Comprehensive investigation into the influence of glycosylation on head and neck squamous cell carcinoma and development of a prognostic model for risk assessment and anticipating immunotherapy.

Background: It has been well established that glycosylation plays a pivotal role in initiation, progression, and therapy resistance of several cancers. However, the correlations between glycosylation and head and neck squamous cell carcinoma (HNSCC) have not been elucidated in detail.

Methods: The paramount genes governing glycosylation were discerned via the utilization of the Protein-Protein Interaction (PPI) network and correlation analysis, coupled with single-cell RNA sequencing (scRNA-seq) analysis.

A SARS-CoV-2 related signature that explores the tumor microenvironment and predicts immunotherapy response in esophageal squamous cell cancer.

Background: The existing therapeutic approaches for combating tumors are insufficient in completely eradicating malignancy, as cancer facilitates tumor relapse and develops resistance to treatment interventions. The potential mechanistic connection between SARS-CoV-2 and ESCC has received limited attention. Therefore, our objective was to investigate the characteristics of SARS-CoV-2-related-genes (SCRGs) in esophageal squamous cancer (ESCC).

The integrated single-cell analysis developed an immunogenic cell death signature to predict lung adenocarcinoma prognosis and immunotherapy.

Background: Research on immunogenic cell death (ICD) in lung adenocarcinoma (LUAD) has been relatively limited. This study aims to create ICD-related signatures for accurate survival prognosis prediction in LUAD patients, addressing the challenge of lacking reliable early prognostic indicators for this type of cancer.

Methods: Using single-cell RNA sequencing (scRNA-seq) analysis, ICD activity in cells was calculated by AUCell algorithm, divided into high- and low-ICD groups according to median values, and key ICD regulatory genes were identified through differential analysis, and these genes were integrated into TCGA data to construct prognostic signatures using LASSO and COX regression analysis, and multi-dimensional analysis of ICD-related signatures in terms of prognosis, immunotherapy, tumor microenvironment (TME), and mutational landscape.

Establishing a prognostic model based on immune-related genes and identification of BIRC5 as a potential biomarker for lung adenocarcinoma patients.

Background: Lung adenocarcinoma (LUAD) is an extraordinarily malignant tumor, with rapidly increasing morbidity and poor prognosis. Immunotherapy has emerged as a hopeful therapeutic modality for lung adenocarcinoma. Furthermore, a prognostic model (based on immune genes) can fulfill the purpose of early diagnosis and accurate prognostic prediction.

A novel signature predicts prognosis and immunotherapy in lung adenocarcinoma based on cancer-associated fibroblasts.

Background: Extensive research has established the significant correlations between cancer-associated fibroblasts (CAFs) and various stages of cancer development, including initiation, angiogenesis, progression, and resistance to therapy. In this study, we aimed to investigate the characteristics of CAFs in lung adenocarcinoma (LUAD) and develop a risk signature to predict the prognosis of patients with LUAD.

Methods: We obtained single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the public database.

A fibroblast-associated signature predicts prognosis and immunotherapy in esophageal squamous cell cancer.

Background: Current paradigms of anti-tumor therapies are not qualified to evacuate the malignancy ascribing to cancer stroma's functions in accelerating tumor relapse and therapeutic resistance. Cancer-associated fibroblasts (CAFs) has been identified significantly correlated with tumor progression and therapy resistance. Thus, we aimed to probe into the CAFs characteristics in esophageal squamous cancer (ESCC) and construct a risk signature based on CAFs to predict the prognosis of ESCC patients.

The integrated single-cell analysis developed a lactate metabolism-driven signature to improve outcomes and immunotherapy in lung adenocarcinoma.

Background: It has been suggested that lactate metabolism (LM) is crucial for the development of cancer. Using integrated single-cell RNA sequencing (scRNA-seq) analysis, we built predictive models based on LM-related genes (LMRGs) to propose novel targets for the treatment of LUAD patients.

Methods: The most significant genes for LM were identified through the use of the AUCell algorithm and correlation analysis in conjunction with scRNA-seq analysis.

Design Appropriate Incision Length for Uniportal Video-Assisted Thoracoscopic Lobectomy: Take into Account Safety and Minimal Invasiveness.

Background:  There is no criterion on the length of the uniportal video-assisted thoracoscopic surgery (UVATS) incision when performing lobectomy. We aimed to develop a nomogram to assist surgeons in designing incision length for different individuals.

Methods:  A cohort consisting of 290 patients were enrolled for nomogram development.

Identification of a Pyroptosis-Related Gene Signature for Predicting the Immune Status and Prognosis in Lung Adenocarcinoma.

Pyroptosis is a form of programmed cell death triggered by the rupture of cell membranes and the release of inflammatory substances; it is essential in the occurrence and development of cancer. A considerable number of studies have revealed that pyroptosis is closely associated to the biological process of several cancers. However, the role of pyroptosis in lung adenocarcinoma (LUAD) remains elusive.