Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) remains a formidable malignancy that significantly impacts human health, and the early diagnosis of HCC holds paramount importance. Therefore, it is imperative to develop an efficacious signature for the early diagnosis of HCC. In this study, we aimed to develop early HCC predictors (eHCC-pred) using machine learning-based methods and compare their performance with existing methods. The enhancements and advancements of eHCC-pred encompassed the following: (i) utilization of a substantial number of samples, including an increased representation of cirrhosis tissues without HCC (CwoHCC) samples for model training and augmented numbers of HCC and CwoHCC samples for model validation; (ii) incorporation of two feature selection methods, namely minimum redundancy maximum relevance and maximum relevance maximum distance, along with the inclusion of eight machine learning-based methods; (iii) improvement in the accuracy of early HCC identification, elevating it from 78.15 to 97% using identical independent datasets; and (iv) establishment of a user-friendly web server. The eHCC-pred is freely accessible at http://www.dulab.com.cn/eHCC-pred/ . Our approach, eHCC-pred, is anticipated to be robustly employed at the individual level for facilitating early HCC diagnosis in clinical practice, surpassing currently available state-of-the-art techniques.

Intercellular Molecular Crosstalk Networks within Invasive and Immunosuppressive Tumor Microenvironment Subtypes Associated with Clinical Outcomes in Four Cancer Types.

Heterogeneity is a critical basis for understanding how the tumor microenvironment (TME) contributes to tumor progression. However, an understanding of the specific characteristics and functions of TME subtypes (subTMEs) in the progression of cancer is required for further investigations into single-cell resolutions. Here, we analyzed single-cell RNA sequencing data of 250 clinical samples with more than 200,000 cells analyzed in each cancer datum. Based on the construction of an intercellular infiltration model and unsupervised clustering analysis, four, three, three, and four subTMEs were revealed in breast, colorectal, esophageal, and pancreatic cancer, respectively. Among the subTMEs, the immune-suppressive subTME (subTME-IS) and matrix remodeling with malignant cells subTME (subTME-MRM) were highly enriched in tumors, whereas the immune cell infiltration subTME (subTME-ICI) and precancerous state of epithelial cells subTME (subTME-PSE) were less in tumors, compared with paracancerous tissues. We detected and compared genes encoding cytokines, chemokines, cytotoxic mediators, PD1, and PD-L1. The results showed that these genes were specifically overexpressed in different cell types, and, compared with normal tissues, they were upregulated in tumor-derived cells. In addition, compared with other subTMEs, the expression levels of PDCD1 and TGFB1 were higher in subTME-IS. The Cox proportional risk regression model was further constructed to identify possible prognostic markers in each subTME across four cancer types. Cell-cell interaction analysis revealed the distinguishing features in molecular pairs among different subTMEs. Notably, ligand-receptor gene pairs, including COL1A1-SDC1, COL6A2-SDC1, COL6A3-SDC1, and COL4A1-ITGA2 between stromal and tumor cells, associated with tumor invasion phenotypes, poor patient prognoses, and tumor advanced progression, were revealed in subTME-MRM. C5AR1-RPS19, LGALS9-HAVCR2, and SPP1-PTGER4 between macrophages and CD8+ T cells, associated with CD8+ T-cell dysfunction, immunosuppressive status, and tumor advanced progression, were revealed in subTME-IS. The spatial co-location information of cellular and molecular interactions was further verified by spatial transcriptome data from colorectal cancer clinical samples. Overall, our study revealed the heterogeneity within the TME, highlighting the potential pro-invasion and pro-immunosuppressive functions and cellular infiltration characteristics of specific subTMEs, and also identified the key cellular and molecular interactions that might be associated with the survival, invasion, immune escape, and classification of cancer patients across four cancer types.

Single-cell and spatial analyses reveal the association between gene expression of glutamine synthetase with the immunosuppressive phenotype of APOE+CTSZ+TAM in cancers.

An immunosuppressive state is regulated by various factors in the tumor microenvironment (TME), including, but not limited to, metabolic plasticity of immunosuppressive cells and cytokines secreted by these cells. We used single-cell RNA-sequencing (scRNA-seq) data and applied single-cell flux estimation analysis to characterize the link between metabolism and cellular function within the hypoxic TME of colorectal (CRC) and lung cancer. In terms of metabolic heterogeneity, we found myeloid cells potentially inclined to accumulate glutamine but tumor cells inclined to accumulate glutamate. In particular, we uncovered a tumor-associated macrophage (TAM) subpopulation, APOE+CTSZ+TAM, that was present in high proportions in tumor samples and exhibited immunosuppressive characteristics through upregulating the expression of anti-inflammatory genes. The proportion of APOE+CTSZ+TAM and regulatory T cells (Treg) were positively correlated across CRC scRNA-seq samples. APOE+CTSZ+TAM potentially interacted with Treg via CXCL16-CCR6 signals, as seen by ligand-receptor interactions analysis. Notably, glutamate-to-glutamine metabolic flux score and glutamine synthetase (GLUL) expression were uniquely higher in APOE+CTSZ+TAM, compared with other cell types within the TME. GLUL expression in macrophages was positively correlated with anti-inflammatory score and was higher in high-grade and invasive tumor samples. Moreover, spatial transcriptome and multiplex immunofluorescence staining of samples showed that APOE+CTSZ+TAM and Treg potentially colocalized in the tissue sections from CRC clinical samples. These results highlight the specific role and metabolic characteristic of the APOE+CTSZ+TAM subpopulation and provide a new perspective for macrophage subcluster-targeted therapeutic interventions or metabolic checkpoint-based cancer therapies.

scWizard: A web-based automated tool for classifying and annotating single cells and downstream analysis of single-cell RNA-seq data in cancers.

The emerging number of single-cell RNA-seq (scRNA-Seq) datasets allows the characterization of cell types across various cancer types. However, there is still lack of effective tools to integrate the various analysis of single-cells, especially for making fine annotation on subtype cells within the tumor microenvironment (TME). We developed scWizard, a point-and-click tool packaging automated process including our developed cell annotation method based on deep neural network learning and 11 downstream analyses methods. scWizard used 113,976 cells across 13 cancer types as a built-in reference dataset for training the hierarchical model enabling to automatedly classify and annotate 7 major cell types and 47 cell subtypes in the TME. scWizard provides a built-in pre-training set for user's flexible choice, and gives a higher accuracy for annotation subtypes of tumor-derived T-lymphocytes/natural killer cells (T/NK) and myeloid cells from different cancer types compared with the existing five methods. scWizard has good robustness in three independent cancer datasets, with an accuracy of 0.98 in annotating major cell types, 0.85 in annotating myeloid cell subtypes and 0.79 in annotating T/NK cell subtypes, indicting the wide applicability of scWizard in different cell types of cancers. Finally, the automatic analysis and visualization function of scWizard are presented by using the intrahepatic cholangiocarcinoma (ICC) scRNA-Seq dataset as a case. scWizard focuses on decoding TME and covers various analysis flows for cancer scRNA-Seq study, and provides an easy-to-use tool and a user-friendly interface for researchers widely, to further accelerate the biological discovery of cancer research.

ARMT: An automatic RNA-seq data mining tool based on comprehensive and integrative analysis in cancer research.

The comprehensive and integrative analysis of RNA-seq data, in different molecular layers from diverse samples, holds promise to address the full-scale complexity of biological systems. Recent advances in gene set variant analysis (GSVA) are providing exciting opportunities for revealing the specific biological processes of cancer samples. However, it is still urgently needed to develop a tool, which combines GSVA and different molecular characteristic analysis, as well as prognostic characteristics of cancer patients to reveal the biological processes of disease comprehensively. Here, we develop ARMT, an automatic tool for RNA-Seq data analysis. ARMT is an efficient and integrative tool with user-friendly interface to analyze related molecular characters of single gene and gene set comprehensively based on transcriptome and genomic data, which builds the bridge for deeper information between genes and pathways, to further accelerate scientific findings. ARMT can be installed easily from https://github.com/Dulab2020/ARMT.

The statistical power of k-mer based aggregative statistics for alignment-free detection of horizontal gene transfer.

Alignment-based database search and sequence comparison are commonly used to detect horizontal gene transfer (HGT). However, with the rapid increase of sequencing depth, hundreds of thousands of contigs are routinely assembled from metagenomics studies, which challenges alignment-based HGT analysis by overwhelming the known reference sequences. Detecting HGT by k-mer statistics thus becomes an attractive alternative. These alignment-free statistics have been demonstrated in high performance and efficiency in whole-genome and transcriptome comparisons. To adapt k-mer statistics for HGT detection, we developed two aggregative statistics T s u m S and T s u m * , which subsample metagenome contigs by their representative regions, and summarize the regional D 2 S and D 2 * metrics by their upper bounds. We systematically studied the aggregative statistics' power at different k-mer size using simulations. Our analysis showed that, in general, the power of T s u m S and T s u m * increases with sequencing coverage, and reaches a maximum power >80% at k = 6, with 5% Type-I error and the coverage ratio >0.2x. The statistical power of T s u m S and T s u m * was evaluated with realistic simulations of HGT mechanism, sequencing depth, read length, and base error. We expect these statistics to be useful distance metrics for identifying HGT in metagenomic studies.

Temsirolimus enhances the efficacy of cetuximab in colon cancer through a CIP2A-dependent mechanism.

PURPOSE: A dozen clinical trials examining a combination of temsirolimus and cetuximab in treating metastatic colon cancer are currently underway. We investigated the role of cancerous inhibitor of protein phosphatase 2A (CIP2A) in the synergism between temsirolimus and cetuximab in colon cancer. METHODS: Five colon cancer cell lines were used for in vitro studies. Signal transduction pathways were assessed by immunoblotting. The synergism between studied drugs was analyzed with combination indexes. Gene silencing was performed using small interfering RNAs. The efficacies of temsirolimus and cetuximab were tested in nude mice with colon cancer xenografts. Transcriptional activity was assessed using a reporter assay. The inhibitors leupeptin, chloroquine, and MG132 were used to assess protein degradation. The association between CIP2A, clinicopathological parameters, and survival was examined by immunohistochemical staining using a tumor tissue microarray. RESULTS: Temsirolimus decreased the resistance of cells to cetuximab by both inhibiting transcription of CIP2A and increasing degradation of CIP2A through the lysosomal-autophagy pathway. The mammalian target of rapamycin (mTOR) protein immunoprecipitated along with CIP2A. Temsirolimus decreased expression of phosphorylated extracellular regulated protein kinase (pErk) and phosphorylated v-akt murine thymoma viral oncogene (pAKT) and decreased the interaction of CIP2A and mTOR in cell lines without the K-ras codon 12 mutation. CIP2A was a prognostic marker only in colon cancer patients with weak expression of pErk or pAKT. CONCLUSIONS: Temsirolimus decreases cellular resistance to cetuximab by regulating CIP2A expression in colon cancer cells. Potential biomarkers for CIP2A inhibitors include pErk and pAKT.

The glutathionylation of p65 modulates NF-κB activity in 15-deoxy-Δ¹²,¹4-prostaglandin J2-treated endothelial cells.

Protein glutathionylation is a posttranslational modification of cysteine residues with glutathione in response to mild oxidative stress. Because 15-deoxy-Δ12,14-prostaglandin J(2) (15d-PGJ(2)) is an electrophilic prostaglandin that can increase glutathione (GSH) levels and augment reactive oxygen species (ROS) production, we hypothesized that it induces NF-κB-p65 glutathionylation and would exert anti-inflammatory effects. Herein, we show that 15d-PGJ(2) suppresses the expression of ICAM-1 and NF-κB-p65 nuclear translocation. 15d-PGJ(2) upregulates the Nrf2-related glutathione synthase gene and thereby increases the GSH levels. Consistent with this, Nrf2 siRNA molecules abolish the inhibition of p65 nuclear translocation in 15d-PGJ(2)-induced endothelial cells (ECs). ECs treated with GSSG show increased thiol modifications of p65 and also a block in TNFα-induced p65 nuclear translocation and ICAM-1 expression, but not in IκBα degradation. However, the overexpression of glutaredoxin 1 was found to be accompanied by a modest increase in NF-κB activity. Furthermore, we found that multiple cysteine residues in p65 are responsible for glutathionylation. 15d-PGJ(2) was observed to induce p65 glutathionylation and is suppressed by a GSH synthesis inhibitor, buthionine sulfoximine, by catalase, and by Nrf2 siRNA molecules. Our results thus indicate that the GSH/ROS-dependent glutathionylation of p65 is likely to be responsible for 15d-PGJ(2)-mediated NF-κB inactivation and for the enhanced inhibitory effects of 15d-PGJ(2) on TNFα-treated ECs.

Dual mechanisms of NF-kappaB inhibition in carnosol-treated endothelial cells.

The increased adhesion of monocytes to injured endothelial layers is a critical early event in atherogenesis. Under inflammatory conditions, there is increased expression of specific cell adhesion molecules on activated vascular endothelial cells, which increases monocyte adhesion. In our current study, we demonstrate a putative mechanism for the anti-inflammatory effects of carnosol, a diterpene derived from the herb rosemary. Our results show that both carnosol and rosemary essential oils inhibit the adhesion of TNFalpha-induced monocytes to endothelial cells and suppress the expression of ICAM-1 at the transcriptional level. Moreover, carnosol was found to exert its inhibitory effects by blocking the degradation of the inhibitory protein IkappaBalpha in short term pretreatments but not in 12 h pretreatments. Our data show that carnosol reduces IKK-beta phosphorylation in pretreatments of less than 3 h. In TNFalpha-treated ECs, NF-kappaB nuclear translocation and transcriptional activity was abolished by up to 12 h of carnosol pretreatment and this was blocked by Nrf-2 siRNA. The long-term inhibitory effects of carnosol thus appear to be mediated through its induction of Nrf-2-related genes. The inhibition of ICAM-1 expression and p65 translocation is reversed by HO-1 siRNA. Carnosol also upregulates the Nrf-2-related glutathione synthase gene and thereby increases the GSH levels after 9 h of exposure. Treating ECs with a GSH synthesis inhibitor, BSO, blocks the inhibitory effects of carnosol. In addition, carnosol increases p65 glutathionylation. Hence, our present findings indicate that carnosol suppresses TNFalpha-induced singling pathways through the inhibition of IKK-beta activity or the upregulation of HO-1 expression. The resulting GSH levels are dependent, however, on the length of the carnosol pretreatment period.