ABSTRACT
Background: Clear cell renal cell carcinoma (ccRCC), which is the most prevalent type of renal cell carcinoma, has a high mortality rate. Lipid metabolism reprogramming is a hallmark of ccRCC progression, but its specific mechanism remains unclear. Here, the relationship between dysregulated lipid metabolism genes (LMGs) and ccRCC progression was investigated. Methods: The ccRCC transcriptome data and patients' clinical traits were obtained from several databases. A list of LMGs was selected, differentially expressed gene screening performed to detect differential LMGs, survival analysis performed, a prognostic model established, and immune landscape evaluated using the CIBERSORT algorithm. Gene Set Variation Analysis and Gene set enrichment analysis were conducted to explore the mechanism by which LMGs affect ccRCC progression. Single-cell RNA-sequencing data were obtained from relevant datasets. Immunohistochemistry and RT-PCR were used to validate the expression of prognostic LMGs. Results: Seventy-one differential LMGs were identified between ccRCC and control samples, and a novel risk score model established comprising 11 LMGs (ABCB4, DPEP1, IL4I1, ENO2, PLD4, CEL, HSD11B2, ACADSB, ELOVL2, LPA, and PIK3R6); this risk model could predict ccRCC survival. The high-risk group had worse prognoses and higher immune pathway activation and cancer development. Conclusion: Our results showed that this prognostic model can affect ccRCC progression.
ABSTRACT
Background: Congenital contractural arachnodactyly (CCA) is an autosomal dominant connective tissue disorder with clinical features of arthrogryposis, arachnodactyly, crumpled ears, scoliosis, and muscular hypoplasia. The heterozygous pathogenic variants in FBN2 have been shown to cause CCA. Fibrillin-2 is related to the elasticity of the tissue and has been demonstrated to play an important role in the constitution of extracellular microfibrils in elastic fibers, providing strength and flexibility to the connective tissue that sustains the body's joints and organs. Methods: We recruited two Chinese families with arachnodactyly and bilateral arthrogryposis of the fingers. Whole-exome sequencing (WES) and co-segregation analysis were employed to identify their genetic etiologies. Three-dimensional protein models were used to analyze the pathogenic mechanism of the identified variants. Results: We have reported two CCA families and identified two novel missense variants in FBN2 (NM_001999.3: c.4093T>C, p.C1365R and c.2384G>T, p.C795F). The structural models of the mutant FBN2 protein in rats exhibited that both the variants could break disulfide bonds. Conclusion: We detected two FBN2 variants in two families with CCA. Our description expands the genetic profile of CCA and emphasizes the pathogenicity of disulfide bond disruption in FBN2.
ABSTRACT
Background: Head and neck squamous cell carcinoma (HNSCC) is a common malignancy of the mucosal epithelium of the oral cavity, pharynx, and larynx. Laryngeal squamous cell carcinoma (LSCC) and oral squamous cell carcinoma are common HNSCC subtypes. Patients with metastatic HNSCC have a poor prognosis. Therefore, identifying molecular markers for the development and progression of HNSCC is essential for improving early diagnosis and predicting patient outcomes. Methods: Gene expression RNA-Seq data and patient clinical traits were obtained from The Cancer Genome Atlas-Head and Neck Squamous Cell Carcinoma (TCGA-HNSC) and Gene Expression Omnibus databases. Differentially expressed gene (DEG) screening was performed using the TCGA-HNSC dataset. Intersection analysis between the DEGs and a list of core matrisome genes obtained from the Matrisome Project was used to identify differentially expressed matrisome genes. A prognostic model was established using univariate and multivariate Cox regression analyses, least absolute shrinkage, and selection operator (LASSO) regression analysis. Immune landscape analysis was performed based on the single-sample gene set enrichment analysis algorithm, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, prognostic value, receiver operating characteristic curve analysis, and gene mutation analyses. Immunohistochemical results regarding prognostic protein levels were obtained from the Human Protein Atlas. Single-gene RNA-sequencing data were obtained from GSE150321 and GSE172577 datasets. CCK-8 and Transwell assays were used to confirm cell proliferation and migration. Results: A total of 1,779 DEGs, including 939 upregulated and 840 downregulated genes, between tumor and normal samples were identified using the TCGA-HNSC microarray data. Intersection analysis revealed 52 differentially expressed matrisome-related genes. After performing univariate and multivariate Cox regression and LASSO analyses, a novel prognostic model based on six matrisome genes (FN1, LAMB4, LAMB3, DMP1, CHAD, and MMRN1) for HNSCC was established. This risk model can successfully predict HNSCC survival. The high-risk group had worse prognoses and higher enrichment of pathways related to cancer development than the low-risk group. Silencing LAMB4 in HNSCC cell lines promoted cell proliferation and migration. Conclusion: This study provides a novel prognostic model for HNSCC. Thus, FN1, LAMB4, LAMB3, DMP1, CHAD, and MMRN1 may be the promising biomarkers for clinical practice.
