A long non-coding RNA signature for predicting survival in patients with colorectal cancer

Single-Cell Transcriptomic Approaches for Decoding Non-Coding RNA Mechanisms in Colorectal Cancer

Non-coding RNAs (ncRNAs) play crucial roles in colorectal cancer (CRC) development and progression. Recent developments in single-cell transcriptome profiling methods have revealed surprising levels of expression variability among seemingly homogeneous cells, suggesting the existence of many more cell types than previously estimated. This review synthesizes recent advances in ncRNA research in CRC, emphasizing single-cell bioinformatics approaches for their analysis. We explore computational methods and tools used for ncRNA identification, characterization, and functional prediction in CRC, with a focus on single-cell RNA sequencing (scRNA-seq) data. The review highlights key bioinformatics strategies, including sequence-based and structure-based approaches, machine learning applications, and multi-omics data integration. We discuss how these computational techniques can be applied to analyze differential expression, perform functional enrichment, and construct regulatory networks involving ncRNAs in CRC. Additionally, we examine the role of bioinformatics in leveraging ncRNAs as diagnostic and prognostic biomarkers for CRC. We also discuss recent scRNA-seq studies revealing ncRNA heterogeneity in CRC. This review aims to provide a comprehensive overview of the current state of single-cell bioinformatics in ncRNA CRC research and outline future directions in this rapidly evolving field, emphasizing the integration of computational approaches with experimental validation to advance our understanding of ncRNA biology in CRC.

Four long noncoding RNAs act as biomarkers in lung adenocarcinoma

Introduction

Lung adenocarcinoma (LUAD) is currently one of the most common malignant tumors worldwide. However, there is a lack of long noncoding RNA (lncRNA)-based effective markers for predicting the prognosis of LUAD patients. We identified four lncRNAs that can effectively predict the prognosis of LUAD patients.

Methods

We used data gene expression profile for 446 patients from The Cancer Genome Atlas database. The patients were randomly divided into a training set and a test set. Significant lncRNAs were identified by univariate regression. Then, multivariate regression was used to identify lncRNAs significantly associated with the survival rate. We constructed four-lncRNA risk formulas for LUAD patients and divided patients into high-risk and low-risk groups. Identified lncRNAs subsequently verified in the test set, and the clinical independence of the lncRNA model was evaluated by stratified analysis. Then mutated genes were identified in the high-risk and low-risk groups. Enrichment analysis was used to determine the relationships between lncRNAs and co-expressed genes. Finally, the accuracy of the model was verified using external database.

Results

A four-lncRNA signature (AC018629.1, AC122134.1, AC119424.1, and AL138789.1) has been verified in the training and test sets to be significantly associated with the overall survival of LUAD patients.

Conclusions

The present study demonstrated that identified four-lncRNA signature can be used as an independent prognostic biomarker for the prediction of survival of LUAD patients.

The Construction and Comprehensive Prognostic Analysis of the LncRNA-Associated Competitive Endogenous RNAs Network in Colorectal Cancer

Competing endogenous RNAs (ceRNAs) are a newly proposed RNA interaction mechanism that has been associated with the tumorigenesis, metastasis, diagnosis, and predicting survival of various cancers. In this study, we constructed a ceRNA network in colorectal cancer (CRC). Then, we sought to develop and validate a composite clinicopathologic–genomic nomogram using The Cancer Genome Atlas (TCGA) database. To construct the ceRNA network in CRC, we analyzed the mRNAseq, miRNAseq data, and clinical information from TCGA database. LncRNA, miRNA, and mRNA signatures were identified to construct risk score as independent indicators of the prognostic value in CRC patients. A composite clinicopathologic–genomic nomogram was developed to predict the overall survival (OS). One hundred sixty-one CRC-specific lncRNAs, 97 miRNAs, and 161 mRNAs were identified to construct the ceRNA network. Multivariate Cox proportional hazards regression analysis indicated that nine-lncRNA signatures, eight-miRNA signatures, and five-mRNA signatures showed a significant prognostic value for CRC. Furthermore, a clinicopathologic–genomic nomogram was constructed in the primary cohort, which performed well in both the primary and validation sets. This study presents a nomogram that incorporates the CRC-specific ceRNA expression profile, clinical features, and pathological factors, which demonstrate its excellent differentiation and risk stratification in predicting OS in CRC patients.

A stroma-related lncRNA panel for predicting recurrence and adjuvant chemotherapy benefit in patients with early-stage colon cancer

The heterogeneity in prognoses and chemotherapeutic responses of colon cancer patients with similar clinical features emphasized the necessity for new biomarkers that help to improve the survival prediction and tailor therapies more rationally and precisely. In the present study, we established a stroma‐related lncRNA signature (SLS) based on 52 lncRNAs to comprehensively predict clinical outcome. The SLS model could not only distinguish patients with different recurrence and mortality risks through univariate analysis, but also served as an independent factor for relapse‐free and overall survival. Compared with the conventionally used TNM stage system, the SLS model clearly possessed higher predictive accuracy. Moreover, the SLS model also effectively screened chemotherapy‐responsive patients, as only patients in the low‐SLS group could benefit from adjuvant chemotherapy. The following cell infiltration and competing endogenous RNA (ceRNA) network functional analyses further confirmed the association between the SLS model and stromal activation‐related biological processes. Additionally, this study also identified three phenotypically distinct colon cancer subtypes that varied in clinical outcome and chemotherapy benefits. In conclusion, our SLS model may be a significant determinant of survival and chemotherapeutic decision‐making in colon cancer and may have a strong clinical transformation value.

Genome-wide expression profiling in colorectal cancer focusing on lncRNAs in the adenoma-carcinoma transition

BACKGROUND

Long non-coding RNAs (lncRNAs) play a fundamental role in colorectal cancer (CRC) development, however, lncRNA expression profiles in CRC and its precancerous stages remain to be explored. We aimed to study whole genomic lncRNA expression patterns in colorectal adenoma-carcinoma transition and to analyze the underlying functional interactions of aberrantly expressed lncRNAs.

METHODS

LncRNA expression levels of colonic biopsy samples (20 CRCs, 20 adenomas (Ad), 20 healthy controls (N)) were analyzed with Human Transcriptome Array (HTA) 2.0. Expression of a subset of candidates was verified by qRT-PCR and in situ hybridization (ISH) analyses. Furthermore, in silico validation was performed on an independent HTA 2.0, on HGU133Plus 2.0 array data and on the TCGA COAD dataset. MiRNA targets of lncRNAs were predicted with miRCODE and lncBase v2 algorithms and miRNA expression was analyzed on miRNA3.0 Array data. MiRNA-mRNA target prediction was performed using miRWALK and c-Met protein levels were analyzed by immunohistochemistry. Comprehensive lncRNA-mRNA-miRNA co-expression pattern analysis was also performed.

RESULTS

Based on our HTA results, a subset of literature-based CRC-associated lncRNAs showed remarkable expression changes already in precancerous colonic lesions. In both Ad vs. normal and CRC vs. normal comparisons 16 lncRNAs, including downregulated LINC02023, MEG8, AC092834.1, and upregulated CCAT1, CASC19 were identified showing differential expression during early carcinogenesis that persisted until CRC formation (FDR-adjusted p < 0.05). The intersection of CRC vs. N and CRC vs. Ad comparisons defines lncRNAs characteristic of malignancy in colonic tumors, where significant downregulation of LINC01752 and overexpression of UCA1 and PCAT1 were found. Two candidates with the greatest increase in expression in the adenoma-carcinoma transition were further confirmed by qRT-PCR (UCA1, CCAT1) and by ISH (UCA1). In line with aberrant expression of certain lncRNAs in tumors, the expression of miRNA and mRNA targets showed systematic alterations. For example, UCA1 upregulation in CRC samples occurred in parallel with hsa-miR-1 downregulation, accompanied by c-Met target mRNA overexpression (p < 0.05).

CONCLUSION

The defined lncRNA sets may have a regulatory role in the colorectal adenoma-carcinoma transition. A subset of CRC-associated lncRNAs showed significantly differential expression in precancerous samples, raising the possibility of developing adenoma-specific markers for early detection of colonic lesions.

An eight-long non-coding RNA signature as a candidate prognostic biomarker for bladder cancer

Backgroud: Bladder cancer (BLCA) is one of the most fatal types of cancer worldwide. However, there are limited methods for us to provide a prognostic prediction of BLCA patients. Therefore, we aimed at developing a lncRNA signature to improve the prognosis prediction of BLCA.

RESULTS

An eight-lncRNA signature was significantly associated with recurrence free survival in BLCA patients from both discovery and validation groups. Furthermore, genes involved in the signature were enriched in extracellular matrix organization pathway. Finally, functional experiments demonstrated that six out of the eight lncRNAs significantly regulated the invasion of BLCA cells.

METHOD

A total of 343 BLCA patients from The Cancer Genome Atlas (TCGA) were employed and randomly divided into training (n=172) and validating (n=171) groups. The lncRNA expression profiles of BLCA patients were screened and a risk-score formula were created and validated according to the Cox regression analysis. Next, WGCNA method was employed to cluster genes that highly correlated with the risk scores based on the profiling data of TCGA dataset and transwell assay was also performed to further investigate the role of these lncRNAs.

CONCLUSIONS

Our results suggested that the eight-lncRNA signature was a candidate prognostic biomarker for predicting tumor recurrence of patients with BLCA.

Downregulation of the long non‑coding RNA FOXD2‑AS1 inhibits cell proliferation, migration and invasion in osteosarcoma

Increasing amounts of long noncoding RNAs (lncRNAs) have been shown to be involved in the development of cancer. Recently, aberrant expression of the lncRNA forkhead box D2 adjacent opposite strand RNA1 (FOXD2-AS1) has been reported to be involved in the progression of several types of human cancer. However, the function and mechanism of FOXD2-AS1 in osteosarcoma (OS) are currently unclear. The present study aimed to investigate the function and mechanism of FOXD2-AS1 in OS. Firstly, it was revealed that the expression levels of FOXD2-AS1 were significantly upregulated in OS tissues and cells, compared with in adjacent tissues and normal cells, as determined using reverse transcription-quantitative polymerase chain reaction. Notably, the overall survival of patients with relatively high FOXD2-AS1 expression in OS tissues was significantly lower than that of patients with relatively low expression, as determined using Kaplan-Meier analysis. In addition, loss-of-function experiments were performed in vivo and in vitro to study the biological effects of FOXD2-AS1. The SOSP-9607 and U2OS OS cell lines were infected with lentivirus-mediated FOXD2-AS1 short hairpin RNA; subsequently, the alterations in cell phenotype and downstream molecules were evaluated. Knockdown of FOXD2-AS1 inhibited the proliferation, migration and invasion of OS cells. Furthermore, the number of cells in the S phase was significantly decreased, which was consistent with the results of the Cell Counting kit 8 proliferation assay. The expression levels of ribonucleotide reductase regulatory subunit M2 and phosphoglycerate dehydrogenase were decreased, as determined by western blotting, following FOXD2-AS1 knockdown. Finally, in a nude mouse model of tumorigenesis, it was revealed that, when FOXD2-AS1 expression was downregulated, tumor growth was significantly reduced and pulmonary metastatic nodules were markedly reduced. The results of the present study suggested that decreased FOXD2-AS1 expression may inhibit the growth, migration and invasion of tumor cells, and it may regulate downstream gene expression. In conclusion, these findings indicated that FOXD2-AS1 may be used as a potential therapeutic target and early tumor marker for the diagnosis and prognosis of OS.