scLM: Automatic Detection of Consensus Gene Clusters Across Multiple Single-cell Datasets

The microRNA target site profile is a novel biomarker in the immunotherapy response

MicroRNAs (miRNAs) bind on the 3' untranslated region (3'UTR) of messenger RNAs (mRNAs) and regulate mRNA expression in physiological and pathological conditions, including cancer. Thus, studies have identified miRNAs as potential biomarkers by correlating the miRNA expression with the expression of important mRNAs and/or clinical outcomes in cancers. However, tumors undergo pervasive 3'UTR shortening/lengthening events through alternative polyadenylation (APA), which varies the number of miRNA target sites in mRNA, raising the number of miRNA target sites (numTS) as another important regulatory axis of the miRNA binding effects. In this study, we developed the first statistical method, BIOMATA-APA, to identify predictive miRNAs based on numTS features. Running BIOMATA-APA on The Cancer Genome Atlas (TCGA) and independent cohort data both with immunotherapy and no immunotherapy, we demonstrated for the first time that the numTS feature 1) distinguishes different cancer types, 2) predicts tumor proliferation and immune infiltration status, 3) explains more variation in the proportion of tumor-infiltrating immune cells, 4) predicts response to immune checkpoint blockade (ICB) therapy, and 5) adds prognostic power beyond clinical and miRNA expression. To the best of our knowledge, this is the first pan-cancer study to systematically demonstrate numTS as a novel type of biomarker representing the miRNA binding effects underlying tumorigenesis and pave the way to incorporate miRNA target sites for miRNA biomarker identification. Another advantage of examining the miRNA binding effect using numTS is that it requires only RNA-Seq data, not miRNAs, thus resulting in high power in the miRNA biomarker identification.

Single-cell sequencing reveals the landscape of the human brain metastatic microenvironment

Brain metastases is the most common intracranial tumor and account for approximately 20% of all systematic cancer cases. It is a leading cause of death in advanced-stage cancer, resulting in a five-year overall survival rate below 10%. Therefore, there is a critical need to identify effective biomarkers that can support frequent surveillance and promote efficient drug guidance in brain metastasis. Recently, the remarkable breakthroughs in single-cell RNA-sequencing (scRNA-seq) technology have advanced our insights into the tumor microenvironment (TME) at single-cell resolution, which offers the potential to unravel the metastasis-related cellular crosstalk and provides the potential for improving therapeutic effects mediated by multifaceted cellular interactions within TME. In this study, we have applied scRNA-seq and profiled 10,896 cells collected from five brain tumor tissue samples originating from breast and lung cancers. Our analysis reveals the presence of various intratumoral components, including tumor cells, fibroblasts, myeloid cells, stromal cells expressing neural stem cell markers, as well as minor populations of oligodendrocytes and T cells. Interestingly, distinct cellular compositions are observed across different samples, indicating the influence of diverse cellular interactions on the infiltration patterns within the TME. Importantly, we identify tumor-associated fibroblasts in both our in-house dataset and external scRNA-seq datasets. These fibroblasts exhibit high expression of type I collagen genes, dominate cell-cell interactions within the TME via the type I collagen signaling axis, and facilitate the remodeling of the TME to a collagen-I-rich extracellular matrix similar to the original TME at primary sites. Additionally, we observe M1 activation in native microglial cells and infiltrated macrophages, which may contribute to a proinflammatory TME and the upregulation of collagen type I expression in fibroblasts. Furthermore, tumor cell-specific receptors exhibit a significant association with patient survival in both brain metastasis and native glioblastoma cases. Taken together, our comprehensive analyses identify type I collagen-secreting tumor-associated fibroblasts as key mediators in metastatic brain tumors and uncover tumor receptors that are potentially associated with patient survival. These discoveries provide potential biomarkers for effective therapeutic targets and intervention strategies.

Systemic analysis identifying PVT1 / DUSP13 axis for microvascular invasion in hepatocellular carcinoma

BACKGROUND

Microvascular invasion (MVI) is an independent detrimental risk factor for tumor recurrence and poor survival in hepatocellular carcinoma (HCC). Competitive endogenous RNA (ceRNA) networks play a pivotal role in the modulation of carcinogenesis and progression among diverse tumor types. However, whether the ceRNA mechanisms are engaged in promoting the MVI process in patients with HCC remains unknown.

METHODS

A ceRNA regulatory network was constructed based on RNA-seq data of patients with HCC from The Cancer Genome Atlas (TCGA) database. In total, 10 hub genes of the ceRNA network were identified using four algorithms: "MCC," "Degree," "Betweenness," and "Stress." Transcriptional expressions were verified by in situ hybridization using clinical samples. Interactions between ceRNA modules were validated by luciferase reporting assay. Logistic regression analysis, correlation analysis, enrichment analysis, promoter region analysis, methylation analysis, and immune infiltration analysis were performed to further investigate the molecular mechanisms and clinical transformation value.

RESULTS

The ceRNA regulatory network featuring a tumor invasion phenotype consisting of 3 long noncoding RNAs, 3 microRNAs, and 93 mRNAs was constructed using transcriptional data from the TCGA database. Systemic analysis and experimentally validation identified a ceRNA network (PVT1/miR-1258/DUSP13 axis) characterized by lipid regulatory potential, immune properties, and abnormal methylation states in patients with HCC and MVI. Meanwhile, 28 transcriptional factors were identified as potential promotors of PVT1 with 3 transcriptional factors MXD3, ZNF580, and KDM1A promising as therapeutic targets in patients with HCC and MVI. Furthermore, miR-1258 was an independent predictor for MVI in patients with HCC.

CONCLUSION

The PVT1/DUSP13 axis is significantly associated with MVI progression in HCC patients. This study provides new insight into mechanisms related to lipids, immune phenotypes, and abnormal epigenetics in oncology research.

scSTEM: clustering pseudotime ordered single-cell data

We develop scSTEM, single-cell STEM, a method for clustering dynamic profiles of genes in trajectories inferred from pseudotime ordering of single-cell RNA-seq (scRNA-seq) data. scSTEM uses one of several metrics to summarize the expression of genes and assigns a p-value to clusters enabling the identification of significant profiles and comparison of profiles across different paths. Application of scSTEM to several scRNA-seq datasets demonstrates its usefulness and ability to improve downstream analysis of biological processes. scSTEM is available at https://github.com/alexQiSong/scSTEM.

Clinical Diagnostic and Prognostic Potential of NDRG1 and NDRG2 in Hepatocellular Carcinoma Patients

Background

Primary liver cancer is still the most common lethal malignancy. The N-myc downstream-regulated gene family (NDRG1–4) is a group of multifunctional proteins associated with carcinogenesis. However, systematic evaluation of the diagnostic and prognostic values of NDRG1 or NDRG2 expression in liver cancer is poorly investigated.

Method

The gene expression matrix of liver hepatocellular carcinoma (LIHC) was comprehensively analyzed by the “limma” and “Dseq2” R packages. The Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) were used to identify the biological functional differences. A single-sample GSEA (ssGSEA) was conducted to quantify the extent of immune cell infiltration. Finally, the clinical and prognostic information of LIHC patients was systematically investigated using Kaplan–Meier analysis and logistic and Cox regression analysis.

Results

Compared with normal tissues, NDRG1 expression was higher, whereas NDRG2 expression was lower in tumor tissues (P <0.001). The area under the receiver operator characteristic curve (AUROC) of NDRG1 and NDRG2 for LIHC was 0.715 and 0.799, respectively. Kaplan–Meier analysis revealed that NDRG1 and NDRG2 were independent clinical prognostic biomarkers for the overall survival (OS, P = 0.001 and 2.9e−06), progression-free interval (PFI, P = 0.028 and 0.005) and disease-specific survival (DSS, P = 0.027 and P <0.001). The C-indexes and calibration plots of the nomogram suggest that NDRG1 and NDRG2 have an effective predictive performance for OS (C-index: 0.676), DSS (C-index: 0.741) and PFI (C-index: 0.630) of liver cancer patients. The mutation rate of NDRG1 in liver cancer reached up to 14%, and DNA methylation levels of NDRG1 and NDRG2 promoters correlated significantly with clinical prognosis.

Conclusions

The mRNA expression and DNA methylation of NDRG superfamily members have the potential for LIHC diagnosis and prognosis via integrative analysis from multiple cohorts.

Quantitative proteomic profiling of hepatocellular carcinoma at different serum alpha-fetoprotein level

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Serum AFP equal to 400 ng/mL is a pivotal turning point not only in prognosis but also metabolic and invasion associated pathways.

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Adjacent noncancerous tissues are not biological normal components at protein level.

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Four druggable targets (C1QBP, HSPE1, CHDH, ITGAL) are identified as potential prognostic biomarkers in hepatocellular carcinoma.

Purpose

Hepatocellular carcinoma (HCC) is characterized by a poor long-term prognosis and high mortality rate. Serum alpha-fetoprotein (AFP) levels show great prognostic value in patients undergoing hepatectomy. This study aims to explore proteomic profiling in HCC samples based on AFP subgroups and identify potential key targets involved in HCC progression.

Methods

Twelve paired tumor and adjacent noncancerous tissue samples were collected from patients with HCC who underwent primary curative resection from January 2012 to December 2013. Clinical information was curated from four tissue microarrays to conduct survival analysis based on serum AFP levels. TMT-based quantitative proteomic analyses and bioinformatics analyses were performed to comprehensively profile molecular features. Immunohistochemistry was carried out to validate protein expression of identified targets. Kaplan-Meier survival analysis was performed to assess the overall survival and recurrence-free survival based on protein expressions.

Results

AFP (400 ng/mL) was a turning point in prognosis, metabolic- and invasion-associated pathways. The mass spectrometry analysis yielded a total of 5573 identified proteins. Annotations of 151 differentially expressed proteins in tumors and 95 proteins in paracancerous tissues (1.2-fold) showed similarities in biological processes, cellular components, molecular functions. Furthermore, differentially expressed hub proteins with five innovatively nominated druggable targets (C1QBP, HSPE1, GLUD2 for tumors and CHDH, ITGAL for paracancerous tissues), of which four (C1QBP, HSPE1, CHDH, ITGAL) targets were associated with poor overall survival (all Log-rank P < 0.05).

Conclusions

Our quantitative proteomics analyses identified four key prognostic biomarkers in HCC and provide opportunities for translational medicine and new treatment.

Mitochondrial-Associated Protein LRPPRC is Related With Poor Prognosis Potentially and Exerts as an Oncogene Via Maintaining Mitochondrial Function in Pancreatic Cancer

Background: The mitochondrial-associated protein leucine-rich pentatricopeptide repeat-containing (LRPPRC) exerts multiple functions involved in physiological processes, including mitochondrial gene translation, cell cycle progression, and tumorigenesis. Previously, LRPPRC was reported to regulate mitophagy by interacting with Bcl-2 and Beclin-1 and thus modifying the activation of PI3KCIII and autophagy. Considering that LRPPRC was found to be negatively associated with survival rate, we hypothesize that LRPPRC may be involved in pancreatic cancer progression via its regulation of autophagy. Methods: Real-time quantitative polymerase chain reaction was performed to detect the expression of LRPPRC in 90 paired pancreatic cancer and adjacent tissues and five pancreatic cancer cell lines. Mitochondrial reactive oxidative species level and function were measured. Mitophagy was measured by performing to detect LC3 levels. Results: By performing a real-time quantitative polymerase chain reaction, the association of LRPPRC with the prognosis of pancreatic cancer was established, and pancreatic cancer tissues had significantly higher LRPPRC expression than adjacent tissues. LRPPRC was negatively associated with the overall survival rate. LRPPRC was also upregulated in pancreatic cancer cell lines. Knockdown of LRPPRC promoted reactive oxidative species accumulation, decreased mitochondrial membrane potential, promoted autophagy/mitophagy, and induced mitochondrial dysfunction. Subsequently, knockdown of LRPPRC inhibited malignant behaviors in PANC-1 cells, including proliferation, migration, invasion, tumor formation, and chemoresistance to gemcitabine. Finally, by inhibiting autophagy/mitophagy using 3-MA, the inhibitory effect of LRPPRC knockdown on proliferation was reversed. Conclusion: Taken together, our results indicate that LRPPRC may act as an oncogene via maintaining mitochondrial homeostasis and could be used as a predictive marker for patient prognosis in pancreatic cancer.

Cancer-associated fibroblast exosomes promote chemoresistance to cisplatin in hepatocellular carcinoma through circZFR targeting signal transducers and activators of transcription (STAT3)/ nuclear factor -kappa B (NF-κB) pathway

Chemoresistance in hepatocellular carcinoma (HCC) has been found to be influenced by exosomal transport of circRNAs. However, the role of circZFR in HCC chemoresistance still remains unclear. In the present study, circZFR was highly expressed in cisplatin (DDP)-resistant HCC cell lines and could regulate DDP resistance of the HCC cells. Also, circZFR was highly expressed in cancer-associated fibroblast (CAFs) and the exosome of CAFs. In addition, supplementation of CAFs in culture medium could promote DDP resistance of HCC cells. In vivo tumor xenograft experiments showed that knockdown of circZFR inhibited tumor growth and weakened DDP resistance, while CAFs-derived exosomes incubation increased the expression of circZFR, inhibited the STAT3/NF-κB pathway, promoted tumor growth, and enhanced DDP resistance. In general, CAFs-derived exosomes deliver circZFR to HCC cells, inhibit the STAT3/NF-κB pathway, and promote HCC development and chemoresistance. The results provided a new sight for the prevention and treatment of chemoresistance in HCC.

Circular RNA SOX5 promotes the proliferation and inhibits the apoptosis of the hepatocellular carcinoma cells by targeting miR-502-5p/synoviolin 1 axis

We aimed to explore the role of circ-SOX5 in the pathogenesis of hepatocellular carcinoma (HCC). The circRNAs in HCC were screened using the GEO database. RT-qPCR was used to detect mRNA expression. Targeting relationships were confirmed by dual luciferase reporter assay and RNA pull-down assay. CCK-8 and EDU staining were used to measure cell viability and proliferation, respectively. Cell apoptosis was determined using flow cytometry. Protein expression was determined by Western blotting. Circ-SOX5 expression was increased in HCC tissues. Inhibition of circ-SOX5 expression reduced the viability, proliferation, and colony formation, and increased the apoptosis of HCC cells. However, miR-502-5p inhibition or overexpression of synoviolin 1 (SYVN1) can reverse the effects of circ-SOX5 knockdown on proliferation and apoptosis. This study demonstrated that the circ-SOX5/miR-502-5p/SYVN1 axis promotes the development of HCC by regulating cell apoptosis. Therefore, circ-SOX5 may be a potential biomarker of HCC.

Comprehensive Analyses of the Expression, Genetic Alteration, Prognosis Significance, and Interaction Networks of m6A Regulators Across Human Cancers

Accumulating lines of evidence indicate that the deregulation of m⁶A is involved in various cancer types. The m⁶A RNA methylation is modulated by m⁶A methyltransferases, demethylases, and reader proteins. Although the aberrant expression of m⁶A RNA methylation contributes to the development and progression of multiple cancer types, the roles of m⁶A regulators across numerous types of cancers remain largely unknown. Here, we comprehensively investigated the expression, genetic alteration, and prognosis significance of 20 commonly studied m⁶A regulators across diverse cancer types using TCGA datasets via bioinformatic analyses. The results revealed that the m⁶A regulators exhibited widespread dysregulation, genetic alteration, and the modulation of oncogenic pathways across TCGA cancer types. In addition, most of the m⁶A regulators were closely relevant with significant prognosis in many cancer types. Furthermore, we also constructed the protein–protein interacting network of the 20 m⁶A regulators, and a more complex interacting regulatory network including m⁶A regulators and their corresponding interacting factors. Besides, the networks between m⁶A regulators and their upstream regulators such as miRNAs or transcriptional factors were further constructed in this study. Finally, the possible chemicals targeting each m⁶A regulator were obtained by bioinformatics analysis and the m⁶A regulators–potential drugs network was further constructed. Taken together, the comprehensive analyses of m⁶A regulators might provide novel insights into the m⁶A regulators’ roles across cancer types and shed light on their potential molecular mechanisms as well as help develop new therapy approaches for cancers.

Identification and Validation of Hub Genes Associated with Bladder Cancer by Integrated Bioinformatics and Experimental Assays

Bladder cancer (BC) is the most common malignant tumor of the urinary system and is associated with high morbidity and mortality; however, the molecular mechanism underlying its occurrence is not clear. In this study, the gene expression profile and related clinical information of GSE13507 were downloaded from the Gene Expression Omnibus (GEO) database. RNA sequencing (RNA-seq) expression data and related clinical information were retrieved from The Cancer Genome Atlas (TCGA) database. Overlapping genes were identified by differential gene expression analysis and weighted gene co-expression network analysis (WGCNA). Then, we carried out functional enrichment analysis to understand the potential biological functions of these co-expressed genes. Finally, we performed a protein-protein interaction (PPI) analysis combined with survival analysis. Using the CytoHubba plug-in of Cytoscape, TROAP, CENPF, PRC1, AURKB, CCNB2, CDC20, TTK, CEP55, ASPM, and CDCA8 were identified as candidate central genes. According to the survival analysis, the high expression of TTK was related to the poor overall survival (OS) of patients with BC. TTK may also affect the bladder tumor microenvironment (TME) by affecting the number of immune cells. The expression level of TTK was verified by immunohistochemistry (IHC) and real-time quantitative polymerase chain reaction (RT-qPCR), and the tumor-promoting effect of TTK in BC cells was confirmed in vitro. Our results also identified the MSC-AS1/hsa-miR-664b-3p/TTK regulatory axis, which may also play an important role in the progression of BC, but further research is needed to verify this result. In summary, our results provide a new idea for accurate early diagnosis, clinical treatment, and prognosis of BC.

Cancer Stem Cells and the Tumor Microenvironment in Gastric Cancer

Gastric cancer (GC) remains one of the leading causes of cancer-related death worldwide. Cancer stem cells (CSCs) might be responsible for tumor initiation, relapse, metastasis and treatment resistance of GC. The tumor microenvironment (TME) comprises tumor cells, immune cells, stromal cells and other extracellular components, which plays a pivotal role in tumor progression and therapy resistance. The properties of CSCs are regulated by cells and extracellular matrix components of the TME in some unique manners. This review will summarize current literature regarding the effects of CSCs and TME on the progression and therapy resistance of GC, while emphasizing the potential for developing successful anti-tumor therapy based on targeting the TME and CSCs.

Berberine Suppresses Stemness and Tumorigenicity of Colorectal Cancer Stem-Like Cells by Inhibiting m6A Methylation

Background

Cancer stem cells (CSCs) are able to survive after cancer therapies, resulting in tumor progression and recurrence, as is seen in colorectal cancer. Therapies targeting CSCs are regarded as novel and promising strategies for efficiently eradicating tumors. Berberine, an isoquinoline alkaloid extracted from the Chinese herbal medicine Coptis chinensis, was found to have antitumor activities against colorectal cancer, without knowing whether it exerts inhibitory effects on colorectal CSCs and the potential mechanisms.

Methods

In this study, we examined the inhibitory roles of Berberine on CSCs derived from HCT116 and HT29 by culturing in serum-free medium. We also examined the effects of Berberine on m⁶A methylation via regulating fat mass and obesity-associated protein (FTO), by downregulating β-catenin.

Results

We examined the effects of Berberine on the tumorigenicity, growth, and stemness of colorectal cancer stem-like cells. The regulatory effect of Berberine on N6-methyladenosine (m⁶A), an abundant mRNA modification, was also examined. Berberine treatment decreased cell proliferation by decreasing cyclin D1 and increasing p27 and p21 and subsequently induced cell cycle arrest at the G₁/G₀ phase. Berberine treatment also decreased colony formation and induced apoptosis. Berberine treatment transcriptionally increased FTO and thus decreased m⁶A methylation, which was reversed by both FTO knockdown and the addition of the FTO inhibitor FB23-2. Berberine induced FTO-related decreases in stemness in HCT116 and HT29 CSCs. Berberine treatment also increased chemosensitivity in CSCs and promoted chemotherapy agent-induced apoptosis. Moreover, we also found that Berberine treatment increased FTO by decreasing β-catenin, which is a negative regulator of FTO.

Conclusions

Our observation that Berberine effectively decreased m⁶A methylation by decreasing β-catenin and subsequently increased FTO suggests a role of Berberine in modulating stemness and malignant behaviors in colorectal CSCs.

PPP1R14B Is a Prognostic and Immunological Biomarker in Pan-Cancer

Recent studies have shown that PPP1R14B was highly expressed in tumor tissues and patients with high expression of PPP1R14B had poor survival rates. However, the function and mechanisms of PPP1R14B in tumor progression remain ill defined. There was also lack of pan-cancer evidence for the relationship between PPP1R14B and various tumor types based on abundant clinical data. We used the TCGA project and GEO databases to perform pan-cancer analysis of PPP1R14B, including expression differences, correlations between expression levels and survival, genetic alteration, immune infiltration, and relevant cellular pathways, to investigate the functions and potential mechanisms of PPP1R14B in the pathogenesis or clinical prognosis of different cancers. Herein, we found that PPP1R14B was involved in the prognosis of pan-cancer and closely related to immune infiltration. Increased PPP1R14B expression correlated with poor prognosis and increased immune infiltration levels in myeloid-derived suppressor cells (MDSCs). Our studies suggest that PPP1R14B can be used as a prognostic biomarker for pan-cancer. Our findings may provide an antitumor strategy targeting PPP1R14B, including manipulation of tumor cell growth or the tumor microenvironment, especially myeloid-derived suppressor cell infiltration.

Comprehensive Analysis to Identify the Epithelial-Mesenchymal Transition-Related Immune Signatures as a Prognostic and Therapeutic Biomarkers in Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is a highly heterogeneous disease with the high rates of the morbidity and mortality due to the lack of the effective prognostic model for prediction.

Aim: To construct a risk model composed of the epithelial–mesenchymal transition (EMT)-related immune genes for the assessment of the prognosis, immune infiltration status, and chemosensitivity.

Methods: We obtained the transcriptome and clinical data of the HCC samples from The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC) databases. The Pearson correlation analysis was applied to identify the differentially expressed EMT-related immune genes (DE-EMTri-genes). Subsequently, the univariate Cox regression was introduced to screen out the prognostic gene sets and a risk model was constructed based on the least absolute shrinkage and selection operator-penalized Cox regression. Additionally, the receiver operating characteristic (ROC) curves were plotted to compare the prognostic value of the newly established model compared with the previous model. Furthermore, the correlation between the risk model and survival probability, immune characteristic, and efficacy of the chemotherapeutics were analyzed by the bioinformatics methods.

Results: Six DE-EMTri-genes were ultimately selected to construct the prognostic model. The area under the curve (AUC) values for 1-, 2-, and 3- year were 0.773, 0.721, and 0.673, respectively. Stratified survival analysis suggested that the prognosis of the low-score group was superior to the high-score group. Moreover, the univariate and multivariate analysis indicated that risk score [hazard ratio (HR) 5.071, 95% CI 3.050, 8.432; HR 4.396, 95% CI 2.624, 7.366; p < 0.001] and stage (HR 2.500, 95% CI 1.721, 3.632; HR 2.111, 95% CI 1.443, 3.089; p < 0.001) served as an independent predictive factors in HCC. In addition, the macrophages, natural killer (NK) cells, and regulatory T (Treg) cells were significantly enriched in the high-risk group. Finally, the patients with the high-risk score might be more sensitive to cisplatin, doxorubicin, etoposide, gemcitabine, and mitomycin C.

Conclusion: We established a reliable EMTri-genes-based prognostic signature, which may hold promise for the clinical prediction.

Expression Levels of Three Key Genes CCNB1, CDC20, and CENPF in HCC Are Associated With Antitumor Immunity

Introduction

Hepatocellular carcinoma (HCC) is the most common primary liver cancer with a low 5-year survival rate. The heterogeneity of HCC makes monotherapy unlikely. The development of diagnostic programs and new treatments targeting common genetic events in the carcinogenic process are providing further insights into the management of HCC. The aim of this study was firstly to validate key genes that are involved in promoting HCC development and as biomarkers for early diagnosis and, secondly, to define their links with antitumor immunity including inhibitory checkpoints.

Methods

Multiple databases including Gene Expression Omnibus (GEO), Gene Expression Profiling Interactive Analysis (GEPIA), Kaplan-Meier Plotter, UALCAN, and Oncomine were used for target gene screening and establishment of a co-expression network. Clinical data and RNAseq of 367 HCC patients were downloaded from the Cancer Genome Atlas (TCGA) database. The diagnostic and prognostic value of screened genes were tested by receiver operating characteristic (ROC) curve and correlation analysis. The links with the key genes in HCC and antitumor immunity were defined using both blood and liver tissue collected prospectively from HCC patients in our center.

Results

Upregulation of CCNB1, CDC20, and CENPF was commonly observed in HCC and are involved in the p53 signal pathway. The hepatic mRNA expression levels of these three genes were strongly associated with patients' prognosis and expressed high value of area under the ROC curve (AUC). Further analysis revealed that these three genes were positively correlated with the gene expression levels of IFN-γ, TNF-α, and IL-17 in peripheral blood. In addition, the expression of CENPF showed positive correlation with the percentage of CD8^pos T cells and negative correlation with the percentage of CD4^pos T cells in the peripheral blood. In the HCC microenvironment, the transcript levels of these three genes and inhibitory checkpoint molecules including PD-1, CTLA-4, and TIM-3 were positively correlated.

Conclusion

The upregulation of CCNB1, CDC20, and CENPF genes was a common event in hepatocarcinogenesis. Expression levels of CCNB1, CDC20, and CENPF showed potential for early diagnosis and prediction of prognosis in HCC patients. There is a close association between three genes and Th1/Th17 cytokines as well as the count of CD4^pos and CD8^pos T cells. The positive correlation between the three genes and inhibitory checkpoint genes, PD-1, CTLA-4, and TIM-3, indicates that these genes are linked with weakened antitumor immunity in HCC. Our findings may provide further insights into developing novel therapies for HCC.

Development of a Toll-Like Receptor-Based Gene Signature That Can Predict Prognosis, Tumor Microenvironment, and Chemotherapy Response for Hepatocellular Carcinoma

Objective: Emerging evidence highlights the implications of the toll-like receptor (TLR) signaling pathway in the pathogenesis and therapeutic regimens of hepatocellular carcinoma (HCC). Herein, a prognostic TLR-based gene signature was conducted for HCC.

Methods: HCC-specific TLRs were screened in the TCGA cohort. A LASSO model was constructed based on prognosis-related HCC-specific TLRs. The predictive efficacy, sensitivity, and independency of this signature was then evaluated and externally verified in the ICGC, GSE14520, and GSE76427 cohorts. The associations between this signature and tumor microenvironment (stromal/immune score, immune checkpoint expression, and immune cell infiltrations) and chemotherapy response were assessed in HCC specimens. The expression of TLRs in this signature was verified in HCC and normal liver tissues by Western blot. Following si-MAP2K2 transfection, colony formation and apoptosis of Huh7 and HepG2 cells were examined.

Results: Herein, we identified 60 HCC-specific TLRs. A TLR-based gene signature (MAP2K2, IRAK1, RAC1, TRAF3, MAP3K7, and SPP1) was conducted for HCC prognosis. High-risk patients exhibited undesirable outcomes. ROC curves confirmed the well prediction performance of this signature. Multivariate Cox regression analysis demonstrated that the signature was an independent prognostic indicator. Also, high-risk HCC was characterized by an increased immune score, immune checkpoint expression, and immune cell infiltration. Meanwhile, high-risk patients displayed higher sensitivity to gemcitabine and cisplatin. The dysregulation of TLRs in the signature was confirmed in HCC. MAP2K2 knockdown weakened colony formation and elevated apoptosis of Huh7 and HepG2 cells.

Conclusion: Collectively, this TLR-based gene signature might assist clinicians to select personalized therapy programs for HCC patients.

scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics

Single-cell omics is the fastest-growing type of genomics data in the literature and public genomics repositories. Leveraging the growing repository of labeled datasets and transferring labels from existing datasets to newly generated datasets will empower the exploration of single-cell omics data. However, the current label transfer methods have limited performance, largely due to the intrinsic heterogeneity among cell populations and extrinsic differences between datasets. Here, we present a robust graph artificial intelligence model, single-cell Graph Convolutional Network (scGCN), to achieve effective knowledge transfer across disparate datasets. Through benchmarking with other label transfer methods on a total of 30 single cell omics datasets, scGCN consistently demonstrates superior accuracy on leveraging cells from different tissues, platforms, and species, as well as cells profiled at different molecular layers. scGCN is implemented as an integrated workflow as a python software, which is available at https://github.com/QSong-github/scGCN .