Blood cell traits and venous thromboembolism in East Asians: Observational and genetic evidence.

Previous studies have indicated that various blood cell traits are associated with a higher risk of venous thromboembolism (VTE). However, the causal relationship remains uncertain. We collected data from the China pulmonary thromboembolism registry study and the China pulmonary health study, using propensity score matching and two-sample Mendelian randomization analyses with summary statistics from genome-wide association studies of blood cell traits and VTE in the East Asian population. Our findings revealed that platelet (PLT) count and hemoglobin (Hb) levels were significantly higher in VTE patients compared to the general population (p value <0.01). Genetically predicted Hb levels were positively associated with VTE, with an odds ratio (OR) of 2.38 (1.13-5.01), p value = 0.022. Similarly, genetically predicted PLT count was positively correlated with VTE, with an OR of 1.33 (1.02-1.74), p value = 0.038. These results suggest a causal relationship and potential targets for prevention.

Oncogenic pathways refine a new perspective on the classification of hepatocellular carcinoma.

BACKGROUND: Genetic alterations in oncogenic pathways are critical for cancer initiation, development, and treatment resistance. However, studies are limited regarding pathways correlated with prognosis, sorafenib, and transcatheter arterial chemoembolization (TACE) in hepatocellular carcinoma (HCC). METHODS: In this study, 1928 patients from 11 independent datasets and a clinical in-house cohort were screened to explore the relationships among canonical pathway alterations in HCC patients. The molecular mechanisms, biological functions, immune landscape, and clinical outcomes among three heterogeneous phenotypes were further explored. RESULTS: We charted the detailed landscape of pathway alterations in the TCGA-LIHC cohort, screened three pivotal pathways (p53, PI3K, and WNT), identified co-occurrence patterns and mutual exclusively, and stratified patients into three altered-pathway dominant phenotypes (ADPs). P53|PI3K ADP characterized by genomic instability (e.g., highest TMB, FGA, FGG, and FGL) indicated an unfavorable prognosis. While, patients in WNT ADP suggested a median prognosis, enhanced immune activation, and sensitivity to PD-L1 therapy. Remarkably, sorafenib and TACE exhibited efficacy for patients in WNT ADP and low frequent alteration phenotype (LFP). Additionally, ADP could work independently of common clinical traits (e.g., AJCC stage) and previous molecular classifications (e.g., iCluster, serum biomarkers). CONCLUSIONS: ADP provides a new perspective for identifying patients at high risk of recurrence and could optimize precision treatment to improve the clinical outcomes in HCC.

Prognosis and Personalized Treatment Prediction in Different Mutation-Signature Hepatocellular Carcinoma.

Introduction: Mutation patterns have been extensively explored to decipher the etiologies of hepatocellular carcinoma (HCC). However, the study and potential clinical role of mutation patterns to stratify high-risk patients and optimize precision therapeutic strategies remain elusive in HCC. Methods: Using exon-sequencing data in public (n=362) and in-house (n=30) cohorts, mutation signatures were extracted to decipher relationships with the etiology and prognosis in HCC. The proteomics (n=159) and cell-line transcriptome data (n=1019) were collected to screen the implication of sensitive drugs. A novel multi-step machine-learning framework was then performed to construct a classification predictor, including recognizing stable reversed gene pairs, establishing a robust prediction model, and validating the robustness of the predictor in five independent cohorts (n=900). Results: Two heterogeneous mutation signature clusters were identified, and a high-risk prognosis cluster was recognized for further analysis. Notably, mutation signature cluster 1 (MSC1) was featured by activated anti-tumor immune and metabolism dysfunctional states, higher genomic instability (high TMB, SNV neoantigen, indel neoantigens, and total neoantigens), and a dismal prognosis. Notably, MSC performed as an independent risk factor than clinical traits (eg, stage, vascular invasion). Additionally, afatinib and canertinib were recognized which might have potential therapeutic implications in MSC1, and the targets of these drugs presented a higher expression in both gene and protein levels in HCC. Discussion: Our studies may provide a promising platform for improving prognosis and tailoring therapy in HCC.

Recent advances and applications of CRISPR-Cas9 in cancer immunotherapy.

The incidence and mortality of cancer are the major health issue worldwide. Apart from the treatments developed to date, the unsatisfactory therapeutic effects of cancers have not been addressed by broadening the toolbox. The advent of immunotherapy has ushered in a new era in the treatments of solid tumors, but remains limited and requires breaking adverse effects. Meanwhile, the development of advanced technologies can be further boosted by gene analysis and manipulation at the molecular level. The advent of cutting-edge genome editing technology, especially clustered regularly interspaced short palindromic repeats (CRISPR-Cas9), has demonstrated its potential to break the limits of immunotherapy in cancers. In this review, the mechanism of CRISPR-Cas9-mediated genome editing and a powerful CRISPR toolbox are introduced. Furthermore, we focus on reviewing the impact of CRISPR-induced double-strand breaks (DSBs) on cancer immunotherapy (knockout or knockin). Finally, we discuss the CRISPR-Cas9-based genome-wide screening for target identification, emphasis the potential of spatial CRISPR genomics, and present the comprehensive application and challenges in basic research, translational medicine and clinics of CRISPR-Cas9.

Bioinformatic analysis and preliminary validation of potential therapeutic targets for COVID-19 infection in asthma patients.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 causes coronavirus disease 19 (COVID-19). The number of confirmed cases of COVID-19 is also rapidly increasing worldwide, posing a significant challenge to human safety. Asthma is a risk factor for COVID-19, but the underlying molecular mechanisms of the asthma-COVID-19 interaction remain unclear. METHODS: We used transcriptome analysis to discover molecular biomarkers common to asthma and COVID-19. Gene Expression Omnibus database RNA-seq datasets (GSE195599 and GSE196822) were used to identify differentially expressed genes (DEGs) in asthma and COVID-19 patients. After intersecting the differentially expressed mRNAs, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify the common pathogenic molecular mechanism. Bioinformatic methods were used to construct protein-protein interaction (PPI) networks and identify key genes from the networks. An online database was used to predict interactions between transcription factors and key genes. The differentially expressed long noncoding RNAs (lncRNAs) in the GSE195599 and GSE196822 datasets were intersected to construct a competing endogenous RNA (ceRNA) regulatory network. Interaction networks were constructed for key genes with RNA-binding proteins (RBPs) and oxidative stress-related proteins. The diagnostic efficacy of key genes in COVID-19 was verified with the GSE171110 dataset. The differential expression of key genes in asthma was verified with the GSE69683 dataset. An asthma cell model was established with interleukins (IL-4, IL-13 and IL-17A) and transfected with siRNA-CXCR1. The role of CXCR1 in asthma development was preliminarily confirmed. RESULTS: By intersecting the differentially expressed genes for COVID-19 and asthma, 393 common DEGs were obtained. GO and KEGG enrichment analyses of the DEGs showed that they mainly affected inflammation-, cytokine- and immune-related functions and inflammation-related signaling pathways. By analyzing the PPI network, we obtained 10 key genes: TLR4, TLR2, MMP9, EGF, HCK, FCGR2A, SELP, NFKBIA, CXCR1, and SELL. By intersecting the differentially expressed lncRNAs for COVID-19 and asthma, 13 common differentially expressed lncRNAs were obtained. LncRNAs that regulated microRNAs (miRNAs) were mainly concentrated in intercellular signal transduction, apoptosis, immunity and other related functional pathways. The ceRNA network suggested that there were a variety of regulatory miRNAs and lncRNAs upstream of the key genes. The key genes could also bind a variety of RBPs and oxidative stress-related genes. The key genes also had good diagnostic value in the verification set. In the validation set, the expression of key genes was statistically significant in both the COVID-19 group and the asthma group compared with the healthy control group. CXCR1 expression was upregulated in asthma cell models, and interference with CXCR1 expression significantly reduced cell viability. CONCLUSIONS: Key genes may become diagnostic and predictive biomarkers of outcomes in COVID-19 and asthma. Video Abstract.

Construction of a diagnostic signature and immune landscape of pulmonary arterial hypertension.

Background: Molecular biomarkers are widely used for disease diagnosis and exploration of pathogenesis. Pulmonary arterial hypertension (PAH) is a rapidly progressive cardiopulmonary disease with delayed diagnosis. Studies were limited regarding molecular biomarkers correlated with PAH from a broad perspective. Methods: Two independent microarray cohorts comprising 73 PAH samples and 36 normal samples were enrolled in this study. The weighted gene co-expression network analysis (WGCNA) was performed to identify the key modules associated with PAH. The LASSO algorithm was employed to fit a diagnostic model. The latent biology mechanisms and immune landscape were further revealed via bioinformatics tools. Results: The WGCNA approach ultimately identified two key modules significantly associated with PAH. For genes within the two models, differential expression analysis between PAH and normal samples further determined nine key genes. With the expression profiles of these nine genes, we initially developed a PAH diagnostic signature (PDS) consisting of LRRN4, PI15, BICC1, PDE1A, TSHZ2, HMCN1, COL14A1, CCDC80, and ABCB1 in GSE117261 and then validated this signature in GSE113439. The ROC analysis demonstrated outstanding AUCs with 0.948 and 0.945 in two cohorts, respectively. Besides, patients with high PDS scores enriched plenty of Th17 cells and neutrophils, while patients with low PDS scores were dramatically related to mast cells and B cells. Conclusion: Our study established a robust and promising signature PDS for diagnosing PAH, with key genes, novel pathways, and immune landscape offering new perspectives for exploring the molecular mechanisms and potential therapeutic targets of PAH.

Integrative bioinformatics analysis to explore a robust diagnostic signature and landscape of immune cell infiltration in sarcoidosis.

Background: The unknown etiology of sarcoidosis with variable clinical features leads to delayed diagnosis and limited therapeutic strategies. Hence, exploring the latent mechanisms and constructing an accessible and reliable diagnostic model of sarcoidosis is vital for innovative therapeutic approaches to improve prognosis. Methods: This retrospective study analyzed transcriptomes from 11 independent sarcoidosis cohorts, comprising 313 patients and 400 healthy controls. The weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis were performed to identify molecular biomarkers. Machine learning was employed to fit a diagnostic model. The potential pathogenesis and immune landscape were detected by bioinformatics tools. Results: A 10-gene signature SARDS consisting of GBP1, LEF1, IFIT3, LRRN3, IFI44, LHFPL2, RTP4, CD27, EPHX2, and CXCL10 was further constructed in the training cohorts by the LASSO algorithm, which performed well in the four independent cohorts with the splendid AUCs ranging from 0.938 to 1.000. The findings were validated in seven independent publicly available gene expression datasets retrieved from whole blood, PBMC, alveolar lavage fluid cells, and lung tissue samples from patients with outstanding AUCs ranging from 0.728 to 0.972. Transcriptional signatures associated with sarcoidosis revealed a potential role of immune response in the development of the disease through bioinformatics analysis. Conclusions: Our study identified and validated molecular biomarkers for the diagnosis of sarcoidosis and constructed the diagnostic model SARDS to improve the accuracy of early diagnosis of the disease.

Integrative insights and clinical applications of single-cell sequencing in cancer immunotherapy.

Recently, immunotherapy has gained increasing popularity in oncology. Several immunotherapies obtained remarkable clinical effects, but the efficacy varied, and only subsets of cancer patients benefited. Breaking the constraints and improving immunotherapy efficacy is extremely important in precision medicine. Whereas traditional sequencing approaches mask the characteristics of individual cells, single-cell sequencing provides multiple dimensions of cellular characterization at the single-cell level, including genomic, transcriptomic, epigenomic, proteomic, and multi-omics. Hence, the complexity of the tumor microenvironment, the universality of tumor heterogeneity, cell composition and cell-cell interactions, cell lineage tracking, and tumor drug resistance mechanisms are revealed in-depth. However, the clinical transformation of single-cell technology is not to the point of in-depth study, especially in the application of immunotherapy. The newly discovered vital cells and tremendous biomarkers facilitate the development of more efficient individualized therapeutic regimens to guide clinical treatment and predict prognosis. This review provided an overview of the progress in distinct single-cell sequencing methods and emerging strategies. For perspective, the expanding utility of combining single-cell sequencing and other technologies was discussed.

Roles of circulating tumor DNA in PD-1/PD-L1 immune checkpoint Inhibitors: Current evidence and future directions.

Circulating tumor DNA (ctDNA) sequencing holds considerable promise for early diagnosis and detection of surveillance and minimal residual disease. Blood ctDNA monitors specific cancers by detecting the alterations found in cancer cells, such as apoptosis and necrosis. Due to the short half-life, ctDNA reflects the actual burden of other treatments on tumors. In addition, ctDNA might be preferable to monitor tumor development and treatment compared with invasive tissue biopsy. ctDNA-based liquid biopsy brings remarkable strength to targeted therapy and precision medicine. Notably, multiple ctDNA analysis platforms have been broadly applied in clinical immunotherapy. Through targeted sequencing, early variations in ctDNA could predict response to immune checkpoint inhibitor (ICI). Several studies have demonstrated a correlation between ctDNA kinetics and anti-PD1 antibodies. The need for further research and development remains, although this biomarker holds significant prospects for early cancer detection. This review focuses on describing the basis of ctDNA and its current utilities in oncology and immunotherapy, either for clinical management or early detection, highlighting its advantages and inherent limitations.

Use of machine learning models to predict prognosis of combined pulmonary fibrosis and emphysema in a Chinese population.

BACKGROUND: Combined pulmonary fibrosis and emphysema (CPFE) is a novel clinical entity with a poor prognosis. This study aimed to develop a clinical nomogram model to predict the 1-, 2- and 3-year mortality of patients with CPFE by using the machine learning approach, and to validate the predictive ability of the interstitial lung disease-gender-age-lung physiology (ILD-GAP) model in CPFE. METHODS: The data of CPFE patients from January 2015 to October 2021 who met the inclusion criteria were retrospectively collected. We utilized LASSO regression and multivariable Cox regression analysis to identify the variables associated with the prognosis of CPFE and generate a nomogram. The Harrell's C index, the calibration curve and the area under the receiver operating characteristic (ROC) curve (AUC) were used to evaluate the performance of the nomogram. Then, we performed likelihood ratio test, net reclassification improvement (NRI), integrated discrimination improvement (IDI) and decision curve analysis (DCA) to compare the performance of the nomogram with that of the ILD-GAP model. RESULTS: A total of 184 patients with CPFE were enrolled. During the follow-up, 90 patients died. After screening out, diffusing lung capacity for carbon monoxide (DLCO), right ventricular diameter (RVD), C-reactive protein (CRP), and globulin were found to be associated with the prognosis of CPFE. The nomogram was then developed by incorporating the above five variables, and it showed a good performance, with a Harrell's C index of 0.757 and an AUC of 0.800 (95% CI 0.736-0.863). Moreover, the calibration plot of the nomogram showed good concordance between the prediction probabilities and the actual observations. The nomogram also improved the discrimination ability of the ILD-GAP model compared to that of the ILD-GAP model alone, and this was substantiated by the likelihood ratio test, NRI and IDI. The significant clinical utility of the nomogram was demonstrated by DCA. CONCLUSION: Age, DLCO, RVD, CRP and globulin were identified as being significantly associated with the prognosis of CPFE in our cohort. The nomogram incorporating the 5 variables showed good performance in predicting the mortality of CPFE. In addition, although the nomogram was superior to the ILD-GAP model in the present cohort, further validation is needed to determine the clinical utility of the nomogram.