ß-elemene promotes miR-127-3p maturation, induces NSCLCs autophagy, and enhances macrophage M1 polarization through exosomal communication.

ß-elemene has been observed to exert inhibitory effects on a multitude of tumors, primarily through multiple pathways such as the inhibition of cancer cell proliferation and the induction of apoptosis. The present study is designed to elucidate the role and underlying mechanisms of ß-elemene in the therapeutic intervention of non-small cell lung cancer (NSCLC). Both in vitro and in vivo experimental models corroborate the inhibitory potency of ß-elemene on NSCLCs. Our findings indicate that ß-elemene facilitates the maturation of miR-127-3p by inhibiting CBX8. Functioning as an upstream regulator of MAPK4, miR-127-3p deactivates the Akt/mTOR/p70S6K pathway by targeting MAPK4, thereby inducing autophagy in NSCLCs. Additionally, ß-elemene augments the packaging of miR-127-3p into exosomes via SYNCRIP. Exosomal miR-127-3p further stimulates M1 polarization of macrophages by suppressing ZC3H4. Taken together, the detailed understanding of the mechanisms through which ß-elemene induces autophagy in NSCLCs and facilitates M1 polarization of macrophages provides compelling scientific evidence supporting its potential utility in NSCLC treatment.

Multi-Dimensional Fragmentomics Enables Early and Accurate Detection of Colorectal Cancer.

Colorectal cancer (CRC) is frequently diagnosed in advanced stages, highlighting the need for developing approaches for early detection. Liquid biopsy using cell-free DNA (cfDNA) fragmentomics is a promising approach, but the clinical application is hindered by complexity and cost. This study aimed to develop an integrated model using cfDNA fragmentomics for accurate, cost-effective early-stage CRC detection. Plasma cfDNA was extracted and sequenced from a training cohort of 360 participants, including 176 CRC patients and 184 healthy controls. An ensemble stacked model comprising five machine learning models was employed to distinguish CRC patients from healthy controls using five cfDNA fragmentomic features. The model was validated in an independent cohort of 236 participants (117 CRC patients and 119 controls) and a prospective cohort of 242 participants (129 CRC patients and 113 controls). The ensemble stacked model showed remarkable discriminatory power between CRC patients and controls, outperforming all base models and achieving a high area under the ROC curve (AUC) of 0.986 in the validation cohort. It reached 94.88% sensitivity and 98% specificity for detecting CRC in the validation cohort, with sensitivity increasing as cancer progressed. The model also demonstrated consistently high accuracy in within-run and between-run tests and across various conditions in healthy individuals. In the prospective cohort, it achieved 91.47% sensitivity and 95.58% specificity. This integrated model capitalizes on the multiplex nature of cfDNA fragmentomics to achieve high sensitivity and robustness, offering significant promise for early CRC detection and broad patient benefit.

Prediction of survival and immunotherapy response by the combined classifier of G protein-coupled receptors and tumor microenvironment in melanoma.

BACKGROUND: Melanoma is the deadliest form of skin tumor, and G protein-coupled receptors (GPCRs) play crucial roles in its carcinogenesis. Furthermore, the tumor microenvironment (TME) affects the overall survival (OS) and the response to immunotherapy. The combination of GPCRs and TME from a multi-omics perspective may help to predict the survival of the melanoma patients and their response to immunotherapy. METHODS: Bulk-seq, single-cell RNA sequencing (scRNA-seq), gene mutations, immunotherapy responses, and clinicopathologic feature data were downloaded from public databases, and prognostic GPCRs and immune cells were screened using multiple machine learning algorithms. The expression levels of GPCRs were detected using real-time quantitative polymerase chain reaction (qPCR) in A375 and HaCaT cell lines. The GPCR-TME classifier was constructed and verified using different cohorts and multi-omics. Gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), and tracking tumor immunophenotype (TIP) were used to identify the key biological pathways among the GPCR-TME subgroups. Then, tumor mutational burden (TMB), vital mutant genes, antigen presentation genes, and immune checkpoints were compared among the subgroups. Finally, the differences in immunotherapy response rates among the GPCR-TME subgroups were investigated. RESULTS: A total of 12 GPCRs and five immune cell types were screened to establish the GPCR-TME classifier. No significant differences in the expression levels of the 12 GPCRs were found in the two cell lines. Patients with high GPCR score or low TME score had a poor OS; thus, the GPCRlow/TMEhigh subgroup had the most favorable OS. The scRNA-seq result revealed that immune cells had a higher GPCR score than tumor and stromal cells. The GPCR-TME classifier acted as an independent prognostic factor for melanoma. GSEA, WGCNA, and TIP demonstrated that the GPCRlow/TMEhigh subgroup was related to the activation and recruitment of anti-tumor immune cells and the positive regulation of the immune response. From a genomic perspective, the GPCRlow/TMEhigh subgroup had higher TMB, and different mutant genes. Ultimately, higher expression levels of antigen presentation genes and immune checkpoints were observed in the GPCRlow/TMEhigh subgroup, and the melanoma immunotherapy cohorts confirmed that the response rate was highest in the GPCRlow/TMEhigh cohort. CONCLUSIONS: We have developed a GPCR-TME classifier that could predict the OS and immunotherapy response of patients with melanoma highly effectively based on multi-omics analysis.

Decoding the metastatic potential and optimal postoperative adjuvant therapy of melanoma based on metastasis score.

Metastasis is a formidable challenge in the prognosis of melanoma. Accurately predicting the metastatic potential of non-metastatic melanoma (NMM) and determining effective postoperative adjuvant treatments for inhibiting metastasis remain uncertain. In this study, we conducted comprehensive analyses of melanoma metastases using bulk and single-cell RNA sequencing data, enabling the construction of a metastasis score (MET score) through diverse machine-learning algorithms. The reliability and robustness of the MET score were validated using various in vitro assays and in vivo models. Our findings revealed a distinct molecular landscape in metastatic melanoma characterized by the enrichment of metastasis-related pathways, intricate cell-cell communication, and heightened infiltration of pro-angiogenic tumor-associated macrophages compared to NMM. Importantly, patients in the high MET score group exhibited poorer prognoses and an immunosuppressive microenvironment, featuring increased infiltration of regulatory T cells and decreased infiltration of CD8+ T cells, compared to the low MET score patient group. Expression of PD-1 was markedly higher in patients with low MET scores. Anti-PD-1 (aPD-1) therapy profoundly affected antitumor immunity activation and metastasis inhibition in these patients. In summary, our study demonstrates the effectiveness of the MET score in predicting melanoma metastatic potential. For patients with low MET scores, aPD-1 therapy may be a potential treatment strategy to inhibit metastasis. Patients with high MET scores may benefit from combination therapies.

Role of PTHrP nuclear localization and carboxyl terminus sequences in postnatal spinal cord development.

Parathyroid hormone-related peptide (PTHrP) acts under physiological conditions to regulate normal development of several tissues and organs. The role of PTHrP in spinal cord development has not been characterized. Pthrp knock in (Pthrp KI) mice were genetically modified to produce PTHrP in which there is a deficiency of the nuclear localization sequence (NLS) and C-terminus. Using this genetically modified mouse model, we have characterized its effect on spinal cord development early postnatally. The spinal cords from Pthrp KI mice displayed a significant reduction in its length, weight, and cross-sectional area compared to wild-type controls. Histologically, there was a decreased development of neurons and glial cells that caused decreased cell proliferation and increased apoptosis. The neural stem cells (NSCs) cultures also revealed decreased cell proliferation and differentiation and increased apoptosis. The proposed mechanism of delayed spinal cord development in Pthrp KI mice may be due to alteration in associated pathways in regulation of cell-division cycles and apoptosis. There was significant downregulation of Bmi-1 and upregulation of cyclin-dependent kinase inhibitors p27, p21, and p16 in Pthrp KI animals. We conclude that NLS and C-terminus peptide segments of PTHrP play an important role in inhibiting cell apoptosis and stimulation of cellular proliferation necessary for normal spinal cord development.