The mitotic spindle-related seven-gene predicts the prognosis and immune microenvironment of lung adenocarcinoma

SEPT9: From pan-cancer to lung squamous cell carcinoma

BACKGROUND

SEPT9 is a pivotal cytoskeletal GTPase that regulates diverse biological processes encompassing mitosis and cytokinesis. While previous studies have implicated SEPT9 in tumorigenesis and development; comprehensive pan-cancer analyses have not been performed. This study aims to systematically explore its role in cancer screening, prognosis, and treatment, addressing this critical gap.

METHODS

Gene and protein expression data containing clinical information were obtained from public databases for pan-cancer analyses. Additionally, clinical samples from 90 patients with lung squamous cell carcinoma (LUSC) were used to further experimentally validate the clinical significance of SEPT9. In addition, the molecular docking tool was used to analyze the affinities between SEPT9 protein and drugs.

RESULTS

SEPT9 is highly expressed in various cancers, and its aberrant expression correlates with genetic alternations and epigenetic modifications, leading to adverse clinical outcomes. Take LUSC as an example, additional dataset analyses and immunohistochemical experiments further confirm the diagnostic and prognostic values as well as the clinical relevance of the SEPT9 gene and protein. Functional enrichment, single-cell expression, and immune infiltration analyses revealed that SEPT9 promotes malignant tumor progression and modulates the immune microenvironments, enabling patients to benefit from immunotherapy. Moreover, drug sensitivity and molecular docking analyses showed that SEPT9 is associated with the sensitivity and resistance of multiple drugs and has stable binding activity with them, including Vorinostat and OTS-964. To harness its prognostic and therapeutic potential in LUSC, a mitotic spindle-associated prognostic model including SEPT9, HSF1, ARAP3, KIF20B, FAM83D, TUBB8, and several clinical characteristics, was developed. This model not only improves clinical outcome predictions but also reshapes the immune microenvironment, making immunotherapy more effective for LUSC patients.

CONCLUSION

This is the first study to systematically analyze the role of SEPT9 in cancers and innovatively apply the mitotic spindle-associated model to LUSC, fully demonstrating its potential as a valuable biomarker for cancer screening and prognosis, and highlighting its application value in promoting immunotherapy and chemotherapy, particularly for LUSC.

Esophageal chemical burns as a risk factor for esophageal malignancies: in-silico analyses - experimental research

Introduction

Esophageal chemical burns often occur through accidental or intentional oral consumption of chemical agents and lead to severe complications such as esophageal stricture, acute perforation, and even death. Esophageal squamous cell carcinoma is a squamous epithelium tumor that lines the normal esophagus. Additionally, adenocarcinomas are tumors located at the interface between the distal esophagus and the proximal gastric and divided into esophageal adenocarcinoma and gastric-cardia adenocarcinoma. Various conditions, such as chemical burns, are considered risk factors in the disease's pathogenesis. In the in-silico study, the authors aim to present the relationship between chemical burns and esophageal cancer by analyzing bioinformatics genetic data.

Methods

The proper gene set was extracted using the 'GEO' database. The string web tool was utilized to form the gene-interaction network. Gephi and Cytoscape software were applied to achieve network analysis.

Results

According to in-silico data, 26 genes, including NCAPH, DLGAP5, CCNB1, KIF11, KIAA0101, CDCA5, BIRC5, NUF2, BUB1B, RRM2, TTK, CDC20, NUSAP1, CCNB2, CCNA2, MELK, TPX2, PRC1, KIF4A, CENPF, TOP2A, CDK1, ASPM, CEP55, BUB1, KIF20A were extracted that can be regarded as the most critical shared genes between chemical burns and esophageal cancer.

Conclusion

In sum, esophageal chemical burns can be related to the occurrence of esophageal cancer. Moreover, esophageal chemical burn is an external factor that upregulates present genes and can be regarded as a worsening prognosis or risk factor for esophageal cancer.

Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis

Background: Endometriosis (EM) is a common gynecological condition in women of reproductive age, with diverse causes and a not yet fully understood pathogenesis. Traditional diagnostics rely on single diagnostic biomarkers and does not integrate a variety of different biomarkers. This study introduces multiple machine learning techniques, enhancing the accuracy of predictive models. A novel diagnostic approach that combines various biomarkers provides a new clinical perspective for improving the diagnostic efficiency of endometriosis, holding significant potential for clinical application. Methods: In this study, GSE51981 was used as a test set, and 11 machine learning algorithms (Lasso, Stepglm, glmBoost, Support Vector Machine, Ridge, Enet, plsRglm, Random Forest, LDA, XGBoost, and NaiveBayes) were employed to construct 113 predictive models for endometriosis. The optimal model was determined based on the AUC values derived from various algorithms. These genes were then evaluated using nine machine learning algorithms (Random Forest, SVM, Gradient Boosting Machine, LASSO, XGB, NNET, Generalized Linear Model, KNN, and Decision Tree) to assess significance scores and identify diagnostic genes for each algorithm. The diagnostic value of these genes was further validated in external datasets from GSE7305, GSE11691, and GSE120103. Results: Analysis of the GSE51981 dataset revealed 62 DEGs. The Stepglm [Both] and plsRglm algorithms identified 30 genes with the most potential using the AUC evaluation. Subsequently, nine machine learning algorithms were applied to select diagnostic genes, leading to the identification of five key diagnostic genes using the LASSO algorithm. The ADAT1 gene exhibited the best single-gene predictive performance, with an AUC of 0.785. A combination of genes (FOS, EPHX1, DLGAP5, PCSK5, and ADAT1) achieves an AUC of 0.836 in the test dataset. Moreover, these genes consistently exhibited an AUC exceeding 0.78 in all validation datasets, demonstrating superior predictive performance. Furthermore, correlation analysis with immune infiltration strengthened their predictive value by demonstrating the close relationship of the diagnostic genes with immune infiltrating cells. Conclusion: A combination of biomarkers consisting of FOS, EPHX1, DLGAP5, PCSK5, and ADAT1 can serve as a diagnostic tool for endometriosis, enhancing diagnostic efficiency. The association of these genes with immune infiltrating cells reveals their potential role in the pathogenesis of endometriosis, providing new insights for early detection and treatment.