Lasso Regression Methods Research Articles

A nomogram to predict stoma creation in elective surgical resection for penetrating Crohn's disease.

The decision to perform a stoma during surgical resection of penetrating Crohn's disease (CD) is a critical consideration. The objective of this study was to identify factors that influence stoma creation and develop a predictive nomogram model to assist surgeons in making optimal surgical decisions. A retrospective study was conducted at a tertiary center from December 1, 2012, to December 1, 2021, involving consecutive patients with penetrating CD who underwent elective surgical resection. The LASSO regression method was used to select preoperative predictors, and a nomogram was constructed using multivariate logistic regression. The performance of the nomogram was validated in an internal cohort by assessing its discrimination, calibration, and clinical usefulness. The study included 405 cases in the training group and 135 cases in the validation group. Nine risk factors for stoma formation were identified, including disease location, fistula resulted from previous anastomosis, absence of preoperative exclusive enteral nutrition support, albumin levels below 35 g/L, C-reactive protein levels above 10 mg/L, hemoglobin levels below 100 g/L, perianal disease, internal fistula, and surgical approach. These risk factors were selected using the LASSO regression method, and a nomogram was developed based on them. The area under the curve and the coefficient of determination (R2) of the nomogram were 0.821 and 0.394, respectively. And the nomogram demonstrated good performance in the validation cohort. The nomogram exhibited good predictive ability for stoma formation during elective surgical resection for penetrating CD, which can assist surgeons in making informed clinical decisions.

Development of a stemness-related prognostic index to provide therapeutic strategies for bladder cancer

Bladder cancer (BC) is a heterogeneous disease with varying clinical outcomes. Recent evidence suggests that cancer progression involves the acquisition of stem-like signatures, and assessing stemness indices help uncover patterns of intra-tumor molecular heterogeneity. We used the one-class logistic regression algorithm to compute the mRNAsi for each sample in BLCA cohort. We subsequently classified BC patients into two subtypes based on 189 mRNAsi-related genes, using the unsupervised consensus clustering. Then, we identified nine hub genes to construct a stemness-related prognostic index (SRPI) using Cox regression, LASSO regression and Random Forest methods. We further validated SRPI using two independent datasets. Afterwards, we examined the molecular and immune characterized of SRPI. Finally, we conducted multiply drug screening and experimental approaches to identify and confirm the most proper agents for patients with high SRPI. Based on the mRNAsi-related genes, BC patients were classified into two stemness subtypes with distinct prognosis, functional annotations, genomic variations and immune profiles. Using the SRPI, we identified a specific subgroup of BC patients with high SRPI, who had a poor response to immunotherapy, and were less sensitive to commonly used chemotherapeutic agents, FGFR inhibitors, and EGFR inhibitors. We further identified that dasatinib was the most promising therapeutic agent for this subgroup of patients. This study provides further insights into the stemness classification of BC, and demonstrates that SRPI is a promising tool for predicting prognosis and therapeutic opportunities for BC patients.

Developing a prognosis and chemotherapy evaluating model for colon adenocarcinoma based on mitotic catastrophe-related genes

Mitotic catastrophe (MC) is a novel form of cell death that plays an important role in the treatment and drug resistance of colon adenocarcinoma (COAD). However, MC related genes in COAD treatment and prognosis evaluation are rarely studied. In this study, the transcriptome data, somatic mutation and copy number variation data were obtained from The Cancer Genome Atlas (TCGA) database. The mitotic catastrophe related genes (MCRGs) were obtained from GENCARDS website. Differential gene analysis was conducted with LIMMA package. Univariate Cox regression analysis was used to identify prognostic related genes. Mutation analysis was performed and displayed by maftools package. RCircos package was used for localizing the position of genes on chromosomes. “Glmnet” R package was applied for constructing a risk model via the LASSO regression method. Consensus clustering analyses was implemented for clustering different subtypes. Functional enrichment analysis through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) methods, immune infiltration analysis via single sample gene set enrichment analysis (ssGSEA), tumor mutation burden and drug sensitivity analysis by pRRophetic R package were also carried out for risk model or molecular subtype’s assessment. Additionally, the connections between the expression of hub genes and overall survival (OS) were obtained from online Human Protein Atlas (HPA) website. Real-Time Quantitative Polymerase Chain Reaction (RT‑qPCR) further validated the expression of hub genes. A total of 207 differentially expressed MCRGs were selected in the TCGA cohort, 23 of which were significantly associated with OS in COAD patients. Subsequently, we constructed risk score prognostic models with 5 hub MCRGs, including SYCE2, SERPINE1, TRIP6, LIMK1, and EEPD1. The high-risk patients suffered from poorer prognosis. Furthermore, we developed a nomogram that gathered age, sex, staging, and risk score to accurately forecast the clinical survival outcomes in 1, 3, and 5 years. The results of functional enrichment suggested a significant correlation between MCRGs characteristics and cancer progression, with important implications for the immune microenvironment. Moreover, patients who displayed high TMB and high risk score showed worse prognosis, and risk characteristics were associated with different chemotherapeutic agents. Finally, RT‑qPCR verified the increased expression of the five MCRGs in clinical samples. The five MCRGs in the prognostic signature were associated with prognosis, and could be treated as reliable prognostic biomarkers and therapeutic targets for COAD patients with distinct clinicopathological characteristics, thereby providing a foundation for the precise application of pertinent drugs in COAD patients.

A simple nomogram for predicting aspiration associated with dysphagia in hospitalized patients after stroke.

Aspiration is a common complication of poststroke dysphagia (PSD) and is associated with poor prognosis and mortality. There is no uniform criterion for determining aspiration associated with dysphagia. The aim of this study was to identify early predictors of aspiration, leading to the development of a simple nomogram for identifying aspiration risk associated with dysphagia in hospitalized patients after stroke. Demographic information and clinical characteristics of 330 patients with PSD in the training cohort were utilized to develop a nomogram. The LASSO regression method was used to screen variables, and logistic regression was used to construct the nomogram. Internal validation was performed with bootstrap in the training cohort, and external validation was performed in the validation cohort of another 82 patients. The area under the curve (AUC), calibration curves, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. Seven variables were selected based on LASSO and multivariate logistic regression. The AUC of the nomogram was 0.834 (95% CI, 0.790-0.878) in the training cohort, 0.806 (95% CI, 0.791-0.880) in the internal validation cohort, and 0.882 (95% CI, 0.810-0.954) in the external validation cohort, which indicated that the model had good discrimination. The calibration and DCA curves showed that the nomogram had good accuracy and clinical utility. In this study, we established a nomogram that can be used to identify the risk of aspiration associated with dysphagia after stroke, and patients may benefit from early screening and preventive care.

Glutamine metabolism-related genes and immunotherapy in nonspecific orbital inflammation were validated using bioinformatics and machine learning

BackgroundNonspecific orbital inflammation (NSOI) is an idiopathic, persistent, and proliferative inflammatory condition affecting the orbit, characterized by polymorphous lymphoid infiltration. Its pathogenesis and progression have been linked to imbalances in tumor metabolic pathways, with glutamine (Gln) metabolism emerging as a critical aspect in cancer. Metabolic reprogramming is known to influence clinical outcomes in various malignancies. However, comprehensive research on glutamine metabolism's significance in NSOI is lacking.MethodsThis study conducted a bioinformatics analysis to identify and validate potential glutamine-related molecules (GlnMgs) associated with NSOI. The discovery of GlnMgs involved the intersection of differential expression analysis with a set of 42 candidate GlnMgs. The biological functions and pathways of the identified GlnMgs were analyzed using GSEA and GSVA. Lasso regression and SVM-RFE methods identified hub genes and assessed the diagnostic efficacy of fourteen GlnMgs in NSOI. The correlation between hub GlnMgs and clinical characteristics was also examined. The expression levels of the fourteen GlnMgs were validated using datasets GSE58331 and GSE105149.ResultsFourteen GlnMgs related to NSOI were identified, including FTCD, CPS1, CTPS1, NAGS, DDAH2, PHGDH, GGT1, GCLM, GLUD1, ART4, AADAT, ASNSD1, SLC38A1, and GFPT2. Biological function analysis indicated their involvement in responses to extracellular stimulus, mitochondrial matrix, and lipid transport. The diagnostic performance of these GlnMgs in distinguishing NSOI showed promising results.ConclusionsThis study successfully identified fourteen GlnMgs associated with NSOI, providing insights into potential novel biomarkers for NSOI and avenues for monitoring disease progression.

Exploring the intestinal ecosystem: from gut microbiota to associations with subtypes of inflammatory bowel disease.

Significant differences have been discovered between subtypes of Crohn's disease (CD) and ulcerative colitis (UC). The role of gut microbiota in promoting the onset of UC and CD is established, but conclusions regarding subtype-specific analyses remain limited. This study aims to explore the influence of gut microbiota on subtypes of UC and CD, offering novel insights into the pathogenesis and treatment of UC and CD.Two-sample Mendelian randomization (MR) analysis was employed to examine the causal relationship between subtypes of UC and CD and gut microbiota composition. Gut microbiota data were sourced from the International Consortium MiBioGen, while UC and CD data were obtained from FINNGEN. Eligible single nucleotide polymorphisms (SNPs) were selected as instrumental variables. Multiple analytical approaches such as inverse variance-weighted (IVW), MR-Egger regression, weighted median, weighted mode, and MR-RAPS were utilized. Sensitivity analyses including MR-Egger intercept test, Cochran's Q test, and leave-one-out analysis were conducted for quality control. Subsequently, we employed multivariable IVW, MR-Egger, weighted median, and LASSO regression methods to identify independently significant genera or families and conducted sensitivity analyses. We have determined that Hungatella, Acidaminococcaceae, and 15 other microbial taxa act as protective factors for various CD and UC subtypes, while Terrisporobacter, Anaerostipes, and 23 other microbial taxa are associated with increased risk for different CD and UC subtypes. Furthermore, through multivariable MR analysis, we have identified significant genera or families with independent effects. Our study confirms a causal relationship between dysbiosis of gut microbiota and the occurrence of CD and UC subtypes. Furthermore, it validates etiological distinctions among different subtypes of CD and UC. A novel approach to adjunctive therapy involving distinct UC or CD subtypes may involve the use of probiotics and represents a potential avenue for future treatments.

Prognostic analysis of patients with gastric cancer based on N6-methyladenosine modification patterns and tumor microenvironment characterization.

Cancers arise from genetic and epigenetic abnormalities that affect oncogenes and tumor suppressor genes, compounded by gene mutations. The N6-methyladenosine (m6A) RNA modification, regulated by methylation regulators, has been implicated in tumor proliferation, differentiation, tumorigenesis, invasion, and metastasis. However, the role of m6A modification patterns in the tumor microenvironment of gastric cancer (GC) remains poorly understood. In this study, we analyzed m6A modification patterns in 267 GC samples utilizing 31 m6A regulators. Using consensus clustering, we identified two unique subgroups of GC. Patients with GC were segregated into high- and low-infiltration cohorts to evaluate the infiltration proportions of the five prognostically significant immune cell types. Leveraging the differential genes in GC, we identified a "green" module via Weighted Gene Co-expression Network Analysis. A risk prediction model was established using the LASSO regression method. The "green" module was connected to both the m6A RNA methylation cluster and immune infiltration patterns. Based on "Module Membership" and "Gene Significance", 37 hub genes were identified, and a risk prediction model incorporating nine hub genes was established. Furthermore, methylated RNA immunoprecipitation and RNA Immunoprecipitation assays revealed that YTHDF1 elevated the expression of DNMT3B, which synergistically promoted the initiation and development of GC. We elucidated the molecular mechanism underlying the regulation of DNMT3B by YTHDF1 and explored the crosstalk between m6A and 5mC modification. m6A RNA methylation regulators are instrumental in malignant progression and the dynamics of tumor microenvironment infiltration of GC. Assessing m6A modification patterns and tumor microenvironment infiltration characteristics in patients with GC holds promise as a valuable prognostic biomarker.

A Prediction Model for Prolonged Hospital Length of Stay (ProLOS) in Coronavirus Disease 2019 (COVID-19) Patients.

Coronavirus disease 2019 (COVID-19) is an acute respiratory infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With the normalization of COVID-19 globally, it is crucial to construct a prediction model that enables clinicians to identify patients at risk for ProLOS based on demographics and serum inflammatory biomarkers. The study included hospitalized patients with a confirmed diagnosis of COVID-19. These patients were randomly grouped into a training (80%) and a test (20%) cohort. The LASSO regression and ten-fold cross-validation method were applied to filter variables. The training cohort utilized multifactorial logistic regression analyses to identify the independent factors of ProLOS in COVID-19 patients. A 4-variable nomogram was created for clinical use. ROC curves were plotted, and the area under the curve (AUC) was calculated to evaluate the model's discrimination; calibration analysis was planned to assess the validity of the nomogram, and decision curve analysis (DCA) was used to evaluate the clinical usefulness of the model. The results showed that among 310 patients with COVID-19, 80 had extended hospitalization (80/310). Four independent risk factors for COVID-19 patients were identified: age, coexisting chronic respiratory diseases, white blood cell count (WBC), and serum albumin (ALB). A nomogram based on these variables was created. The AUC in the training cohort was 0.808 (95% CI: 0.75 - 0.8671), and the AUC in the test cohort was 0.815 (95% CI: 0.7031 - 0.9282). The model demonstrates good calibration and can be used with threshold probabilities ranging from 0% to 100% to obtain clinical net benefits. A predictive model has been created to accurately predict whether the hospitalization duration of COVID-19 patients will be prolonged. This model incorporates serum WBC, ALB levels, age, and the presence of chronic respiratory system diseases.

Penerapan Metode Regresi Least Absolute Shrinkage and Selection Operator (LASSO) dan Regresi Linier untuk Memprediksi Tingkat Kemiskinan di Indonesia

Poverty is defined as a situation where a person or family is unable to meet the basic needs for survival, such as clothing, food, shelter, and education. This study aims to compare the accuracy of the Least Absolute Shrinkage and Selection Operator (Lasso) regression method and linear regression in predicting poverty levels in Indonesia and to choose the best model from the two methods used. The results of this study indicate that of the two algorithms used, namely the linear regression algorithm and Lasso regression, the algorithm that has a higher level of accuracy for predicting poverty rates in each province in Indonesia is the linear regression algorithm because it has a lower MSE value and has the value of R^2 is closer to 1 than the Lasso regression algorithm. In addition, the results of the analysis show that the variables that have the highest influence on poverty rates in provinces in Indonesia are education, as well as the Human Development Index (IPM).Keywords: Poverty Rate Prediction, Lasso Regression, Linear Regression

Single-cell transcriptomics reveals heterogeneity in esophageal squamous epithelial cells and constructs models for predicting patient prognosis and immunotherapy.

Esophageal squamous cell carcinoma (ESCC), characterized by its high invasiveness and malignant potential, has long been a formidable challenge in terms of treatment. A variety of advanced analytical techniques are employed, including single-cell RNA sequencing (scRNA-seq), cell trajectory inference, transcription factor regulatory network analysis, GSVA enrichment analysis, mutation profile construction, and the inference of potential immunotherapeutic drugs. The purpose is to conduct a more comprehensive exploration of the heterogeneity among malignant squamous epithelial cell subgroups within the ESCC microenvironment and establish a model for predicting the prognosis and immunotherapy outcomes of ESCC patients. An analysis was conducted through scRNA-seq, and three Cluster of malignant epithelial cells were identified using the infer CNV method. Cluster 0 was found to exhibit high invasiveness, whereas Cluster 1 displayed prominent characteristics associated with epithelial-mesenchymal transition. Confirmation of these findings was provided through cell trajectory analysis, which positioned Cluster 0 at the initiation stage of development and Cluster 1 at the final developmental stage. The abundance of Cluster 0-2 groups in TCGA-LUAD samples was assessed using ssGSEA and subsequently categorized into high and low-expression groups. Notably, it was observed that Cluster 0-1 had a significant impact on survival (p<0.05). Furthermore, GSVA enrichment analysis demonstrated heightened activity in hallmark pathways for Cluster 0, whereas Cluster 1 exhibited notable enrichment in pathways related to cell proliferation. It is noteworthy that a prognostic model was established utilizing feature genes from Cluster 0-1, employing the Lasso and stepwise regression methods. The results revealed that in TCGA and GSE53624 cohorts, the low-risk group demonstrated significantly higher overall survival and increased levels of immune infiltration. An examination of four external immunotherapy cohorts unveiled that the low-risk group exhibited improved immunotherapeutic efficacy. Additionally, more meaningful treatment options were identified for the low-risk group. The findings revealed distinct interactions between malignant epithelial cells of ESCC and subgroups within the tumor microenvironment. Two cell clusters, strongly linked to survival, were pinpointed, and a signature was formulated. This signature is expected to play a crucial role in identifying and advancing precision medicine approaches for the treatment of ESCC.

Research on the Implementation Path of Tourism Contributing to the Development of Rural Revitalization Combined with Elastic Regression Network Algorithm

Abstract This paper analyzes the power mechanism of tourism to help rural revitalization by elaborating the core content of rural tourism and rural revitalization. It also combines the advantages of the Lasso regression method and ridge regression method to form a flexible regression network model, which is used to predict the amount of tourists and tourists’ demand for rural tourism so as to put forward the implementation path of the tourism industry to help rural revitalization development. To verify the effectiveness of the proposed algorithm, data analysis is carried out in rural areas of L city as an example. The results show that compared with the Lasso regression model, the average absolute percentage error decreased by 2.41%, the average absolute error decreased by 2,233, and the root-mean-square error decreased by 2,967, which indicates that the elasticity regression network prediction model has a stronger generalization ability and a better prediction ability, and it can predict the number of tourists more accurately, and it can provide data for the tourism industry to contribute to rural revitalization and development. Reference.

Lasso regression method for a compositional covariate regularised by the norm L1 pairwise logratio

Lasso regression methods include a penalty function expressed in terms of a norm defined in the space of model coefficients. The norm plays a key role as regards the way coefficients can become irrelevant in the model. For models with a compositional covariate, the norm should be coherent with the Aitchison geometry. The proposed method is based on a newly-defined compositional norm called L1 pairwise logratio. The novel approach allows one to construct an appropriate basis through a sequential binary partition for discriminating between balances that influence the response variable and those that have no effect. This generalised Lasso regression scheme is illustrated with the analysis of a geochemical data set.

Prediction of acute kidney injury following coronary artery bypass graft surgery in elderly Chinese population

BackgroundAcute kidney injury (AKI) is a common and serious complication following coronary artery bypass graft (CABG) surgery. Advanced age is an independent risk factor for the development of AKI, and the incidence of AKI in the elderly increases more rapidly than that in younger patients. This study aimed to develop and validate the risk prediction model for AKI after CABG in elderly patients.MethodsPatients were retrospectively recruited from January 2019 to December 2020. AKI after CABG was defined according to the criteria of Kidney Disease Improving Global Outcomes (KDIGO). The entire population was divided into the derivation set and the verification set using random split sampling (ratio: 7:3). Lasso regression method was applied to screen for the variables in the derivation set. Decision curve analysis (DCA) and receiver operating characteristic (ROC) curves were plotted to analyze the predictive ability of the model for AKI risk in the derivation set and the verification set.ResultsA total of 2155 patients were enrolled in this study. They were randomly divided into the derivation set (1509 cases) and the validation set (646 cases). Risk factors associated with AKI were selected by Lasso regression including T2DM, diabetes mellitus type intraoperative use of intra-aortic ballon pump (IABP), cardiopulmonary bypass (CPB), epinephrine, isoprenaline, and so on. The model was established by Lasso logistic regression. The area under the ROC curve (AUC) of the model for the derivation set was 0.754 (95% CI: 0.720 − 0.789), and that for the validation cohort was 0.718 (95% CI: 0.665 − 0.771).ConclusionIn this study, the model with significant preoperative and intraoperative variables showed good prediction performance for AKI following CABG in elderly patients to optimize postoperative treatment strategies and improve early prognosis.

A Predictive Study on the Content of Epigallocatechin Gallate (EGCG) in Yunnan Large Leaf Tea Trees Based on the Nomogram Model

To explore the changes in epigallocatechin gallate (EGCG) content in tea under abiotic stress conditions, we collected tea samples, along with corresponding soil and altitude data, and utilized the measured data for single-factor analysis. At the same time, the LASSO regression method, which is rarely used in agriculture, was employed to screen modeling factors, a prediction model was established, and the Akaike information criterion (AIC) was introduced to compare the goodness of fit. The results show that LASSO screening reduced the AIC value of the model by 13.8%. The average area under the curve of the training set and the validation set was 0.81 and 0.76, respectively, and the calibration curve also showed good consistency. Based on the nomogram model, a visual prediction system was developed, and the content prediction curve was introduced for detailed soil evaluation. The accuracy rate reached 75% after external verification. This study provides a theoretical basis for elucidating the prediction and intervention of Pu’er tea quality under abiotic stress conditions.

Enhancing Feature Selection Optimization for COVID-19 Microarray Data

The utilization of gene selection techniques is crucial when dealing with extensive datasets containing limited cases and numerous genes, as they enhance the learning processes and improve overall outcomes. In this research, we introduce a hybrid method that combines the binary reptile search algorithm (BRSA) with the LASSO regression method to effectively filter and reduce the dimensionality of a gene expression dataset. Our primary objective was to pinpoint genes associated with COVID-19 by examining the GSE149273 dataset, which focuses on respiratory viral (RV) infections in individuals with asthma. This dataset suggested a potential increase in ACE2 expression, a critical receptor for the SARS-CoV-2 virus, along with the activation of cytokine pathways linked to COVID-19. Our proposed BRSA method successfully identified six significant genes, including ACE2, IFIT5, and TRIM14, that are closely related to COVID-19, achieving an impressive maximum classification accuracy of 87.22%. By conducting a comparative analysis against four existing binary feature selection algorithms, we demonstrated the effectiveness of our hybrid approach in reducing the dimensionality of features, while maintaining a high classification accuracy. As a result, our hybrid approach shows great promise for identifying COVID-19-related genes and could be an invaluable tool for other studies dealing with very large gene expression datasets.

Optimizing Linear Regression Models with Lasso and Ridge Regression: A Study on UAE Financial Behavior during COVID-19

This study examines the way individuals in the United Arab Emirates handled their finances during the COVID-19 pandemic, with a focus on optimizing linear regression models utilizing Lasso and Ridge Regression methods. Using the World Bank's enormous Global Findex dataset, we navigated the obstacles of multicollinearity and missing values. Our investigation showed that specific economic indicators and demographic characteristics, including gender and educational attainment, are crucial in affecting financial choices during crises. Our study carefully analyzed the performance of Lasso and Ridge Regression in the context of the Global Findex dataset. Ridge Regression was outperformed by Lasso, which is renowned for its feature selection and complexity reduction abilities. This research provides a comprehensive knowledge of the way advanced regression approaches might improve predictive modeling in such settings, shedding light on the complex dynamics of financial activity during a worldwide crisis.

Integrated performance measurement system for Slovak heating industry: A balanced scorecard approach

The prerequisite for businesses’ success, competitiveness, and non-bankruptcy is their performance. An effective performance measurement system is a suitable tool for measuring and improving business performance. The development in performance measures moved from financial measures focused on company profitability to measurement systems combining different methods, approaches, and tools. The paper aims to identify key performance indicators for Slovak heating companies based on the developed integrated performance measurement system. The analysis sampled 292 Slovak companies within SK NACE 35 (heating industry). The performance measurement system was built on balanced scorecard principles, while the least absolute shrinkage and selection operator (Lasso regression) method was used to select financial indicators. Based on the combination of the above methods, a performance measurement system framework for the analyzed sample of businesses was created. The results show that when managing performance, the analyzed businesses should focus on the following financial performance indicators: Receivables turnover ratio, Return on equity, Return on costs, Total debt to total assets, Material intensity, Labor to revenue ratio, Netto cash flow to assets, Net working capital to total assets, and Short-term liabilities to assets. When building performance measurement system based on balanced scorecard principles, financial indicators were supplemented by non-financial ones. In addition to the original balanced scorecard principles, the performance measurement system was extended by environmental constituents. Also, the paper’s deliverable combines Lasso regression and balanced scorecard principles in order to select key performance indices. AcknowledgmentThis paper is prepared within the grant scheme VEGA No. 1/0741/20 (the application of variant methods in detecting symptoms of possible bankruptcy of Slovak businesses in order to ensure their sustainable development).

An association study of clock genes with major depressive disorder

ObjectiveTo study the relationship between clock genes and Major Depressive Disorder (MDD). MethodsGEO database was used to obtain the chip data and clinical information of datasets GSE98793, GSE39653 and GSE52790. The differentially expressed clock genes were found through the analysis of the differentially expressed genes between MDD and healthy controls. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes Pathway (KEGG) enrichment analysis were performed on the differential expressed clock genes. Lasso Regression and Support Vector Machine (SVM) method were used for screening the differential expressed clock genes. Logistic regression was used to establish a diagnostic model for depression with the screened genes. Receiver Operating Characteristic (ROC) Curve was used to verify the model. Gene differential expression analysis was performed for MDD with high scores and MDD with low scores in the diagnostic model. Gene Set Enrichment Analysis (GSEA) enrichment analysis was performed for differentially expressed genes. Single-gene GSEA was used to analyze each gene in the model separately. Cibersort method was used to analyze the immune infiltration of MDD and healthy controls, and the correlation between immune cells and clock genes was analyzed. Cytoscape was used to analyze the clock gene interaction network. The DGIdb website was used to predict potentially effective therapeutic drugs for clock genes closely related to MDD. ResultsSix genes were identified by differential expression analysis of clock genes between MDD and healthy controls. GO and KEGG enrichment analysis of 6 genes showed that their pathways were concentrated such as circadian rhythm, rhythmic process, TGF − beta signaling pathway, longevity regulating pathway-multiple species, adipocytokine signaling pathway and so on. Lasso regression and SVM were used to screen out 5 clock genes (HDAC1, ID3, NFIL3, PRKAA1, TNF) for MDD. The diagnostic model of depression was established according to the 5 clock genes. The area under the curve (AUC) of the established depression diagnostic model was 0.686. Gene difference analysis was performed between MDD patients with high score of clock gene diagnostic model and MDD patients with low score. GSEA was performed for the differential genes showed that the most enriched pathways were:adipocytokine signaling pathway, TGF beta signaling pathway, oxidative phosphorylation, primary immunodeficiency, and so on. The single gene GSEA showed that the most enriched pathways were Toll like receptor signaling pathway, glucolipid metabolism, amino acid metabolism, neuroactive ligand receptor interaction, and so on. The results of immune infiltration analysis showed that NK cells resting and Macrophages M2 were different between MDD and control groups. In MDD, the gene closely related to NK cells resting was HDAC1, and the genes closely related to Macrophages M2 were HDAC1 and NFIL3. The RNA interactions network of clock genes shows that the regulation process is complex, which can provide a reference for subsequent related research. Potential therapeutic drugs predict display, among the 5 clock genes, TNF, HDAC1, and PRKAA1 may have potential effective therapeutic drugs. ConclusionAmong all CLOCK genes, HDAC1, ID3, NFIL3, PRKAA1, TNF are closely related to MDD. Among them, TNF, HDAC1, and PRKAA1 may have potential effective therapeutic drugs.

Trade-offs among productive-living-ecological lands for facilitating low-carbon and efficient cities: evidences in 30 major Chinese cities from 2015 to 2020

ABSTRACT Low-carbon and efficient city, a sustainable city development mode, requires the land to carry more people and economy, but cause no ecological damage, which is essentially a trade-off among diverse land functional zones (LFZs), e.g. living, productive, and ecological lands, from a land system perspective. Unfortunately, there is little knowledge about how to trade off LFZs for building low-carbon and efficient cities. To fill the gap, the study first proposes a fine-grained LFZ mapping method to sort the land into seven kinds of LFZs based on multimodal geographic data; then, the study defines carbon efficiency (CE) as a reference to evaluate efficient cities considering their economics, society, and ecology; finally, the study presents a cross lasso regression method to explore how LFZ patterns as well as their temporal changes influence CE, based on which LFZ features are optimized at both national and city levels for improving CE and facilitating efficient cities. The proposed methods are verified in 30 major Chinese cities from 2015 to 2020. The experimental results indicate that the 30 cities have different LFZ patterns in area proportion, morphology, and temporal change, where LFZ proportions are master control factors to CE; and LFZ feature trade-off at national level works better than per city, as it can improve CE by 58.5% compared to that in 2020, which increases population carrying capacity by 88.5% and economy producing capacity by 81.0% with no change in carbon emission, or reduces carbon emission by 55.2% by keeping population and economy constant. These findings directly contribute to low-carbon and efficient city planning, and facilitating sustainable city development.

Characterizing the key genes of COVID-19 that regulate tumor immune microenvironment and prognosis in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC), a highly heterogeneous malignant tumor associated with a poor prognosis, is a common cause of cancer-related deaths worldwide, with a limited survival benefit for patients despite ongoing therapeutic breakthroughs. Coronavirus disease 2019 (COVID-19), a severe infectious disease caused by severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), is a global pandemic and a serious threat to human health. The increased susceptibility to SARS-CoV-2 infection and a poor prognosis in patients with cancer necessitate the exploration of the potential link between the two. No studies have investigated the relationship of COVID-19 genes with the prognosis and tumor development in patients with HCC. We screened prognosis-related COVID-19 genes in HCC, performed molecular typing, developed a stable and reliable COVID-19 genes signature for predicting survival, characterized the immune microenvironment in HCC patients, and explored new molecular therapeutic targets. Datasets of HCC patients, including RNA sequencing data and clinical information, were obtained from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases. Prognosis-related COVID-19 genes were identified by univariate Cox analysis. Molecular typing of HCC was performed using the consensus non-negative matrix factorization method (cNMF), followed by the analysis of survival, tumor microenvironment, and pathway enrichment for each subtype. Prognostic signatures were constructed using LASSO-Cox regression models, and receiver operating characteristic (ROC) curves were used to validate the predictive performance of the signature. The same approach was used for the test and external validation sets. Seven software packages were applied to determine the abundance of immune infiltration in HCC patients and investigate its relationship with the risk scores. Gene set enrichment analysis (GSEA) was used to explore the potential mechanisms by which the COVID-19 genes affect hepatocarcinogenesis and prognosis. Three types of machine learning methods were combined to identify the most critical genes in the signature and localize their expression at the single cell level. We identified 53 prognosis-related COVID-19 genes and classified HCC into two molecular subtypes (C1, C2) by using the NMF method. The prognosis of C2 was significantly better than that of C1, and the two subtypes differed remarkably in terms of the tumor immune microenvironment and biological functions. The 17 COVID-19 genes were screened using the LASSO regression method to develop a 17 COVID-19 genes signature, which demonstrated a good predictive performance for 1-, 2- and 3-year OS of patients with HCC. The risk score as an independent prognostic factor for HCC has better predictive accuracy than traditional clinical variables. Patients in the TCGA cohort were categorized by risk score into the high- and low-risk groups, with the high-risk group mainly enriched in the immune modulation-related pathways and the low-risk group mainly enriched in the metabolism-related pathways, suggesting that the COVID-19 genes may affect disease progression and prognosis by regulating the tumor immune microenvironment and metabolism in HCC. NOL10 was identified as the most critical gene in the signature and hypothesized to be a potential therapeutic target for HCC. Objectively, the COVID-19 genes signature developed in this study, as an independent prognostic factor in HCC patients, is closely associated with the prognosis and tumor immune microenvironment of HCC patients and indicates that they may regulate the development of HCC in multiple ways, providing us with new perspectives for understanding the molecular mechanisms of HCC and finding effective therapeutic targets.