Identification of a rank-based radiomic signature with individualized prognostic value for lung adenocarcinoma in a multi-cohort study.

OBJECTIVES: Radiomics provides an opportunity to evaluate cancer prognosis noninvasively. However, the susceptibility of the radiomic quantitative features to multicenter effects, leads to the clinical dilemma of the radiomic signatures. This study aimed to develop a radiomic signature to circumvent multicenter effects, achieving the individualized prognostic assessment of lung adenocarcinoma (LUAD). METHODS: Using computed tomography (CT) imaging of 234 stage I-IIIA LUAD patients derived from three public multicenter cohorts, we proposed a rank-based method that utilized the relative rank patterns of quantitative values between radiomic feature pairs within individual patients and established a feature pair signature for LUAD prognosis. We collected a new clinical cohort with 162 LUAD patients for independent validation. RESULTS: A rank-based radiomic signature, consisting of 12 feature pairs, was developed, and it could determine the mortality risk for an individual according to the rank patterns of 12 feature pairs within the patient's CT imaging. The prognostic performance of the rank-based signature was effectively validated in the new clinical cohort (log-rank P = 0.0051, C-index = 0.73), whereas other signatures lost their prognostic ability across centers. The novel proposed radiomic nomogram significantly improved the prognostic performance of clinicopathological factors. The further radiogenomic analyses revealed the underlying biological characteristics (e.g., Stemness, Ferroptosis, 'ECM') reflected by the rank-based radiomic signature. CONCLUSIONS: This multicenter study illustrates the accuracy and stability of the rank-based radiomic signature for LUAD prognosis, and demonstrates a unique advantage of clinical individualized application. The biological characteristics underlying the rank-based radiomic signature would accelerate its clinical application.

Alpha-1 antitrypsin targeted neutrophil elastase protects against sepsis-induced inflammation and coagulation in mice via inhibiting neutrophil extracellular trap formation.

AIMS: Sepsis pathophysiology is complex and identifying effective treatments for sepsis remains challenging. The study aims to identify effective drugs and targets for sepsis through transcriptomic analysis of sepsis patients, repositioning analysis of compounds, and validation by animal models. MAIN METHODS: GSE185263 obtained from the GEO database that includes gene expression profiles of 44 healthy controls and 348 sepsis patients categorized by severity. Bioinformatic algorithms revealed the molecular, function, and immune characteristics of the sepsis, and constructed sepsis-related protein-protein interaction networks. Subsequently, Random Walk with Restart analysis was applied to identify candidate drugs for sepsis, which were tested in animal models for survival, inflammation, coagulation, and multi-organ damage. KEY FINDINGS: Our analysis found 1862 genes linked to sepsis development, enriched in functions like neutrophil extracellular trap formation (NETs) and complement/coagulation cascades. With disease progression, immune activation-associated cells were inhibited, while immune suppression-associated cells were activated. Next, the drug repositioning method identified candidate drugs, such as alpha-1 antitrypsin, that may play a therapeutic role by targeting neutrophil elastase (NE) to inhibit NETs. Animal experiments proved that alpha-1 antitrypsin treatment can improve the survival rate, reduce sepsis score, reduce the levels of inflammation markers in serum, and alleviate muti-organ morphological damage in mice with sepsis. The further results showed that α-1 antitrypsin can inhibit the NETs by suppressing the NE for the treatment of sepsis. SIGNIFICANCE: Alpha-1 antitrypsin acted on the NE to inhibit NETs thereby protecting mice from sepsis-induced inflammation and coagulation.

Body composition parameters correlate with the endoscopic severity in Crohn's disease patients treated with infliximab.

Background: The disease activity status and behavior of Crohn's disease (CD) can reflect the severity of the disease, and changes in body composition are common in CD patients. Aims: The aim of this study was to investigate the relationship between body composition parameters and disease severity in CD patients treated with infliximab (IFX). Methods: Patients with CD assessed with the simple endoscopic score (SES-CD) and were treated with IFX were retrospectively collected, and body composition parameters at the level of the 3rd lumbar vertebrae were calculated from computed tomography (CT) scans of the patients. The correlation of patients' body composition parameters with disease activity status and disease behavior was analyzed, and the diagnostic value of the relevant parameters was assessed using receiver operating characteristic (ROC) curves. Results: A total of 106 patients were included in this study. There were significant differences in the subcutaneous adiposity index (SAI) (p = 0.010), the visceral adiposity index (VAI) (p < 0.001), the skeletal muscle mass index (SMI) (p < 0.001), and decreased skeletal muscle mass (p < 0.001) among patients with different activity status. After Spearman and multivariate regression analysis, SAI (p = 0.006 and p = 0.001), VAI (p < 0.001 and p < 0.001), and SMI (p < 0.001and p = 0.007) were identified as independent correlates of disease activity status (both disease activity and moderate-to-severe activity), with disease activity status independently positively correlated with SAI and SMI and independently negatively correlated with VAI. In determining the disease activity and moderate-to-severe activity status, SMI performed best relative to SAI and VAI, with areas under the ROC curve of 0.865 and 0.801, respectively. SAI (p = 0.015), SMI (p = 0.011) and decreased skeletal muscle mass (p = 0.027) were significantly different between different disease behavior groups (inflammatory disease behavior group, complex disease behavior group) but were not independent correlates (p > 0.05). Conclusion: Body composition parameters of CD patients treated with IFX correlate with the endoscopic disease severity, and SMI can be used as a reliable indicator of disease activity status.

Imaging-based assessment of body composition in patients with Crohn's disease: a systematic review.

BACKGROUND AND AIMS: Body composition changes in patients with Crohn's disease (CD) have received increasing attention in recent years. This review aims to describe the changes in body composition in patients with CD on imaging and to analyze and summarize the prognostic value of body composition. METHODS: We systematically searched Web of Science, PubMed, Embase, Cochrane Library, and Medline via OVID for literature published before November 2022, and two researchers independently evaluated the quality of the retrieved literature. RESULTS: A total of 39 publications (32 cohort studies and 7 cross-sectional studies) involving 4219 patients with CD were retrieved. Imaging methods for body composition assessment, including dual-energy X-ray absorptiometry (DXA), computed tomography (CT) and magnetic resonance imaging (MRI), were included in this review. The study found that patients with CD typically have more visceral adipose tissue and less skeletal muscle mass, and the prevalence of sarcopenia and visceral obesity was significantly different in different studies (sarcopenia: 16-100%; visceral obesity: 5.3-30.5%). Available studies suggest that changes in the body composition of CD patients are significantly related to inflammatory status, disease behavior, poor outcomes, and drug efficacy. CONCLUSION: Altered body composition can be a significant predictor of poor outcomes for CD patients. Therefore, the body composition of CD patients may serve as a potential therapeutic target to help optimize disease management strategies in clinical practice.

Hierarchical identification of a transcriptional panel for the histological diagnosis of lung neuroendocrine tumors.

Background: Lung cancer is a complex disease composed of neuroendocrine (NE) and non-NE tumors. Accurate diagnosis of lung cancer is essential in guiding therapeutic management. Several transcriptional signatures have been reported to distinguish between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) belonging to non-NE tumors. This study aims to identify a transcriptional panel that could distinguish the histological subtypes of NE tumors to complement the morphology-based classification of an individual. Methods: A public dataset with NE subtypes, including 21 small-cell lung cancer (SCLC), 56 large-cell NE carcinomas (LCNECs), and 24 carcinoids (CARCIs), and non-NE subtypes, including 85 ADC and 61 SCC, was used as a training set. In the training set, consensus clustering was first used to filter out the samples whose expression patterns disagreed with their histological subtypes. Then, a rank-based method was proposed to develop a panel of transcriptional signatures for determining the NE subtype for an individual, based on the within-sample relative gene expression orderings of gene pairs. Twenty-three public datasets with a total of 3,454 samples, which were derived from fresh-frozen, formalin-fixed paraffin-embedded, biopsies, and single cells, were used for validation. Clinical feasibility was tested in 10 SCLC biopsy specimens collected from cancer hospitals via bronchoscopy. Results: The NEsubtype-panel was composed of three signatures that could distinguish NE from non-NE, CARCI from non-CARCI, and SCLC from LCNEC step by step and ultimately determine the histological subtype for each NE sample. The three signatures achieved high average concordance rates with 97.31%, 98.11%, and 90.63%, respectively, in the 23 public validation datasets. It is worth noting that the 10 clinic-derived SCLC samples diagnosed via immunohistochemical staining were also accurately predicted by the NEsubtype-panel. Furthermore, the subtype-specific gene expression patterns and survival analyses provided evidence for the rationality of the reclassification by the NEsubtype-panel. Conclusion: The rank-based NEsubtype-panel could accurately distinguish lung NE from non-NE tumors and determine NE subtypes even in clinically challenging samples (such as biopsy). The panel together with our previously reported signature (KRT5-AGR2) for SCC and ADC would be an auxiliary test for the histological diagnosis of lung cancer.

Predicting Chemo-Radiotherapy Sensitivity With Concordant Survival Benefit in Non-Small Cell Lung Cancer via Computed Tomography Derived Radiomic Features.

Background: To identify a computed tomography (CT) derived radiomic signature for the options of concurrent chemo-radiotherapy (CCR) in patients with non-small cell lung cancer (NSCLC). Methods: A total of 226 patients with NSCLC receiving CCR were enrolled from public dataset, and allocated to discovery and validation sets based on patient identification number. Using CT images of 153 patients in the discovery dataset, we pre-selected a list of radiomic features significantly associated with 5-year survival rate and adopted the least absolute shrinkage and selection operator regression to establish a predictive radiomic signature for CCR treatment. We performed transcriptomic analyzes of the signature, and evaluated its association with molecular lesions and immune landscapes in a dataset with matched CT images and transcriptome data. Furthermore, we identified CCR resistant genes positively correlated with resistant scores of radiomic signature and screened essential resistant genes for NSCLC using genome-scale CRIPSR data. Finally, we combined DrugBank and Genomics of Drug Sensitivity in Cancer databases to excavate candidate therapeutic agents for patients with CCR resistance, and validated them using the Connectivity Map dataset. Results: The radiomic signature consisting of nine features was established, and then validated in the dataset of 73 patients receiving CCR log-rank P = 0.0005, which could distinguish patients into resistance and sensitivity groups, respectively, with significantly different 5-year survival rate. Furthermore, the novel proposed radiomic nomogram significantly improved the predictive performance (concordance indexes) of clinicopathological factors. Transcriptomic analyzes linked our signature with important tumor biological processes (e.g. glycolysis/glucoseogenesis, ribosome). Then, we identified 36 essential resistant genes, and constructed a gene-agent network including 10 essential resistant genes and 35 candidate therapeutic agents, and excavated AT-7519 as the therapeutic agent for patients with CCR resistance. The therapeutic efficacy of AT-7519 was validated that significantly more resistant genes were down-regulated induced by AT-7519, and the degree gradually increased with the enhanced doses. Conclusions: This study illustrated that radiomic signature could non-invasively predict therapeutic efficacy of patients with NSCLC receiving CCR, and indicated that patients with CCR resistance might benefit from AT-7519 or CCR treatment combined with AT-7519.

Identifying metabolic reprogramming phenotypes with glycolysis-lipid metabolism discoordination and intercellular communication for lung adenocarcinoma metastasis.

Tumor metastasis imposes metabolic requirements for escaping from primary tissues, producing vulnerability in treatment. This study aimed to explore the metabolic reprogramming relevant to lung adenocarcinoma (LUAD) metastasis and decode the underlying intercellular alterations. Using the gene expression profiles of 394 LUAD samples derived from The Cancer Genome Atlas (TCGA), we identified 11 metastasis-related metabolic genes involved in glycolysis and lipid metabolism, and defined three metabolic reprogramming phenotypes (MP-I, -II, and -III) using unsupervised clustering. MP-III with the highest glycolytic and lowest lipid metabolic levels exhibited the highest metastatic potency and poorest survival in TCGA and six independent cohorts totaling 1,235 samples. Genomic analyses showed that mutations in TP53 and KEAP1, and deletions in SETD2 and PBRM1 might drive metabolic reprogramming in MP-III. Single-cell RNA-sequencing data from LUAD validated a metabolic evolutionary trajectory from normal to MP-II and MP-III, through MP-I. The further intercellular communications revealed that MP-III interacted uniquely with endothelial cells and fibroblasts in the ANGPTL pathway, and had stronger interactions with endothelial cells in the VEGF pathway. Herein, glycolysis-lipid dysregulation patterns suggested metabolic reprogramming phenotypes relevant to metastasis. Further insights into the oncogenic drivers and microenvironmental interactions would facilitate the treatment of LUAD metastasis in the future.