Integrated single-cell sequencing, spatial transcriptome sequencing and bulk RNA sequencing highlights the molecular characteristics of parthanatos in gastric cancer.

BACKGROUND: Parthanatos is a novel programmatic form of cell death based on DNA damage and PARP-1 dependency. Nevertheless, its specific role in the context of gastric cancer (GC) remains uncertain. METHODS: In this study, we integrated multi-omics algorithms to investigate the molecular characteristics of parthanatos in GC. A series of bioinformatics algorithms were utilized to explore clinical heterogeneity of GC and further predict the clinical outcomes. RESULTS: Firstly, we conducted a comprehensive analysis of the omics features of parthanatos in various human tumors, including genomic mutations, transcriptome expression, and prognostic relevance. We successfully identified 7 cell types within the GC microenvironment: myeloid cell, epithelial cell, T cell, stromal cell, proliferative cell, B cell, and NK cell. When compared to adjacent non-tumor tissues, single-cell sequencing results from GC tissues revealed elevated scores for the parthanatos pathway across multiple cell types. Spatial transcriptomics, for the first time, unveiled the spatial distribution characteristics of parthanatos signaling. GC patients with different parthanatos signals often exhibited distinct immune microenvironment and metabolic reprogramming features, leading to different clinical outcomes. The integration of parthanatos signaling and clinical indicators enabled the creation of novel survival curves that accurately assess patients' survival times and statuses. CONCLUSIONS: In this study, the molecular characteristics of parthanatos' unicellular and spatial transcriptomics in GC were revealed for the first time. Our model based on parthanatos signals can be used to distinguish individual heterogeneity and predict clinical outcomes in patients with GC.

Mendelian randomization suggests a causal relationship between gut dysbiosis and thyroid cancer.

Background: Alterations in gut microbiota composition and function have been linked to the development and progression of thyroid cancer (TC). However, the exact nature of the causal relationship between them remains uncertain. Methods: A bidirectional two-sample Mendelian randomization (TSMR) analysis was conducted to assess the causal connection between gut microbiota (18,340 individuals) and TC (6,699 cases combined with 1,613,655 controls) using data from a genome-wide association study (GWAS). The primary analysis used the inverse-variance weighted (IVW) method to estimate the causal effect, with supplementary approaches including the weighted median, weighted mode, simple mode, and MR-Egger. Heterogeneity and pleiotropy were assessed using the Cochrane Q test, MR-Egger intercept test, and MR-PRESSO global test. A reverse TSMR analysis was performed to explore reverse causality. Results: This study identified seven microbial taxa with significant associations with TC. Specifically, the genus Butyrivibrio (OR: 1.127, 95% CI: 1.008-1.260, p = 0.036), Fusicatenibacter (OR: 1.313, 95% CI: 1.066-1.618, p = 0.011), Oscillospira (OR: 1.240, 95% CI: 1.001-1.536, p = 0.049), Ruminococcus2 (OR: 1.408, 95% CI: 1.158-1.711, p < 0.001), Terrisporobacter (OR: 1.241, 95% CI: 1.018-1.513, p = 0.032) were identified as risk factors for TC, while The genus Olsenella (OR: 0.882, 95% CI: 0.787-0.989, p = 0.031) and Ruminococcaceae UCG004 (OR: 0.719, 95% CI: 0.566-0.914, p = 0.007) were associated with reduced TC risk. The reverse MR analysis found no evidence of reverse causality and suggested that TC may lead to increased levels of the genus Holdemanella (ß: 0.053, 95% CI: 0.012~0.094, p = 0.011) and decreased levels of the order Bacillales (ß: -0.075, 95% CI: -0.143~-0.006, p = 0.033). No significant bias, heterogeneity, or pleiotropy was detected in this study. Conclusion: This study suggests a potential causal relationship between gut microbiota and TC, providing new insights into the role of gut microbiota in TC. Further research is needed to explore the underlying biological mechanisms.

Integrated multi-omics identified the novel intratumor microbiome-derived subtypes and signature to predict the outcome, tumor microenvironment heterogeneity, and immunotherapy response for pancreatic cancer patients.

Background: The extremely malignant tumour known as pancreatic cancer (PC) lacks efficient prognostic markers and treatment strategies. The microbiome is crucial to how cancer develops and responds to treatment. Our study was conducted in order to better understand how PC patients' microbiomes influence their outcome, tumour microenvironment, and responsiveness to immunotherapy. Methods: We integrated transcriptome and microbiome data of PC and used univariable Cox regression and Kaplan-Meier method for screening the prognostic microbes. Then intratumor microbiome-derived subtypes were identified using consensus clustering. We utilized LASSO and Cox regression to build the microbe-related model for predicting the prognosis of PC, and utilized eight algorithms to assess the immune microenvironment feature. The OncoPredict package was utilized to predict drug treatment response. We utilized qRT-PCR to verify gene expression and single-cell analysis to reveal the composition of PC tumour microenvironment. Results: We obtained a total of 26 prognostic genera in PC. And PC samples were divided into two microbiome-related subtypes: Mcluster A and B. Compared with Mcluster A, patients in Mcluster B had a worse prognosis and higher TNM stage and pathological grade. Immune analysis revealed that neutrophils, regulatory T cell, CD8+ T cell, macrophages M1 and M2, cancer associated fibroblasts, myeloid dendritic cell, and activated mast cell had remarkably higher infiltrated levels within the tumour microenvironment of Mcluster B. Patients in Mcluster A were more likely to benefit from CTLA-4 blockers and were highly sensitive to 5-fluorouracil, cisplatin, gemcitabine, irinotecan, oxaliplatin, and epirubicin. Moreover, we built a microbe-derived model to assess the outcome. The ROC curves showed that the microbe-related model has good predictive performance. The expression of LAMA3 and LIPH was markedly increased within pancreatic tumour tissues and was linked to advanced stage and poor prognosis. Single-cell analysis indicated that besides cancer cells, the tumour microenvironment of PC was also rich in monocytes/macrophages, endothelial cells, and fibroblasts. LIPH and LAMA3 exhibited relatively higher expression in cancer cells and neutrophils. Conclusion: The intratumor microbiome-derived subtypes and signature in PC were first established, and our study provided novel perspectives on PC prognostic indicators and treatment options.

LncRNA IGFL2-AS1 Promotes the Proliferation, Migration, and Invasion of Colon Cancer Cells and is Associated with Patient Prognosis.

BACKGROUND: LncRNAs play an important role in tumor initiation and development. However, the underlying involvement of lncRNA expression in colorectal carcinoma remains to be clarified. METHODS: All analyses were performed in R software v4.0, SPSS v13.0, and GraphPad Prism 8. The "limma" package was used to identify differentially expressed lncRNAs between two groups with the threshold of |logFC| >1 and P <0.05. The "Survival" package was used to conduct survival analysis. HCT8 and SE480 cell lines were used to conduct further phenotype experiments, including transwell, wound-healing, CCK8 and colony formation assay. Gene set enrichment analysis was used to explore the biological pathway difference in high and low IGFL2-AS1 patients. RESULTS: The lncRNA IGFL2-AS1 was highly expressed in colon adenocarcinoma (COAD) tissue and cell lines (HCT116, HCT8, HCT129, and SW480). The COAD patients with high IGFL2-AS1 were associated with a worse prognosis. Meanwhile, the knockdown of IGFL2-AS1 could significantly suppress the proliferation and invasion of COAD cells. Gene set enrichment analysis showed that the top five biological pathways involving IGFL2-AS1 were angiogenesis, epithelial-mesenchymal transition, KRAS signaling, myogenesis, and coagulation. Western blot results showed that the inhibition of IGFL2-AS1 could significantly reduce the N-cadherin, HIF1A and KRAS protein expression, yet increase the E-cadherin protein level. IGFL2-AS1 was also positively correlated with M0 macrophages, M2 macrophages, and neutrophils but negatively correlated with CD4+ memory T cells and CD8+ T cells. CONCLUSION: IGFL1-AS1 could seriously worsen patient outcomes and facilitate COAD progression, thus serving as an independent tumor marker.

Exosomal linc-FAM138B from cancer cells alleviates hepatocellular carcinoma progression via regulating miR-765.

Exosomes are small vesicles with a diameter of 30-150 nm secreted by cells, which can be used as signal carriers to transfer nucleic acids, proteins, lipids and other functional substances to the recipient cells and play a role in cell communication. Hepatocellular carcinoma is the fourth most common cause of cancer-related death worldwide. Studies have shown that long non-coding RNAs (lncRNAs) are involved in the development and progression of many types of tumors. Our present study found that linc-FAM138B was reduced in HCC tissues and cell lines, low expression of linc-FAM138B indicated a poor prognosis in HCC patients. Interestingly, linc-FAM138B could be packaged into cancer cells. And exo-FAM138B inhibited the proliferation, migration and invasion of HCC cells. Furthermore, linc-FAM138B sponged miR-765 levels. And exo-si-FAM138B promoted HCC progression, while deletion of miR-765 reversed the role of exo-si-FAM138B. In vivo tumorigenesis experiments showed that exo-FAM138B suppressed HCC growth via modulating miR-765. In conclusion, exo-linc-FAM138B secreted by cancer cells inhibited HCC development via targeting miR-765, which provided a new idea and perspective for in-depth understanding of the complex signal regulation in HCC process.