Impact of lncRNA SOX9-AS1 overexpression on the prognosis and progression of intrahepatic cholangiocarcinoma

Plasma-derived exosomal long noncoding RNAs of pancreatic cancer patients as novel blood-based biomarkers of disease

BACKGROUND

Pancreatic cancer (PaCa) is one of the most intractable and fatal malignancies and is associated with the dysregulation of long noncoding RNAs (lncRNAs), which are a large class of noncoding RNAs larger than 200 nt that act as competing endogenous RNAs or sponges for miRNAs to induce tumour biological behaviours. However, their clinical value in treating pancreatic cancer has been poorly explained, but they are essential for improving the prognosis of PaCa patients.

METHODS

We analysed the plasma-derived exosomal lncRNA profiles of PaCa patients by using whole-transcriptome sequencing analysis and identified significantly differentially expressed lncRNAs, including LINC01268, LINC02802, AC124854.1, and AL132657.1. In the current study, the expression levels of four plasma-derived exosomal lncRNAs in PaCa plasma were validated via quantitative real-time polymerase chain reaction (qRT‒PCR). The relationship between the expression of the four lncRNAs and the clinicopathological features of patients with PaCa was also evaluated.

RESULTS

We demonstrated that exosomal LINC01268, LINC02802, AC124854.1 and AL132657.1 were highly expressed in PaCa plasma compared with those in normal controls; moreover, they were positively correlated with the serum expression of carbohydrate antigen 19-9 (CA19-9). The receiver operating characteristic curves (AUCs) of the four lncRNAs were 0.8421, 0.6544, 0.7190, and 0.6321, and the AUC value of the combination of the four exosomal lncRNAs increased to 0.8476, with a sensitivity of 0.72 and specificity of 0.89. These results suggested that the plasma-derived exosomal genes LINC01268, LINC02802, AC124854.1, and AL132657.1 may be novel diagnostic markers for PaCa.

CONCLUSIONS

Our research demonstrated that the plasma-derived exosomal lncRNAs of PaCa patients are novel blood-based biomarkers of disease.

Multiomics-Based Feature Extraction and Selection for the Prediction of Lung Cancer Survival

Lung cancer is a global health challenge, hindered by delayed diagnosis and the disease's complex molecular landscape. Accurate patient survival prediction is critical, motivating the exploration of various -omics datasets using machine learning methods. Leveraging multi-omics data, this study seeks to enhance the accuracy of survival prediction by proposing new feature extraction techniques combined with unbiased feature selection. Two lung adenocarcinoma multi-omics datasets, originating from the TCGA and CPTAC-3 projects, were employed for this purpose, emphasizing gene expression, methylation, and mutations as the most relevant data sources that provide features for the survival prediction models. Additionally, gene set aggregation was shown to be the most effective feature extraction method for mutation and copy number variation data. Using the TCGA dataset, we identified 32 molecular features that allowed the construction of a 2-year survival prediction model with an AUC of 0.839. The selected features were additionally tested on an independent CPTAC-3 dataset, achieving an AUC of 0.815 in nested cross-validation, which confirmed the robustness of the identified features.

LncRNA SOX9-AS1 triggers a transcriptional program involved in lipid metabolic reprogramming, cell migration and invasion in triple-negative breast cancer

At the molecular level, triple-negative breast cancer (TNBC) is frequently categorized as PAM50 basal-like subtype, but despite the advances in molecular analyses, the clinical outcome for these subtypes is uncertain. Long non-coding RNAs (lncRNAs) are master regulators of genes involved in hallmarks of cancer, which makes them suitable biomarkers for breast cancer (BRCA) diagnosis and prognosis. Here, we evaluated the regulatory role of lncRNA SOX9-AS1 in these subtypes. Using the BRCA-TCGA cohort, we observed that SOX9-AS1 was significantly overexpressed in basal-like and TNBC in comparison with other BRCA subtypes. Survival analyzes showed that SOX9-AS1 overexpression was associated with a favorable prognosis in TNBC and basal-like patients. To study the functions of SOX9-AS1, we determined the expression levels in a panel of nine BRCA cell lines finding increased levels in MDA-MB-468 and HCC1187 TNBC. Using subcellular fractionation in these cell lines, we ascertained that SOX9-AS1 was located in the cytoplasmic compartment. In addition, we performed SOX9-AS1 gene silencing using two short-harping constructs, which were transfected in both cell models and performed a genome-wide RNA-seq analysis. Data showed that 351 lncRNAs and 740 mRNAs were differentially expressed in MDA-MB-468 while 56 lncRNAs and 100 mRNAs were modulated in HCC1187 cells (Log2FC < - 1.5 and > 1.5, p.adj value < 0.05). Pathway analysis revealed that the protein-encoding genes potentially regulate lipid metabolic reprogramming, and epithelial-mesenchymal transition (EMT). Expression of lipid metabolic-related genes LIPE, REEP6, GABRE, FBP1, SCD1, UGT2B11, APOC1 was confirmed by RT-qPCR. Functional analysis demonstrated that the knockdown of SOX9-AS1 increases the triglyceride synthesis, cell migration and invasion in both two TNBC cell lines. In conclusion, high SOX9-AS1 expression predicts an improved clinical course in patients, while the loss of SOX9-AS1 expression enhances the aggressiveness of TNBC cells.