML models were constructed for classification and prognosis prediction, and validated in an independent Gene Expression Omnibus (GEO) dataset. The study revealed a significant relationship between mitochondrial damage and immune infiltration in LUAD. We identified a gene module closely associated with the clinical stages of LUAD. The ML models for classification and prognosis that were constructed demonstrated good effectiveness and generalization capabilities. Mitochondrial damage-related genes are crucial in LUAD progression and linked to immune infiltration. The gene module and models identified have potential applications in LUAD classification and prognosis, offering novel markers for precision medicine. This study uncovers the relationship between mitochondrial damage and immune infiltration in LUAD, paving the way for molecular classification, prognosis prediction, and personalized treatment strategies.

BackgroundThe clinical outcomes are not always favorable in certain thyroid cancer patients. The effect of Forkhead-box family on immune cells infiltration and tumor microenvironment in thyroid cancer was explored. The role of FOXP2 in tumor invasion and recurrence was investigated consequently.MethodsTIMER and GEPIA were firstly employed to compare FOXPs expression in normal and cancer tissues from multiple human cancers. The results from database were confirmed by quantitative Real Time-PCR and Western blot in matched thyroid cancer and adjacent normal tissues, in addition to a panel of thyroid cancer cell lines and normal thyroid cell. GEPIA platform was employed to discover the possibility of FOXPs as prognostic indicator. TISIBD and UACLCAN were then employed to estimate the influence of FOXPs on lymph node metastasis and tumor staging. GEPIA analysis was initially employed to analyze correlation of FOXPs and tumor immune infiltrating cells, and TIMER dataset was then included for standardization according to tumor purity.ResultDifferent member of FOXPs showed divergence in expression in various cancer tissues. Lower FOXP1, FOXP2 and higher FOXP3, FOXP4 levels could be identified in thyroid cancer tissues when compared with matched normal tissue. There was an inverse correlation between FOXP2, FOXP4 and immune invasion, whereas FOXP1 and FOXP3 were positively correlated. FOXPs showed remarkable correlations with multiply immune cells. More importantly, only FOXP2 showed the significant effect on recurrence and tumor staging.ConclusionAs immune regulatory factor, the reduction of FOXP2 may affect tumor microenvironments and immune cells infiltration, enhance tumor immune escape, and promote recurrence of thyroid cancer. FOXP2 could be a new potential diagnostic and prognostic marker.

Thyroid cancer (TC) is a common malignant tumor of the endocrine system, and its morbidity and mortality are in the high places. Recent studies have focused on exploring biological markers and targeted therapy for TC. This research aims to elucidate the role of LINC00106 in the progression of TC and the regulatory mechanisms. Differential level of LINC00106 in a downloaded profile containing TC and normal tissues from GEPIA database was analyzed. Subsequently, its level in TC tissues and cell lines was detected by quantitative Real Time-Polymerase Chain Reaction (qRT-PCR). The relationship between LINC00106 level and clinical data of TC patients was assessed, including age, tumor staging, lymphatic metastasis, and overall survival. After transfection of si-LINC00106, TC cell metastasis was evaluated by wound healing and transwell assay. Relative levels of E-cadherin, N-cadherin, β-catenin, and Vimentin regulated by LINC00106 were determined using qRT-PCR and Western blot. LINC00106 was downregulated in TC tissues than normal ones. Its level was correlated to tumor staging, lymphatic metastasis and overall survival in TC patients. The knockdown of LINC00106 in BCPCP and TPC-1 cells enhanced migratory and invasive abilities and triggered the process of epithelial-mesenchymal transition (EMT). LINC00106 is lowly expressed in TC specimens, which attenuates migratory and invasive abilities in TC by inhibiting EMT as a tumor suppressor.

BackgroundThyroid cancer-related deaths mostly result from metastasis. It was reported that the immunometabolism associated enzyme interleukin-4-induced-1 (IL4I1) was related to tumor metastasis. The present study was intended to investigate the effects of IL4I1 on thyroid cancer metastasis and its relationship with the prognosis.MethodsData from Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to find out the different mRNA expressions of IL4I1 between thyroid cancer and normal tissues. And Human Protein Atlas (HPA) was used to assess IL4I1 protein expression. To further differentiate thyroid cancer from normal tissues and estimate the impact of IL4I1 on the prognosis, the receiver operating characteristic curve (ROC) and Kaplan–Meier (KM) method was performed. The protein–protein interaction (PPI) network was established using STRING, and functional enrichment analyses were conducted by “clusterProfiler” package. Then, we assayed the correlation between IL4I1 and some related molecules. The relationship between IL4I1 and immune infiltration was performed using “Gene Set Variation Analysis (GSVA)” package in TCGA and tumor-immune system interaction database (TISIDB). Finally, we did in vitro experiments in order to further prove the bioeffects of IL4I1 on metastasis.ResultsThe expression of IL4I1 mRNA and IL4I1 protein was significantly upregulated in thyroid cancer tissues. The increment of IL4I1 mRNA expression was related to high-grade malignancy, lymph node metastases and extrathyroidal extension. The ROC curve displayed the cutoff value of 0.782, with the sensitivity of 77.5% and the specificity of 77.8%. KM survival analysis showed that there was a worse PFS in patients with high IL4I1 expression than those with low IL4I1 expression (p = 0.013). Further study indicated that IL4I1 was associated with lactate, body fluid secretion, positive regulation of T cell differentiation, and cellular response to nutrients in Gene Ontology (GO) analysis. Moreover, IL4I1 was found correlated with immune infiltration. Finally, the in vitro experiments revealed the promotion of IL4I1 on cancer cell proliferation, migration and invasion.ConclusionsThe increased IL4I1 expression is markedly correlated with the immune imbalance in the tumor microenvironment (TME) and predicts poor survival in thyroid cancer. This study reveals the potential clinical biomarker of poor prognosis and the target of immune therapy in thyroid cancer.

BackgroundSIRT4, a protein localized in the mitochondria, is one of the least characteristic members of the sirtuin family. It is known that SIRT4 has deacetylase activity and plays a role in energy metabolism, but little is known about its possible role in carcinogenesis. Recently, several studies have suggested that SIRT4 may function as either a tumor oncogene or a tumor suppressor gene. However, its relationship with thyroid cancer remains unclear.MethodsWe stably overexpressed SIRT4 or silenced its expression in the human thyroid cancer cell line BCPAP by means of lentiviral vectors. We conducted a variety of tests, such as CCK-8, wound healing, migration, and invasion assays, to investigate the role of SIRT4 in the proliferation, migration, and invasion abilities of thyroid cancer cells. We also investigated the effects of SIRT4 overexpression on cell cycle progression and apoptosis of BCPAP cells and studied the role of glutamine metabolism in the effects of SIRT4 on BCPAP cell migration and invasion. Finally, we analyzed SIRT4 expression levels in thyroid cancer specimens by immunohistochemistry and investigated their association with clinicopathological features.ResultsOverexpression of SIRT4 inhibited the proliferation, migration, and invasion abilities of BCPAP thyroid cancer cells, blocked the cell cycle in the G0/G1 phase, and induced apoptosis. Mechanistically, SIRT4 inhibited BCPAP migration and invasion by inhibiting glutamine metabolism. Moreover, we found that SIRT4 protein levels in thyroid cancer tissues were markedly lower than in their non-neoplastic tissue counterparts (P<0.001).ConclusionSIRT4 plays a pivotal role in the growth and metastasis of thyroid cancer cells and could be a potential therapeutic target in thyroid cancer.

BackgroundTumor immune microenvironment (TIME) plays a crucial role in cancer development. However, the prognostic significance of immune-related genes (IRGs) in thyroid cancer (THCA) is unclear.MethodsThe Cancer Genome Atlas (TCGA)-THCA dataset was downloaded. The CIBERSORT algorithm was used to determine immune cell infiltration and a Weighted Gene Co-expression Network Analysis (WGCNA) was executed to obtain immune cell-related genes. Univariate Cox analysis was performed to screen prognostic genes and THCA samples were categorized into different immune cell-related clusters. The correlations between clusters and THCA prognosis and clinical characteristics were explored. Differentially expressed genes (DEGs) between THCA and controls from TCGA-THCA were identified. Macrophage and lymphocyte abundances, IFN-γ, wound healing, and TGF-beta levels were determined using the single set gene set enrichment analysis (GSEA), and THCA samples were categorized into different immune-related clusters, and corresponding genes were obtained from WGCNA. DEGs, IRGs, and immune-related clusters genes were subjected to overlap analysis to obtain differentially expressed IRGs (DE-IRGs), and these were subjected to least absolute shrinkage and selection operator (LASSO) and multivariate Cox analyses to identify prognosis-related genes. THCA samples were divided into high/low-risk groups based on the median risk score. Furthermore, the prognostic model’s utility in predicting immunotherapy response was analyzed. The potential therapeutic drugs were obtained. The expression of the corresponding genes in 10 pairs of clinical specimens was evaluated and those of proteins were analyzed by immunofluorescence assay.ResultsTCGA-THCA samples were categorized into two immune cell-related clusters based on 141 prognostic immune cell-related genes. Significant differences in survival and clinical characteristics such as T Stage between clusters. In total, 16,648 DEGs between THCA and control samples were extracted. THCA samples were categorized into two immune-related clusters and were found to affect the prognosis and TIME of THCA. By using LASSO and multivariate Cox analyses for 88 DE-IRGs, three prognostic IRGs, namely FLNC, IL18, and MMP17 were identified. The TIDE score of the low-risk group was significantly lower than that of the other one, indicating that these samples were more responsive to immunotherapy. The 50% inhibitory concentration (IC50) of camptothecin, methotrexate, rapamycin, and others were notably different between the risk groups.ConclusionBased on bioinformatics analysis, we constructed an immune-related prognosis model for THCA, which is expected to provide new ideas for studies related to the prognosis and treatment of THCA.

Anoikis is intricately associated with the malignant progression of cancer. Thyroid cancer (THCA) is the most common endocrine tumor, metastasis is closely related to treatment response and prognosis of THCA. Hence, it is imperative to comprehensively identify predictive prognostic genes and novel molecular targets for effective THCA therapy. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were utilized to mine differentially expressed anoikis-related (DE-ARGs). Then, the prognostic genes were identified and a risk signature was constructed for THCA using univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) method. Furthermore, the associations between risk signature and immune infiltration, immunotherapy, as well as potential mechanisms of action were determined using multiple R packages and Wilcoxon test. Finally, Mendelian randomized (MR) analysis was conducted to investigate the causal relationship between the prognostic genes and THCA. In total, six prognostic genes (LRRC75A, METTL7B, ADRA1B, TPD52L1, TNFRSF10C, and CXCL8) related to anoikis were identified, and the corresponding risk signature were constructed to assess the survival time of THCA patients. Immunocorrelation analysis demonstrated the anoikis-relevant risk signature could be used to evaluate immunotherapy effects in THCA patients, and the infiltration of immune cells was correlated with the degree of risk in THCA patients. According to two-sample MR analysis, there was the significant causal relationship between CXCL8 and THCA (odds ratio [OR] > 1 & p< 0.05), and the increase of its gene expression would lead to an increased risk of THCA. Furthermore, real-time quantitative polymerase chain reaction (RT-qPCR) confirmed the upregulated expression patterns of these prognostic genes in THCA tissues. In conclusion, we constructed the risk signature related to anoikis for THCA, which might have important clinical significance for improving the quality of life and treatment effect of THCA patients.

Head and neck squamous cell carcinoma (HNSCC) contributes significantly to global health challenges, presenting primarily in the oral cavity, pharynx, nasopharynx, and larynx. HNSCC has a high propensity for lymphatic metastasis. Diffuse large B-cell lymphoma (DLBCL), the most common subtype of non-Hodgkin lymphoma, exhibits significant heterogeneity and aggressive behavior, leading to high mortality rates. Epstein-Barr virus (EBV) is notably associated with DLBCL and certain types of HNSCC. The purpose of this study is to elucidate the molecular and immune interplay between HNSCC and DLBCL using bioinformatics and machine learning (ML) to identify shared biomarkers and potential therapeutic targets. Differentially expressed genes (DEGs) were identified using the "limma" package in R from the HNSCC dataset in The Cancer Genome Atlas (TCGA) database, and relevant modules were selected through weighted gene co-expression network analysis (WGCNA) from a DLBCL dataset in the Gene Expression Omnibus (GEO) database. Based on their intersection genes, functional enrichment analyses were conducted using Gene Ontology (GO), Disease Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Protein-protein interaction (PPI) networks and ML algorithms were employed to screen for biomarkers. The prognostic value of these biomarkers was evaluated using Kaplan-Meier (K-M) survival analysis and receiver operating characteristic (ROC) curve analyses. The Human Protein Atlas (HPA) database facilitated the examination of messenger RNA (mRNA) and protein expressions. Further analyses of mutations, immune infiltration, drug predictions, and pan-cancer impacts were performed. Additionally, single-cell RNA sequencing (scRNA-seq) data analysis at the cell type level was conducted to provide deeper insights into the tumor microenvironment. From 2,040 DEGs and 1,983 module-related genes, 85 shared genes were identified. PPI analysis with six algorithms proposed 21 prospective genes, followed ML examination yielded 16 candidates. Survival and ROC analyses pinpointed four hub genes-ACACB, MMP8, PAX5, and TNFAIP6-as significantly associated with patient outcomes, demonstrating high predictive capabilities. Evaluations of mutations and immune infiltration, coupled with drug prediction and a comprehensive cancer analysis, highlighted these biomarkers' roles in tumor immune response and treatment efficacy. The scRNA-seq data analysis revealed an increased abundance of fibroblasts, epithelial cells and mononuclear phagocyte system (MPs) in HNSCC tissues compared to lymphoid tissues. MMP8 showed higher expression in five cell types in HNSCC tissues, while TNFAIP6 and PAX5 exhibited higher expression in specific cell types. Leveraging bioinformatics and ML, this study identified four pivotal genes with significant diagnostic capabilities for DLBCL and HNSCC. The survival analysis corroborates their diagnostic accuracy, supporting the development of a diagnostic nomogram to assist in clinical decision-making.

Identification of novel biomarkers and immune infiltration characteristics of ischemic stroke based on comprehensive bioinformatic analysis and machine learning

Emerging evidence has suggested a potential pathological association between early-onset left-sided colorectal cancer (EOLCC) and metabolic syndrome (MetS). However, the underlying genetic and molecular mechanisms remain insufficiently elucidated. This study aimed to identify and characterize key biomarkers associated with the progression and treatment response of MetS-related EOLCC. An in-hospital cohort was utilized to assess the clinical implications of primary tumor location in early-onset colorectal cancer (EOCRC). Differentially expressed genes (DEGs) and weighted gene coexpression network analysis (WGCNA) were employed to identify genes potentially associated with MetS-related EOLCC. Functional enrichment analyses were conducted to explore the underlying mechanisms. Candidate biomarkers were screened using random forest (RF) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Survival relevance, expression profiles, and diagnostic performance were analyzed to identify key biomarkers. Treatment responses were evaluated, and potential therapeutic compounds were identified through molecular docking. Single-cell RNA sequencing (scRNA-seq) data and in vitro experiments were used to validate gene expression and functional characteristics. The in-hospital cohort revealed a higher proportion of EOLCC among EOCRC patients. Using the edgeR package and WGCNA, we identified coexpressed genes common to both EOLCC and MetS, significantly enriched in pathways associated with stromal remodeling and metabolic regulation. Machine learning algorithms highlighted three candidate biomarkers. Among them, only CD151 was associated with prognosis and advanced disease stage. CD151 was strongly correlated with stromal remodeling and chemoresistance. Additionally, potential therapeutic compounds targeting MetS-related EOLCC were identified via molecular docking. scRNA-seq analysis confirmed the expression and functional patterns of CD151, particularly in tumor cells. The bioinformatics results were further validated through quantitative real-time PCR (qRT-PCR), western blotting, and immunohistochemical (IHC) staining. This study identified CD151 as a key biomarker in MetS-related EOLCC, offering valuable insights into prognosis, tumor biology, and personalized treatment strategies. CD151 may serve as a reference for future research and clinical applications targeting this disease subtype.

Insulin/IGF-I hybrid receptors play a major role in IGF-I signaling in thyroid cancer

Increasing evidence has indicated a connection between bipolar disorder (BD) and arteriosclerosis (AS), yet the specific molecular mechanisms remain unclear. This study aims to investigate the hub genes and molecular pathways for BD with AS. BD-related dataset GSE12649 were downloaded from the Gene Expression Omnibus database and differentially expressed genes (DEGs) and key module genes derived from Limma and weighted gene co-expression network analyses (WGCNA) were identified. AS-related genes were sourced from the DisGeNET database, and the overlapping genes between DEGs and AS-related genes were characterized as differentially expressed arteriosclerosis-related genes (DE-ASRGs). The functional enrichment analysis, protein-protein interaction (PPI) network and three machine learning algorithms were performed to explore the hub genes, which were validated with two external validation sets. Additionally, immune infiltration was performed in BD. Overall, 67 DE-ASRGs were found to be overlapping between the DEGs and AS-related genes. Functional enrichment analysis highlighted the cancer pathways between BD and AS. We identified seven candidate hub genes (CTSD, IRF3, NPEPPS, ST6GAL1, HIF1A, SOX9 and CX3CR1). Eventually, two hub genes (CX3CR1 and ST6GAL1) were identified as BD and AS co-biomarkers by using machine learning algorithms. Immune infiltration had revealed the disorder of immunocytes. This study identified the hub genes CX3CR1 and ST6GAL1 in BD and AS, providing new insights for further research on the bioinformatic mechanisms of BD with AS and contributing to the diagnosis and prevention of AS in psychiatric clinical practice.

The study aimed to investigate the molecular mechanisms underlying kidney stones by analyzing gene expression profiles. They focused on identifying differentially expressed genes (DEGs), performing gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, and screening optimal feature genes using various machine learning algorithms. Data from the GSE73680 dataset, comprising normal renal papillary tissues and Randall's Plaque (RP) tissues, were downloaded from the GEO database. DEGs were identified using the limma R package, followed by GSEA and WGCNA to explore functional modules. Functional enrichment analysis was conducted using KEGG and Disease Ontology. Various machine learning algorithms were used for screening the most suitable feature genes, which were then assessed for their expression and diagnostic significance through Wilcoxon rank-sum tests and ROC curves. GSEA and correlation analysis were performed on optimal feature genes, and immune cell infiltration was assessed using the CIBERSORT algorithm. 412 DEGs were identified, with 194 downregulated and 218 upregulated genes in kidney stone samples. GSEA revealed enriched pathways related to metabolic processes, immune response, and disease states. WGCNA identified modules correlated with kidney stones, particularly the yellow module. Functional enrichment analysis highlighted pathways involved in metabolism, immune response, and disease pathology. Through machine learning algorithms, KLK1 and MMP10 were identified as optimal feature genes, significantly upregulated in kidney stone samples, with high diagnostic value. GSEA further elucidated their biological functions and pathway associations. The study comprehensively analyzed gene expression profiles to uncover molecular mechanisms underlying kidney stones. KLK1 and MMP10 were identified as potential diagnostic markers and key players in kidney stone progression. Functional enrichment analysis provided insights into their roles in metabolic processes, immune response, and disease pathology. These results contribute significantly to a better understanding of kidney stone pathogenesis and may inform future diagnostic and therapeutic strategies.

Lipid metabolism reprogramming played an important role in cancer occurrence, development, and immune regulation. The aim of this study was to identify and validate lipid metabolism-related genes (LMRGs) associated with the phenotype, prognosis, and immunological characteristics of lung squamous cell carcinoma (LUSC). In the TCGA cohort, bioinformatics and survival analysis were used to identify lipid metabolism-related differentially expressed genes (DEGs) associated with the prognosis of LUSC. PTGIS/HRASLS knockdown and overexpression effects on the LUSC phenotype were analyzed in vitro experiments. Based on the expression distribution of PTGIS/HRASLS, LUSC patients were divided into two clusters by consensus clustering. Clinical information, prognosis, immune infiltration, expression of immune checkpoints, and tumor mutation burden (TMB) level were compared between the TCGA and GSE4573 cohorts. The genes related to clustering and tumor immunity were screened by weighted gene coexpression network analysis (WGCNA), and the target module genes were analyzed by functional enrichment analysis, protein-protein interaction (PPI) analysis, and immune correlation analysis. 191 lipid metabolism-related DEGs were identified, of which 5 genes were independent prognostic genes of LUSC. PTGIS/HRASLS were most closely related to LUSC prognosis and immunity. RT-qPCR, western blot (WB) analysis, and immunohistochemistry (IHC) showed that the expression of PTGIS was low in LUSC, while HRASLS was high. Functionally, PTGIS promoted LUSC proliferation, migration, and invasion, while HRASLS inhibited LUSC proliferation, migration, and invasion. The two clusters' expression and distribution of PTGIS/HRASLS had the opposite trend. Cluster 1 was associated with lower pathological staging (pT, pN, and pTNM stages), better prognosis, stronger immune infiltration, higher expression of immune checkpoints, and higher TMB level than cluster 2. WGCNA found that 28 genes including CD4 and IL10RA were related to the expression of PTGIS/HRASLS and tumor immune infiltration. PTGIS/HRASLS in the GSE4573 cohort had the same effect on LUSC prognosis and tumor immunity as the TCGA cohort. PTGIS and HRASLS can be used as new therapeutic targets for LUSC as well as biomarkers for prognosis and tumor immunity, which has positive significance for guiding the immunotherapy of LUSC.

PurposeThis study aimed to identify novel biomarkers for preeclampsia (PE) diagnosis by integrating Weighted Gene Co-expression Network Analysis (WGCNA) with machine learning techniques.Patients and methodsWe obtained the PE dataset GSE25906 from the gene expression omnibus (GEO) database. Analysis of differentially expressed genes (DEGs) and module genes with Limma and Weighted Gene Co-expression Network analysis (WGCNA). Candidate hub genes for PE were identified using machine learning. Subsequently, we used western-blotting (WB) and real-time fluorescence quantitative (qPCR) to verify the expression of F13A1 and SCCPDH in preeclampsia patients. Finally, we estimated the extent of immune cell infiltration in PE samples by employing the CIBERSORT algorithms.ResultsOur findings revealed that F13A1 and SCCPDH were the hub genes of PE. The nomogram and two candidate hub genes had high diagnostic values (AUC: 0.90 and 0.88, respectively). The expression levels of F13A1 and SCCPDH were verified by WB and qPCR. CIBERSORT analysis confirmed that the PE group had a significantly larger proportion of plasma cells and activated dendritic cells and a lower portion of resting memory CD4 + T cells.ConclusionThe study proposes F13A1 and SCCPDH as potential biomarkers for diagnosing PE and points to an improvement in early detection. Integration of WGCNA with machine learning could enhance biomarker discovery in complex conditions like PE and offer a path toward more precise and reliable diagnostic tools.