Prognostic implication of UBE2C + CD8 + T cell in neoadjuvant immune checkpoint blockade plus chemotherapy for locally advanced esophageal cancer.

BACKGROUND: Immune checkpoint blockers (ICBs) plus chemotherapy as neoadjuvant therapy for patients with esophageal cancer (EC) has gained substantial attention. This study aimed to investigate the early and mid-term outcome of neoadjuvant ICBs plus chemotherapy and discover immune-associated predictors of major pathological response (MPR) for locally advanced EC. METHOD: Patients with locally advanced EC who received neoadjuvant ICBs plus chemotherapy were retrospectively included between June 2019 to December 2021. Conjoint analysis of Bulk-RNA seq (GSE165252) and scRNA seq (GSE188900) were used to investigate potential prognostic factors and immunological mechanisms, then multiplexed immunofluorescence was applied to validate. RESULTS: 76 patients were included. A total of 21 (27.6 %) patients achieved MPR, with 13 (17.1 %) attaining a pathological complete response. Over a median follow-up of 1.8 years, 6 (7.9 %) patients died and 21 (27.6 %) experienced disease recurrence within 0.6 to 2.1 years after surgery. The overall survival rate and recurrence-free survival rate were 93.3 + 2.9 % and 84.8 + 4.2 % at 12 months, 90.8 + 3.7 % and 67.1 + 6.4 % at 24 months, and 90.8 + 3.7 % and 62.9 + 7.2 % at 36 months, respectively. Patients achieving MPR had a significantly lower risk of recurrence compared to non-responders (9.5 % vs 34.5 %, P = 0.017). Analysis of bulk-RNA seq and scRNA-seq revealed that UBE2C and UBE2C + CD8 + T cells were adverse prognostic factors. Immunohistochemistry demonstrated that the non-MPR group had a higher infiltration of UBE2C + immune cells than MPR group after neoadjuvant treatment. Multiplexed immunofluorescence confirmed that infiltrating UBE2C + CD8 + T cells in MPR group were significantly fewer than non-MPR group after neoadjuvant treatment, indicating their poor prognostic role for EC. CONCLUSIONS: Neoadjuvant ICBs plus chemotherapy shows promising efficacy in locally advanced EC, with MPR being a significant predictor of lower recurrence risk. Immunological analyses identified UBE2C + CD8 + T cells as adverse prognostic factors, suggesting their potential as biomarkers for patient stratification and treatment response.

Construction of a New Ferroptosis-related Prognosis Model for Survival Prediction in Colorectal Cancer.

AIM: This study was designed to develop a ferroptosis-related gene signature for guiding the prognostic prediction in colorectal cancer (CRC) and to explore the potential in the molecular functions of the gene signature. BACKGROUND: Ferroptosis is mainly characterized by lipid peroxide accumulation on the cell membranes in an iron-dependent manner, resulting in cellular oxidative stress, metabolic disorders, and, ultimately, cell death. This study aimed to develop a prognostic ferroptosis signature in CRC and explore its potential molecular function. OBJECTIVE: The present work was designed to devise a ferroptosis signature for CRC prognosis and explore its potential molecular function. METHODS: Single-cell RNA sequencing data GSE161277 and transcriptome sequencing data GSE17537 and TCGA-CRC from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases were downloaded, respectively. Quality control, dimension reduction, clustering, and clustering of single-cell RNA sequencing (scRNA- seq) data were performed using the Seurat package. A total of 259 ferroptosis-correlated genes from the FerrDb database were acquired. The single sample gene set enrichment analysis (ssGSEA) was performed to calculate the scores of genes related to ferroptosis. ESTIMATE was used to calculate immune infiltration. Independent prognostic factors were determined by performing Weighted Gene Co-Expression Network Analysis (WGCNA), univariate and Cox analyses, and Lasso analyses were used to search for independent prognostic factors. RESULTS: From the scRNA-seq (GSE161277) dataset, 22 cell clusters were initially identified, and according to immune cell markers, only 8 types of cells (Follicular B, central memory T cell, Epithelial, Natural killer T cell, Plasma B, M1 macrophage, Fibroblasts, and Mast cell) were finally determined to be related to CRC prognosis. The results of the scRNA-seq analysis showed that the score of ferroptosis-related genes was higher in tumour tissues and in 8 types of cells in tumour samples. In the TCGA dataset, CRC samples were divided into ferroptosis-related high scores, ferroptosis-related median scores, and ferroptosis-related low scores. Immune cell analysis revealed that ferroptosis- related high scores had the highest abundance of immune cells. An 11-gene signature was developed by WGCNA, univariate Cox, and Lasso Cox regression. The prediction ability of the signature was successfully validated in the GSE17537 dataset. A comprehensive nomogram combining the 11 signature genes and clinical parameters could effectively predict the overall survival of CRC patients. CONCLUSIONS: The present molecular signature established based on the 11 ferroptosis-related genes performed well in assessing CRC prognosis. The present discoveries could inspire further research on ferroptosis, providing a new direction for CRC management.

Development and validation of CT-based radiomics deep learning signatures to predict lymph node metastasis in non-functional pancreatic neuroendocrine tumors: a multicohort study.

Background: Lymph node status is an important factor for the patients with non-functional pancreatic neuroendocrine tumors (NF-PanNETs) with respect to the surgical methods, prognosis, recurrence. Our aim is to develop and validate a combination model based on contrast-enhanced CT images to predict the lymph node metastasis (LNM) in NF-PanNETs. Methods: Retrospective data were gathered for 320 patients with NF-PanNETs who underwent curative pancreatic resection and CT imaging at two institutions (Center 1, n = 236 and Center 2, n = 84) between January 2010 and March 2022. RDPs (Radiomics deep learning signature) were developed based on ten machine-learning techniques. These signatures were integrated with the clinicopathological factors into a nomogram for clinical applications. The evaluation of the model's performance was conducted through the metrics of the area under the curve (AUC). Findings: The RDPs showed excellent performance in both centers with a high AUC for predicting LNM and disease-free survival (DFS) in Center 1 (AUC, 0.88; 95% CI: 0.84-0.92; DFS, p < 0.05) and Center 2 (AUC, 0.91; 95% CI: 0.85-0.97; DFS, p < 0.05). The clinical factors of vascular invasion, perineural invasion, and tumor grade were associated with LNM (p < 0.05). The combination nomogram showed better prediction capability for LNM (AUC, 0.93; 95% CI: 0.89-0.96). Notably, our model maintained a satisfactory predictive ability for tumors at the 2-cm threshold, demonstrating its effectiveness across different tumor sizes in Center 1 (≤2 cm: AUC, 0.90 and >2 cm: AUC, 0.86) and Center 2 (≤2 cm: AUC, 0.93 and >2 cm: AUC, 0.91). Interpretation: Our RDPs may have the potential to preoperatively predict LNM in NF-PanNETs, address the insufficiency of clinical guidelines concerning the 2-cm threshold for tumor lymph node dissection, and provide precise therapeutic strategies. Funding: This work was supported by JSPS KAKENHI Grant Number JP22K20814; the Rare Tumor Research Special Project of the National Natural Science Foundation of China (82141104) and Clinical Research Special Project of Shanghai Municipal Health Commission (202340123).

Single-cell dissection, hdWGCNA and deep learning reveal the role of oxidatively stressed plasma cells in ulcerative colitis.

Ulcerative colitis (UC) develops as a result of complex interactions between various cell types in the mucosal microenvironment. In this study, we aim to elucidate the pathogenesis of ulcerative colitis at the single-cell level and unveil its clinical significance. Using single-cell RNA sequencing and high-dimensional weighted gene co-expression network analysis, we identify a subpopulation of plasma cells (PCs) with significantly increased infiltration in UC colonic mucosa, characterized by pronounced oxidative stress. Combining 10 machine learning approaches, we find that the PC oxidative stress genes accurately distinguish diseased mucosa from normal mucosa (independent external testing AUC=0.991, sensitivity=0.986, specificity=0.909). Using MCPcounter and non-negative matrix factorization, we identify the association between PC oxidative stress genes and immune cell infiltration as well as patient heterogeneity. Spatial transcriptome data is used to verify the infiltration of oxidatively stressed PCs in colitis. Finally, we develop a gene-immune convolutional neural network deep learning model to diagnose UC mucosa in different cohorts (independent external testing AUC=0.984, sensitivity=95.9%, specificity=100%). Our work sheds light on the key pathogenic cell subpopulations in UC and is essential for the development of future clinical disease diagnostic tools.

Identification of antigen-presentation related B cells as a key player in Crohn's disease using single-cell dissecting, hdWGCNA, and deep learning.

Crohn's disease (CD) arises from intricate intercellular interactions within the intestinal lamina propria. Our objective was to use single-cell RNA sequencing to investigate CD pathogenesis and explore its clinical significance. We identified a distinct subset of B cells, highly infiltrated in the CD lamina propria, that expressed genes related to antigen presentation. Using high-dimensional weighted gene co-expression network analysis and nine machine learning techniques, we demonstrated that the antigen-presenting CD-specific B cell signature effectively differentiated diseased mucosa from normal mucosa (Independent external testing AUC = 0.963). Additionally, using MCPcounter and non-negative matrix factorization, we established a relationship between the antigen-presenting CD-specific B cell signature and immune cell infiltration and patient heterogeneity. Finally, we developed a gene-immune convolutional neural network deep learning model that accurately diagnosed CD mucosa in diverse cohorts (Independent external testing AUC = 0.963). Our research has revealed a population of B cells with a potential promoting role in CD pathogenesis and represents a fundamental step in the development of future clinical diagnostic tools for the disease.

A precise molecular subtyping of ulcerative colitis reveals the immune heterogeneity and predicts clinical drug responses.

BACKGROUND AND AIMS: We sought to identify novel molecular subtypes of ulcerative colitis (UC) based on large-scale cohorts and establish a clinically applicable subtyping system for the precision treatment of the disease. METHODS: Eight microarray profiles containing colon samples from 357 patients were utilized. Expression heterogeneity was screened out and stable subtypes were identified among UC patients. Immune infiltration pattern and biological agent response were compared among subtypes to assess the value in guiding treatment. The relationship between PRLR and TNFSF13B genes with the highest predictive value was further validated by functional experiments. RESULTS: Three stable molecular subtypes were successfully identified. Immune cell infiltration analysis defined three subtypes as innate immune activated UC (IIA), whole immune activated UC (WIA), and immune homeostasis like UC (IHL). Notably, the response rate towards biological agents (infliximab/vedolizumab) in WIA patients was the lowest (less than 10%), while the response rate in IHL patients was the highest, ranging from 42 to 60%. Among the featured genes of subtypes, the ratio of PRLR to TNFSF13B could effectively screen for IHL UC subtype suitable for biological agent therapies (Area under curve: 0.961-0.986). Furthermore, we demonstrated that PRLR expressed in epithelial cells could inhibit the expression of TNFSF13B in monocyte-derived macrophages through the CXCL1-NF-κB pathway. CONCLUSIONS: We identified three stable UC subtypes with a heterogeneous immune pattern and different response rates towards biological agents for the first time. We also established a precise molecular subtyping system and classifier to predict clinical drug response and provide individualized treatment strategies for UC patients.

Cell metabolism-based optimization strategy of CAR-T cell function in cancer therapy.

Adoptive cell therapy (ACT) using chimeric antigen receptor (CAR)-modified T cells has revolutionized the field of immune-oncology, showing remarkable efficacy against hematological malignancies. However, its success in solid tumors is limited by factors such as easy recurrence and poor efficacy. The effector function and persistence of CAR-T cells are critical to the success of therapy and are modulated by metabolic and nutrient-sensing mechanisms. Moreover, the immunosuppressive tumor microenvironment (TME), characterized by acidity, hypoxia, nutrient depletion, and metabolite accumulation caused by the high metabolic demands of tumor cells, can lead to T cell "exhaustion" and compromise the efficacy of CAR-T cells. In this review, we outline the metabolic characteristics of T cells at different stages of differentiation and summarize how these metabolic programs may be disrupted in the TME. We also discuss potential metabolic approaches to improve the efficacy and persistence of CAR-T cells, providing a new strategy for the clinical application of CAR-T cell therapy.

Minimally invasive versus open McKeown esophagectomy for patients with esophageal squamous cell carcinoma after neoadjuvant PD-1 inhibitor plus chemotherapy.

Introduction: The purpose of this study was to compare short and mid-term outcomes in esophageal squamous cell carcinoma (ESCC) patients undergoing open or minimally invasive McKeown esophagectomy (MIE) after neoadjuvant PD-1 inhibitor plus chemotherapy. Methods: Patients with locally advanced ESCC underwent open or minimally invasive McKeown esophagectomy after neoadjuvant PD-1 inhibitor plus chemotherapy were retrospectively included from June 2019 to June 2021. The baseline characteristics, pathological data, short-and mid-term outcomes were collected and compared based on the surgical approach. Results: A total of 35 patients were included in the study. An open procedure was performed for 13 patients (37.1%), and 22 (62.9%) patients underwent MIE after neoadjuvant therapy. Compared with open group, MIE group had shorter operative times (350.8± 117.8 vs. 277.9 ± 30.2 min, P = 0.009). The total number of resected lymph nodes was not significantly different, but more left recurrent laryngeal lymph nodes were harvested from the Open group (2.6 ± 3.2 vs. 0.9 ± 1.7, P = 0.047). The median follow-up time was 1.42 years (range, 0.35-2.59 years) from the first day of treatment. Three patients (8.6%) died during follow-up, one in the open surgery group and two in the MIE group. There were six (17.1%) patients developed recurrence, three in each group. The 2-year cumulative survival rates were 92.3 ± 7.4% and 89.5 ± 7.1% for the open and MIE groups, respectively. Overall survival was not different between the two surgical approaches. Conclusions: MIE might be safe and feasible for patients with locally advanced ESCC undergoing neoadjuvant PD-1 inhibitor plus chemotherapy.

A High-Throughput Sequencing Data-Based Classifier Reveals the Metabolic Heterogeneity of Hepatocellular Carcinoma.

Metabolic heterogeneity plays a key role in poor outcomes in malignant tumors, but its role in hepatocellular carcinoma (HCC) remains largely unknown. In the present study, we aim to disentangle the metabolic heterogeneity features of HCC by developing a classification system based on metabolism pathway activities in high-throughput sequencing datasets. As a result, HCC samples were classified into two distinct clusters: cluster 1 showed high levels of glycolysis and pentose phosphate pathway activity, while cluster 2 exhibited high fatty acid oxidation and glutaminolysis status. This metabolic reprogramming-based classifier was found to be highly correlated with several clinical variables, including overall survival, prognosis, TNM stage, and 𝛼-fetoprotein (AFP) expression. Of note, activated oncogenic pathways, a higher TP53 mutation rate, and increased stemness were also observed in cluster 1, indicating a causal relationship between metabolic reprogramming and carcinogenesis. Subsequently, distinct metabolism-targeted therapeutic strategies were proven in human HCC cell lines, which exhibit the same metabolic properties as corresponding patient samples based on this classification system. Furthermore, the metabolic patterns and effects of different types of cells in the tumor immune microenvironment were explored by referring to both bulk and single-cell data. It was found that malignant cells had the highest overall metabolic activities, which may impair the anti-tumor capacity of CD8+ T cells through metabolic competition, and this provided a potential explanation for why immunosuppressive cells had higher overall metabolic activities than those with anti-tumor functions. Collectively, this study established an HCC classification system based on the gene expression of energy metabolism pathways. Its prognostic and therapeutic value may provide novel insights into personalized clinical practice in patients with metabolic heterogeneity.

Single-cell dissection reveals the role of DNA damage response patterns in tumor microenvironment components contributing to colorectal cancer progression and immunotherapy.

Colorectal cancer (CRC) is one of the leading malignant cancers. DNA damage response (DDR), referring to the molecular process of DNA damage, is emerging as a promising field in targeted cancer therapy. However, the engagement of DDR in the remodeling of the tumor microenvironment is rarely studied. In this study, by sequential nonnegative matrix factorization (NMF) algorithm, pseudotime analysis, cell-cell interaction analysis, and SCENIC analysis, we have shown that DDR genes demonstrate various patterns among different cell types in CRC TME (tumor microenvironment), especially in epithelial cells, cancer-associated fibroblasts, CD8+ T cells, tumor-associated macrophages, which enhance the intensity of intercellular communication and transcription factor activation. Furthermore, based on the newly identified DDR-related TME signatures, cell subtypes including MNAT+CD8+T_cells-C5, POLR2E+Mac-C10, HMGB2+Epi-C4, HMGB1+Mac-C11, PER1+Mac-C5, PER1+CD8+T_cells-C1, POLR2A+Mac-C1, TDG+Epi-C5, TDG+CD8+T_cells-C8 are determined as critical prognostic factors for CRC patients and predictors of immune checkpoint blockade (ICB) therapy efficacy in two public CRC cohorts, TCGA-COAD and GSE39582. Our novel and systematic analysis on the level of the single-cell analysis has revealed the unique role of DDR in remodeling CRC TME for the first time, facilitating the prediction of prognosis and guidance of personalized ICB regimens in CRC.

Systematic single-cell dissecting reveals heterogeneous oncofetal reprogramming in the tumor microenvironment of gastric cancer.

Oncofetal reprogramming of the tumor microenvironment is clinically relevant. This study used the non-negative matrix factorial (NMF) algorithm for single-cell RNA sequencing data of gastric cancer (GC) based on embryonic stem genes. Pseudotime analysis, cell-cell interaction analysis, and SCENIC analysis revealed that cancer-associated fibroblasts (CAFs), tumor-associated endothelial cells (TECs), and tumor-associated macrophages (TAMs) have different oncofetal reprogramming that affects cell function, enhances intercellular communication, and activates multiple transcription factors in these cells. Furthermore, based on the signatures of the newly defined oncofetal cell subtypes and expression profiles of large cohorts in GC patients, we determined that GJA1 + TEC-C2, IFITM1 + CAF-C3, PODXL + TEC-C1, SFRP2 + CAF-C2, and SRSF7 + CAF-C1 are crucial prognostic factors for GC patients and predictors of immune checkpoint blockade in GC. Cell subtypes were validated by immunohistochemical methods. Our novel, profound, and systematic analysis of the oncofetal reprogramming of GC may facilitate the development of improved drugs for treating GC.

ITGB1 as a prognostic biomarker correlated with immune suppression in gastric cancer.

INTRODUCTION: Gastric cancer is one of the common malignant tumors with a high incidence and mortality in China. Prognostic biomarkers and potential predictors of the treatment efficacy of gastric cancer urgently need to be identified. Integrin-ß (ITGB) is a superfamily of integrins and is involved in cell adhesion, tissue repair, immune response, and tumor metastasis. METHODS: We analyzed ITGB1 expression in our hospital samples of the gastric cancer cohort. And the public data of The Cancer Genome Atlas stomach adenocarcinoma (TCGA-STAD), The Asian Cancer Research Group (ACRG)/GSE62254, and GSE15459 data sets were analyzed by using the bioinformatic methods. The relationships between ITGB1 expression and clinicopathological features, patient prognosis, activation of the Wnt/ß-catenin signaling pathway, and tumor immunosuppressive factors were also explored. RESULTS: The positive rate of ITGB1 expression in the Fudan University Shanghai Cancer Center gastric cancer tumor tissues was 61.4% (258/420) and correlated with deep invasion (p = 0.017), an advanced clinical stage (p = 0.011), and a poor prognosis (p < 0.05). The TCGA-STAD/ACRG/GSE15459 cohorts also showed similar results. ITGB1 is one of the upstream molecules of the Wnt/ß-catenin signaling pathway and is correlated with tumor immune suppression. In gastric cancer, we found a correlation between ITGB1 expression and Wnt/ß-catenin signaling pathway activity. In the TCGA-STAD/ACRG/GSE15459 cohorts, ITGB1 expression was positively associated with immunosuppressive factors and negatively associated with immunoactive factors. Patients with low ITGB1 expression exhibited a significantly high immunotherapy response ratio according to an analysis of tumor immune dysfunction and exclusion (TIDE), which may indicate that ITGB1 is a potential predictor of immunotherapy efficacy. CONCLUSIONS: ITGB1 affects the prognosis in gastric cancer patients and plays a core role in immune suppression in gastric cancer.

Single-nucleus RNA sequencing reveals the shared mechanisms inducing cognitive impairment between COVID-19 and Alzheimer's disease.

Alzheimer's disease (AD)-like cognitive impairment, a kind of Neuro-COVID syndrome, is a reported complication of SARS-CoV-2 infection. However, the specific mechanisms remain largely unknown. Here, we integrated single-nucleus RNA-sequencing data to explore the potential shared genes and pathways that may lead to cognitive dysfunction in AD and COVID-19. We also constructed ingenuity AD-high-risk scores based on AD-high-risk genes from transcriptomic, proteomic, and Genome-Wide Association Studies (GWAS) data to identify disease-associated cell subtypes and potential targets in COVID-19 patients. We demonstrated that the primary disturbed cell populations were astrocytes and neurons between the above two dis-eases that exhibit cognitive impairment. We identified significant relationships between COVID-19 and AD involving synaptic dysfunction, neuronal damage, and neuroinflammation. Our findings may provide new insight for future studies to identify novel targets for preventive and therapeutic interventions in COVID-19 patients.

Construction and validation of a novel ten miRNA-pair based signature for the prognosis of clear cell renal cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most predominate pathological subtype of renal cell carcinoma, causing a recurrence or metastasis rate as high as 20% to 40% after operation, for which effective prognostic signature is urgently needed. METHODS: The mRNA and miRNA profiles of ccRCC specimens were collected from the Cancer Genome Atlas. MiRNA-pair risk score (miPRS) for each miRNA pair was generated as a signature and validated by univariate and multivariate Cox proportional hazards regression analysis. Functional enrichment was performed, and immune cells infiltration, as well as tumor mutation burden (TMB), and immunophenoscore (IPS) were evaluated between high and low miPRS groups. Target gene-prediction and differentially expressed gene-analysis were performed based on databases of miRDB, miRTarBase, and TargetScan. Multivariate Cox proportional hazards regression analysis was adopted to establish the prognostic model and Kaplan-Meier survival analysis was performed. FINDINGS: A novel 10 miRNA-pair based signature was established. Area under the time-dependent receiver operating curve proved the performance of the signature in the training, validation, and testing cohorts. Higher TMB, as well as the higher CTLA4-negative PD1-negative IPS, were discovered in high miPRS patients. A prognostic model was built based on miPRS (1 year-, 5 year-, 10 year- ROC-AUC=0.92, 0.84, 0.82, respectively). INTERPRETATION: The model based on miPRS is a novel and valid tool for predicting the prognosis of ccRCC. FUNDING: This study was supported by research grants from the China National Natural Scientific Foundation (81903972, 82002018, and 82170752) and Shanghai Sailing Program (19YF1406700 and 20YF1406000).

Robust Validation and Comprehensive Analysis of a Novel Signature Derived from Crucial Metabolic Pathways of Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with a dismal prognosis. PDAC have extensively reprogrammed metabolic characteristics influenced by interactions with normal cells, the effects of the tumor microenvironment and oncogene-mediated cell-autonomous pathways. In this study, we found that among all cancer hallmarks, metabolism played an important role in PDAC. Subsequently, a 16-gene prognostic signature was established with genes derived from crucial metabolic pathways, including glycolysis, bile acid metabolism, cholesterol homeostasis and xenobiotic metabolism (gbcx). The signature was used to distinguish overall survival in multiple cohorts from public datasets as well as a validation cohort followed up by us at Shanghai Cancer Center. Notably, the gbcx-related risk score (gbcxMRS) also accurately predicted poor PDAC subtypes, such as pure-basal-like and squamous types. At the same time, it also predicted PDAC recurrence. The gbcxMRS was also associated with immune cells, especially CD8 T cells, Treg cells. Furthermore, a high gbcxMRS may indicate high drug sensitivity to irinotecan and docetaxel and CTLA4 inhibitor immunotherapy. Taken together, these results indicate a robust and reproducible metabolic-related signature based on analysis of the overall pathogenesis of pancreatic cancer, which may have excellent prognostic and therapeutic implications for PDAC.

Machine Learning Model in Predicting Sarcopenia in Crohn's Disease Based on Simple Clinical and Anthropometric Measures.

Sarcopenia is associated with increased morbidity and mortality in Crohn's disease. The present study is aimed at investigating the different diagnostic performance of different machine learning models in identifying sarcopenia in Crohn's disease. Patients diagnosed with Crohn's disease at our center provided clinical, anthropometric, and radiological data. The cross-sectional CT slice at L3 was used for segmentation and the calculation of body composition. The prevalence of sarcopenia was calculated, and the clinical parameters were compared. A total of 167 patients were included in the present study, of which 127 (76.0%) were male and 40 (24.0%) were female, with an average age of 36.1 ± 14.3 years old. Based on the previously defined cut-off value of sarcopenia, 118 (70.7%) patients had sarcopenia. Seven machine learning models were trained with the randomly allocated training cohort (80%) then evaluated on the validation cohort (20%). A comprehensive comparison showed that LightGBM was the most ideal diagnostic model, with an AUC of 0.933, AUCPR of 0.970, sensitivity of 72.7%, and specificity of 87.0%. The LightGBM model may facilitate a population management strategy with early identification of sarcopenia in Crohn's disease, while providing guidance for nutritional support and an alternative surveillance modality for long-term patient follow-up.

Transcriptomic Analysis and Novel Gene Pair-Based Signatures for Hepatitis B-Related Hepatocellular Carcinoma.

Hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) misses the opportunity for surgery because it is not detected early. The molecular mechanism of hepatitis B-related liver cancer needs further understanding, and effective diagnostic and prognostic models are in urgent need. Expression profiles from the Cancer Genome Atlas (TCGA) Liver Hepatocellular Carcinoma (LIHC) and GSE121248, GSE94660, GSE76724 and GSE14520 from Gene Expression Omnibus (GEO) database were obtained. Differentially expressed genes (DEGs) between normal and tumor HBV-related HCC samples. Gene pairs are generated by comparing the expression levels of every two DEGs. The diagnostic signature of pairs of DEGs was built using cross-validation Lasso and Best Subset Selection regression. Hub genes and significant modules were screened by Cytoscape, and potential drugs were predicted by DGIdb. The gene-pair based prognostic signature was established by Cox proportional hazards regression model. xCell and ssGSEA were utilized to reveal the cell composition and cancer hallmarks to get an elucidation for the risk. A total of 457 DEGs were screened. A powerful diagnostic signature of two pairs of DEGs was built and validated in TCGA-LIHC and GEO datasets repeatedly with assured performance. Ten Hub genes were screened out. The prognostic signature of four gene pairs had good efficacy both in training and validation cohorts, with stromal score and several hallmarks related to the increasing of risk. Taken together, the study provided sight into the molecular mechanism as well as a novel strategy for the early diagnosis and prognosis for HBV-related HCC.

Comprehensive Analysis of the Systemic Transcriptomic Alternations and Inflammatory Response during the Occurrence and Progress of COVID-19.

The pandemic of the coronavirus disease 2019 (COVID-19) has posed huge threats to healthcare systems and the global economy. However, the host response towards COVID-19 on the molecular and cellular levels still lacks full understanding and effective therapies are in urgent need. Here, we integrate three datasets, GSE152641, GSE161777, and GSE157103. Compared to healthy people, 314 differentially expressed genes were identified, which were mostly involved in neutrophil degranulation and cell division. The protein-protein network was established and two significant subsets were filtered by MCODE: ssGSEA and CIBERSORT, which comprehensively revealed the alternation of immune cell abundance. Weighted gene coexpression network analysis (WGCNA) as well as GO and KEGG analyses unveiled the role of neutrophils and T cells during the progress of the disease. Based on the hospital-free days after 45 days of follow-up and statistical methods such as nonnegative matrix factorization (NMF), submap, and linear correlation analysis, 31 genes were regarded as the signature of the peripheral blood of COVID-19. Various immune cells were identified to be related to the prognosis of the patients. Drugs were predicted for the genes in the signature by DGIdb. Overall, our study comprehensively revealed the relationship between the inflammatory response and the disease course, which provided strategies for the treatment of COVID-19.