SMGR: a joint statistical method for integrative analysis of single-cell multi-omics data

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

T-reg transcriptomic signatures identify response to check-point inhibitors

Regulatory T cells (Tregs) is a subtype of CD4+ T cells that produce an inhibitory action against effector cells. In the present work we interrogated genomic datasets to explore the transcriptomic profile of breast tumors with high expression of Tregs. Only 0.5% of the total transcriptome correlated with the presence of Tregs and only four transcripts, BIRC6, MAP3K2, USP4 and SMG1, were commonly shared among the different breast cancer subtypes. The combination of these genes predicted favorable outcome, and better prognosis in patients treated with checkpoint inhibitors. Twelve up-regulated genes coded for proteins expressed at the cell membrane that included functions related to neutrophil activation and regulation of macrophages. A positive association between MSR1 and CD80 with macrophages in basal-like tumors and between OLR1, ABCA1, ITGAV, CLEC5A and CD80 and macrophages in HER2 positive tumors was observed. Expression of some of the identified genes correlated with favorable outcome and response to checkpoint inhibitors: MSR1, CD80, OLR1, ABCA1, TMEM245, and ATP13A3 predicted outcome to anti PD(L)1 therapies, and MSR1, CD80, OLR1, ANO6, ABCA1, TMEM245, and ATP13A3 to anti CTLA4 therapies, including a subgroup of melanoma treated patients. In this article we provide evidence of genes strongly associated with the presence of Tregs that modulates the response to check point inhibitors.

Prognosis, Immune Microenvironment Infiltration and Immunotherapy Response in Clear Cell Renal Cell Carcinoma Based on Cuproptosis-related Immune Checkpoint Gene Signature

Background: Immune checkpoint genes (ICGs), which are the cornerstone of immunotherapy, influence the incidence and progression of clear cell renal cell carcinoma (ccRCC). It is important to note that there is not much data in the literature to determine how cuproptosis and antitumor immunity are related. Methods: On the basis of The Cancer Genome Atlas ccRCC dataset (n=526), cuproptosis-related ICGs (CICGs) were used to identify distinct molecular subtypes. Using the Cox regression method, a risk signature was constructed and externally validated using the ICGC (n=91) and primary ccRCC subgroups of GSE22541 (n=24). The molecular and immune characteristics and efficacy of immunotherapy in the subgroups defined by the risk score were investigated. Four risk CICGs were verified through in vitro experiment. Results: We identified two unique molecular subgroups with substantial prognostic differences based on 17 CICGs. The two subtypes clearly differ in terms of the tumor immune microenvironment (TME). A predictive risk signature (CD276, HLA-E, LGALS9, and TNFRSF18) was created and externally confirmed, and their expressions were validated by realtime PCR. The multivariate Cox regression analysis demonstrated that this signature could independently predict survival. Thus, a credible nomogram incorporating the signature, age, stage, and grade was constructed, and discrimination was confirmed using the C-index, calibration curve, and decision curve analyses. The underlying implications for immune checkpoint inhibitors, the landscape of the TME, and single-cell level localization are depicted. In addition, its accuracy in forecasting actual immunotherapeutic results has been proven (CheckMate025 and TCGA-SKCM cohorts). The sensitivity of the two risk groups to various drug-targeted therapy methods was analyzed. Conclusions: The data provided here provide the groundwork for creating customized therapeutic options for individuals with ccRCC. The findings also suggested that researching the cuproptosis-based pathway might improve ccRCC patient better prognosis, development of anti-tumor immunity, and therapeutic strategies for immunotherapy.

Microfluidic design in single-cell sequencing and application to cancer precision medicine

Single-cell sequencing (SCS) is a crucial tool to reveal the genetic and functional heterogeneity of tumors, providing unique insights into the clonal evolution, microenvironment, drug resistance, and metastatic progression of cancers. Microfluidics is a critical component of many SCS technologies and workflows, conferring advantages in throughput, economy, and automation. Here, we review the current landscape of microfluidic architectures and sequencing techniques for single-cell omics analysis and highlight how these have enabled recent applications in oncology research. We also discuss the challenges and the promise of microfluidics-based single-cell analysis in the future of precision oncology.

Integrating Single-Cell RNA-Seq and Bulk RNA-Seq Data to Explore the Key Role of Fatty Acid Metabolism in Breast Cancer

Cancer immune escape is associated with the metabolic reprogramming of the various infiltrating cells in the tumor microenvironment (TME), and combining metabolic targets with immunotherapy shows great promise for improving clinical outcomes. Among all metabolic processes, lipid metabolism, especially fatty acid metabolism (FAM), plays a major role in cancer cell survival, migration, and proliferation. However, the mechanisms and functions of FAM in the tumor immune microenvironment remain poorly understood. We screened 309 fatty acid metabolism-related genes (FMGs) for differential expression, identifying 121 differentially expressed genes. Univariate Cox regression models in The Cancer Genome Atlas (TCGA) database were then utilized to identify the 15 FMGs associated with overall survival. We systematically evaluated the correlation between FMGs' modification patterns and the TME, prognosis, and immunotherapy. The FMGsScore was constructed to quantify the FMG modification patterns using principal component analysis. Three clusters based on FMGs were demonstrated in breast cancer, with three patterns of distinct immune cell infiltration and biological behavior. An FMGsScore signature was constructed to reveal that patients with a low FMGsScore had higher immune checkpoint expression, higher immune checkpoint inhibitor (ICI) scores, increased immune microenvironment infiltration, better survival advantage, and were more sensitive to immunotherapy than those with a high FMGsScore. Finally, the expression and function of the signature key gene NDUFAB1 were examined by in vitro experiments. This study significantly demonstrates the substantial impact of FMGs on the immune microenvironment of breast cancer, and that FMGsScores can be used to guide the prediction of immunotherapy efficacy in breast cancer patients. In vitro experiments, knockdown of the NDUFAB1 gene resulted in reduced proliferation and migration of MCF-7 and MDA-MB-231 cell lines.

Detection of sFas, sCD137, and IL-27 Cytokines as Potential Biomarkers for Hepatocellular Carcinoma Diagnosis

Purpose

Hepatocellular carcinoma (HCC), a prevalent type of liver cancer, is mainly diagnosed in the advanced stage, leading to a high mortality rate. Recent advances have identified peripheral cytokines as a potential tool to predict disease outcomes and inform therapeutic decisions. Hence, in this study, we aim to build a predictive model for HCC based on serum levels of different cytokines.

Patients and Methods

We used immunoassay to quantify the concentrations of IL-27, MIP-1β, Perforin, sCD137, sFas, and TNF-α in the serum of 38 HCC patients and 15 healthy controls. Logistic regression was then used to construct classification models detecting HCC based on these cytokines. A nomogram of the best-performing model was generated to visualize HCC prediction.

Results

sFas and MIP-1β were found to be significantly higher in HCC patients compared to controls. Predictive models based on cytokine levels combining sFas, sCD137, and IL-27 performed the best in distinguishing HCC patients from healthy controls. This model has a bias-corrected area under the receiver operating characteristic (ROC) curve (AUC) of 0.948, a sensitivity of 92.11%, a specificity of 93.33%, and an accuracy of 0.925.

Conclusion

Our findings suggest that serum cytokines have the potential to be utilized in HCC screening to improve detection rates.

Toll-like Receptor 4 Inflammatory Perspective on Doxorubicin-Induced Cardiotoxicity

Doxorubicin (Dox) is one of the most frequently used chemotherapeutic drugs in a variety of cancers, but Dox-induced cardiotoxicity diminishes its therapeutic efficacy. The underlying mechanisms of Dox-induced cardiotoxicity are still not fully understood. More significantly, there are no established therapeutic guidelines for Dox-induced cardiotoxicity. To date, Dox-induced cardiac inflammation is widely considered as one of the underlying mechanisms involved in Dox-induced cardiotoxicity. The Toll-like receptor 4 (TLR4) signaling pathway plays a key role in Dox-induced cardiac inflammation, and growing evidence reports that TLR4-induced cardiac inflammation is strongly linked to Dox-induced cardiotoxicity. In this review, we outline and address all the available evidence demonstrating the involvement of the TLR4 signaling pathway in different models of Dox-induced cardiotoxicity. This review also discusses the effect of the TLR4 signaling pathway on Dox-induced cardiotoxicity. Understanding the role of the TLR4 signaling pathway in Dox-induced cardiac inflammation might be beneficial for developing a potential therapeutic strategy for Dox-induced cardiotoxicity.

Systematic pan-cancer analysis identifies cuproptosis-related gene DLAT as an immunological and prognostic biomarker

Lipoylated dihydrolipoamide S-acetyltransferase (DLAT), the component E2 of the multi-enzyme pyruvate dehydrogenase complex, is one of the key molecules of cuproptosis. However, the prognostic value and immunological role of DLAT in pan-cancer are still unclear. Using a series of bioinformatics approaches, we studied combined data from different databases, including the Cancer Genome Atlas, Genotype Tissue-Expression, the Cancer Cell Line Encyclopedia, Human Protein Atlas, and cBioPortal to investigate the role of DLAT expression in prognosis and tumor immunity response. We also reveal the potential correlations between DLAT expression and gene alterations, DNA methylation, copy number variation (CNV), tumor mutational burden (TMB), microsatellite instability (MSI), tumor microenvironment (TME), immune infiltration levels, and various immune-related genes across different cancers. The results show that DLAT displays abnormal expression within most malignant tumors. Through gene set enrichment analysis (GSEA), we found that DLAT was significantly associated with immune-related pathways. Further, the expression of DLAT was also confirmed to be correlated with the tumor microenvironment and diverse infiltration of immune cells, especially tumor-associated macrophages (TAMs). In addition, we found that DLAT is co-expressed with genes encoding major histocompatibility complex (MHC), immunostimulators, immune inhibitors, chemokines, and chemokine receptors. Meanwhile, we demonstrate that DLAT expression is correlated with TMB in 10 cancers and MSI in 11 cancers. Our study reveals that DLAT plays an essential role in tumorigenesis and cancer immunity, which may be used to function as a prognostic biomarker and potential target for cancer immunotherapy.

Immune heterogeneity in cardiovascular diseases from a single-cell perspective

A variety of immune cell subsets occupy different niches in the cardiovascular system, causing changes in the structure and function of the heart and vascular system, and driving the progress of cardiovascular diseases (CVDs). The immune cells infiltrating the injury site are highly diverse and integrate into a broad dynamic immune network that controls the dynamic changes of CVDs. Due to technical limitations, the effects and molecular mechanisms of these dynamic immune networks on CVDs have not been fully revealed. With recent advances in single-cell technologies such as single-cell RNA sequencing, systematic interrogation of the immune cell subsets is feasible and will provide insights into the way we understand the integrative behavior of immune populations. We no longer lightly ignore the role of individual cells, especially certain highly heterogeneous or rare subpopulations. We summarize the phenotypic diversity of immune cell subsets and their significance in three CVDs of atherosclerosis, myocardial ischemia and heart failure. We believe that such a review could enhance our understanding of how immune heterogeneity drives the progression of CVDs, help to elucidate the regulatory roles of immune cell subsets in disease, and thus guide the development of new immunotherapies.

Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT

Kinetic modeling is an essential tool in systems biology research, enabling the quantitative analysis of biological systems and predicting their behavior. However, the development of kinetic models is a complex and time-consuming process. In this article, we propose a novel approach called KinModGPT, which generates kinetic models directly from natural language text. KinModGPT employs GPT as a natural language interpreter and Tellurium as an SBML generator. We demonstrate the effectiveness of KinModGPT in creating SBML kinetic models from complex natural language descriptions of biochemical reactions. KinModGPT successfully generates valid SBML models from a range of natural language model descriptions of metabolic pathways, protein-protein interaction networks, and heat shock response. This article demonstrates the potential of KinModGPT in kinetic modeling automation.

Development and validation of two redox-related genes associated with prognosis and immune microenvironment in endometrial carcinoma

In recent studies, the tumourigenesis and development of endometrial carcinoma (EC) have been correlated significantly with redox. We aimed to develop and validate a redox-related prognostic model of patients with EC to predict the prognosis and the efficacy of immunotherapy. We downloaded gene expression profiles and clinical information of patients with EC from the Cancer Genome Atlas (TCGA) and the Gene Ontology (GO) dataset. We identified two key differentially expressed redox genes (CYBA and SMPD3) by univariate Cox regression and utilised them to calculate the risk score of all samples. Based on the median of risk scores, we composed low-and high-risk groups and performed correlation analysis with immune cell infiltration and immune checkpoints. Finally, we constructed a nomogram of the prognostic model based on clinical factors and the risk score. We verified the predictive performance using receiver operating characteristic (ROC) and calibration curves. CYBA and SMPD3 were significantly related to the prognosis of patients with EC and used to construct a risk model. There were significant differences in survival, immune cell infiltration and immune checkpoints between the low-and high-risk groups. The nomogram developed with clinical indicators and the risk scores was effective in predicting the prognosis of patients with EC. In this study, a prognostic model constructed based on two redox-related genes (CYBA and SMPD3) were proved to be independent prognostic factors of EC and associated with tumour immune microenvironment. The redox signature genes have the potential to predict the prognosis and the immunotherapy efficacy of patients with EC.

spaCI: deciphering spatial cellular communications through adaptive graph model

Cell-cell communications are vital for biological signalling and play important roles in complex diseases. Recent advances in single-cell spatial transcriptomics (SCST) technologies allow examining the spatial cell communication landscapes and hold the promise for disentangling the complex ligand-receptor (L-R) interactions across cells. However, due to frequent dropout events and noisy signals in SCST data, it is challenging and lack of effective and tailored methods to accurately infer cellular communications. Herein, to decipher the cell-to-cell communications from SCST profiles, we propose a novel adaptive graph model with attention mechanisms named spaCI. spaCI incorporates both spatial locations and gene expression profiles of cells to identify the active L-R signalling axis across neighbouring cells. Through benchmarking with currently available methods, spaCI shows superior performance on both simulation data and real SCST datasets. Furthermore, spaCI is able to identify the upstream transcriptional factors mediating the active L-R interactions. For biological insights, we have applied spaCI to the seqFISH+ data of mouse cortex and the NanoString CosMx Spatial Molecular Imager (SMI) data of non-small cell lung cancer samples. spaCI reveals the hidden L-R interactions from the sparse seqFISH+ data, meanwhile identifies the inconspicuous L-R interactions including THBS1-ITGB1 between fibroblast and tumours in NanoString CosMx SMI data. spaCI further reveals that SMAD3 plays an important role in regulating the crosstalk between fibroblasts and tumours, which contributes to the prognosis of lung cancer patients. Collectively, spaCI addresses the challenges in interrogating SCST data for gaining insights into the underlying cellular communications, thus facilitates the discoveries of disease mechanisms, effective biomarkers and therapeutic targets.

Construction and validation of a novel ferroptosis-related signature for evaluating prognosis and immune microenvironment in ovarian cancer

Ovarian cancer (OV) is the most lethal form of gynecological malignancy worldwide, with limited therapeutic options and high recurrence rates. However, research focusing on prognostic patterns of ferroptosis-related genes (FRGs) in ovarian cancer is still lacking. From the 6,406 differentially expressed genes (DEGs) between TCGA-OV (n = 376) and GTEx cohort (n = 180), we identified 63 potential ferroptosis-related genes. Through the LASSO-penalized Cox analysis, 3 prognostic genes, SLC7A11, ZFP36, and TTBK2, were finally distinguished. The time-dependent ROC curves and K-M survival analysis performed powerful prognostic ability of the 3-gene signature. Stepwise, we constructed and validated the nomogram based on the 3-gene signature and clinical features, with promising prognostic value in both TCGA (p-value < .0001) and ICGC cohort (p-value = .0064). Gene Set Enrichment Analysis elucidated several potential pathways between the groups stratified by 3-gene signature, while the m6A gene analysis implied higher m6A level in the high-risk group. We applied the CIBERSORT algorithm to distinct tumor immune microenvironment between two groups, with less activated dendritic cells (DCs) and plasma cells, more M0 macrophages infiltration, and higher expression of key immune checkpoint molecules (CD274, CTLA4, HAVCR2, and PDCD1LG2) in the high-risk group. In addition, the low-risk group exhibited more favorable immunotherapy and chemotherapy responses. Collectively, our findings provided new prospects in the role of ferroptosis-related genes, as a promising prediction tool for prognosis and immune responses, in order to assist personalized treatment decision-making among ovarian cancer patients.

NLRP3 Gene Polymorphisms in Rheumatoid Arthritis and Primary Sjogren's Syndrome Patients

Aim: The activation of NLRP3 inflammasome leads to the stimulation of cytokines and is significantly involved in the pathogenesis and progression of autoimmune diseases. The purpose of this study is to examine the associations of NLRP3 gene polymorphisms with rheumatoid arthritis (RA) and primary Sjogren's syndrome (SS) patients. Methods: A total of 239 patients with RA, 285 patients with primary SS, and 170 healthy controls were enrolled. Genomic DNA was extracted from peripheral blood mononuclear cells, and gene polymorphisms were genotyped through the TaqMan assay. Antinuclear antibody (ANA), anti-Ro, and anti-CCP antibodies were detected using immunofluorescence immunoassay. Results: The T allele of rs4612666 CT elevated the susceptibility to RA disease. The RF titer during diagnosis of RA was significantly high in RA patients with the A allele of rs12079994 G/A polymorphism. The titer of anti-CCP during diagnosis of RA was high in the absence of the C allele of rs10754558 C/G polymorphisms in RA patients. Antinuclear antibody and anti-CCP were positively associated with the A allele of rs12079994 G/A polymorphism in primary SS. The C allele of rs4612666 C/T was negatively associated with ANA in primary SS. Conclusions: The results have shown that NLRP3 gene polymorphisms may play a role in the pathogenesis of RA and primary SS.

A novel prognostic related lncRNA signature associated with amino acid metabolism in glioma

Background

Glioma is one of the deadliest malignant brain tumors in adults, which is highly invasive and has a poor prognosis, and long non-coding RNAs (lncRNAs) have key roles in the progression of glioma. Amino acid metabolism reprogramming is an emerging hallmark in cancer. However, the diverse amino acid metabolism programs and prognostic value remain unclear during glioma progression. Thus, we aim to find potential amino-related prognostic glioma hub genes, elaborate and verify their functions, and explore further their impact on glioma.

Methods

Glioblastoma (GBM) and low-grade glioma (LGG) patients' data were downloaded from TCGA and CCGA datasets. LncRNAs associated with amino acid metabolism were discriminated against via correlation analysis. LASSO analysis and Cox regression analysis were conducted to identify lncRNAs related to prognosis. GSVA and GSEA were performed to predict the potential biological functions of lncRNA. Somatic mutation data and CNV data were further built to demonstrate genomic alterations and the correlation between risk scores. Human glioma cell lines U251 and U87-MG were used for further validation in vitro experiments.

Results

There were eight amino-related lncRNAs in total with a high prognostic value that were identified via Cox regression and LASSO regression analyses. The high risk-score group presented a significantly poorer prognosis compared with the low risk-score group, with more clinicopathological features and characteristic genomic aberrations. Our results provided new insights into biological functions in the above signature lncRNAs, which participate in the amino acid metabolism of glioma. LINC01561 is one of the eight identified lncRNAs, which was adopted for further verification. In in vitro experiments, siRNA-mediated LINC01561 silencing suppresses glioma cells' viability, migration, and proliferation.

Conclusion

Novel amino-related lncRNAs associated with the survival of glioma patients were identified, and a lncRNA signature can predict glioma prognosis and therapy response, which possibly has vital roles in glioma. Meanwhile, it emphasized the importance of amino acid metabolism in glioma, particularly in providing deeper research at the molecular level.

Prognostic value of nectin-4 in human cancers: A meta-analysis

Background

Many reports have described that abnormal nectin-4 expression may be used as a prognostic marker in many tumors. However, these studies failed to reach a consensus. Here, we performed a meta-analysis to comprehensively evaluate the prognostic value of nectin-4 in cancers.

Methods

Relevant studies were identified through a comprehensive search of PubMed, EMBASE and Web of science until August 31, 2022. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were used to evaluate the relationship between nectin-4 expression and overall survival (OS) and disease-free survival/progression-free survival/relapse-free survival (DFS/PFS/RFS). Odds ratios (ORs) with 95% CIs were applied to assess the relationship between nectin-4 expression and clinicopathologic features. Subgroup analysis was performed to explore the sources of heterogeneity. Sensitivity analysis and funnel plot were used to test the reliability of the results. All data analyses were performed using STATA version 12.0 software.

Results

Fifteen articles involving 2245 patients were included in the meta-analysis. The pooled analysis showed that high nectin-4 expression was significantly associated with poor OS (HR: 1.75, 95% CI: 1.35-2.28). There was no relationship between high nectin-4 expression and DFS/PFS/RFS (HR: 178, 95% CI: 0.78-4.08).Subgroup analyses revealed that that high nectin-4 expression mainly presented adverse OS in esophageal cancer (EC) (HR: 1.78, 95% CI: 1.30-2.44) and gastric cancer (GC) (HR: 1.92, 95% CI: 1.43-2.58). We also found that high nectin-4 expression was associated with tumor diameter (big vs small) (OR: 1.96, 95% CI: 1.02-3.75), tumor stage (III-IV vs I-II) (OR: 2.04, 95% CI: 1.01-4.12) and invasion depth (T3+T4 vs T2+T1) (OR: 3.95, 95% CI: 2.06-7.57).

Conclusions

Nectin-4 can be used as an effective prognostic indicator for specific cancers.

Characterization of prognostic value and immunological roles of RAB22A in hepatocellular carcinoma

Background

The protein-coding gene RAB22A, a member of the RAS oncogene family, is amplified or overexpressed in certain cancers. However, its action mechanism in hepatocellular carcinoma (HCC) remains unclear. Here, we aimed to examine the connection between RAB22A and survival prognosis in HCC and explore the biological significance of RAB22A.

Methods

A database-based pan-cancer expression analysis of RAB22A was performed. Kaplan-Meier analysis and Cox regression were performed to evaluate the association between RAB22A expression and survival prognosis in HCC. Using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA), various potential biological functions and regulatory pathways of RAB22A in HCC were discovered. Tumor immune infiltration was studied using the single sample gene set enrichment analysis (ssGSEA) method. N6-methyladenosine modifications and the regulatory network of competitive endogenous RNA (ceRNA) were verified in the TCGA cohort.

Results

RAB22A was upregulated in HCC samples and cell lines. A high RAB22A expression in HCC was strongly correlated with sex, race, age, weight, TNM stage, pathological stage, tumor status, histologic grade, TP53 mutation status, and alpha fetal protein (AFP) levels. Overexpression of RAB22A indicated a poor prognosis was related to overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). GO and KEGG analyses revealed that the differentially expressed genes related to RAB22A might be involved in the proteasomal protein catabolic process, ncRNA processing, ribosome ribosomal subunit, protein serine/threonine kinase activity, protein serine kinase activity, Endocytosis, and non-alcoholic fatty liver disease. GSEA analyses revealed that the differentially expressed genes related to RAB22A might be involved in the T cell receptor, a co-translational protein, that binds to the membrane, axon guidance, ribosome, phagocytosis, and Eukaryotic translation initiation. RAB22A was correlated with N6-methyladenosine expression in HCC and established RAB22A-related ceRNA regulatory networks. Finally,RAB22A expression was positively connected the levels of infiltrating with T helper cells, Tcm cells, and Th2 cells,In contrast, we observed negatively correlations with cytotoxic cells, DCs, and pDCs cells.Moreover,RAB22A expression showed a strong correlation with various immunomarkergroups in HCC.

Conclusions

RAB22A is a potential therapeutic target for improving HCC prognosis and is closely related to immune cell infiltration.

WWOX-rs13338697 genotype predicts therapeutic efficacy of ADI-PEG 20 for patients with advanced hepatocellular carcinoma

BACKGROUND

Previous studies have identified three single nucleotide polymorphisms (SNPs): GALNT14-rs9679162, WWOX-rs13338697 and rs6025211. Their genotypes are associated with therapeutic outcomes in hepatocellular carcinoma (HCC). Herein, we examined whether these SNP genotypes could predict the clinical outcome of HCC patients treated with ADI-PEG 20.

METHODS

Totally 160 patients with advanced HCC, who had previously been enrolled in clinical trials, including 113 receiving ADI-PEG 20 monotherapy (cohort-1) and 47 receiving FOLFOX/ADI-PEG 20 combination treatment (cohort-2), were included retrospectively.

RESULTS

The WWOX-rs13338697-GG genotype was associated with favorable overall survival in cohort-1 patients (P = 0.025), whereas the rs6025211-TT genotype was associated with unfavorable time-to-tumor progression in cohort-1 (P = 0.021) and cohort-1 plus 2 patients (P = 0.008). As ADI-PEG 20 can reduce plasma arginine levels, we examined its pretreatment levels in relation to the WWOX-rs13338697 genotypes. Pretreatment plasma arginine levels were found to be significantly higher in patients carrying the WWOX-rs13338697-GG genotype (P = 0.006). We next examined the association of the WWOX-rs13338697 genotypes with WWOX tissue protein levels in 214 paired (cancerous/noncancerous) surgically resected HCC tissues (cohort-3). The WWOX-rs13338697-GG genotype was associated with decreased tissue levels of WWOX and ASS1. Mechanistic studies showed that WWOX and ASS1 levels were downregulated in hypoxic HCC cells. Silencing WWOX to mimic low WWOX protein expression in HCC in patients with the WWOX-rs13338697-GG genotype, enhanced HIF1A increment under hypoxia, further decreased ASS1, and increased cell susceptibility to ADI-PEG 20.

COMCLUSION

In summary, the WWOX-rs13338697 and rs6025211 genotypes predicted treatment outcomes in ADI-PEG 20-treated advanced HCC patients. The WWOX-rs13338697-GG genotype was associated with lower tissue WWOX and ASS1 levels and higher pretreatment plasma arginine levels, resembling an arginine auxotrophic phenotype requires excessive extracellular arginine supply. Silencing WWOX to mimic HCC with the WWOX-rs13338697-GG genotype further stimulated HCC cell response to hypoxia through increased HIF1A expression, leading to further reduction of ASS1 and thus increased cell susceptibility to ADI-PEG 20.

TYK2 correlates with immune infiltration: A prognostic marker for head and neck squamous cell carcinoma

Tyrosine kinase 2 (TYK2) is a member of the Janus kinase (JAK) family and is involved in immune and inflammatory signaling. TYK2 is overexpressed in several types of cancers and promotes the invasion and proliferation of cancer cells. Nevertheless, the roles of TYK2 in the prognosis and immune infiltration of head and neck squamous cell carcinoma (HNSCC) remain to be elucidated. In this study, the expression of TYK2 in HNSCC was evaluated based on the data retrieved from multiple databases and quantitative real-time polymerase chain reaction (qRT-PCR) analysis. The prognostic potential of TYK2 in patients with HNSCC was analyzed by Kaplan-Meier curves and Cox regression analysis. A TYK2-related risk assessment model was subsequently constructed by Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and stepwise multivariate Cox regression analysis. The association between the expression of TYK2 and the tumor immune microenvironment, immune checkpoints, and drug sensitivity was explored various packages in R. Cell function assays were finally used for exploring the effects of TYK2 on the growth and metastasis of HNSCC tumors. The expression of TYK2 was significantly upregulated in HNSCC and was found to be closely correlated with HPV status, gender, clinical grade, and TP53 mutation status. Survival analysis suggested that TYK2 is associated with better survival outcomes and acts as an independent prognostic indicator of HNSCC. The model constructed herein also performed well in terms of predicting patient prognosis. The expression of TYK2 was positively associated with the population of tumor-infiltrating immune cells, expression of immune checkpoint genes, and antitumor drug susceptibility. Functionally, TYK2 knockdown significantly promoted the proliferation, migration, and invasion of HNSCC cell lines in vitro. The findings demonstrated that TYK2 could serve as a suppressor of tumor growth and holds significant promise as a novel biomarker for assessing the prognosis of patients with HNSCC and aid in immunotherapy against HNSCC.

Identification and verification of a novel epigenetic-related gene signature for predicting the prognosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a common malignant tumor with a poor prognosis. Epigenetic dysregulation is now considered to be related to hepatocarcinogenesis. However, it is unclear how epigenetic-related genes (ERGs) contribute to the prognosis of HCC. In this study, we used the TCGA database to identify prognostic ERGs that were differentially expressed in HCC patients. Then, using least absolute shrinkage and selection operator (LASSO) regression analysis, a six-gene signature was constructed, and patients were divided into high- and low-risk groups. Validation was performed on HCC patients from the ICGC database. Patients in the high-risk group had a significantly lower chance of survival than those in the low-risk group (p &lt; 0.001 in both databases). The predictive ability of the signature was determined by the receiver operating characteristic (ROC) curve. The risk score was then shown to be an independent prognostic factor for the overall survival (OS) of HCC patients based on the results of univariate and multivariate analyses. We also created a practical nomogram combining the prognostic model with other clinical features. Moreover, functional enrichment analysis revealed that these genes are linked to tumor immunity. In conclusion, our findings showed that a novel six-gene signature related to epigenetics can accurately predict the occurrence and prognosis of HCC.

Multi-omics analysis: Paving the path toward achieving precision medicine in cancer treatment and immuno-oncology

The acceleration of large-scale sequencing and the progress in high-throughput computational analyses, defined as omics, was a hallmark for the comprehension of the biological processes in human health and diseases. In cancerology, the omics approach, initiated by genomics and transcriptomics studies, has revealed an incredible complexity with unsuspected molecular diversity within a same tumor type as well as spatial and temporal heterogeneity of tumors. The integration of multiple biological layers of omics studies brought oncology to a new paradigm, from tumor site classification to pan-cancer molecular classification, offering new therapeutic opportunities for precision medicine. In this review, we will provide a comprehensive overview of the latest innovations for multi-omics integration in oncology and summarize the largest multi-omics dataset available for adult and pediatric cancers. We will present multi-omics techniques for characterizing cancer biology and show how multi-omics data can be combined with clinical data for the identification of prognostic and treatment-specific biomarkers, opening the way to personalized therapy. To conclude, we will detail the newest strategies for dissecting the tumor immune environment and host–tumor interaction. We will explore the advances in immunomics and microbiomics for biomarker identification to guide therapeutic decision in immuno-oncology.

Transmembrane and coiled-coil domains 3 is a diagnostic biomarker for predicting immune checkpoint blockade efficacy in hepatocellular carcinoma

Liver hepatocellular carcinoma (LIHC) is a malignancy with a high mortality and morbidity rate worldwide. However, the pathogenesis of LIHC has still not been thoroughly studied. Transmembrane and coiled-coil domains 3 (TMCO3) encodes a monovalent cation, a member of the proton transducer 2 (CPA2) family of transporter proteins. In the present study, TMCO3 expression and its relationship with cancer prognosis, as well as its immunological role in LIHC were studied by bioinformatic analysis. We found the significant overexpression of TMCO3 in LIHC in the TCGA, HCCDB, and GEO databases. In LIHC patients, high TMCO3 expression was related to poorer overall survival (OS) and TMCO3 had good predictive accuracy for prognosis. Moreover, TMCO3 was linked to the infiltrates of certain immune cells in LIHC. The correlation of TMCO3 with immune checkpoints was also revealed. Moreover, patients with LIHC with low TMCO3 expression showed a better response to immune checkpoint blockade (ICB) than those with LIHC with high TMCO3 expression. GO and KEGG enrichment analyses indicated that TMCO3 was probably involved in the microtubule cytoskeleton organization involved in mitosis, small GTPase mediated signal transduction, and TGF-β pathway. In conclusion, TMCO3 may be a potential biomarker for LIHC prognosis and immunotherapy.

Identification of the hub and prognostic genes in liver hepatocellular carcinoma via bioinformatics analysis

Hepatocellular carcinoma (HCC) is a common malignancy. However, the molecular mechanisms of the progression and prognosis of HCC remain unclear. In the current study, we merged three Gene Expression Omnibus (GEO) datasets and combined them with The Cancer Genome Atlas (TCGA) dataset to screen differentially expressed genes. Furthermore, protein‒protein interaction (PPI) and weighted gene coexpression network analysis (WGCNA) were used to identify key gene modules in the progression of HCC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses indicated that the terms were associated with the cell cycle and DNA replication. Then, four hub genes were identified (AURKA, CCNB1, DLGAP5, and NCAPG) and validated via the expression of proteins and transcripts using online databases. In addition, we established a prognostic model using univariate Cox proportional hazards regression and least absolute shrinkage and selection operator (LASSO) regression. Eight genes were identified as prognostic genes, and four genes (FLVCR1, HMMR, NEB, and UBE2S) were detrimental gens. The areas under the curves (AUCs) at 1, 3 and 5 years were 0.622, 0.69, and 0.684 in the test dataset, respectively. The effective of prognostic model was also validated using International Cancer Genome Consortium (ICGC) dataset. Moreover, we performed multivariate independent prognostic analysis using multivariate Cox proportional hazards regression. The results showed that the risk score was an independent risk factor. Finally, we found that all prognostic genes had a strong positive correlation with immune infiltration. In conclusion, this study identified the key hub genes in the development and progression of HCC and prognostic genes in the prognosis of HCC, which was significant for the future diagnosis and prognosis of HCC.