TMSB4X: A novel prognostic marker for non-small cell lung cancer

Wide-spectrum profiling of plasma cell-free RNA and the potential for health-monitoring

Circulating cell-free RNA (cfRNA) has emerged as a promising analyte for disease detection. However, the comprehensive profiling of diverse cfRNA types remains under-characterized. Here, we applied a new wide-spectrum cfRNA sequencing method and simultaneously captured rRNA, tRNA, mRNA, miRNA, lncRNA and all mitochondrial RNA. The cfRNA compositions, size distributions and highly abundant cfRNA genes were analysed for each type of cfRNA. We depicted the cfRNA cell types of origin profiles of 66 generally healthy individuals and found that BMI showed a significant impact on the kidney-derived cfRNA proportion. Three individuals with some liver problems were identified because of relatively high levels of hepatocyte-specific cfRNA. The abundance levels of different genes and RNA types, including mRNA, miRNA and lncRNA, were significantly correlated with the liver function test results. The genes of individual cfRNA variances were enriched in pathways associated with common diseases such as liver diseases, virus infections, cancers and metabolic diseases. This study provided a profiling of cfRNA and displayed the potential of cfRNA as a biomarker in health monitoring.

Single-cell transcriptomic analysis of canine insulinoma reveals distinct sub-populations of insulin-expressing cancer cells

Canine malignant insulinoma is a rare, highly metastatic and life-threatening neuroendocrine tumour of pancreatic beta cells. To map the single-cell transcriptomic landscape of canine insulinoma for the first time, transcriptomic profiles of 5,532 cells were captured from two spontaneous insulinomas (Patient 1 and 2) and one associated metastasis (Patient 2) in two Boxer dogs. Distinct cancer, endocrine, and immune cell populations were identified. Notably, all three tumour samples contained two transcriptionally distinct insulin-expressing tumour cell populations (INS+ and INS+FOS low ), characterised here for the first time. These two cancer cell populations significantly differed by ~ 8,000 differentially expressed genes (DEGs), particularly tumour suppressor genes (e.g. TP53, EGR1) and cancer-related pathways (e.g., MAPK, p53). In contrast, COX7A2L was one of a few genes ubiquitously expressed and significantly upregulated (> 20-fold) in both insulin-expressing tumour populations compared to other captured populations. Both populations were also characterised by expression of chromogranin/secretogranin neuroendocrine tumour marker genes (e.g. CHGA, SCGN). There were far fewer gene expression differences observed between insulin-expressing tumour cells from the two patients (~ 600 DEGs) than between the two cancer cell populations within each patient. These DEGs included CLTRN, TMSB4X, CSRP2, LGALS2, and C15orf48. Unexpectedly for a tumour of endocrine origin, the metastasis in Patient 2 exhibited > 20-70 fold upregulation of exocrine pancreatic genes including CLPS, PRSS2, PRSS and CTRC. Immune cell analyses identified distinct infiltrating immune populations, including memory T cells and macrophages and revealed likely tumour-immune interactions, including the CD40-CD40L interaction. This study provides the first single-cell RNA sequencing (scRNA-seq) analysis of naturally occurring insulinoma in any species, revealing tumour cell heterogeneity, novel immune microenvironment features, and potential therapeutic targets. Despite its small scale, the findings highlight the utility of scRNA-seq in veterinary oncology and its translational potential for pancreatic neuroendocrine tumours across species.

Supplementary Information

The online version contains supplementary material available at 10.1186/s44356-025-00026-3.

TMSB4X is a regulator of inflammation-associated ferroptosis, and promotes the proliferation, migration and invasion of hepatocellular carcinoma cells

BACKGROUND

Ferroptosis and inflammation are involved in cancer progression. The aim of this study was to identify inflammation-associated ferroptosis regulators in hepatocellular carcinoma (HCC).

METHODS

FerrDb database was searched for ferroptosis-related genes. RNA sequencing data and clinicopathologic information of HCC patients were downloaded from the Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network analysis was applied to obtain the genes probably involved in inflammation-associated ferroptosis. Univariate Cox regression analysis was conducted to screen prognostic genes, and 10 machine learning algorithms were combined to find the optimal strategy to evaluate the prognosis of the patients based on the prognosis-related genes. The patients were divided into high risk group and low risk group, and the differentially expressed genes were obtained. Thymosin beta 4 X-linked (TMSB4X) was overexpressed or knocked down in HCC cell lines, and then qPCR, CCK-8, Transwell, flow cytometery assays were performed to detect the change of HCC cells' phenotypes, and Western blot was used to detect the change of ferroptosis markers.

RESULTS

157 genes related to inflammation and ferroptosis in HCC were obtained by WGCNA. rLasso algorithm, with the highest C-index, screened out 29 hub genes, and this model showed good efficacy to predict the prognosis of HCC patients. The patients in high risk group and low risk groups showed distinct molecular characteristics. TMSB4X was the most important gene which dominated the classification, and it was highly expressed in HCC samples. TMSB4X promoted the viability, migration and invasion, and repressed ferroptosis of HCC cells.

CONCLUSION

The risk model constructed based on the inflammation-associated ferroptosis regulators is effective to predict the clinical outcome of HCC patients. TMSB4X, involved in inflammation-associated ferroptosis, is a potential biomarker and therapeutic target for HCC.

Improving platelet-RNA-based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification

Liquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community on which methods are the most effective or how to process the data. To circumvent this, we performed a large-scale study using various machine-learning techniques. First, we took a closer look at existing datasets and filtered out some patients to assert data collection quality. The final data collection included platelet RNA samples acquired from 1397 cancer patients (17 types of cancer) and 354 asymptomatic, presumed healthy, donors. Then, we assessed an array of different machine-learning models and techniques (e.g., feature selection of RNA transcripts) in pan-cancer detection and multiclass classification. Our results show that simple logistic regression performs the best, reaching a 68% cancer detection rate at a 99% specificity level, and multiclass classification accuracy of 79.38% when distinguishing between five cancer types. In summary, by revisiting classical machine-learning models, we have exceeded the previously used method by 5% and 9.65% in cancer detection and multiclass classification, respectively. To ease further research, we open-source our code and data processing pipelines (https://gitlab.com/jopekmaksym/improving-platelet-rna-based-diagnostics), which we hope will serve the community as a strong baseline.

ScHGSC-IGDC: Identifying genes with differential correlations of high-grade serous ovarian cancer based on single-cell RNA sequencing analysis

Due to the high heterogeneity of ovarian cancer (OC), it occupies the main cause of cancer-related death among women. As the most aggressive and frequent subtype of OC, high-grade serous cancer (HGSC) represents around 70 % of all patients. With the booming progress of single-cell RNA sequencing (scRNA-seq), unique and subtle changes among different cell states have been identified including novel risk genes and pathways. Here, our present study aims to identify differentially correlated core genes between normal and tumor status through HGSC scRNA-seq data analysis. R package high-dimension Weighted Gene Co-expression Network Analysis (hdWGCNA) was implemented for building gene interaction networks based on HGSC scRNA-seq data. DiffCorr was integrated for identifying differentially correlated genes between tumor and their adjacent normal counterparts. Software Cytoscape was implemented for constructing and visualizing biological networks. Real-time qPCR (RT-qPCR) was utilized to confirm expression pattern of new genes. We introduced ScHGSC-IGDC (Identifying Genes with Differential Correlations of HGSC based on scRNA-seq analysis), an in silico framework for identifying core genes in the development of HGSC. We detected thirty-four modules in the network. Scores of new genes with opposite correlations with others such as NDUFS5, TMSB4X, SERPINE2 and ITPR2 were identified. Further survival and literature validation emphasized their great values in the HGSC management. Meanwhile, RT-qPCR verified expression pattern of NDUFS5, TMSB4X, SERPINE2 and ITPR2 in human OC cell lines and tissues. Our research offered novel perspectives on the gene modulatory mechanisms from single cell resolution, guiding network based algorithms in cancer etiology field.

Integrating spatial and single-cell transcriptomics reveals tumor heterogeneity and intercellular networks in colorectal cancer

Single cell RNA sequencing (scRNA-seq), a powerful tool for studying the tumor microenvironment (TME), does not preserve/provide spatial information on tissue morphology and cellular interactions. To understand the crosstalk between diverse cellular components in proximity in the TME, we performed scRNA-seq coupled with spatial transcriptomic (ST) assay to profile 41,700 cells from three colorectal cancer (CRC) tumor-normal-blood pairs. Standalone scRNA-seq analyses revealed eight major cell populations, including B cells, T cells, Monocytes, NK cells, Epithelial cells, Fibroblasts, Mast cells, Endothelial cells. After the identification of malignant cells from epithelial cells, we observed seven subtypes of malignant cells that reflect heterogeneous status in tumor, including tumor_CAV1, tumor_ATF3_JUN | FOS, tumor_ZEB2, tumor_VIM, tumor_WSB1, tumor_LXN, and tumor_PGM1. By transferring the cellular annotations obtained by scRNA-seq to ST spots, we annotated four regions in a cryosection from CRC patients, including tumor, stroma, immune infiltration, and colon epithelium regions. Furthermore, we observed intensive intercellular interactions between stroma and tumor regions which were extremely proximal in the cryosection. In particular, one pair of ligands and receptors (C5AR1 and RPS19) was inferred to play key roles in the crosstalk of stroma and tumor regions. For the tumor region, a typical feature of TMSB4X-high expression was identified, which could be a potential marker of CRC. The stroma region was found to be characterized by VIM-high expression, suggesting it fostered a stromal niche in the TME. Collectively, single cell and spatial analysis in our study reveal the tumor heterogeneity and molecular interactions in CRC TME, which provides insights into the mechanisms underlying CRC progression and may contribute to the development of anticancer therapies targeting on non-tumor components, such as the extracellular matrix (ECM) in CRC. The typical genes we identified may facilitate to new molecular subtypes of CRC.