Background Lactate, a byproduct of glucose metabolism, is primarily utilized for gluconeogenesis and numerous cellular and organismal life processes. Interestingly, many studies have demonstrated a correlation between lactate metabolism and tumor development. However, the relationship between long non-coding RNAs (lncRNAs) and lactate metabolism in papillary thyroid cancer (PTC) remains to be explored. Methods Lactate metabolism-related lncRNAs (LRLs) were obtained by differential expression and correlation analyses, and the risk model was further constructed by least absolute shrinkage and selection operator analysis (Lasso) and Cox analysis. Clinical, immune, tumor mutation, and enrichment analyses were performed based on the risk model. The expression level of six LRLs was tested using RT-PCR. Results This study found several lncRNAs linked to lactate metabolism in both The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets. Using Cox regression analysis, 303 lactate LRLs were found to be substantially associated with prognosis. Lasso was done on the TCGA cohort. Six LRLs were identified as independent predictive indicators for the development of a PTC prognostic risk model. The cohort was separated into two groups based on the median risk score (0.39717 -0.39771). Subsequently, Kaplan-Meier survival analysis and multivariate Cox regression analysis revealed that the high-risk group had a lower survival probability and that the risk score was an independent predictive factor of prognosis. In addition, a nomogram that can easily predict the 1-, 3-, and 5-year survival rates of PTC patients was established. Furthermore, the association between PTC prognostic factors and tumor microenvironment (TME), immune escape, as well as tumor somatic mutation status was investigated in high- and low-risk groups. Lastly, gene expression analysis was used to confirm the differential expression levels of the six LRLs. Conclusion In conclusion, we have constructed a prognostic model that can predict the prognosis, mutation status, and TME of PTC patients. The model may have great clinical significance in the comprehensive evaluation of PTC patients.
Macroautophagy is a highly conserved eukaryotic cellular pathway involving the engulfment of macromolecules, organelles, and invading microbes by a double-membrane compartment and subsequent lysosomal degradation. The mechanisms that regulate macroautophagy, and the interaction of its components with other cellular pathways, have remained unclear. Here, we performed proximity-dependent biotin identification (BioID) on 39 core human macroautophagy proteins, identifying over 700 unique high confidence proximity interactors with new putative connections between macroautophagic and essential cellular processes. Of note, we identify members of the OSBPL (oxysterol binding protein like) family as Atg8-family protein interactors. We subsequently conducted comprehensive screens of the OSBPL family for LC3B-binding and roles in xenophagy and aggrephagy. OSBPL7 and OSBPL11 emerged as novel lipid transfer proteins required for macroautophagy of selective cargo. Altogether, our proximity interaction map provides a valuable resource for the study of autophagy and highlights the critical role of membrane contact site proteins in the pathway. BioID: proximity-dependent biotin identification; GO: gene ontology; OSBPL: oxysterol binding protein like; VAPA: VAMP associated protein A; VAPB: VAMP associated protein B and C
BioID: proximity-dependent biotin identification; GO: gene ontology; OSBPL: oxysterol binding protein like; VAPA: VAMP associated protein A; VAPB: VAMP associated protein B and C.
Background Accumulating evidence suggests that N6-methyladenosine (m6A) RNA methylation plays an important role in tumor proliferation and growth. However, its effect on the clinical prognosis, immune infiltration, and immunotherapy response of thyroid cancer patients has not been investigated in detail. Methods Clinical data and RNA expression profiles of thyroid cancer were extracted from the Cancer Genome Atlas-thyroid carcinoma (TCGA-THCA) and preprocessed for consensus clustering. The risk model was constructed based on differentially expressed genes (DEGs) using Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression analyses. The associations between risk score and clinical traits, immune infiltration, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), immune infiltration, and immunotherapy were assessed. Immunohistochemistry was used to substantiate the clinical traits of our samples. Results Gene expression analysis showed that 17 genes, except YHTDF2, had significant differences (vs healthy control, P <0.001). Consensus clustering yielded 2 clusters according to their clinical features and estimated a poorer prognosis for Cluster 1 ( P =0.03). The heatmap between the 2 clusters showed differences in T ( P <0.01), N ( P <0.001) and stage ( P <0.01). Based on univariate Cox and LASSO regression, a risk model consisting of three high-risk genes (KIAA1429, RBM15, FTO) was established, and the expression difference between normal and tumor tissues of three genes was confirmed by immunohistochemical results of our clinical tissues. KEGG and GSEA analyses showed that the risk DEGs were related mainly to proteolysis, immune response, and cancer pathways. The levels of immune infiltration in the high- and low-risk groups were different mainly in iDCs ( P <0.05), NK cells ( P <0.05), and type-INF-II ( P <0.001). Immunotherapy analysis yielded 30 drugs associated with the expression of each gene and 20 drugs associated with the risk score. Conclusions Our risk model can act as an independent marker for thyroid cancer and provides promising immunotherapy targets for its treatment.
Macroautophagy is a highly conserved eukaryotic cellular pathway involving the engulfment of macromolecules, organelles, and invading microbes by a double-membrane compartment and subsequent lysosomal degradation. The mechanisms that regulate macroautophagy, and the interaction of its components with other cellular pathways, have remained unclear. Here, we performed proximity-dependent biotin identification (BioID) on 39 core human macroautophagy proteins, identifying over 700 unique high confidence proximity interactors with new putative connections between macroautophagic and essential cellular processes. Of note, we identify members of the OSBPL (oxysterol binding protein like) family as Atg8-family protein interactors. We subsequently conducted comprehensive screens of the OSBPL family for LC3B-binding and roles in xenophagy and aggrephagy. OSBPL7 and OSBPL11 emerged as novel lipid transfer proteins required for macroautophagy of selective cargo. Altogether, our proximity interaction map provides a valuable resource for the study of autophagy and highlights the critical role of membrane contact site proteins in the pathway. BioID: proximity-dependent biotin identification; GO: gene ontology; OSBPL: oxysterol binding protein like; VAPA: VAMP associated protein A; VAPB: VAMP associated protein B and C
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