Identification of a novel snoRNA expression signature associated with overall survival in patients with lung adenocarcinoma: A comprehensive analysis based on RNA sequencing dataset

Characterization the prognosis role and effects of snoRNAs in melanoma patients

Melanoma is a melanocyte-derived malignant cancer and is known for its early metastasis and high mortality rates. It is a highly cutaneous tumour disease that could be related to the abnormal immune microenvironment, and the identification of reliable diagnostic and prognostic markers is crucial for improving patient outcomes. In the search for biomarkers, various types of RNAs have been discovered and recognized as reliable prognostic markers. Among these, small nucleolar RNAs (snoRNAs) have emerged as a promising avenue for studying early diagnosis and prognostic markers in tumours due to their widespread presence in tissues, tumour specificity and stability. In our study, we analysed snoRNAs data from melanoma samples in the TCGA-SKCM cohort and developed a prognostic model comprising 12 snoRNAs (SNORD9, SNORA31, SNORD14E, SNORA14A, SNORA5A, SNORD83A, SNORA75, AL096855, AC007684, SNORD14A, SNORA65 and AC004839). This model exhibited unique prognostic accuracy and demonstrated a significant correlation with the immune infiltration tumour microenvironment. Additionally, analysis of the GSE213145 dataset, which explored the sensitivity and resistance of immune checkpoint inhibitors, further supported the potential of snoRNAs as prognostic markers for immunotherapy. Overall, our study contributes reliable prognostic and immune-related biomarkers for melanoma patients. These findings can offer valuable insights for the future discovery of novel melanoma treatment strategies and hold promise for improving clinical outcomes in melanoma patients.

Luciferase Expressing Preclinical Model Systems Representing the Different Molecular Subtypes of Colorectal Cancer

Colorectal cancer (CRC) is a heterogeneous disease. More insight into the biological diversity of CRC is needed to improve therapeutic outcomes. Established CRC cell lines are frequently used and were shown to be representative models of the main subtypes of CRC at the genomic and transcriptomic level. In the present work, we established stable, luciferase expressing derivatives from 10 well-established CRC cell lines, generated spheroids and subcutaneous xenograft tumors in nude mice, and performed comparative characterization of these model systems. Transcriptomic analyses revealed the close relation of cell lines with their derived spheroids and xenograft tumors. The preclinical model systems clustered with patient tumor samples when compared to normal tissue thereby confirming that cell-line-based tumor models retain specific characteristics of primary tumors. Xenografts showed different differentiation patterns and bioluminescence imaging revealed metastatic spread to the lungs. In addition, the models were classified according to the CMS classification system, with further sub-classification according to the recently identified two intrinsic epithelial tumor cell states of CRC, iCMS2 and iCMS3. The combined data showed that regarding primary tumor characteristics, 3D-spheroid cultures resemble xenografts more closely than 2D-cultured cells do. Furthermore, we set up a bioluminescence-based spheroid cytotoxicity assay in order to be able to perform dose-response relationship studies in analogy to typical monolayer assays. Applying the established assay, we studied the efficacy of oxaliplatin. Seven of the ten used cell lines showed a significant reduction in the response to oxaliplatin in the 3D-spheroid model compared to the 2D-monolayer model. Therapy studies in selected xenograft models confirmed the response or lack of response to oxaliplatin treatment. Analyses of differentially expressed genes in these models identified CAV1 as a possible marker of oxaliplatin resistance. In conclusion, we established a combined 2D/3D, in vitro/in vivo model system representing the heterogeneity of CRC, which can be used in preclinical research applications.

Midsize noncoding RNAs in cancers: a new division that clarifies the world of noncoding RNA or an unnecessary chaos?

Most of the human genome is made out of noncoding RNAs (ncRNAs). These ncRNAs do not code for proteins but carry a vast number of important functions in human cells such as: modification and processing other RNAs (tRNAs, rRNAs, snRNAs, snoRNAs, miRNAs), help in the synthesis of ribosome proteins, initiation of DNA replication, regulation of transcription, processing of pre-messenger mRNA during its maturation and much more. The ncRNAs also have a significant impact on many events that occur during carcinogenesis in cancer cells, such as: regulation of cell survival, cellular signaling, apoptosis, proliferation or even influencing the metastasis process. The ncRNAs may be divided based on their length, into short and long, where 200 nucleotides is the "magic" border. However, a new division was proposed, suggesting the creation of the additional group called midsize noncoding RNAs, with the length ranging from 50-400 nucleotides. This new group may include: transfer RNA (tRNA), small nuclear RNAs (snRNAs) with 7SK and 7SL, small nucleolar RNAs (snoRNAs), small Cajal body-specific RNAs (scaRNAs) and YRNAs. In this review their structure, biogenesis, function and influence on carcinogenesis process will be evaluated. What is more, a question will be answered of whether this new division is a necessity that clears current knowledge or just creates an additional misunderstanding in the ncRNA world?

Identification of Small Nucleolar RNA SNORD60 as a Potential Biomarker and Its Clinical Significance in Lung Adenocarcinoma

Non-small-cell lung cancer (NSCLC) is the leading cause of cancer deaths in the world and often diagnosed at an advanced stage, so it is urgent to explore the pathogenesis and new diagnostic biomarkers. Accumulated evidences suggested that small nucleolar RNAs (snoRNAs) played a key role in the development and progression of NSCLC. To examine differential expression snoRNA profile and identify snoRNAs with clinical significance in lung adenocarcinoma (LUAD), The Cancer Genome Atlas (TCGA) LUAD RNA sequencing dataset was used to investigate differential expression snoRNA signatures and compared with snoRNA PCR array analysis in pair-matched LUAD tissues. The diagnostic ability of SONRD60 was assessed using a receiver operating characteristic (ROC) curve. The Kaplan-Meier method was used to plot survival curves. Univariate and multivariate Cox regression analyses were used to investigate the prognostic effect of SNORD60 expression on LUAD. The results showed that SNORD60 was a significantly upregulated snoRNA after intersection analysis in LUAD cases. SNORD60 has 74.2% sensitivity and 75.3% specificity for the diagnosis of LUAD. Increased SNORD60 expression was linked with lymph node metastases and the TNM stage (P < 0.05). Pathological T category and lymph node metastases were independent prognostic factors for overall survival in a multivariate Cox regression study. Our findings demonstrated that SNORD60, a small nucleolar RNA, has an oncogenic function in LUAD and might be used as a new early diagnostic biomarker for LUAD.

Comprehensive analysis of molecular mechanism and a novel prognostic signature based on small nuclear RNA biomarkers in gastric cancer patients

Small nuclear RNAs (snRNAs) are rarely reported in cancer. This study is based on The Cancer Genome Atlas genome-wide data set to explore the prognostic value and molecular mechanism of snRNAs in gastric cancer (GC). Gene ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis were used to explore the molecular mechanism of snRNAs. A total of 351 patients were included in the survival analysis, and 14 prognostic snRNAs were identified using multivariate survival analysis. We constructed a prognostic signature containing nine snRNAs, which can signally classify patients into high- and low-risk phenotypes (adjusted P < 0.0001, hazard ratio = 2.671, 95% confidence interval = 1.850–3.858). Combining the molecular mechanisms obtained by the three functional enrichment approaches, we concluded that this prognostic signature snRNAs participated in classical tumor-related signaling pathways, including Notch, PI3K, toll-like receptor, etc.; cell adhesion; cell cycle; cell proliferation; and other biological processes that affect the biological phenotype of cancer cells. We also found significant downregulation of the abundance of immune cell infiltrates and immune microenvironment scores for high-risk phenotypes of GC patients. In conclusion, this study has identified 14 prognostic snRNAs signally associated with GC overall survival and also constructed a novel prognostic signature containing nine prognostic snRNAs.