Upregulation of TMCO3 Promoting Tumor Progression and Contributing to the Poor Prognosis of Hepatocellular Carcinoma

m6A-Mediated TMCO3 Promotes Hepatocellular Carcinoma Progression by Facilitating the Membrane Translocation and Activation of AKT

The transmembrane and coiled-coil domains 3 (TMCO3) are highly expressed in many tumors. However, the underlying mechanisms governing the way in which TMCO3 affects the progression of hepatocellular carcinoma (HCC) remain unclear. This study screens out the molecule TMCO3 with high N6-methyladenosine (m6A) modification level in tumor samples compared to the adjacent non-cancerous tissues of three pairs of HCC patients through Methylated RNA Immunoprecipitation Sequencing (MeRIP-seq) and RNA sequencing (RNA-seq). Subsequently, the oncogenic effect of TMCO3 in HCC is verified through in vivo and in vitro experiments. AlkB Homolog 5 (ALKBH5), an m6A demethylase of TMCO3 is then screened out. The following experiments demonstrate that TMCO3 can activate AKT directly through the Phosphatidylinositol-3-Kinase (PI3K) pathway, thus promoting the progression of HCC. Meanwhile, the phosphorylation site on TMCO3: the 85th amino acid-serine, and mutation of this site can directly impair the activity and membrane translocation of AKT is found. Finally, the carcinogenic effect of TMCO3 is further elucidated in HCC through the orthotopic treatment model and the hydrodynamic tail vein injection treatment model. The findings can provide a potential target for targeted AKT treatment in patients with HCC and verify a possible prognostic marker in HCC.

Integrative transcriptome analysis identifies a crotonylation gene signature for predicting prognosis and drug sensitivity in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) stands as the most prevalent and treatment-resistant malignant tumour, characterized by a dismal prognosis. Croton acylation (CA) has recently gained attention as a critical factor in cancer pathogenesis. This study sought to rapidly identify prognostic features of HCC linked to CA. Differential analysis was conducted between tumour tissues and adjacent non-tumour tissues in the TCGA-LIHC and GSE76427 datasets to uncover differentially expressed genes (DEG1 and DEG2). The intersection of DEG1 and DEG2 highlighted DEGs with consistent expression patterns. Single-sample gene set enrichment analysis scores were calculated for 18 lysine crotonylation-related genes (LCRGs) identified in prior research, showing significant differences between tumour and normal groups. Subsequently, weighted gene co-expression network analysis was employed to identify key module genes correlated with the LCRG score. Candidate genes were identified by overlapping consistently expressed DEGs with key module genes. Prognostic features were identified, and risk scores were determined via regression analysis. Patients were categorized into risk groups based on the optimal cutoff value. Gene set enrichment analysis (GSEA) and immunoassays were also performed. The prognostic features were further validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). A total of 88 candidate genes were identified from 1179 consistently expressed DEGs and 4200 key module genes. Seven prognostic features were subsequently identified: TMCO3, RAP2A, ITGAV, ZFYVE26, CHST9, HMGN4, and KLHL21. GSEA revealed that DEGs between risk groups were primarily associated with chylomicron metabolism, among other pathways. Additionally, activated CD4+ T cells demonstrated the strongest positive correlation with risk scores, and most immune checkpoints showed significant differences between risk groups, with ASXL1 exhibiting the strongest correlation with risk scores. The Tumour Immune Dysfunction and Exclusion score was notably higher in the high-risk group. Moreover, in both the TCGA-LIHC and ICGC-LIRI-JP datasets, the expression of other prognostic features was elevated in tumour tissues, with the exception of CHST9. RT-qPCR confirmed the increased expression of TMCO3, RAP2A, ITGAV, ZFYVE26, and HMGN4. This study establishes a risk model for HCC based on seven crotonylation-associated prognostic features, offering a theoretical framework for the diagnosis and treatment of HCC.

Platelet-derived circRNAs signature in patients with gastroenteropancreatic neuroendocrine tumors

BACKGROUND

Neuroendocrine tumors (NETs) early diagnosis is a clinical challenge that require a deep understanding of molecular and genetic features of this heterogeneous group of neoplasms. However, few biomarkers exist to aid diagnosis and to predict prognosis and treatment response. In the oncological field, tumor-educated platelets (TEPs) have been implicated as central players in the systemic and local responses to tumor growth, thereby altering tumor specific RNA profile. Although TEPs have been found to be enriched in RNAs, few studies have investigated the potential of a type of RNA, circular RNAs (circRNA), as platelet-derived biomarkers for cancer. In this proof-of-concept study, we aim to demonstrate whether the circRNAs signature of tumor educated platelets can be used as a liquid biopsy biomarker for the detection of gastroenteropancreatic (GEP)-NETs and the prediction of the early response to treatment.

METHODS

We performed a 24-months, prospective proof-of-concept study in men and women with histologically proven well-differentiated G1-G2 GEP-NET, aged 18-80 years, naïve to treatment. We performed a RNAseq analysis of circRNAs obtained from TEPs samples of 10 GEP-NETs patients at baseline and after 3 months from therapy (somatostatin analogs or surgery) and from 5 patients affected by non-malignant endocrinological diseases enrolled as a control group.

RESULTS

Statistical analysis based on p < 0.05 resulted in the identification of 252 circRNAs differentially expressed between GEP-NET and controls of which 109 were up-regulated and 143 were down-regulated in NET patients. Further analysis based on an FDR value ≤ 0.05 resulted in the selection of 5 circRNAs all highly significant downregulated. The same analysis on GEP-NETs at baseline and after therapy in 5 patients revealed an average of 4983 remarkably differentially expressed circRNAs between follow-up and baseline samples of which 2648 up-regulated and 2334 down-regulated, respectively. Applying p ≤ 0.05 and FDR ≤ 0.05 filters, only 3/5 comparisons gave statistically significant results.

CONCLUSIONS

Our findings identified for the first time a circRNAs signature from TEPs as potential diagnostic and predictive biomarkers for GEP-NETs.