Original Article Construction and validation of an EGFR-related risk signature identified SHC1 as a prognostic biomarker for lung adenocarcinoma Hanqin Cao, Bohao Sun, Jing Wang, Shanshan Wu, Na Shi, Jing Zhang, Yichen Wu, Hao Wang

Background: The metabolism of fatty acids is essential in the initiation and progression of cervical cancer. The objective of this research is to develop a prognostic model associated with fatty acid metabolism and to identify the FASN protein within this framework, with the intention of investigating its function in cervical squamous cell carcinoma (CESC) and its correlation with patient prognosis. Methods: This prognostic model was developed using least absolute shrinkage and selection operator (LASSO) regression analysis on the training cohort derived from The Cancer Genome Atlas (TCGA) dataset. A nomogram for overall survival (OS) was effectively constructed by integrating the model risk scores with clinical characteristics. Furthermore, we analyzed various correlations involving pathway enrichment, therapeutic approaches, immune cell infiltration, and the prognostic model itself. The functional significance of the FASN gene was examined through Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and gene set enrichment analysis (GSEA). In addition, we performed an in-depth assessment of the correlation between FASN expression and various clinical parameters sourced from the TCGA database, which encompassed tumor (T), node (N), and metastasis (M) stages, age, weight, and histological grade. Utilizing the “rms” R package, we constructed a nomogram model that combines clinical attributes with levels of FASN expression. Moreover, immunohistochemical analyses were performed to evaluate FASN expression in a cohort of 30 cases diagnosed with cervical cancer. Results: We developed a prognostic model that includes eight genes: FASN, ACAT2, HMGCS1, IL4I1, ACAA2, SERINC1, CEL, and TP53INP2. The association between the altered expression of these genes and patient survival outcomes was confirmed through Kaplan-Meier survival analysis. Both univariate and multivariate Cox regression analyses revealed that FASN functions as an independent prognostic factor for OS within the TCGA cohort. Increased expression of FASN was associated with advanced T and N stages, higher clinical stages, older age, lower weight, greater histological grades, and a poorer prognosis. A nomogram was developed that integrates FASN expression with clinical variables to predict the likelihood of OS for patients at 1, 3, and 5 years. Additionally, immunohistochemical analysis indicated that FASN expression was significantly elevated in cervical cancer tissues when compared to adjacent non-cancerous tissues in a study involving 30 patients. Conclusions: This study clarified a specific signature associated with fatty acid metabolism, specifically FASN, which is connected to both the initiation and progression of CESC. Furthermore, FASN may serve as a prognostic marker for individuals diagnosed with CESC, thus providing fresh insights for the formulation of clinical treatment strategies.

Recent advances in next-generation sequencing (NGS) technology have greatly accelerated the need for efficient annotation to accurately interpret clinically relevant genetic variants in human diseases. Therefore, it is crucial to develop appropriate analytical tools to improve the interpretation of disease variants. Given the unique genetic characteristics of mitochondria, including haplogroup, heteroplasmy, and maternal inheritance, we developed a suite of variant analysis toolkits specifically designed for primary mitochondrial diseases: the Mitochondrial Missense Variant Annotation Tool (MmisAT) and the Mitochondrial Missense Variant Pathogenicity Predictor (MmisP). MmisAT can handle protein-coding variants from both nuclear DNA and mtDNA and generate 349 annotation types across six categories. It processes 4.78 million variant data in 76 min, making it a valuable resource for clinical and research applications. Additionally, MmisP provides pathogenicity scores to predict the pathogenicity of genetic variations in mitochondrial disease. It has been validated using cross-validation and external datasets and demonstrated higher overall discriminant accuracy with a receiver operating characteristic (ROC) curve area under the curve (AUC) of 0.94, outperforming existing pathogenicity predictors. In conclusion, the MmisAT is an efficient tool that greatly facilitates the process of variant annotation, expanding the scope of variant annotation information. Furthermore, the development of MmisP provides valuable insights into the creation of disease-specific, phenotype-specific, and even gene-specific predictors of pathogenicity, further advancing our understanding of specific fields.

Background: PI3K/Akt/mTOR pathway is crucial in some cancers, but its relation with tumor-infiltrating immune cells in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) is unanalyzed. This study aimed to determine if ACTR3 overexpression affects CC survival and explore its impact on the tumor immune microenvironment. Methods: Research investigated ACTR3 expression levels related to PI3K/Akt/mTOR pathway and its influence on tumor immunology and clinical outcomes. Methods included RNA-seq data analysis, immune cell infiltration evaluation, survival analysis, gene enrichment analysis, and single-cell RNA-seq data integration. ACTR3 expression in cervical cancer specimens was evaluated by immunohistochemistry. Results: Least absolute shrinkage and selection operator (LASSO) Cox regression identified ten key genes including ACTR3 with prognostic value. High ACTR3 expression correlated with poor outcomes, suggesting it as a prognostic biomarker. The prognostic model was validated by time-dependent receiver operating characteristic (ROC) curves for 1-, 3-, and 5-year survival. A nomogram combining ACTR3 expression and clinical parameters estimated patient survival. Single-cell RNA sequencing showed ACTR3-expressing immune cells in CESC include dendritic cells (DCs), T cells, and tissue stem cells. Conclusion: This research elucidated a distinct signature linked to the PI3K/Akt/mTOR signaling pathway, particularly focusing on ACTR3, which plays a role in both the onset and advancement of CESC. Moreover, ACTR3 has the potential to act as a prognostic biomarker for patients diagnosed with CESC, thereby offering novel perspectives for the development of clinical therapeutic approaches.

Background: Glioma is a prevalent and aggressive form of brain neoplasm, characterized by a 5-year survival rate of less than 10%. Despite the encouraging outcomes demonstrated by numerous prognostic models for gliomas in preliminary research, these models frequently do not meet anticipated results when subjected to external validation. Our goal is to uncover potential prognostic biomarkers and therapeutic targets by concentrating on mismatch repair-related genes (MRRGs) that are significantly linked to glioma. Methods: We employed least absolute shrinkage and selection operator (LASSO) Cox regression to develop a multigene signature based on MRRGs. The functional implications of the EXO1 gene were evaluated through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). We analyzed the correlation between EXO1 gene expression and immune cell infiltration using single-sample GSEA (ssGSEA). Moreover, we undertook a comprehensive examination of the correlation between EXO1 expression and several clinical parameters derived from clinical samples obtained from the TCGA database. The parameters assessed encompassed World Health Organization (WHO) grade, isocitrate dehydrogenase (IDH) wild-type status, the status of 1p/19q non-co-deletion, and patient age. Additionally, we executed a thorough prognostic evaluation of EXO1 across various subgroups defined by clinical parameters. Utilizing the “rms” R package, we constructed a nomogram model that amalgamates clinical characteristics and EXO1 expression levels. Immunohistochemical techniques were utilized to assess EXO1 expression in sixty glioma cases. Results: A comparative analysis of the expression of 23 MRRGs between glioma and normal samples revealed that 22 MRRGs were upregulated in glioma tissues. Univariate analysis indicated that 20 of these MRRGs were significantly differentially expressed (P<0.05). The LASSO algorithm reduced this set to seven key genes: EXO1, POLD2, POLD4, RFC1, RFC2, RFC4, and RPA3. Kaplan-Meier survival analysis confirmed the association between the aberrant expression of these genes and patient survival outcomes. GO and KEGG enrichment analyses highlighted the role of EXO1 in crucial biological processes and pathways, including the cell cycle and DNA repair mechanisms. Increased expression of EXO1 was correlated with higher WHO grades, IDH wild type, 1p/19q non-codel, and poor prognosis. A nomogram that combines EXO1 with clinical parameters has been developed to assist in predicting the overall survival probabilities of patients at 1-year intervals. The calibration chart revealed that effectiveness of the nomogram was accurate (c-index =0.850). Immunohistochemical evaluations showed that EXO1 expression levels were significantly elevated in 60 glioma tissues compared to adjacent non-tumorous tissues. Conclusions: In summary, our results indicate a marked elevation in EXO1 expression levels within gliomas, which correlates strongly with clinical pathological characteristics and unfavorable prognosis. Moreover, EXO1 emerges as a promising candidate biomarker and potential therapeutic target for glioma, likely playing a critical role in mediating immune infiltration within this malignancy.