Molecular classification reveals the sensitivity of lung adenocarcinoma to radiotherapy and immunotherapy: multi-omics clustering based on similarity network fusion.

BACKGROUND: Due to individual differences in tumors and immune systems, the response rate to immunotherapy is low in lung adenocarcinoma (LUAD) patients. Combinations with other therapeutic strategies improve the efficacy of immunotherapy in LUAD patients. Although radioimmunotherapy has been demonstrated to effectively suppress tumors, the underlying mechanisms still need to be investigated. METHODS: Total RNA from LUAD cells was sequenced before and after radiotherapy to identify differentially expressed radiation-associated genes. The similarity network fusion (SNF) algorithm was applied for molecular classification based on radiation-related genes, immune-related genes, methylation data, and somatic mutation data. The changes in gene expression, prognosis, immune cell infiltration, radiosensitivity, chemosensitivity, and sensitivity to immunotherapy were assessed for each subtype. RESULTS: We used the SNF algorithm and multi-omics data to divide TCGA-LUAD patients into three subtypes. Patients with the CS3 subtype had the best prognosis, while those with the CS1 and CS2 subtypes had poorer prognoses. Among the strains tested, CS2 exhibited the most elevated immune cell infiltration and expression of immune checkpoint genes, while CS1 exhibited the least. Patients in the CS2 subgroup were more likely to respond to PD-1 immunotherapy. The CS2 patients were most sensitive to docetaxel and cisplatin, while the CS1 patients were most sensitive to paclitaxel. Experimental validation of signature genes in the CS2 subtype showed that inhibiting the expression of RHCG and TRPA1 could enhance the sensitivity of lung cancer cells to radiation. CONCLUSIONS: In summary, this study identified a risk classifier based on multi-omics data that can guide treatment selection for LUAD patients.

Small noncoding RNA dysregulation is implicated in manganism in a rat model of methylcyclopentadienyl manganese tricarbonyl-induced unrepaired striatum damage.

The accumulation of excessively high manganese levels within the brain can contribute to a series of Parkinsonian symptoms referred to as manganism. The gasoline antiknock additive Methylcyclopentadienyl Manganese Tricarbonyl (MMT) is an environmental source of manganese exposure and can induce manganism in rats. While some prior reports have demonstrated the differential expression of small noncoding RNAs (sncRNAs) in patients with Parkinson's disease (PD), the degree of sncRNA dysfunction in manganism has yet to be clearly documented. As sncRNAs such as transfer RNA-derived small RNAs (tsRNAs) and ribosomal RNA-derived small RNAs (rsRNAs) exhibit high levels of modifications such as 3' terminal 3'-phosphate and 2',3'-cyclic phosphate modifications that disrupt the process of adapter ligation and m1A, m3C, m1G, and m22G RNA methylation, these transcripts are not detected in traditional small RNA-sequencing studies. Here, differential sncRNA expression was analyzed by comparing a rat model of MMT-induced unrepaired striatum damage to appropriate control samples via PANDORA-Seq, which can detect highly modified sncRNAs. Following the removal of sncRNA modifications, this approach identified 599 sncRNAs that were differentially expressed in the striatum of MMT-exposed rats relative to controls, as well as 1155 sncRNAs that were differentially expressed in Mn-treated and control rats. Additional functional analyses were performed to predict the putative targets of these sncRNAs, implicating a role for such sncRNA dysregulation in the pathogenesis of manganism in this rat model system.

Histone acetylation-related IncRNA: Potential biomarkers for predicting prognosis and immune response in lung adenocarcinoma, and distinguishing hot and cold tumours.

Background: Histone acetylation-related lncRNAs (HARlncRNAs) play significant roles in various cancers, but their impact on lung adenocarcinoma (LUAD) remains unclear. This study aimed to develop a new HARlncRNA-based prognostic model for LUAD and to explore its potential biological mechanisms. Methods: We identified 77 histone acetylation genes based on previous studies. HARlncRNAs related to prognosis were screened by co-expression, univariate and multivariate analyses, and least absolute shrinkage selection operator regression (LASSO). Afterward, a prognostic model was established based on the screened HARlncRNAs. We analysed the relationship between the model and immune cell infiltration characteristics, immune checkpoint molecule expression, drug sensitivity, and tumour mutational burden (TMB). Finally, the entire sample was divided into three clusters to further distinguish between hot and cold tumours. Results: A seven-HARlncRNA-based prognostic model was established for LUAD. The area under the curve (AUC) of the risk score was the highest among all the analysed prognostic factors, indicating the accuracy and robustness of the model. The patients in the high-risk group were predicted to be more sensitive to chemotherapeutic, targeted, and immunotherapeutic drugs. It was worth noting that clusters could effectively identify hot and cold tumours. In our study, clusters 1 and 3 were considered hot tumours that were more sensitive to immunotherapy drugs. Conclusion: We developed a risk-scoring model based on seven prognostic HARlncRNAs that promises to be a new tool for evaluating the prognosis and efficacy of immunotherapy in patients with LUAD.

Nanoscale Zeolitic Imidazolate Framework (ZIF)-8 in Cancer Theranostics: Current Challenges and Prospects.

Cancer severely threatens human health and has remained the leading cause of disease-related death for decades. With the rapid advancement of nanomedicine, nanoscale metal-organic frameworks are believed to be potentially applied in the treatment and biomedical imaging for various tumors. Zeolite imidazole framework (ZIF)-8 attracts increasing attention due to its high porosity, large specific surface area, and pH-responsiveness. The designs and modifications of ZIF-8 nanoparticles, as well as the strategy of drug loading, demand a multifaceted and comprehensive understanding of nanomaterial features and tumor characteristics. We searched for studies on ZIF-8-based nanoplatforms in tumor theranostics on Web of Science from 2015 to 2022, mainly focused on the research published in the past 3 years, summarized the progress of their applications in tumor imaging and treatment, and discussed the favorable aspects of ZIF-8 nanoparticles for tumor theranostics as well as the future opportunities and potential challenges. As a kind of metal-organic framework material full of potential, ZIF-8 can be expected to be combined with more therapeutic systems in the future and continue to contribute to all aspects of tumor therapy and diagnosis.

GPR87 promotes tumor cell invasion and mediates the immunogenomic landscape of lung adenocarcinoma.

The purpose of this study is to examine the association between G protein-coupled receptor 87 (GPR87) and lung adenocarcinoma (LUAD) metastasis and immune infiltration. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets extract clinical data. According to the TCGA database, increased GPR87 expression predicts poor overall survival, progression-free interval, and disease-specific survival in LUAD patients. The meta-analysis also reveals a significant association between high GPR87 expression and poor overall survival. Moreover, functional experiments demonstrate that GPR87 silencing reduces LUAD cell invasion and migration. Immunoblotting shows that GPR87 knockdown decreased Vimentin and N-cadherin expression and increased E-cadherin expression in LUAD cells. GPR87 expression in LUAD is positively correlated with immune infiltration. In addition, GPR87 expression is associated with immune and chemotherapy resistance in LUAD patients. Our findings indicate that GPR87 promotes tumor progression and is correlated with immune infiltration, suggesting GPR87 as a possible biomarker for prognosis prediction in LUAD.

Protein Arginine Methylation: An Emerging Modification in Cancer Immunity and Immunotherapy.

In recent years, protein arginine methyltransferases (PRMTs) have emerged as new members of a gene expression regulator family in eukaryotes, and are associated with cancer pathogenesis and progression. Cancer immunotherapy has significantly improved cancer treatment in terms of overall survival and quality of life. Protein arginine methylation is an epigenetic modification function not only in transcription, RNA processing, and signal transduction cascades, but also in many cancer-immunity cycle processes. Arginine methylation is involved in the activation of anti-cancer immunity and the regulation of immunotherapy efficacy. In this review, we summarize the most up-to-date information on regulatory molecular mechanisms and different underlying arginine methylation signaling pathways in innate and adaptive immune responses during cancer. We also outline the potential of PRMT-inhibitors as effective combinatorial treatments with immunotherapy.

LncRNA PCAT1 activates SOX2 and suppresses radioimmune responses via regulating cGAS/STING signalling in non-small cell lung cancer.

BACKGROUND: The expression of long non-coding RNA (lncRNA) prostate cancer-associated ncRNA transcripts 1 (PCAT1) is increased in non-small cell lung cancer (NSCLC). It stimulates tumour growth and metastasis, but its role in the radioimmune responses remain unknown. We aimed to explore the impacts of PCAT1 on tumorigenesis and radioimmune responses and the underlying molecular mechanisms in NSCLC. METHODS: Comprehensive bioinformatics analysis was performed to identify immunosuppressive lncRNAs involved with tumour invasion in NSCLC. The expression levels of PCAT1 were analysed by in situ hybridisation in 55 paired NSCLC tissues and adjacent normal tissues. Both loss- and gain-of-function assays were performed to examine the effects of PCAT1 and SOX2 on NSCLC cell behaviours in vivo and in vitro. Bioinformatic analyses, chromatin isolation by RNA purification (ChIRP) and dual-luciferase reporter assays were applied to validate the regulatory effects of PCAT1 on SOX2 expression. Chromatin immunoprecipitation, luciferase and rescue assays were utilised to identify the relationship between SOX2 and the cGAS/stimulator of interferon genes (STING) signalling. RESULTS: PCAT1 was immunosuppressive and related with NSCLC invasion. Increased PCAT1 was negatively correlated with immune cell infiltration in NSCLC. PCAT1 knockdown restrained proliferation, increased apoptosis, and repressed cell metastasis in vivo and in vitro. PCAT1 activated SOX2 that accelerated tumorigenesis and immunosuppression. SOX2 promoted tumour growth through inhibiting cytotoxic T-cell immunity. Moreover, SOX2 restrained cGAS transcription and hampered downstream type I interferon (IFN)-induced immune responses. Inhibition of PCAT1/SOX2 in collaboration with radiation further inhibited tumour growth, and initiated the cGAS/STING signalling pathway, which enhanced the immune responses of radiotherapy in NSCLC. CONCLUSIONS: PCAT1/SOX2 axis promoted tumorigenesis and immunosuppression through inhibition of cGAS/STING signalling-mediated T-cell activation. Inhibition of PCAT1 and SOX2 synergised with radiotherapy to activate the immune response and could serve as potential therapeutic targets.

Research progress of the application of mesenchymal stem cells in chronic inflammatory systemic diseases.

Chronic inflammatory systemic diseases are the result of the body's immune imbalance, with a long course and recurring episodes. Immunosuppressants are the main treatment, but not all patients respond well to it. Being capable of both self-renewal and differentiation into multiple tissue cells and low immunogenicity, mesenchymal stem cell is a promising treatment for chronic inflammatory systemic diseases. In this article, we describe the research progress and clinical application of mesenchymal stem cells in chronic inflammatory systemic diseases and look for influencing factors and biomarkers that can predict the outcome of patient with mesenchymal stem cell transplantation.