Gene expression profile based classification models of psoriasis

The biomedical applications of artificial intelligence: an overview of decades of research

A significant area of computer science called artificial intelligence (AI) is successfully applied to the analysis of intricate biological data and the extraction of substantial associations from datasets for a variety of biomedical uses. AI has attracted significant interest in biomedical research due to its features: (i) better patient care through early diagnosis and detection; (ii) enhanced workflow; (iii) lowering medical errors; (v) lowering medical costs; (vi) reducing morbidity and mortality; (vii) enhancing performance; (viii) enhancing precision; and (ix) time efficiency. Quantitative metrics are crucial for evaluating AI implementations, providing insights, enabling informed decisions, and measuring the impact of AI-driven initiatives, thereby enhancing transparency, accountability, and overall impact. The implementation of AI in biomedical fields faces challenges such as ethical and privacy concerns, lack of awareness, technology unreliability, and professional liability. A brief discussion is given of the AI techniques, which include Virtual screening (VS), DL, ML, Hidden Markov models (HMMs), Neural networks (NNs), Generative models (GMs), Molecular dynamics (MD), and Structure-activity relationship (SAR) models. The study explores the application of AI in biomedical fields, highlighting its enhanced predictive accuracy, treatment efficacy, diagnostic efficiency, faster decision-making, personalised treatment strategies, and precise medical interventions.

Identification of candidate biomarkers correlated with the pathogenesis of breast cancer patients

Breast cancer (BC) is the second leading cause of cancer-related death in females, followed by lung cancer. Disadvantages exist in conventional diagnostic techniques of BC, such as radiation risk. The present study integrated bioinformatics analysis with machine learning to elucidate potential key candidate genes associated with the tumorigenesis of BC. Eleven datasets were downloaded from the Gene Expression Omnibus (GEO) database and were consolidated into two independent cohorts (training cohort and validation cohort) after batch-effect removal. We employed "limma" package to screen differentially expressed genes (DEGs) between BC and adjacent normal breast samples. Subsequently, the most reliable diagnostic indicators were identified utilizing LASSO-Logistic regression, SVM-RFE and multivariate stepwise Logistic regression analysis. Logistic model and nomogram were created based on these hub genes and applied in external validation cohort to verify the robustness of the model. As a result, a total of six hub genes connected with BC pathogenesis were identified, including CD300LG, IGSF10, FAM83D, MAMDC2, COMP and SEMA3G. Then, a diagnostic model of BC on the basis of these genes was established. ROC analysis of the diagnostic model illustrated that AUC of the training cohort was 0.978 (0.962, 0.995). In the validation cohort, AUC of training set and validation set were 0.936 (0.910, 0.961) and 0.921 (0.870, 0.972), respectively. This indicated that the model was reliable in separating BC patients from healthy individuals. The model may assist in early diagnosis of BC with implications for improving the prognosis of BC patients.

Deciphering the Etiologies of Adult Erythroderma: An Updated Guide to Presentations, Diagnostic Tools, Pathophysiologies, and Treatments

Erythroderma, an inflammatory skin condition characterized by widespread erythema with variable degrees of exfoliation, pustulation, or vesiculobullous formation, is associated with high morbidity and mortality. Determining the underlying cause of erythroderma frequently presents a diagnostic challenge, which may contribute to the condition's relatively poor prognosis. This review covers the clinical presentation, pathophysiology, diagnosis, and treatment of erythroderma. It discusses similarities and differences among the many underlying etiologies of the condition and differences between erythrodermic and non-erythrodermic presentations of the same dermatosis. Finally, this article explores current research that may provide future tools in the diagnosis and management of erythroderma.

AI in Psoriatic Disease: Scoping Review

BACKGROUND

Artificial intelligence (AI) has many applications in numerous medical fields, including dermatology. Although the majority of AI studies in dermatology focus on skin cancer, there is growing interest in the applicability of AI models in inflammatory diseases, such as psoriasis. Psoriatic disease is a chronic, inflammatory, immune-mediated systemic condition with multiple comorbidities and a significant impact on patients' quality of life. Advanced treatments, including biologics and small molecules, have transformed the management of psoriatic disease. Nevertheless, there are still considerable unmet needs. Globally, delays in the diagnosis of the disease and its severity are common due to poor access to health care systems. Moreover, despite the abundance of treatments, we are unable to predict which is the right medication for the right patient, especially in resource-limited settings. AI could be an additional tool to address those needs. In this way, we can improve rates of diagnosis, accurately assess severity, and predict outcomes of treatment.

OBJECTIVE

This study aims to provide an up-to-date literature review on the use of AI in psoriatic disease, including diagnostics and clinical management as well as addressing the limitations in applicability.

METHODS

We searched the databases MEDLINE, PubMed, and Embase using the keywords "AI AND psoriasis OR psoriatic arthritis OR psoriatic disease," "machine learning AND psoriasis OR psoriatic arthritis OR psoriatic disease," and "prognostic model AND psoriasis OR psoriatic arthritis OR psoriatic disease" until June 1, 2023. Reference lists of relevant papers were also cross-examined for other papers not detected in the initial search.

RESULTS

Our literature search yielded 38 relevant papers. AI has been identified as a key component in digital health technologies. Within this field, there is the potential to apply specific techniques such as machine learning and deep learning to address several aspects of managing psoriatic disease. This includes diagnosis, particularly useful for remote teledermatology via photographs taken by patients as well as monitoring and estimating severity. Similarly, AI can be used to synthesize the vast data sets already in place through patient registries which can help identify appropriate biologic treatments for future cohorts and those individuals most likely to develop complications.

CONCLUSIONS

There are multiple advantageous uses for AI and digital health technologies in psoriatic disease. With wider implementation of AI, we need to be mindful of potential limitations, such as validation and standardization or generalizability of results in specific populations, such as patients with darker skin phototypes.

The state of artificial intelligence for systemic dermatoses: Background and applications for psoriasis, systemic sclerosis, and much more

Artificial intelligence (AI) has been steadily integrated into dermatology, with AI platforms already attempting to identify skin cancers and diagnose benign versus malignant lesions. Although not as widely known, AI programs have also been utilized as diagnostic and prognostic tools for dermatologic conditions with systemic or extracutaneous involvement, especially for diseases with autoimmune etiologies. We have provided a primer on commonly used AI platforms and the practical applicability of these algorithms in dealing with psoriasis, systemic sclerosis, and dermatomyositis as a microcosm for future directions in the field. With a rapidly changing landscape in dermatology and medicine as a whole, AI could be a versatile tool to support clinicians and enhance access to care.

Gulf War Illness Is Associated with Host Gut Microbiome Dysbiosis and Is Linked to Altered Species Abundance in Veterans from the BBRAIN Cohort

Gulf War Illness (GWI) is a debilitating condition marked by chronic fatigue, cognitive problems, pain, and gastrointestinal (GI) complaints in veterans who were deployed to the 1990-1991 Gulf War. Fatigue, GI complaints, and other chronic symptoms continue to persist more than 30 years post-deployment. Several potential mechanisms for the persistent illness have been identified and our prior pilot study linked an altered gut microbiome with the disorder. This study further validates and builds on our prior preliminary findings of host gut microbiome dysbiosis in veterans with GWI. Using stool samples and Multidimensional Fatigue Inventory (MFI) data from 89 GW veteran participants (63 GWI cases and 26 controls) from the Boston biorepository, recruitment, and integrative network (BBRAIN) for Gulf War Illness, we found that the host gut bacterial signature of veterans with GWI showed significantly different Bray-Curtis beta diversity than control veterans. Specifically, a higher Firmicutes to Bacteroidetes ratio, decrease in Akkermansia sp., Bacteroides thetaiotamicron, Bacteroides fragilis, and Lachnospiraceae genera and increase in Blautia, Streptococcus, Klebsiella, and Clostridium genera, that are associated with gut, immune, and brain health, were shown. Further, using MaAsLin and Boruta algorithms, Coprococcus and Eisenbergiella were identified as important predictors of GWI with an area under the curve ROC predictive value of 74.8%. Higher self-reported MFI scores in veterans with GWI were also significantly associated with an altered gut bacterial diversity and species abundance of Lachnospiraceae and Blautia. These results suggest potential therapeutic targets for veterans with GWI that target the gut microbiome and specific symptoms of the illness.

Metagenomics reveals unique gut mycobiome biomarkers in psoriasis

PURPOSE

In present, the diagnosis of psoriasis is mainly based on the patient's typical clinical manifestations, dermoscopy and skin biopsy, and unlike other immune diseases, psoriasis lacks specific indicators in the blood. Therefore, we are required to search novel biomarkers for the diagnosis of psoriasis.

METHODS

In this study, we analyzed the composition and the differences of intestinal fungal communities composition between psoriasis patients and healthy individuals in order to find the intestinal fungal communities associated with the diagnosis of psoriasis. We built a machine learning model and identified potential microbial markers for the diagnosis of psoriasis.

RESULTS

The results of AUROC (area under ROC) showed that Aspergillus puulaauensis (AUROC = 0.779), Kazachstania africana (AUROC = 0.750) and Torulaspora delbrueckii (AUROC = 0.745) had high predictive ability (AUROC > 0.7) for predicting psoriasis, While Fusarium keratoplasticum (AUROC = 0.670) was relatively lower (AUROC < 0.7).

CONCLUSION

The strategy based on the prediction of intestinal fungal communities provides a new idea for the diagnosis of psoriasis and is expected to become an auxiliary diagnostic method for psoriasis.

GPR15LG regulates psoriasis-like inflammation by down-regulating inflammatory factors on keratinocytes

Psoriasis is a common chronic inflammatory skin disease characterized by aberrant proliferation of keratinocytes and infiltration of immune cells. We previously found that GPR15LG protein is highly expressed in psoriasis lesional skin and it positively regulates psoriatic keratinocyte proliferation. Our data also showed that GPR15LG could regulate the activity of NF-κB pathway, which is associated with psoriatic inflammation. In the present study, we demonstrated that Gpr15lg (ortholog of GPR15LG) knockdown attenuated the severity of imiquimod (IMQ)-induced psoriasis-like inflammation in mice. Such an effect was achieved by down-regulating the expression of inflammatory cytokines interleukin (IL)-1α, IL-1β, tumor necrosis factor (TNF)-α and S100A7. Consistently, GPR15LG knockdown in vitro significantly downgraded the expression of inflammatory factors in the cellular model of psoriasis. These results suggested that GPR15LG could be involved in the development of psoriasis by regulating inflammation.

Identification of immune characteristic biomarkers and therapeutic targets in cuproptosis for sepsis by integrated bioinformatics analysis and single-cell RNA sequencing analysis

Background

Cuproptosis is a copper-dependent cell death that is connected to the development and immune response of multiple diseases. However, the function of cuproptosis in the immune characteristics of sepsis remains unclear.

Method

We obtained two sepsis datasets (GSE9960 and GSE134347) from the GEO database and classified the raw data with R packages. Cuproptosis-related genes were manually curated, and differentially expressed cuproptosis-related genes (DECuGs) were identified. Afterwards, we applied enrichment analysis and identified key DECuGs by performing machine learning techniques. Then, the immune cell infiltrations and correlation between DECuGs and immunocyte features were created by the CIBERSORT algorithm. Subsequently, unsupervised hierarchical clustering analysis was performed based on key DECuGs. We then constructed a ceRNA network based on key DECuGs by using multi-step computational strategies and predicted potential drugs in the DrugBank database. Finally, the role of these key genes in immune cells was validated at the single-cell RNA level between septic patients and healthy controls.

Results

Overall, 16 DECuGs were obtained, and most of them had lower expression levels in sepsis samples. Afterwards, we obtained six key DECuGs by performing machine learning. Then, the LIPT1-T-cell CD4 memory resting was the most positively correlated DECuG-immunocyte pair. Subsequently, two different subclusters were identified by six DECuGs. Bioinformatics analysis revealed that there were different immune characteristics between the two subclusters. Moreover, we identified the key lncRNA OIP5-AS1 within the ceRNA network and obtained 4 drugs that may represent novel drugs for sepsis. Finally, these key DECuGs were statistically significantly dysregulated in another validation set and showed a major distribution in monocytes, T cells, B cells, NK cells and platelets at the single-cell RNA level.

Conclusion

These findings suggest that cuproptosis might promote the progression of sepsis by affecting the immune system and metabolic dysfunction, which provides a new direction for understanding potential pathogenic processes and therapeutic targets in sepsis.

Artificial intelligence in psoriasis: Where we are and where we are going

Artificial intelligence (AI) is a field of computer science that involves the development of programs designed to replicate human cognitive processes and the analysis of complex data. In dermatology, which is predominantly a visual-based diagnostic field, AI has become increasingly important in improving professional processes, particularly in the diagnosis of psoriasis. In this review, we summarized current AI applications in psoriasis: (i) diagnosis, including identification, classification, lesion segmentation, lesion severity and area scoring; (ii) treatment, including prediction treatment efficiency and prediction candidate drugs; (iii) management, including e-health and preventive medicine. Key challenges and future aspects of AI in psoriasis were also discussed, in hope of providing potential directions for future studies.

Identifying Macrophage-Related Genes in Ulcerative Colitis Using Weighted Coexpression Network Analysis and Machine Learning

Ulcerative colitis (UC) is an inflammatory bowel disease of unknown cause that typically affects the colon and rectum. Innate intestinal immunity, including macrophages, plays a significant role in the pathological development of UC. Using the CIBERSORT algorithm, we observed elevated levels of 22 types of immune cell infiltrates, as well as increased M1 and decreased M2 macrophages in UC compared to normal colonic mucosa. Weighted gene coexpression network analysis (WGCNA) was used to identify modules associated with macrophages and UC, resulting in the identification of 52 macrophage-related genes (MRGs) that were enriched in macrophages at single-cell resolution. Consensus clustering based on these 52 MRGs divided the integrated UC cohorts into three subtypes. Machine learning algorithms were used to identify ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1), sodium- and chloride-dependent neutral and basic amino acid transporter B(0+) (SLC6A14), and 3-hydroxy-3-methylglutaryl-CoA synthase 2 (HMGCS2) in the training set, and their diagnostic value was validated in independent validation sets. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) revealed the main biological effects, and that interleukin-17 was one of several signaling pathways enriched by the three genes. We also constructed a competitive endogenous RNA (CeRNA) network reflecting a potential posttranscriptional regulatory mechanism. Expression of diagnostic markers was validated in vivo and in biospecimens, and our immunohistochemistry (IHC) results confirmed that HMGCS2 gradually decreased during the transformation of UC to colorectal cancer. In conclusion, ENPP1, SLC6A14, and HMGCS2 are associated with macrophages and the progression of UC pathogenesis and have good diagnostic value for patients with UC.

Principles, applications, and future of artificial intelligence in dermatology

This paper provides an overview of artificial-intelligence (AI), as applied to dermatology. We focus our discussion on methodology, AI applications for various skin diseases, limitations, and future opportunities. We review how the current image-based models are being implemented in dermatology across disease subsets, and highlight the challenges facing widespread adoption. Additionally, we discuss how the future of AI in dermatology might evolve and the emerging paradigm of large language, and multi-modal models to emphasize the importance of developing responsible, fair, and equitable models in dermatology.

Pathomechanism of Pruritus in Psoriasis and Atopic Dermatitis: Novel Approaches, Similarities and Differences

Pruritus is defined as an unpleasant sensation that elicits a desire to scratch. Nearly a third of the world's population may suffer from pruritus during their lifetime. This symptom is widely observed in numerous inflammatory skin diseases-e.g., approximately 70-90% of patients with psoriasis and almost every patient with atopic dermatitis suffer from pruritus. Although the pathogenesis of atopic dermatitis and psoriasis is different, the complex intricacies between several biochemical mediators, enzymes, and pathways seem to play a crucial role in both conditions. Despite the high prevalence of pruritus in the general population, the pathogenesis of this symptom in various conditions remains elusive. This review aims to summarize current knowledge about the pathogenesis of pruritus in psoriasis and atopic dermatitis. Each molecule involved in the pruritic pathway would merit a separate chapter or even an entire book, however, in the current review we have concentrated on some reports which we found crucial in the understanding of pruritus. However, the pathomechanism of pruritus is an extremely complex and intricate process. Moreover, many of these signaling pathways are currently undergoing detailed analysis or are still unexplained. As a result, it is currently difficult to take an objective view of how far we have come in elucidating the pathogenesis of pruritus in the described diseases. Nevertheless, considerable progress has been made in recent years.

Identification of immune-related lncRNA in sepsis by construction of ceRNA network and integrating bioinformatic analysis

BACKGROUND

Sepsis is a high mortality disease which seriously threatens human life and health, for which the pathogenetic mechanism still unclear. There is increasing evidence showed that immune and inflammation responses are key players in the development of sepsis pathology. LncRNAs, which act as ceRNAs, have critical roles in various diseases. However, the regulatory roles of ceRNA in the immunopathogenesis of sepsis have not yet been elucidated.

RESULTS

In this study, we aimed to identify immune biomarkers associated with sepsis. We first generated a global immune-associated ceRNA (IMCE) network based on data describing interactions pairs of gene-miRNA and miRNA-lncRNA. Afterward, we excavated a dysregulated sepsis immune-associated ceRNA (SPIMC) network from the global IMCE network by means of a multi-step computational approach. Functional enrichment indicated that lncRNAs in SPIMC network have pivotal roles in the immune mechanism underlying sepsis. Subsequently, we identified module and hub genes (CD4 and STAT4) via construction of a sepsis immune-related PPI network. Then, we identified hub genes based on the modular structure of PPI network and generated a ceRNA subnetwork to analyze key lncRNAs associated with sepsis. Finally, 6 lncRNAs (LINC00265, LINC00893, NDUFA6-AS1, NOP14-AS1, PRKCQ-AS1 and ZNF674-AS1) that identified as immune biomarkers of sepsis. Moreover, the CIBERSORT algorithm and the infiltration of circulating immune cells types were performed to identify the inflammatory state of sepsis. Correlation analyses between immune cells and sepsis immune biomarkers showed that the LINC00265 was strongly positive correlated with the macrophages M2 (r = 0.77).

CONCLUSION

Collectively, these results may suggest that these lncRNAs (LINC00265, LINC00893, NDUFA6-AS1, NOP14-AS1, PRKCQ-AS1 and ZNF674-AS1) played important roles in the immune pathogenesis of sepsis and provide potential therapeutic targets for further researches on immune therapy treatment in patients with sepsis.

Emerging roles of a chemoattractant receptor GPR15 and ligands in pathophysiology

Chemokine receptors play a central role in the maintenance of immune homeostasis and development of inflammation by directing leukocyte migration to tissues. GPR15 is a G protein-coupled receptor (GPCR) that was initially known as a co-receptor for human immunodeficiency virus (HIV) and simian immunodeficiency virus (SIV), with structural similarity to other members of the chemoattractant receptor family. Since the discovery of its novel function as a colon-homing receptor of T cells in mice a decade ago, GPR15 has been rapidly gaining attention for its involvement in a variety of inflammatory and immune disorders. The recent identification of its natural ligand C10orf99, a chemokine-like polypeptide strongly expressed in gastrointestinal tissues, has established that GPR15-C10orf99 is a novel signaling axis that controls intestinal homeostasis and inflammation through the migration of immune cells. In addition, it has been demonstrated that C10orf99-independent functions of GPR15 and GPR15-independent activities of C10orf99 also play significant roles in the pathophysiology. Therefore, GPR15 and its ligands are potential therapeutic targets. To provide a basis for the future development of GPR15- or GPR15 ligand-targeted therapeutics, we have summarized the latest advances in the role of GPR15 and its ligands in human diseases as well as the molecular mechanisms that regulate GPR15 expression and functions.

High-throughput proteomics profiling-derived signature associated with chemotherapy response and survival for stage II/III colorectal cancer

Adjuvant chemotherapy (ACT) is usually used to reduce the risk of disease relapse and improve survival for stage II/III colorectal cancer (CRC). However, only a subset of patients could benefit from ACT. Thus, there is an urgent need to identify improved biomarkers to predict survival and stratify patients to refine the selection of ACT. We used high-throughput proteomics to analyze tumor and adjacent normal tissues of stage II/III CRC patients with /without relapse to identify potential markers for predicting prognosis and benefit from ACT. The machine learning approach was applied to identify relapse-specific markers. Then the artificial intelligence (AI)-assisted multiplex IHC was performed to validate the prognostic value of the relapse-specific markers and construct a proteomic-derived classifier for stage II/III CRC using 3 markers, including FHL3, GGA1, TGFBI. The proteomics profiling-derived signature for stage II/III CRC (PS) not only shows good accuracy to classify patients into high and low risk of relapse and mortality in all three cohorts, but also works independently of clinicopathologic features. ACT was associated with improved disease-free survival (DFS) and overall survival (OS) in stage II (pN0) patients with high PS and pN2 patients with high PS. This study demonstrated the clinical significance of proteomic features, which serve as a valuable source for potential biomarkers. The PS classifier provides prognostic value for identifying patients at high risk of relapse and mortality and optimizes individualized treatment strategy by detecting patients who may benefit from ACT for survival.

Multi-organ single-cell analysis reveals an on/off switch system with potential for personalized treatment of immunological diseases

Prioritization of disease mechanisms, biomarkers, and drug targets in immune-mediated inflammatory diseases (IMIDs) is complicated by altered interactions between thousands of genes. Our multi-organ single-cell RNA sequencing of a mouse IMID model, namely collagen-induced arthritis, shows highly complex and heterogeneous expression changes in all analyzed organs, even though only joints showed signs of inflammation. We organized those into a multi-organ multicellular disease model, which shows predicted molecular interactions within and between organs. That model supports that inflammation is switched on or off by altered balance between pro- and anti-inflammatory upstream regulators (URs) and downstream pathways. Meta-analyses of human IMIDs show a similar, but graded, on/off switch system. This system has the potential to prioritize, diagnose, and treat optimal combinations of URs on the levels of IMIDs, subgroups, and individual patients. That potential is supported by UR analyses in more than 600 sera from patients with systemic lupus erythematosus.

Psoriatic Resolved Skin Epidermal Keratinocytes Retain Disease-Residual Transcriptomic and Epigenomic Profiles

The disease-residual transcriptomic profile (DRTP) within psoriatic healed/resolved skin and epidermal tissue-resident memory T (TRM) cells have been proposed to be crucial for the recurrence of old lesions. However, it is unclear whether epidermal keratinocytes are involved in disease recurrence. There is increasing evidence regarding the importance of epigenetic mechanisms in the pathogenesis of psoriasis. Nonetheless, the epigenetic changes that contribute to the recurrence of psoriasis remain unknown. The aim of this study was to elucidate the role of keratinocytes in psoriasis relapse. The epigenetic marks 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC) were visualized using immunofluorescence staining, and RNA sequencing was performed on paired never-lesional and resolved epidermal and dermal compartments of skin from psoriasis patients. We observed diminished 5-mC and 5-hmC amounts and decreased mRNA expression of the ten-eleven translocation (TET) 3 enzyme in the resolved epidermis. SAMHD1, C10orf99, and AKR1B10: the highly dysregulated genes in resolved epidermis are known to be associated with pathogenesis of psoriasis, and the DRTP was enriched in WNT, TNF, and mTOR signaling pathways. Our results suggest that epigenetic changes detected in epidermal keratinocytes of resolved skin may be responsible for the DRTP in the same regions. Thus, the DRTP of keratinocytes may contribute to site-specific local relapse.

Diagnostic Test Accuracy of Genetic Tests in Diagnosing Psoriasis: A Systematic Review

The pathogenesis of psoriasis involves the interaction of several environmental and genetic factors. Predicting the disease risk cannot depend on individual genetic alleles. Consequently, some studies have evaluated the use of genetic risk scores that combine several psoriasis susceptibility loci to increase the accuracy of predicting/diagnosing the disease. This meta-analysis summarizes the evidence regarding using genetic risk scores (GRS) in the diagnosis or prediction of psoriasis. A search of MEDLINE/PubMed, the Latin American Caribbean Health Sciences Literature (LILACS) database, Cochrane Library, Scopus, Web of Science, and ProQuest was conducted in July 2022. The primary objective was to record the area under the curve (AUC) for GRS of psoriasis. Secondary objectives included characteristics of studies and patients. The risk of bias (ROB) was assessed using the PROBAST tool. Five studies fulfilled the eligibility criteria of this review. None of the studies described the clinical criteria (reference standard) that were employed to diagnose psoriasis. The AUCs of the 11 GRS models ranged from 0.6029-0.8583 (median: 0.75). Marked heterogeneity was detected (Cochran Q: 1250.051, p < 0.001, and I2 index: 99.2%). So, pooling of the results of the included studies was not performed. The ROB was high for all studies and clinical application was not described. Genetic risk scores are promising tools for the prediction of psoriasis with fair to good accuracy. However, further research is required to identify the most accurate combination of loci and to validate the scores in variable ethnicities.

GPR15 expressed in T lymphocytes from RA patients is involved in leukocyte chemotaxis to the synovium

The rheumatoid arthritis (RA) inflammatory process occurs in the joints where immune cells are attracted into the synovium to promote remodeling and tissue damage. GPR15 is a G protein-coupled receptor (GPCR) located on chromosome 3 and has similarity in its sequence with chemokine receptors. Recent evidence indicates that GPR15 may be associated with modulation of the chronic inflammatory response. We evaluated the expression of GPR15 and GPR15L in blood and synovial tissue samples from RA patients, as well as to perform a functional migration assay in response to GPR15L. The expression of GPR15 and c10orf99/gpr15l mRNA was analyzed by RT-qPCR. Samples of synovial fluid and peripheral blood were analyzed for CD45+CD3+CD4+GPR15+ and CD45+CD3+CD8+GPR15+ T cell frequency comparing RA patients versus control subjects by flow cytometry. Migration assays were performed using PBMCs isolated from these individuals in response to the synthetic GPR15 ligand. Statistical analysis included Kruskal-Wallis test, T-test, or Mann-Whitney U test, according to data distribution. A higher expression in the mRNA for GPR15 was identified in early RA subjects. The frequencies of CD4+/CD8+ GPR15+ T lymphocytes are higher in RA patients comparing with healthy subjects. Also, the frequency CD4+/CD8+ GPR15+ T lymphocytes are higher in synovial fluid of established RA patients comparing with OA patients. GPR15 and GPR15L are present in the synovial tissue of RA patients and GPR15L promotes migration of PBMCs from RA patients and healthy subjects. Our results suggest that GPR15/GPR15L have a pathogenic role in RA and their antagonizing could be a therapeutic approach in RA.

An application based on bioinformatics and machine learning for risk prediction of sepsis at first clinical presentation using transcriptomic data

Background: Linking genotypic changes to phenotypic traits based on machine learning methods has various challenges. In this study, we developed a workflow based on bioinformatics and machine learning methods using transcriptomic data for sepsis obtained at the first clinical presentation for predicting the risk of sepsis. By combining bioinformatics with machine learning methods, we have attempted to overcome current challenges in predicting disease risk using transcriptomic data.

Methods: High-throughput sequencing transcriptomic data processing and gene annotation were performed using R software. Machine learning models were constructed, and model performance was evaluated by machine learning methods in Python. The models were visualized and interpreted using the Shapley Additive explanation (SHAP) method.

Results: Based on the preset parameters and using recursive feature elimination implemented via machine learning, the top 10 optimal genes were screened for the establishment of the machine learning models. In a comparison of model performance, CatBoost was selected as the optimal model. We explored the significance of each gene in the model and the interaction between each gene through SHAP analysis.

Conclusion: The combination of CatBoost and SHAP may serve as the best-performing machine learning model for predicting transcriptomic and sepsis risks. The workflow outlined may provide a new approach and direction in exploring the mechanisms associated with genes and sepsis risk.

Framework for feature selection of predicting the diagnosis and prognosis of necrotizing enterocolitis

Neonatal necrotizing enterocolitis (NEC) occurs worldwide and is a major source of neonatal morbidity and mortality. Researchers have developed many methods for predicting NEC diagnosis and prognosis. However, most people use statistical methods to select features, which may ignore the correlation between features. In addition, because they consider a small dimension of characteristics, they neglect some laboratory parameters such as white blood cell count, lymphocyte percentage, and mean platelet volume, which could be potentially influential factors affecting the diagnosis and prognosis of NEC. To address these issues, we include more perinatal, clinical, and laboratory information, including anemia—red blood cell transfusion and feeding strategies, and propose a ridge regression and Q-learning strategy based bee swarm optimization (RQBSO) metaheuristic algorithm for predicting NEC diagnosis and prognosis. Finally, a linear support vector machine (linear SVM), which specializes in classifying high-dimensional features, is used as a classifier. In the NEC diagnostic prediction experiment, the area under the receiver operating characteristic curve (AUROC) of dataset 1 (feeding intolerance + NEC) reaches 94.23%. In the NEC prognostic prediction experiment, the AUROC of dataset 2 (medical NEC + surgical NEC) reaches 91.88%. Additionally, the classification accuracy of the RQBSO algorithm on the NEC dataset is higher than the other feature selection algorithms. Thus, the proposed approach has the potential to identify predictors that contribute to the diagnosis of NEC and stratification of disease severity in a clinical setting.

Development and validation of a gene signature predicting the risk of postmenopausal osteoporosis

AIMS

We aimed to develop a gene signature that predicts the occurrence of postmenopausal osteoporosis (PMOP) by studying its genetic mechanism.

METHODS

Five datasets were obtained from the Gene Expression Omnibus database. Unsupervised consensus cluster analysis was used to determine new PMOP subtypes. To determine the central genes and the core modules related to PMOP, the weighted gene co-expression network analysis (WCGNA) was applied. Gene Ontology enrichment analysis was used to explore the biological processes underlying key genes. Logistic regression univariate analysis was used to screen for statistically significant variables. Two algorithms were used to select important PMOP-related genes. A logistic regression model was used to construct the PMOP-related gene profile. The receiver operating characteristic area under the curve, Harrell's concordance index, a calibration chart, and decision curve analysis were used to characterize PMOP-related genes. Then, quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the expression of the PMOP-related genes in the gene signature.

RESULTS

We identified three PMOP-related subtypes and four core modules. The muscle system process, muscle contraction, and actin filament-based movement were more active in the hub genes. We obtained five feature genes related to PMOP. Our analysis verified that the gene signature had good predictive power and applicability. The outcomes of the GSE56815 cohort were found to be consistent with the results of the earlier studies. qRT-PCR results showed that RAB2A and FYCO1 were amplified in clinical samples.

CONCLUSION

The PMOP-related gene signature we developed and verified can accurately predict the risk of PMOP in patients. These results can elucidate the molecular mechanism of RAB2A and FYCO1 underlying PMOP, and yield new and improved treatment strategies, ultimately helping PMOP monitoring.Cite this article: Bone Joint Res 2022;11(8):548-560.

Identification of diagnostic signatures associated with immune infiltration in Alzheimer’s disease by integrating bioinformatic analysis and machine-learning strategies

Objective

As a chronic neurodegenerative disorder, Alzheimer’s disease (AD) is the most common form of progressive dementia. The purpose of this study was to identify diagnostic signatures of AD and the effect of immune cell infiltration in this pathology.

Methods

The expression profiles of GSE109887, GSE122063, GSE28146, and GSE1297 were downloaded from the Gene Expression Omnibus (GEO) database to obtain differentially expressed genes (DEGs) between AD and control brain samples. Functional enrichment analysis was performed to reveal AD-associated biological functions and key pathways. Besides, we applied the Least Absolute Shrinkage Selection Operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) analysis to screen potential diagnostic feature genes in AD, which were further tested in AD brains of the validation cohort (GSE5281). The discriminatory ability was then assessed by the area under the receiver operating characteristic curves (AUC). Finally, the CIBERSORT algorithm and immune cell infiltration analysis were employed to assess the inflammatory state of AD.

Results

A total of 49 DEGs were identified. The functional enrichment analysis revealed that leukocyte transendothelial migration, cytokine receptor interaction, and JAK-STAT signaling pathway were enriched in the AD group. MAF basic leucine zipper transcription factor F (MAFF), ADCYAP1, and ZFP36L1 were identified as the diagnostic biomarkers of AD with high discriminatory ability (AUC = 0.850) and validated in AD brains (AUC = 0.935). As indicated from the immune cell infiltration analysis, naive B cells, plasma cells, activated/resting NK cells, M0 macrophages, M1 macrophages, resting CD4⁺ T memory cells, resting mast cells, memory B cells, and resting/activated dendritic cells may participate in the development of AD. Additionally, all diagnostic signatures presented different degrees of correlation with different infiltrating immune cells.

Conclusion

MAFF, ADCYAP1, and ZFP36L1 may become new candidate biomarkers of AD, which were closely related to the pathogenesis of AD. Moreover, the immune cells mentioned above may play crucial roles in disease occurrence and progression.

Deep learning‐based microarray cancer classification and ensemble gene selection approach

Malignancies and diseases of various genetic origins can be diagnosed and classified with microarray data. There are many obstacles to overcome due to the large size of the gene and the small number of samples in the microarray. A combination strategy for gene expression in a variety of diseases is described in this paper, consisting of two steps: identifying the most effective genes via soft ensembling and classifying them with a novel deep neural network. The feature selection approach combines three strategies to select wrapper genes and rank them according to the k‐nearest neighbour algorithm, resulting in a very generalisable model with low error levels. Using soft ensembling, the most effective subsets of genes were identified from three microarray datasets of diffuse large cell lymphoma, leukaemia, and prostate cancer. A stacked deep neural network was used to classify all three datasets, achieving an average accuracy of 97.51%, 99.6%, and 96.34%, respectively. In addition, two previously unreported datasets from small, round blue cell tumors (SRBCTs)and multiple sclerosis‐related brain tissue lesions were examined to show the generalisability of the model method.

Early detection of Alzheimer's disease using single nucleotide polymorphisms analysis based on gradient boosting tree

Alzheimer's disease (AD) is a degenerative disorder that attacks nerve cells in the brain. AD leads to memory loss and cognitive & intellectual impairments that can influence social activities and decision-making. The most common type of human genetic variation is single nucleotide polymorphisms (SNPs). SNPs are beneficial markers of complex gene-disease. Many common and serious diseases, such as AD, have associated SNPs. Detection of SNP biomarkers linked with AD could help in the early prediction and diagnosis of this disease. The main objective of this paper is to predict and diagnose AD based on SNPs biomarkers with high classification accuracy in the early stages. One of the most concerning problems is the high number of features. Thus, the paper proposes a comprehensive framework for early AD detection and detecting the most significant genes based on SNPs analysis. Usage of machine learning (ML) techniques to identify new biomarkers of AD is also suggested. In the proposed system, two feature selection techniques are separately checked: the information gain filter and Boruta wrapper. The two feature selection techniques were used to select the most significant genes related to AD in this system. Filter methods measure the relevance of features by their correlation with dependent variables, while wrapper methods measure the usefulness of a subset of features by training a model on it. Gradient boosting tree (GBT) has been applied on all AD genetic data of neuroimaging initiative phase 1 (ADNI-1) and Whole-Genome Sequencing (WGS) datasets by using two feature selection techniques. In the whole-genome approach ADNI-1, results revealed that the GBT learning algorithm scored an overall accuracy of 99.06% in the case of using Boruta feature selection. Using information gain feature selection, the proposed system achieved an average accuracy of 94.87%. The results show that the proposed system is preferable for the early detection of AD. Also, the results revealed that the Boruta wrapper feature selection is superior to the information gain filter technique.

GPR15L is an epithelial inflammation-derived pruritogen

Itch is an unpleasant sensation that often accompanies chronic dermatological conditions. Although many of the itch receptors and the neural pathways underlying this sensation are known, the identity of endogenous ligands is still not fully appreciated. Using an unbiased bioinformatic approach, we identified GPR15L as a candidate pruritogen whose expression is robustly up-regulated in psoriasis and atopic dermatitis. Although GPR15L was previously shown to be a cognate ligand of the receptor GPR15, expressed in dermal T cells, here we show that it also contributes to pruritogenesis by activating Mas-related G protein-coupled receptors (MRGPRs). GPR15L can selectively stimulate mouse dorsal root ganglion neurons that express Mrgpra3 and evokes intense itch responses. GPR15L causes mast cell degranulation through stimulation of MRGPRX2 and Mrgprb2. Genetic disruption of GPR15L expression attenuates scratch responses in a mouse model of psoriasis. Our study reveals unrecognized features of GRP15L, showing that it is a potent itch-inducing agent.

C10orf99/GPR15L Regulates Proinflammatory Response of Keratinocytes and Barrier Formation of the Skin

The epidermis, outermost layer of the skin, forms a barrier and is involved in innate and adaptive immunity in an organism. Keratinocytes participate in all these three protective processes. However, a regulator of keratinocyte protective responses against external dangers and stresses remains elusive. We found that upregulation of the orphan gene 2610528A11Rik was a common factor in the skin of mice with several types of inflammation. In the human epidermis, peptide expression of G protein-coupled receptor 15 ligand (GPR15L), encoded by the human ortholog C10orf99, was highly induced in the lesional skin of patients with atopic dermatitis or psoriasis. C10orf99 gene transfection into normal human epidermal keratinocytes (NHEKs) induced the expression of inflammatory mediators and reduced the expression of barrier-related genes. Gene ontology analyses showed its association with translation, mitogen-activated protein kinase (MAPK), mitochondria, and lipid metabolism. Treatment with GPR15L reduced the expression levels of filaggrin and loricrin in human keratinocyte 3D cultures. Instead, their expression levels in mouse primary cultured keratinocytes did not show significant differences between the wild-type and 2610528A11Rik deficient keratinocytes. Lipopolysaccharide-induced expression of Il1b and Il6 was less in 2610528A11Rik deficient mouse keratinocytes than in wild-type, and imiquimod-induced psoriatic dermatitis was blunted in 2610528A11Rik deficient mice. Furthermore, repetitive subcutaneous injection of GPR15L in mouse ears induced skin inflammation in a dose-dependent manner. These results suggest that C10orf99/GPR15L is a primary inducible regulator that reduces the barrier formation and induces the inflammatory response of keratinocytes.

FeCO3, constructing the network biomarkers using the inter-feature correlation coefficients and its application in detecting high-order breast cancer biomarkers

Aims:

This study aims to formulate the inter-feature correlation as the engineered features.

Background:

Modern biotechnologies tend to generate a huge number of characteristics of a sample, while an OMIC dataset usually has a few dozens or hundreds of samples due to the high costs of generating the OMIC data. So many bio-OMIC studies assumed the inter-feature independence and selected a feature with a high phenotype-association.

Objective:

However, many features are closely associated with each other due to their physical or functional interactions, which may be utilized as a new view of features.

Method:

This study proposed a feature engineering algorithm based on the correlation coefficients (FeCO3) by utilizing the correlations between a given sample and a few reference samples. A comprehensive evaluation was carried out for the proposed FeCO3 network features using 24 bio-OMIC datasets.

Result:

The experimental data suggested that the newly calculated FeCO3 network features tended to achieve better classification performances than the original features, using the same popular feature selection and classification algorithms. The FeCO3 network features were also consistently supported by the literature. FeCO3 was utilized to investigate the high-order engineered biomarkers of breast cancer, and detected the PBX2 gene (Pre-B-Cell Leukemia Transcription Factor 2) as one of the candidate breast cancer biomarkers. Although the two methylated residues cg14851325 (Pvalue=8.06e-2) and cg16602460 (Pvalue=1.19e-1) within PBX2 did not have statistically significant association with breast cancers, the high-order inter-feature correlations showed a significant association with breast cancers.

Conclusion:

The proposed FeCO3 network features calculated the high-order inter-feature correlations as novel features, and may facilitate the investigations of complex diseases from this new perspective. The source code is available in FigShare at 10.6084/m9.figshare.13550051 or the web site http://www.healthinformaticslab.org/supp/ .

A Novel CpG Methylation Risk Indicator for Predicting Prognosis in Bladder Cancer

Purpose

Bladder cancer (BLCA) is one of the most common cancers worldwide. In a large proportion of BLCA patients, disease recurs and/or progress after resection, which remains a major clinical issue in BLCA management. Therefore, it is vital to identify prognostic biomarkers for treatment stratification. We investigated the efficiency of CpG methylation for the potential to be a prognostic biomarker for patients with BLCA.

Patients and Methods

Overall, 357 BLCA patients from The Cancer Genome Atlas (TCGA) were randomly separated into the training and internal validation cohorts. Least absolute shrinkage and selector operation (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) were used to select candidate CpGs and build the methylation risk score model, which was validated for its prognostic value in the validation cohort by Kaplan–Meier analysis. Hazard curves were generated to reveal the risk nodes throughout the follow-up. Gene Set Enrichment Analysis (GSEA) was used to reveal the potential biological pathways associated with the methylation model. Quantitative real-time polymerase chain reaction (PCR) and western blotting were performed to verify the expression level of the methylated genes.

Results

After incorporating the CpGs obtained by the two algorithms, CpG methylation of eight genes corresponding to TNFAIP8L3, KRTDAP, APC, ZC3H3, COL9A2, SLCO4A1, POU3F3, and ADARB2 were prominent candidate predictors in establishing a methylation risk score for BLCA (MRSB), which was used to divide the patients into high- and low-risk progression groups (p < 0.001). The effectiveness of the MRSB was validated in the internal cohort (p < 0.001). In the MRSB high-risk group, the hazard curve exhibited an initial wide, high peak within 10 months after treatment, whereas some gentle peaks around 2 years were noted. Furthermore, a nomogram comprising MRSB, age, sex, and tumor clinical stage was developed to predict the individual progression risk, and it performed well. Survival analysis implicated the effectiveness of MRSB, which remains significant in all the subgroup analysis based on the clinical features. A functional analysis of MRSB and the corresponding genes revealed potential pathways affecting tumor progression. Validation of quantitative real-time PCR and western blotting revealed that TNFAIP8L3 was upregulated in the BLCA tissues.

Conclusion

We developed the MRSB, an eight-gene-based methylation signature, which has great potential to be used to predict the post-surgery progression risk of BLCA.

Application of Genetic Algorithm-Based Support Vector Machine in Identification of Gene Expression Signatures for Psoriasis Classification: A Hybrid Model

BACKGROUND

Psoriasis is a chronic autoimmune disease impairing significantly the quality of life of the patient. The diagnosis of the disease is done via a visual inspection of the lesional skin by dermatologists. Classification of psoriasis using gene expression is an important issue for the early and effective treatment of the disease. Therefore, gene expression data and selection of suitable gene signatures are effective sources of information.

METHODS

We aimed to develop a hybrid classifier for the diagnosis of psoriasis based on two machine learning models of the genetic algorithm and support vector machine (SVM). The method also conducts gene signature selection. A publically available gene expression dataset was used to test the model.

RESULTS

A number of 181 probe sets were selected among the original 54,675 probes using the hybrid model with a prediction accuracy of 100% over the test set. A number of 10 hub genes were identified using the protein-protein interaction network. Nine out of 10 identified genes were found in significant modules.

CONCLUSIONS

The results showed that the genetic algorithm improved the SVM classifier performance significantly implying the ability of the proposed model in terms of detecting relevant gene expression signatures as the best features.

Immune changes beyond Th2 pathways during rapid multifood immunotherapy enabled with omalizumab

BACKGROUND

Multifood oral immunotherapy (mOIT) with adjunctive anti-IgE (omalizumab, XOLAIR® ) treatment affords safe, effective, and rapid desensitization to multiple foods, although the specific immune mechanisms mediating this desensitization remain to be fully elucidated.

METHODS

Participants in our phase 2 mOIT trial (NCT02643862) received omalizumab from baseline to week 16 and mOIT from week 8 to week 36. We compared the immune profile of PBMCs and plasma taken at baseline, week 8, and week 36 using high-dimensional mass cytometry, component-resolved diagnostics, the indirect basophil activation test, and Luminex.

RESULTS

We found (i) decreased frequency of IL-4+ peanut-reactive CD4+ T cells and a marked downregulation of GPR15 expression and CXCR3 frequency among γδ and CD8+ T-cell subsets at week 8 during the initial, omalizumab-alone induction phase; (ii) significant upregulation of the skin-homing receptor CCR4 in peanut-reactive CD4+ T and Th2 effector memory (EM) cells and of cutaneous lymphocyte-associated antigen (CLA) in peanut-reactive CD8+ T and CD8+ EM cells; (iii) downregulation of CD86 expression among antigen-presenting cell subsets; and (iv) reduction in pro-inflammatory cytokines, notably IL-17, at week 36 post-OIT. We also observed significant attenuation of the Th2 phenotype post-OIT, defined by downregulation of IL-4 peanut-reactive T cells and OX40 in Th2EM cells, increased allergen component-specific IgG4/IgE ratio, and decreased allergen-driven activation of indirectly sensitized basophils.

CONCLUSIONS

This exploratory study provides novel comprehensive insight into the immune underpinnings of desensitization through omalizumab-facilitated mOIT. Moreover, this study provides encouraging results to support the complex immune changes that can be induced by OIT.

MGRFE: Multilayer Recursive Feature Elimination Based on an Embedded Genetic Algorithm for Cancer Classification

Microarray gene expression data have become a topic of great interest for cancer classification and for further research in the field of bioinformatics. Nonetheless, due to the "large p, small n" paradigm of limited biosamples and high-dimensional data, gene selection is becoming a demanding task, which is aimed at selecting a minimal number of discriminatory genes associated closely with a phenotype. Feature or gene selection is still a challenging problem owing to its nondeterministic polynomial time complexity and thus most of the existing feature selection algorithms utilize heuristic rules. A multilayer recursive feature elimination method based on an embedded integer-coded genetic algorithm, MGRFE, is proposed here, which is aimed at selecting the gene combination with minimal size and maximal information. On the basis of 19 benchmark microarray datasets including multiclass and imbalanced datasets, MGRFE outperforms state-of-the-art feature selection algorithms with better cancer classification accuracy and a smaller selected gene number. MGRFE could be regarded as a promising feature selection method for high-dimensional datasets especially gene expression data. Moreover, the genes selected by MGRFE have close biological relevance to cancer phenotypes. The source code of our proposed algorithm and all the 19 datasets used in this paper are available at https://github.com/Pengeace/MGRFE-GaRFE.

A dynamic recursive feature elimination framework (dRFE) to further refine a set of OMIC biomarkers

MOTIVATION

A feature selection algorithm may select the subset of features with the best associations with the class labels. The recursive feature elimination (RFE) is a heuristic feature screening framework and has been widely used to select the biological OMIC biomarkers. This study proposed a dynamic recursive feature elimination (dRFE) framework with more flexible feature elimination operations. The proposed dRFE was comprehensively compared with 11 existing feature selection algorithms and five classifiers on the eight difficult transcriptome datasets from a previous study, the ten newly collected transcriptome datasets and the five methylome datasets.

RESULTS

The experimental data suggested that the regular RFE framework did not perform well, and dRFE outperformed the existing feature selection algorithms in most cases. The dRFE-detected features achieved Acc=1.0000 for the two methylome datasets GSE53045 and GSE66695. The best prediction accuracies of the dRFE-detected features were 0.9259, 0.9424, and 0.8601 for the other three methylome datasets GSE74845, GSE103186, and GSE80970, respectively. Four transcriptome datasets received Acc=1.0000 using the dRFE-detected features, and the prediction accuracies for the other six newly collected transcriptome datasets were between 0.6301 and 0.9917.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Decoding Psoriasis: Integrated Bioinformatics Approach to Understand Hub Genes and Involved Pathways

BACKGROUND

Psoriasis is a chronic immune mediated skin disorder with global prevalence of 0.2- 11.4%. Despite rare mortality, the severity of the disease could be understood by the accompanying comorbidities, that has even led to psychological problems among several patients. The cause and the disease mechanism still remain elusive.

OBJECTIVE

To identify potential therapeutic targets and affecting pathways for better insight of the disease pathogenesis.

METHOD

The gene expression profile GSE13355 and GSE14905 were retrieved from NCBI, Gene Expression Omnibus database. The GEO profiles were integrated and the DEGs of lesional and non-lesional psoriasis skin were identified using the affy package in R software. The Kyoto Encyclopaedia of Genes and Genomes pathways of the DEGs were analyzed using clusterProfiler. Cytoscape, V3.7.1 was utilized to construct protein interaction network and analyze the interactome map of candidate proteins encoded in DEGs. Functionally relevant clusters were detected through Cytohubba and MCODE.

RESULTS

A total of 1013 genes were differentially expressed in lesional skin of which 557 were upregulated and 456 were downregulated. Seven dysregulated genes were extracted in non-lesional skin. The disease gene network of these DEGs revealed 75 newly identified differentially expressed gene that might have a role in development and progression of the disease. GO analysis revealed keratinocyte differentiation and positive regulation of cytokine production to be the most enriched biological process and molecular function. Cytokines -cytokine receptor was the most enriched pathways. Among 1013 identified DEGs in lesional group, 36 DEGs were found to have altered genetic signature including IL1B and STAT3 which are also reported as hub genes. CCNB1, CCNA2, CDK1, IL1B, CXCL8, MKI 67, ESR1, UBE2C, STAT1 and STAT3 were top 10 hub gene.

CONCLUSION

The hub genes, genomic altered DEGs and other newly identified differentially dysregulated genes would improve our understanding of psoriasis pathogenesis, moreover, the hub genes could be explored as potential therapeutic targets for psoriasis.

Use of Artificial Intelligence in Dermatology

Artificial intelligence (AI) has emerged as a major frontier in computer science research. Although AI has been available for some time and found its application in many fields of medicine, its use in dermatology is comparatively new and limited. A sound understanding of the concepts of AI is essential for dermatologists as skin conditions with their abundant clinical and dermatoscopic data and images can potentially be the next big thing in the application of AI in medicine. There are already a number of artificial intelligence studies focusing on skin disorders, such as skin cancer, psoriasis, atopic dermatitis and onychomycosis. This article presents an overview of AI and new developments relevant to dermatology, examining both its current applications and future potential.

Multi-view radiomics and dosiomics analysis with machine learning for predicting acute-phase weight loss in lung cancer patients treated with radiotherapy

We propose a multi-view data analysis approach using radiomics and dosiomics (R&D) texture features for predicting acute-phase weight loss (WL) in lung cancer radiotherapy. Baseline weight of 388 patients who underwent intensity modulated radiation therapy (IMRT) was measured between one month prior to and one week after the start of IMRT. Weight change between one week and two months after the commencement of IMRT was analyzed, and dichotomized at 5% WL. Each patient had a planning CT and contours of gross tumor volume (GTV) and esophagus (ESO). A total of 355 features including clinical parameter (CP), GTV and ESO (GTV&ESO) dose-volume histogram (DVH), GTV radiomics, and GTV&ESO dosiomics features were extracted. R&D features were categorized as first- (L1), second- (L2), higher-order (L3) statistics, and three combined groups, L1 + L2, L2 + L3 and L1 + L2 + L3. Multi-view texture analysis was performed to identify optimal R&D input features. In the training set (194 earlier patients), feature selection was performed using Boruta algorithm followed by collinearity removal based on variance inflation factor. Machine-learning models were developed using Laplacian kernel support vector machine (lpSVM), deep neural network (DNN) and their averaged ensemble classifiers. Prediction performance was tested on an independent test set (194 more recent patients), and compared among seven different input conditions: CP-only, DVH-only, R&D-only, DVH + CP, R&D + CP, R&D + DVH and R&D + DVH + CP. Combined GTV L1 + L2 + L3 radiomics and GTV&ESO L3 dosiomics were identified as optimal input features, which achieved the best performance with an ensemble classifier (AUC = 0.710), having statistically significantly higher predictability compared with DVH and/or CP features (p < 0.05). When this performance was compared to that with full R&D-only features which reflect traditional single-view data, there was a statistically significant difference (p < 0.05). Using optimized multi-view R&D input features is beneficial for predicting early WL in lung cancer radiotherapy, leading to improved performance compared to using conventional DVH and/or CP features.

A lncRNA prognostic signature associated with immune infiltration and tumour mutation burden in breast cancer

Current studies have shown that long non-coding RNAs (lncRNAs) may serve as prognostic biomarkers in multiple cancers. Therefore, we postulated that expression patterns of multiple lncRNAs combined into a single signature could improve clinicopathological risk stratification and prediction of overall survival rate for breast cancer patients. Two algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to select candidate lncRNAs. Univariate and multivariate Cox regression analyses were employed to construct a seven-lncRNA signature for breast cancer. Stratified analysis revealed that the signature was significantly associated with multiple clinicopathological risk factors. For clinical use, we developed a nomogram model to predict overall survival and odds of death for breast cancer patients. Single-sample gene set enrichment analysis (ssGSEA), CIBERSORT algorithm and ESTIMATE method were employed to assess the relative immune cell infiltrations of each sample. Differentially infiltration of immune cells and diverse tumour mutation burden (TMB) scores might give rise to the efficacy of lncRNA signature for predicting the overall survival of patients. Correlation analysis implied that LINC01215 was associated with multiple immune-related signalling pathways. A seven-lncRNA prognostic signature is a reliable tool to predict the prognosis of breast cancer patients.

Tumor mutational burden related classifier is predictive of response to PD-L1 blockade in locally advanced and metastatic urothelial carcinoma

BACKGROUND

Immunotherapy has made encouraging progress in the treatment of urothelial carcinoma, but only a small percentage of patients respond effectively to the immune checkpoint blockade (ICB). Our study aims to develop a classifier could effectively predict the response to ICB.

METHODS

Support vector machines recursive feature elimination (SVM-RFE) algorithm was used to feature selection, then compared nine common binary classification algorithms through machine learning, we selected the classifier with the highest prediction performance (LASSO logistics classifier). Ten-fold cross-validation was used to avoid the overfitting effect.

RESULTS

We developed a classifier on a urothelial carcinoma cohort treated with PD-L1 inhibitor Atzolizumab (IMvigor210 cohort, n = 272) and calculated a tumor mutational burden-related LASSO score (TLS) using the LASSO algorithm, which was significantly correlated with Tumor mutational burden (TMB) and neoantigen burden. We validated the efficacy of TLS in predicting prognosis and immunotherapy benefit in internal (IMvigor210) and external validation set (TCGA-BLCA, n = 406), respectively.

CONCLUSIONS

After in-depth analysis, we provide a new idea for stratified treatment of such patients, that is, patients with high TLS should use ICB and also may benefit from hormone therapy, while patients with low TLS respond poorly to ICB and maybe benefit from targeting TGFβ.

Altered Hippocampal Epigenetic Regulation Underlying Reduced Cognitive Development in Response to Early Life Environmental Insults

The hippocampus is involved in learning and memory and undergoes significant growth and maturation during the neonatal period. Environmental insults during this developmental timeframe can have lasting effects on brain structure and function. This study assessed hippocampal DNA methylation and gene transcription from two independent studies reporting reduced cognitive development stemming from early life environmental insults (iron deficiency and porcine reproductive and respiratory syndrome virus (PRRSv) infection) using porcine biomedical models. In total, 420 differentially expressed genes (DEGs) were identified between the reduced cognition and control groups, including genes involved in neurodevelopment and function. Gene ontology (GO) terms enriched for DEGs were associated with immune responses, angiogenesis, and cellular development. In addition, 116 differentially methylated regions (DMRs) were identified, which overlapped 125 genes. While no GO terms were enriched for genes overlapping DMRs, many of these genes are known to be involved in neurodevelopment and function, angiogenesis, and immunity. The observed altered methylation and expression of genes involved in neurological function suggest reduced cognition in response to early life environmental insults is due to altered cholinergic signaling and calcium regulation. Finally, two DMRs overlapped with two DEGs, VWF and LRRC32, which are associated with blood brain barrier permeability and regulatory T-cell activation, respectively. These results support the role of altered hippocampal DNA methylation and gene expression in early life environmentally-induced reductions in cognitive development across independent studies.

Artificial Intelligence in Dermatology-Where We Are and the Way to the Future: A Review

Although artificial intelligence has been available for some time, it has garnered significant interest recently and has been popularized by major companies with its applications in image identification, speech recognition and problem solving. Artificial intelligence is now being increasingly studied for its potential uses in medicine. A sound understanding of the concepts of this emerging field is essential for the dermatologist as dermatology has abundant medical data and images that can be used to train artificial intelligence for patient care. There are already a number of artificial intelligence studies focusing on skin disorders such as skin cancer, psoriasis, atopic dermatitis and onychomycosis. This article aims to present a basic introduction to the concepts of artificial intelligence as well as present an overview of the current research into artificial intelligence in dermatology, examining both its current applications and its future potential.

Review-Current Concepts in Inflammatory Skin Diseases Evolved by Transcriptome Analysis: In-Depth Analysis of Atopic Dermatitis and Psoriasis

During the last decades, high-throughput assessment of gene expression in patient tissues using microarray technology or RNA-Seq took center stage in clinical research. Insights into the diversity and frequency of transcripts in healthy and diseased conditions provide valuable information on the cellular status in the respective tissues. Growing with the technique, the bioinformatic analysis toolkit reveals biologically relevant pathways which assist in understanding basic pathophysiological mechanisms. Conventional classification systems of inflammatory skin diseases rely on descriptive assessments by pathologists. In contrast to this, molecular profiling may uncover previously unknown disease classifying features. Thereby, treatments and prognostics of patients may be improved. Furthermore, disease models in basic research in comparison to the human disease can be directly validated. The aim of this article is not only to provide the reader with information on the opportunities of these techniques, but to outline potential pitfalls and technical limitations as well. Major published findings are briefly discussed to provide a broad overview on the current findings in transcriptomics in inflammatory skin diseases.