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Abstract Background Adenosine monophosphate-activated protein kinase (AMPK) is associated with the development of liver hepatocellular carcinoma (LIHC). AMPKα2, an α2 subunit of AMPK, is encoded by PRKAA2, and functions as the catalytic core of AMPK. However, the role of AMPKα2 in the LIHC tumor immune environment is unclear. Methods RNA-seq data were obtained from the Cancer Genome Atlas and Genotype-Tissue Expression databases. Using the single-cell RNA-sequencing dataset for LIHC obtained from the China National Genebank Database, the communication between malignant cells and T cells in response to different PRKAA2 expression patterns was evaluated. In addition, the association between PRKAA2 expression and T-cell evolution during tumor progression was explored using Pseudotime analysis, and the role of PRKAA2 in metabolic reprogramming was explored using the R “scMetabolis” package. Functional experiments were performed in LIHC HepG2 cells. Results AMPK subunits were expressed in tissue-specific and substrate-specific patterns. PRKAA2 was highly expressed in LIHC tissues and was associated with poor patient prognosis. Tumors with high PRKAA2 expression displayed an immune cold phenotype. High PRKAA2 expression significantly promoted LIHC immune escape. This result is supported by the following evidence: 1) the inhibition of major histocompatibility complex class I (MHC-I) expression through the regulation of interferon-gamma activity in malignant cells; 2) the promotion of CD8+ T-cell exhaustion and the formation of CD4+ Treg cells in T cells; 3) altered interactions between malignant cells and T cells in the tumor immune environment; and 4) induction of metabolic reprogramming in malignant cells. Conclusions Our study indicate that PRKAA2 may contribute to LIHC progression by promoting metabolic reprogramming and tumor immune escape through theoretical analysis, which offers a theoretical foundation for developing PRKAA2-based strategies for personalized LIHC treatment.

Abstract Dysregulation of R-loop homeostasis is closely related to various human diseases, including cancer. However, the causality of aberrant R-loops in tumor progression remains unclear. In this study, using single-cell RNA-sequencing datasets from lung adenocarcinoma (LUAD), we constructed an R-loop scoring model to characterize the R-loop state according to the identified R-loop regulators related to EGFR mutations, tissue origins, and TNM stage. We then evaluated the relationships of the R-loop score with the tumor microenvironment (TME) and treatment response. Furthermore, the potential roles of FANCI-mediated R-loops in LUAD were explored using a series of in vitro experiments. Results showed that malignant cells with low R-loop scores displayed glycolysis and epithelial–mesenchymal transition pathway activation and immune escape promotion, thereby hampering the antitumor therapeutic effects. Cell communication analysis suggested that low R-loop scores contributed to T cell exhaustion. We subsequently validated the prognostic value of R-loop scores by using bulk transcriptome datasets across 33 tumor types. The R-loop scoring model well predicted patients’ therapeutic response to targeted therapy, chemotherapy, or immunotherapy in 32 independent cohorts. Remarkably, changes in R-loop distribution mediated by FANCI deficiency blocked the activity of Ras signaling pathway, suppressing tumor-cell proliferation and dissemination. In conclusion, this study reveals the underlying molecular mechanism of metabolic reprogramming and T cell exhaustion under R-loop score patterns, and the changes in R-loops mediated by R-loop regulators resulting in tumor progression. Therefore, incorporating anticancer methods based on R-loop or R-loop regulators into the treatment schemes of precision medicine may be beneficial.

Currently, no clinically relevant non-invasive biomarkers are available for screening of multiple cancer types. In this study, we developed a serum diagnostic signature based on 5-methylcytosine (m5C)-related miRNAs (m5C-miRNAs) for multiple-cancer detection. Serum miRNA expression data and the corresponding clinical information of patients were collected from the Gene Expression Omnibus database. Serum samples were then randomly assigned to the training or validation cohort at a 1:1 ratio. Using the identified m5C-miRNAs, an m5C-miRNA signature for cancer detection was established using a support vector machine algorithm. The constructed m5C-miRNA signature displayed excellent accuracy, and its areas under the curve were 0.977, 0.934, and 0.965 in the training cohort, validation cohort, and combined training and validation cohort, respectively. Moreover, the diagnostic capability of the m5C-miRNA signature was unaffected by patient age or sex or the presence of noncancerous disease. The m5C-miRNA signature also displayed satisfactory performance for distinguishing tumor types. Importantly, in the detection of early-stage cancers, the diagnostic performance of the m5C-miRNA signature was obviously superior to that of conventional tumor biomarkers. In summary, this work revealed the value of serum m5C-miRNAs in cancer detection and provided a new strategy for developing non-invasive and cost effective tools for large-scale cancer screening.

Messenger RNA vaccines are considered to be a promising strategy in cancer immunotherapy, while their application on mesothelioma is still largely uncharacterized. This study aimed to identify potential antigens in mesothelioma for anti-mesothelioma mRNA vaccine development, and further determine the immune subtypes of mesothelioma for selection of suitable candidates from an extremely heterogeneous population. Gene expression data and corresponding clinicopathological information were obtained from the TCGA and gene expression omnibus, respectively. Then, the genetic alterations were compared and visualized using cBioPortal, and differentially expressed genes and their prognostic signatures were identified by GEPIA. The relationship between tumor-infiltrating immune cells and the expression of tumor antigens was systematically evaluated by TIMER online. Finally, the immune subtypes and immune landscape of mesothelioma were separately analyzed using consensus cluster and graph learning-based dimensional reduction. A total of five potential tumor antigens correlated with prognosis and infiltration of antigen-presenting cells, including AUNIP, FANCI, LASP1, PSMD8, and XPO5 were identified. Based on the expression of immune-related genes, patients with mesothelioma were divided into two immune subtypes (IS1 and IS2). Each subtype exhibited differential molecular, cellular and clinical properties. Patients with the IS1 subtype were characterized by an immune “cold” phenotype, displaying superior survival outcomes, whereas those with the IS2 subtype were characterized by an immune “hot” and immunosuppressive phenotype. Furthermore, immune checkpoints and immunogenic cell death modulators were differentially expressed between the IS1 and IS2 immune subtype tumors. The immunogenomic landscape of mesothelioma revealed a complex tumor immune microenvironment between individual patients. AUNIP, FANCI, LASP1, PSMD8, and XPO5 are putative antigens for the development of anti-mesothelioma mRNA vaccine and patients with the IS1 subtype may be considered for vaccination.

The performance of implanted biomaterials is largely determined by their interaction with the host immune system. As a fibrous-like 3D network, fibrin matrix formed at the interfaces of tissue and material, whose effects on dendritic cells (DCs) remain unknown. Here, a bone plates implantation model was developed to evaluate the fibrin matrix deposition and DCs recruitment in vivo. The DCs responses to fibrin matrix were further analyzed by a 2D and 3D fibrin matrix model in vitro. In vivo results indicated that large amount of fibrin matrix deposited on the interface between the tissue and bone plates, where DCs were recruited. Subsequent in vitro testing denoted that DCs underwent significant shape deformation and cytoskeleton reorganization, as well as mechanical property alteration. Furthermore, the immune function of imDCs and mDCs were negatively and positively regulated, respectively. The underlying mechano-immunology coupling mechanisms involved RhoA and CDC42 signaling pathways. These results suggested that fibrin plays a key role in regulating DCs immunological behaviors, providing a valuable immunomodulatory strategy for tissue healing, regeneration and implantation.

The “writers” of four types of adenosine (A)-related RNA modifications (N6-methyladenosine, N1-methyladenosine, alternative polyadenylation, as well as A-to-inosine RNA editing) are closely related to the tumorigenesis and progression of many cancer types, including skin cutaneous melanoma (SKCM). However, the potential roles of the crosstalk between these RNA modification “writers” in the tumor microenvironment (TME) remain unclear. The RNA modification patterns were identified using an unsupervised clustering method. Subsequently, based on differentially expressed genes responsible for the aforementioned RNA modification patterns, an RNA modification “writer” scoring model (W_Score) was constructed to quantify the RNA modification-associated subtypes in individual patients. Moreover, a correlation analysis for W_Score and the TME characteristics, clinical features, molecular subtypes, drug sensitivities, immune responses, and prognosis was performed. We identified three RNA modification patterns, corresponding to distinct tumor immune microenvironment characteristics and survival outcomes. Based on the W_Score score, which was extracted from the RNA modification-related signature genes, patients with SKCM were divided into high- and low-W_Score groups. The low-W_Score group was characterized by better survival outcomes and strengthened immunocyte infiltration. Further analysis showed that the low-W_Score group was positively associated with higher tumor mutation burden and PD-L1 expression. Of note, two immunotherapy cohorts demonstrated that patients with low W_Score exhibited long-term clinical benefits and an enhanced immune response. This study is the first to systematically analyze four types of A-related RNA modifications in SKCM, revealing that these “writers” essentially contribute to TME complexity and diversity. We quantitatively evaluated the RNA modification patterns in individual tumors, which could aid in developing personalized immunotherapy strategies for patients.

The administration of mRNA-based tumour vaccines is considered a promising strategy in tumour immunotherapy, although its application against kidney renal clear cell carcinoma (KIRC) is still at its infancy stage. The purpose of this study was to identify potential antigens and to further select suitable patients for vaccination. Gene expression data and clinical information were retrieved from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. GEPIA2 was used to evaluate the prognostic value of selected antigens. The relationship of antigens presenting cell infiltration with antigen expression was evaluated by TIMER, and immune subtypes were determined using unsupervised cluster analysis. Tumour antigens LRP2 and DOCK8, which are associated with prognosis and tumour-infiltrating antigen-presenting cells, were identified in KIRC. A total of six immune subtypes were identified, and patients with immune subtype 1–4 (IS1–4) tumours had an immune ‘cold’ phenotype, a higher tumour mutation burden, and poor survival. Moreover, these immune subtypes showed significant differences in the expression of immune checkpoint and immunogenic cell death modulators. Finally, the immune landscape of KIRC revealed the immune-related cell components in individual patients. This study suggests that LRP2 and DOCK8 are potential KIRC antigens in the development of mRNA vaccines, and patients with immune subtypes IS1–4 are suitable for vaccination.