Long non-coding (lnc)RNA profiling and the role of a key regulator lnc-PNRC2-1 in the transforming growth factor-1-induced epithelial-mesenchymal transition of CNE1 nasopharyngeal carcinoma cells

lncRNAs'p potential roles in the pathogenesis of cancer via interacting with signaling pathways; special focus on lncRNA-mediated signaling dysregulation in lung cancer

Lung cancer ranks among the most lethal types of cancer globally, with a high occurrence and fatality rate. The spread of cancer to other parts of the body, known as metastasis, is the primary cause of treatment failure and death in lung cancer cases. Current approaches for treating advanced lung cancer typically involve a combination of chemotherapy and targeted therapy. However, the majority of patients ultimately develop resistance to these treatments, leading to a worsened prognosis. In recent years, cancer biology research has predominantly focused on the role of protein-encoding genes in cancer development. Long non-coding RNAs (lncRNAs) are transcripts over 200 nucleotides in length that do not encode proteins but are crucial RNA molecules involved in numerous biological functions. While many functions of lncRNAs remain unknown, some have been linked to human diseases, including cancer. Studies have demonstrated that lncRNAs interact with other large molecules in the cell, such as proteins, DNA, and RNA, influencing various critical aspects of cancer. LncRNAs play a significant role in regulating gene expression and have a crucial function in the transcriptional regulation of cancer cells. They mediate various biological and clinical processes such as invasion, metastasis, apoptosis, and cell proliferation. Dysregulation of lncRNAs has been found to impact the process of carcinogenesis through advanced technologies like RNA sequencing and microarrays. Collectively, these long non-coding RNAs hold promise as potential biomarkers and therapeutic targets for human cancers. In this segment, we provide a comprehensive summary of the literature on the characteristics and formation of lncRNAs, along with an overview of their current known roles in lung cancer.

Unboxing the network among long non-coding RNAs and TGF-β signaling in cancer

Deeper analysis of molecular mechanisms arising in tumor cells is an unmet need to provide new diagnostic and therapeutic strategies to prevent and treat tumors. The transforming growth factor β (TGF-β) signaling has been steadily featured in tumor biology and linked to poor prognosis of cancer patients. One pro-tumorigenic mechanism induced by TGF-β is the epithelial-to-mesenchymal transition (EMT), which can initiate cancer dissemination, enrich the tumor stem cell population, and increase chemoresistance. TGF-β signals via SMAD proteins, ubiquitin ligases, and protein kinases and modulates the expression of protein-coding and non-coding RNA genes, including those encoding larger than 500 nt transcripts, defined as long non-coding RNAs (lncRNAs). Several reports have shown lncRNAs regulating malignant phenotypes by directly affecting epigenetic processes, transcription, and post-transcriptional regulation. Thus, this review aims to update and summarize the impact of TGF-β signaling on the expression of lncRNAs and the function of such lncRNAs as regulators of TGF-β signaling, and how these networks might impact specific hallmarks of cancer.

Identifying important microbial and genomic biomarkers for differentiating right- versus left-sided colorectal cancer using random forest models

BACKGROUND

Colorectal cancer (CRC) is a heterogeneous disease, with subtypes that have different clinical behaviours and subsequent prognoses. There is a growing body of evidence suggesting that right-sided colorectal cancer (RCC) and left-sided colorectal cancer (LCC) also differ in treatment success and patient outcomes. Biomarkers that differentiate between RCC and LCC are not well-established. Here, we apply random forest (RF) machine learning methods to identify genomic or microbial biomarkers that differentiate RCC and LCC.

METHODS

RNA-seq expression data for 58,677 coding and non-coding human genes and count data for 28,557 human unmapped reads were obtained from 308 patient CRC tumour samples. We created three RF models for datasets of human genes-only, microbes-only, and genes-and-microbes combined. We used a permutation test to identify features of significant importance. Finally, we used differential expression (DE) and paired Wilcoxon-rank sum tests to associate features with a particular side.

RESULTS

RF model accuracy scores were 90%, 70%, and 87% with area under curve (AUC) of 0.9, 0.76, and 0.89 for the human genomic, microbial, and combined feature sets, respectively. 15 features were identified as significant in the model of genes-only, 54 microbes in the model of microbes-only, and 28 genes and 18 microbes in the model with genes-and-microbes combined. PRAC1 expression was the most important feature for differentiating RCC and LCC in the genes-only model, with HOXB13, SPAG16, HOXC4, and RNLS also playing a role. Ruminococcus gnavus and Clostridium acetireducens were the most important in the microbial-only model. MYOM3, HOXC4, Coprococcus eutactus, PRAC1, lncRNA AC012531.25, Ruminococcus gnavus, RNLS, HOXC6, SPAG16 and Fusobacterium nucleatum were most important in the combined model.

CONCLUSIONS

Many of the identified genes and microbes among all models have previously established associations with CRC. However, the ability of RF models to account for inter-feature relationships within the underlying decision trees may yield a more sensitive and biologically interconnected set of genomic and microbial biomarkers.