Single-cell transcriptomics reveal DHX9 in mature B cell as a dynamic network biomarker before lymph node metastasis in CRC

Exploring the Role of Cellular Interactions in the Colorectal Cancer Microenvironment

Colorectal cancer (CRC) stands as one of the tumors with globally high incidence and mortality rates. In recent years, researchers have extensively explored the role of the tumor immune microenvironment (TME) in CRC, highlighting the crucial influence of immune cell populations in driving tumor progression and shaping therapeutic outcomes. The TME encompasses an array of cellular and noncellular constituents, spanning tumor cells, immune cells, myeloid cells, and tumor-associated fibroblasts, among others. However, the cellular composition within the TME is highly dynamic, evolving throughout different stages of tumor progression. These shifts in cell subpopulation proportions lead to a gradual transition in the immune response, shifting from an early antitumor growth to a late-stage environment that supports tumor survival. Therefore, it is crucial to further investigate and understand the complex interactions among the various cell populations within the TME. In this review, we explore the key cellular components of varying origins, subpopulations with shared origins, and noncellular elements within the CRC TME, examining their interconnections and critical considerations for developing personalized and precise immunotherapy strategies.

Multi-omics with dynamic network biomarker algorithm prefigures organ-specific metastasis of lung adenocarcinoma

Efficacious strategies for early detection of lung cancer metastasis are of significance for improving the survival of lung cancer patients. Here we show the marker genes and serum secretome foreshadowing the lung cancer site-specific metastasis through dynamic network biomarker (DNB) algorithm, utilizing two clinical cohorts of four major types of lung cancer distant metastases, with single-cell RNA sequencing (scRNA-seq) of primary lesions and liquid chromatography-mass spectrometry data of sera. Also, we locate the intermediate status of cancer cells, along with its gene signatures, in each metastatic state trajectory that cancer cells at this stage still have no specific organotropism. Furthermore, an integrated neural network model based on the filtered scRNA-seq data is successfully constructed and validated to predict the metastatic state trajectory of cancer cells. Overall, our study provides an insight to locate the pre-metastasis status of lung cancer and primarily examines its clinical application value, contributing to the early detection of lung cancer metastasis in a more feasible and efficacious way.

Combined Transcriptomics and Metabolomics Identify Regulatory Mechanisms of Porcine Vertebral Chondrocyte Development In Vitro

Porcine body length is closely related to meat production, growth, and reproductive performance, thus playing a key role in the profitability of the pork industry. Cartilage development is critical to longitudinal elongation of individual vertebrae. This study isolated primary porcine vertebral chondrocytes (PVCs) to clarify the complex mechanisms of elongation. We used transcriptome and target energy metabolome technologies to confirm crucial genes and metabolites in primary PVCs at different differentiation stages (0, 4, 8, and 12 days). Pairwise comparisons of the four stages identified 4566 differentially expressed genes (DEGs). Time-series gene cluster and functional analyses of these DEGs revealed four clusters related to metabolic processes, cartilage development, vascular development, and cell cycle regulation. We constructed a transcriptional regulatory network determining chondrocyte maturation. The network indicated that significantly enriched transcription factor (TF) families, including zf-C2H2, homeobox, TF_bZIP, and RHD, are important in cell cycle and differentiation processes. Further, dynamic network biomarker (DNB) analysis revealed that day 4 was the tipping point for chondrocyte development, consistent with morphological and metabolic changes. We found 24 DNB DEGs, including the TFs NFATC2 and SP7. Targeted energy metabolome analysis showed that most metabolites were elevated throughout chondrocyte development; notably, 16 differentially regulated metabolites (DRMs) were increased at three time points after cell differentiation. In conclusion, integrated metabolome and transcriptome analyses highlighted the importance of amino acid biosynthesis in chondrocyte development, with coordinated regulation of DEGs and DRMs promoting PVC differentiation via glucose oxidation. These findings reveal the regulatory mechanisms underlying PVC development and provide an important theoretical reference for improving pork production.

Latent space search based multimodal optimization with personalized edge-network biomarker for multi-purpose early disease prediction

Considering that cancer is resulting from the comutation of several essential genes of individual patients, researchers have begun to focus on identifying personalized edge-network biomarkers (PEBs) using personalized edge-network analysis for clinical practice. However, most of existing methods ignored the optimization of PEBs when multimodal biomarkers exist in multi-purpose early disease prediction (MPEDP). To solve this problem, this study proposes a novel model (MMPDENB-RBM) that combines personalized dynamic edge-network biomarkers (PDENB) theory, multimodal optimization strategy and latent space search scheme to identify biomarkers with different configurations of PDENB modules (i.e. to effectively identify multimodal PDENBs). The application to the three largest cancer omics datasets from The Cancer Genome Atlas database (i.e. breast invasive carcinoma, lung squamous cell carcinoma and lung adenocarcinoma) showed that the MMPDENB-RBM model could more effectively predict critical cancer state compared with other advanced methods. And, our model had better convergence, diversity and multimodal property as well as effective optimization ability compared with the other state-of-art methods. Particularly, multimodal PDENBs identified were more enriched with different functional biomarkers simultaneously, such as tissue-specific synthetic lethality edge-biomarkers including cancer driver genes and disease marker genes. Importantly, as our aim, these multimodal biomarkers can perform diverse biological and biomedical significances for drug target screen, survival risk assessment and novel biomedical sight as the expected multi-purpose of personalized early disease prediction. In summary, the present study provides multimodal property of PDENBs, especially the therapeutic biomarkers with more biological significances, which can help with MPEDP of individual cancer patients.

MAPKAPK2, a potential dynamic network biomarker of α-synuclein prior to its aggregation in PD patients

One of the important pathological features of Parkinson's disease (PD) is the pathological aggregation of α-synuclein (α-Syn) in the substantia nigra. Preventing the aggregation of α-Syn has become a potential strategy for treating PD. However, the molecular mechanism of α-Syn aggregation is unclear. In this study, using the dynamic network biomarker (DNB) method, we first identified the critical time point when α-Syn undergoes pathological aggregation based on a SH-SY5Y cell model and found that DNB genes encode transcription factors that regulated target genes that were differentially expressed. Interestingly, we found that these DNB genes and their neighbouring genes were significantly enriched in the cellular senescence pathway and thus proposed that the DNB genes HSF1 and MAPKAPK2 regulate the expression of the neighbouring gene SERPINE1. Notably, in Gene Expression Omnibus (GEO) data obtained from substantia nigra, prefrontal cortex and peripheral blood samples, the expression level of MAPKAPK2 was significantly higher in PD patients than in healthy people, suggesting that MAPKAPK2 has potential as an early diagnostic biomarker of diseases related to pathological aggregation of α-Syn, such as PD. These findings provide new insights into the mechanisms underlying the pathological aggregation of α-Syn.

Low expression of the dynamic network markers FOS/JUN in pre-deteriorated epithelial cells is associated with the progression of colorectal adenoma to carcinoma

BACKGROUND

Deterioration of normal intestinal epithelial cells is crucial for colorectal tumorigenesis. However, the process of epithelial cell deterioration and molecular networks that contribute to this process remain unclear.

METHODS

Single-cell data and clinical information were downloaded from the Gene Expression Omnibus (GEO) database. We used the recently proposed dynamic network biomarker (DNB) method to identify the critical stage of epithelial cell deterioration. Data analysis and visualization were performed using R and Cytoscape software. In addition, Single-Cell rEgulatory Network Inference and Clustering (SCENIC) analysis was used to identify potential transcription factors, and CellChat analysis was conducted to evaluate possible interactions among cell populations. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set variation analysis (GSVA) analyses were also performed.

RESULTS

The trajectory of epithelial cell deterioration in adenoma to carcinoma progression was delineated, and the subpopulation of pre-deteriorated epithelial cells during colorectal cancer (CRC) initialization was identified at the single-cell level. Additionally, FOS/JUN were identified as biomarkers for pre-deteriorated epithelial cell subpopulations in CRC. Notably, FOS/JUN triggered low expression of P53-regulated downstream pro-apoptotic genes and high expression of anti-apoptotic genes through suppression of P53 expression, which in turn inhibited P53-induced apoptosis. Furthermore, malignant epithelial cells contributed to the progression of pre-deteriorated epithelial cells through the GDF signaling pathway.

CONCLUSIONS

We demonstrated the trajectory of epithelial cell deterioration and used DNB to characterize pre-deteriorated epithelial cells at the single-cell level. The expression of DNB-neighboring genes and cellular communication were triggered by DNB genes, which may be involved in epithelial cell deterioration. The DNB genes FOS/JUN provide new insights into early intervention in CRC.

The enigmatic helicase DHX9 as a candidate prognostic biomarker for resected pancreatic ductal adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, and current therapies have limited efficacy on PDAC. The DEAH-box helicase 9 (DHX9) is widely reported to influence cell biological behavior via regulating DNA replication, genomic stability, transcription, translation, and microRNA biogenesis. However, the prognostic role of DHX9 in PDAC remains unclear. Thus, the objective of this study is to investigate the prognostic value of DHX9 expression in PDAC patients.

Methods

Tumor specimens from PDAC patients with surgical resection were obtained, and DHX9 was stained and analyzed in this study. Univariate and multivariate Cox regression analyses were utilized to identify independent risk factors of overall survival (OS) and recurrence-free survival (RFS). The prognostic nomograms for predicting OS and RFS were established to obtain superior predictive power.

Results

Among the enrolled 110 patients, 61 patients were identified as having high expression of DHX9. The correlation analysis revealed that higher DHX9 expression in PDAC was prone to have advanced N stage (p = 0.010) and TNM stage (p = 0.017). For survival, the median OS (21.0 vs. 42.0 months, p < 0.001) and RFS (12.0 vs. 24.0 months, p < 0.001) of patients in the high DHX9 group were significantly shorter than those in the low DHX9 group. Within the univariate and multivariate analyses, American Joint Committee on Cancer (AJCC) N stage (p = 0.036) and DHX9 expression (p = 0.041) were confirmed as independent prognostic factors of OS, while nerve invasion (p = 0.031) and DHX9 expression (p = 0.005) were independent prognostic factors of RFS. Finally, the novel prognostic nomograms for OS and RFS were established and showed superior predictive accuracy.

Conclusion

This study identified the independent prognostic value of DHX9 for RFS and OS in resected PDAC patients, and higher DHX9 expression was prone to have an earlier recurrence and shorter OS. Therefore, DHX9 may be a promising and valuable biomarker and a potential target for treating PDAC. More accurate and promising predictive models would be achieved when DHX9 is incorporated into nomograms.