Interstitial lung diseases (ILDs), or diffuse pulmonary lung disease, are a subset of lung diseases that primarily affect lung alveoli and the space around interstitial tissue and bronchioles. It clinically manifests as progressive dyspnea, and patients often exhibit a varied decrease in pulmonary diffusion function. Recently, variants in telomere biology-related genes have been identified as genetic lesions of ILDs. Here, we enrolled 82 patients with interstitial pneumonia from 2017 to 2021 in our hospital to explore the candidate gene mutations of these patients via whole-exome sequencing. After data filtering, a novel heterozygous mutation (NM_025099: p.Gly131Arg) of CTC1 was identified in two affected family members. As a component of CST (CTC1-STN1-TEN1) complex, CTC1 is responsible for maintaining telomeric structure integrity and has also been identified as a candidate gene for IPF, a special kind of chronic ILD with insidious onset. Simultaneously, real-time PCR revealed that two affected family members presented with short telomere lengths, which further confirmed the effect of the mutation in the CTC1 gene. Our study not only expanded the mutation spectrum of CTC1 and provided epidemiological data on ILDs caused by CTC1 mutations but also further confirmed the relationship between heterozygous mutations in CTC1 and ILDs, which may further contribute to understanding the mechanisms underlying ILDs.
Background: RNA modifications, especially N6-methyladenosine, N1-methyladenosine and 5-methylcytosine, play an important role in the progression of cardiovascular disease. However, its regulatory function in dilated cardiomyopathy (DCM) remains to be undefined.
Methods: In the study, key RNA modification regulators (RMRs) were screened by three machine learning models. Subsequently, a risk prediction model for DCM was developed and validated based on these important genes, and the diagnostic efficiency of these genes was assessed. Meanwhile, the relevance of these genes to clinical traits was explored. In both animal models and human subjects, the gene with the strongest connection was confirmed. The expression patterns of important genes were investigated using single-cell analysis.
Results: A total of 4 key RMRs were identified. The risk prediction models were constructed basing on these genes which showed a good accuracy and sensitivity in both the training and test set. Correlation analysis showed that insulin-like growth factor binding protein 2 (IGFBP2) had the highest correlation with left ventricular ejection fraction (LVEF) (R = -0.49, P = 0.00039). Further validation expression level of IGFBP2 indicated that this gene was significantly upregulated in DCM animal models and patients, and correlation analysis validation showed a significant negative correlation between IGFBP2 and LVEF (R = -0.87; P = 6*10-5). Single-cell analysis revealed that this gene was mainly expressed in endothelial cells.
Conclusion: In conclusion, IGFBP2 is an important biomarker of left ventricular dysfunction in DCM. Future clinical applications could possibly use it as a possible therapeutic target.
Background: Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. Nonetheless, the accurate diagnosis of this condition continues to pose a challenge when relying on conventional diagnostic techniques. Cell death is a key factor in the pathogenesis of AF. Existing investigations suggest that cuproptosis may also contribute to AF. This investigation aimed to identify a novel diagnostic gene signature associated with cuproptosis for AF using ensemble learning methods and discover the connection between AF and cuproptosis.
Results: Two genes connected to cuproptosis, including solute carrier family 31 member 1 (SLC31A1) and lipoic acid synthetase (LIAS), were selected by integration of random forests and eXtreme Gradient Boosting algorithms. Subsequently, a diagnostic model was constructed that includes the two genes for AF using the Light Gradient Boosting Machine (LightGBM) algorithm with good performance (the area under the curve value > 0.75). The microRNA-transcription factor-messenger RNA network revealed that homeobox A9 (HOXA9) and Tet methylcytosine dioxygenase 1 (TET1) could target SLC31A1 and LIAS in AF. Functional enrichment analysis indicated that cuproptosis might be connected to immunocyte activities. Immunocyte infiltration analysis using the CIBERSORT algorithm suggested a greater level of neutrophils in the AF group. According to the outcomes of Spearman's rank correlation analysis, there was a negative relation between SLC31A1 and resting dendritic cells and eosinophils. The study found a positive relationship between LIAS and eosinophils along with resting memory CD4+ T cells. Conversely, a negative correlation was detected between LIAS and CD8+ T cells and regulatory T cells.
Conclusions: This study successfully constructed a cuproptosis-related diagnostic model for AF based on the LightGBM algorithm and validated its diagnostic efficacy. Cuproptosis may be regulated by HOXA9 and TET1 in AF. Cuproptosis might interact with infiltrating immunocytes in AF.
Background: HMGB1 (high mobility group box B-1) exhibits crucial role in tumor genesis and development, including lung cancer. Whereas, more HMGB1-related details in non-small cell lung cancer (NSCLC) are still largely unclear.
Methods: The HMGB1 and inflammatory factors in malignant (MPE) and non-malignant pleural effusion (BPE) were determined by ELISA. Additionally, qRT-PCR, western blot, or immunohistochemistry were used to determine HMGB1, drug-resistant and apoptotic proteins' expressions in NSCLC A549, A549-DDP cell lines, and xenograft model. Cell viability, migration/ invasion, and apoptosis were analyzed using MTT, Transwell, and flow cytometry assays, respectively.
Results: Inflammatory factors and HMGB1 expressions in MPE were significantly higher than BPE of NSCLC. Compared with preoperative and adjacent tissues, significantly higher HMGB1, drug-resistant protein, and anti-apoptotic protein expressions were observed in recurrent tissues. Overexpressed HMGB1 induced NSCLC cells to exhibit stronger aggressive, proliferative, and drug-resistant features. The related abilities were reversed when HMGB1 was interfered. Overexpressed HMGB1 showed a similar co-localization with drug resistant protein P-gp in cytoplasm in xenograft model, while low HMGB1 expression localized in cell nucleus.
Conclusions: HMGB1 overexpression significantly promoted the malignant progression and cisplatin resistance of NSCLC in vitro and in vivo.
Background: RNA methylation modifications, such as N1-methyladenosine/N6-methyladenosine /N5-methylcytosine (m1A/m6A/m5C), are the most common RNA modifications and are crucial for a number of biological processes. Nonetheless, the role of RNA methylation modifications of m1A/m6A/m5C in the pathogenesis of renal interstitial fibrosis (RIF) remains incompletely understood.
Methods: Firstly, we downloaded 2 expression datasets from the GEO database, namely GSE22459 and GSE76882. In a differential analysis of these datasets between patients with and without RIF, we selected 33 methylation-related genes (MRGs). We then applied a PPI network, LASSO analysis, SVM-RFE algorithm, and RF algorithm to identify key MRGs.
Results: We eventually obtained five candidate MRGs (WTAP, ALKBH5, YTHDF2, RBMX, and ELAVL1) to forecast the risk of RIF. We created a nomogram model derived from five key MRGs, which revealed that the nomogram model may be advantageous to patients. Based on the selected five significant MRGs, patients with RIF were classified into two MRG patterns using consensus clustering, and the correlation between the five MRGs, the two MRG patterns, and the genetic pattern with immune cell infiltration was shown. Moreover, we conducted GO and KEGG analyses on 768 DEGs between MRG clusters A and B to look into their different involvement in RIF. To measure the MRG patterns, a PCA algorithm was developed to determine MRG scores for each sample. The MRG scores of the patients in cluster B were higher than those in cluster A.
Conclusions: Ultimately, we concluded that cluster A in the two MRG patterns identified on these five key m1A/m6A/m5C regulators may be associated with RIF.
Background: Copper-induced cell death (cuproptosis) is a new regulatory cell death mechanism. Long noncoding RNAs (lncRNAs) are related to tumor immunity and metastasis. However, the correlation of cuproptosis-related lncRNAs with the immunotherapy response and prognosis of lung adenocarcinoma (LUAD) patients is not clear.
Methods: We obtained the clinical characteristics and transcriptome data from TCGA-LUAD dataset (containing 539 LUAD and 59 paracancerous tissues). By utilizing LASSO-penalized Cox regression analysis, we identified a prognostic signature composed of cuproptosis-related lncRNAs. This signature was then utilized to segregate patients into two different risk categories based on their respective risk scores. The identification of differentially expressed genes (DEGs) between high- and low-risk groups was carried out using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. We evaluated the immunotherapy response by analyzing tumor mutational burden (TMB), immunocyte infiltration and Tumor Immune Dysfunction and Exclusion (TIDE) web application. The "pRRophetic" R package was utilized to conduct further screening of potential therapeutic drugs for their sensitivity.
Results: We ultimately identified a prognostic risk signature that includes six cuproptosis-related lncRNAs (AP003778.1, AC011611.2, CRNDE, AL162632.3, LY86-AS1, and AC090948.1). Compared with clinical characteristics, the signature was significantly correlated with prognosis following the control of confounding variables (HR = 2.287, 95% CI = 1.648-3.174, p ˂ 0.001), and correctly predicted 1-, 2-, and 3-year overall survival (OS) rates (AUC value = 0.725, 0.715, and 0.662, respectively) in LUAD patients. In terms of prognosis, patients categorized as low risk exhibited more positive results in comparison to those in the high-risk group. The enrichment analysis showed that the two groups had different immune signaling pathways. Immunotherapy may offer a more appropriate treatment option for high-risk patients due to their higher TMB and lower TIDE scores. The higher risk score may demonstrate increased sensitivity to bexarotene, cisplatin, epothilone B, and vinorelbine.
Conclusions: Based on cuproptosis-related lncRNAs, we constructed and validated a novel risk signature that may be used to predict immunotherapy efficacy and prognosis in LUAD patients.
Background: The study aimed to establish a prognostic survival model with 8 pyroptosis-and-cuproptosis-related genes to examine the prognostic effect in patients of hepatocellular carcinoma (HCC).
Methods: We downloaded gene expression data and clinical information of HCC patients from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO). The clustering analysis and cox regression with LASSO were used for constructing an 8 PCmRNAs survival model. Using TCGA, ICGC and GEO cohort, the overall survival (OS) between high- and low- risk group was determined. We also evaluated independent prognostic indicators using univariate and multivariate analyses. The relatively bioinformatics analysis, including immune cell infiltration, function enrichment and drug sensitivity analyses, was performed as well. The gene expression of 8 PCmRNAs in vitro were validated in several HCC cell lines by qRT-PCR and Western blot. The relationship between GZMA and Fludarabine were further checked by CCK-8 assay.
Results: The survival prognostic model was constructed with ATP7A, GLS, CDKN2A, BAK1, CHMP4B, NLRP6, NOD1 and GZMA using data from TCGA cohort. The ICGC and GEO cohort were used for model validation. Receiver operating characteristic (ROC) curves showed a good survival prediction by this model. Risk scores had the highest predictable value for survival among Stage, Age, Gender and Grade. Most Immune cells and immune functions were decreased in high-risk group. Besides, function enrichment analyses showed that steroid metabolic process, hormone metabolic process, collagen - containing extracellular matrix, oxidoreductase activity and pyruvate metabolism were enriched. Potential drugs targeted different PCDEGs like Nelarabine, Dexamethasone and Fludarabine were found as well. ATP7A, GLS, CDKN2A, BAK1, CHMP4B, NOD1 were upregulated while NLRP6 and GZMA were downregulated in most HCC cell lines. The potential therapy of Fludarabine was demonstrated when GZMA was low expressed in Huh7 cell line.
Conclusion: We constructed a novel 8-gene (ATP7A, GLS, CDKN2A, BAK1, CHMP4B, NLRP6, NOD1 and GZMA) prognostic model and explored potential functional information and microenvironment of HCC, which might be worthy of clinical application. In addition, several potential chemotherapy drugs were screened and Fludarabine might be effective for HCC patients whose GZMA was low expressed.
Background: Glioma stem cells (GSCs) are responsible for glioma recurrence and drug resistance, yet the mechanisms underlying their maintenance remains unclear. This study aimed to identify enhancer-controlled genes involved in GSCs maintenance and elucidate the mechanisms underlying their regulation.
Methods: We analyzed RNA-seq data and H3K27ac ChIP-seq data from GSE119776 to identify differentially expressed genes and enhancers, respectively. Gene Ontology analysis was performed for functional enrichment. Transcription factors were predicted using the Toolkit for Cistrome Data Browser. Prognostic analysis and gene expression correlation was conducted using the Chinese Glioma Genome Atlas (CGGA) data. Two GSC cell lines, GSC-A172 and GSC-U138MG, were isolated from A172 and U138MG cell lines. qRT-PCR was used to detect gene transcription levels. ChIP-qPCR was used to detect H3K27ac of enhancers, and binding of E2F4 to target gene enhancers. Western blot was used to analyze protein levels of p-ATR and γH2AX. Sphere formation, limiting dilution and cell growth assays were used to analyze GSCs growth and self-renewal.
Results: We found that upregulated genes in GSCs were associated with ataxia-telangiectasia-mutated-and-Rad3-related kinase (ATR) pathway activation, and that seven enhancer-controlled genes related to ATR pathway activation (LIN9, MCM8, CEP72, POLA1, DBF4, NDE1, and CDKN2C) were identified. Expression of these genes corresponded to poor prognosis in glioma patients. E2F4 was identified as a transcription factor that regulates enhancer-controlled genes related to the ATR pathway activation, with MCM8 having the highest hazard ratio among genes positively correlated with E2F4 expression. E2F4 bound to MCM8 enhancers to promote its transcription. Overexpression of MCM8 partially restored the inhibition of GSCs self-renewal, cell growth, and the ATR pathway activation caused by E2F4 knockdown.
Conclusion: Our study demonstrated that E2F4-mediated enhancer activation of MCM8 promotes the ATR pathway activation and GSCs characteristics. These findings offer promising targets for the development of new therapies for gliomas.
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