Identification of MFI2-AS1, a Novel Pivotal lncRNA for Prognosis of Stage III/IV Colorectal Cancer

Construction and validation of a deep learning prognostic model based on digital pathology images of stage III colorectal cancer

BACKGROUND

TNM staging is the main reference standard for prognostic prediction of colorectal cancer (CRC), but the prognosis heterogeneity of patients with the same stage is still large. This study aimed to classify the tumor microenvironment of patients with stage III CRC and quantify the classified tumor tissues based on deep learning to explore the prognostic value of the developed tumor risk signature (TRS).

METHODS

A tissue classification model was developed to identify nine tissues (adipose, background, debris, lymphocytes, mucus, smooth muscle, normal mucosa, stroma, and tumor) in whole-slide images (WSIs) of stage III CRC patients. This model was used to extract tumor tissues from WSIs of 265 stage III CRC patients from The Cancer Genome Atlas and 70 stage III CRC patients from the Sixth Affiliated Hospital of Sun Yat-sen University. We used three different deep learning models for tumor feature extraction and applied a Cox model to establish the TRS. Survival analysis was conducted to explore the prognostic performance of TRS.

RESULTS

The tissue classification model achieved 94.4 % accuracy in identifying nine tissue types. The TRS showed a Harrell's concordance index of 0.736, 0.716, and 0.711 in the internal training, internal validation, and external validation sets. Survival analysis showed that TRS had significant predictive ability (hazard ratio: 3.632, p = 0.03) for prognostic prediction.

CONCLUSION

The TRS is an independent and significant prognostic factor for PFS of stage III CRC patients and it contributes to risk stratification of patients with different clinical stages.

Exploring the Key Signaling Pathways and ncRNAs in Colorectal Cancer

Colorectal cancer (CRC) is the third most prevalent cancer to be diagnosed, and it has a substantial mortality rate. Despite numerous studies being conducted on CRC, it remains a significant health concern. The disease-free survival rates notably decrease as CRC progresses, emphasizing the urgency for effective diagnostic and therapeutic approaches. CRC development is caused by environmental factors, which mostly lead to the disruption of signaling pathways. Among these pathways, the Wingless/Integrated (Wnt) signaling pathway, Phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/AKT/mTOR) signaling pathway, Mitogen-Activated Protein Kinase (MAPK) signaling pathway, Transforming Growth Factor-β (TGF-β) signaling pathway, and p53 signaling pathway are considered to be important. These signaling pathways are also regulated by non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs). They have emerged as crucial regulators of gene expression in CRC by changing their expression levels. The altered expression patterns of these ncRNAs have been implicated in CRC progression and development, suggesting their potential as diagnostic and therapeutic targets. This review provides an overview of the five key signaling pathways and regulation of ncRNAs involved in CRC pathogenesis that are studied to identify promising avenues for diagnosis and treatment strategies.

Somatic Copy Number Alterations in Colorectal Cancer Lead to a Differentially Expressed ceRNA Network (ceRNet)

Colorectal cancer (CRC) represents the second deadliest malignancy worldwide. Around 75% of CRC patients exhibit high levels of chromosome instability that result in the accumulation of somatic copy number alterations. These alterations are associated with the amplification of oncogenes and deletion of tumor-ppressor genes and contribute to the tumoral phenotype in different malignancies. Even though this relationship is well known, much remains to be investigated regarding the effect of said alterations in long non-coding RNAs (lncRNAs) and, in turn, the impact these alterations have on the tumor phenotype. The present study aimed to evaluate the role of differentially expressed lncRNAs coded in regions with copy number alterations in colorectal cancer patient samples. We downloaded RNA-seq files of the Colorectal Adenocarcinoma Project from the The Cancer Genome Atlas (TCGA) repository (285 sequenced tumor tissues and 41 non-tumor tissues), evaluated differential expression, and mapped them over genome sequencing data with regions presenting copy number alterations. We obtained 78 differentially expressed (LFC > 1|< -1, padj < 0.05) lncRNAs, 410 miRNAs, and 5028 mRNAs and constructed a competing endogenous RNA (ceRNA) network, predicting significant lncRNA-miRNA-mRNA interactions. Said network consisted of 30 lncRNAs, 19 miRNAs, and 77 mRNAs. To understand the role that our ceRNA network played, we performed KEGG and GO analysis and found several oncogenic and anti-oncogenic processes enriched by the molecular players in our network. Finally, to evaluate the clinical relevance of the lncRNA expression, we performed survival analysis and found that C5orf64, HOTAIR, and RRN3P3 correlated with overall patient survival. Our results showed that lncRNAs coded in regions affected by SCNAs form a complex gene regulatory network in CCR.

Role of Non-Coding RNAs in TGF-β Signalling in Glioma

Brain tumours and Gliomas, in particular, are among the primary causes of cancer mortality worldwide. Glioma diagnosis and therapy have not significantly improved despite decades of efforts. Autocrine TGF-β signalling promotes glioma proliferation, invasion, epithelial-to-mesenchymal transition (EMT), and drug resistance. Non-coding RNAs such as miRNA, lncRNA, and circRNAs have emerged as critical transcriptional and post-transcriptional regulators of TGF-β pathway components in glioma. Here, we summarize the complex regulatory network among regulatory ncRNAs and TGF-β pathway during Glioma pathogenesis and discuss their role as potential therapeutic targets for Gliomas.

LncRNAs in colorectal cancer: Biomarkers to therapeutic targets

Colorectal cancer (CRC) is the third leading cause of cancer-related death in men and women worldwide. As early detection is associated with lower mortality, novel biomarkers are urgently needed for timely diagnosis and appropriate management of patients to achieve the best therapeutic response. Long noncoding RNAs (lncRNAs) have been reported to play essential roles in CRC progression. Accordingly, the regulatory roles of lncRNAs should be better understood in general and for identifying diagnostic, prognostic and predictive biomarkers in CRC specifically. In this review, the latest advances on the potential diagnostic and prognostic lncRNAs as biomarkers in CRC samples were highlighted, Current knowledge on dysregulated lncRNAs and their potential molecular mechanisms were summarized. The potential therapeutic implications and challenges for future and ongoing research in the field were also discussed. Finally, novel insights on the underlying mechanisms of lncRNAs were examined as to their potential role as biomarkers and therapeutic targets in CRC. This review may be used to design future studies and advanced investigations on lncRNAs as biomarkers for the diagnosis, prognosis and therapy in CRC.

Establishment and validation of a redox-related long non-coding RNAs prognostic signature in head and neck squamous cell carcinoma

Reduction and oxidation (redox) reactions occur in living organisms as part of normal cellular metabolism. Here, we established a novel redox-related long non-coding RNAs (rrlncRNAs) signature to predict the prognosis and therapeutic response in Head and neck squamous cell carcinoma (HNSCC). The expression profile and clinical information were obtained from the TCGA project. In total, 10 differently expressed rrlncRNAs associated with prognosis were identified and involved in a prognostic risk score signature by the least absolute shrinkage and selection operator penalized Cox analysis. The area under the receiver operating characteristic curves of the survival rates predicted by the rrlncRNAs signature over one, two, and three years were found to be 0.651, 0.670, and 0.679. Following the completion of the Kaplan-Meier survival study, we discovered that the lower-risk cohort exhibited a much longer overall survival period in contrast with the higher-risk cohort. Univariate and multivariable Cox regression analyses demonstrated that the risk score independently served as a significant predictive factor. GO annotation and KEGG pathway analyses illustrated that the rrlncRNAs signature was strongly associated with immune-related functions as well as signaling pathways. The tumor-infiltrating immune cells, tumor microenvironment, immune-related functions, HLA gene family expression, immune checkpoint genes expression, and somatic variants differed substantially between the low- and high-risk cohorts. Moreover, patients in low-risk group were predicted to present a favorable immunotherapy responsiveness, while in contrast, the high-risk group patients might have a stronger sensitivity to "docetaxel". According to our findings, the rrlncRNAs signature showed an excellent prognosis predictive value and might indicate therapeutic response to immunotherapy in HNSCC.

lncRNA2919 Suppresses Rabbit Dermal Papilla Cell Proliferation via trans-Regulatory Actions

Hair follicles (HFs) are complex organs that grow cyclically during mammals' growth and development. Long non-coding RNAs (lncRNAs) cannot be translated into proteins and play crucial roles in many biological processes. In our previous study, candidate lncRNAs associated with HF cyclic regeneration were screened, and we identified that the novel lncRNA, lncRNA2919, was significantly expressed during catagen. Here, we identified that lncRNA2919 has no coding potentiality and is highly expressed in the cell nucleus, and downregulates HF growth and development-related genes, inhibits cell proliferation, and promotes cell apoptosis in rabbit dermal papilla cells. lncRNA2919 recruits STAT1 to form a compound. As a key transcription factor, STAT1 regulates the transcriptional expression of KRTAP11-1. Our study revealed that lncRNA2919 is involved in HF cyclic regeneration through the trans-regulatory lncRNA2919-STAT1-KRTAP11-1 axis. This study elucidates the mechanism through which lncRNA2919 regulates HF growth and development and the role of lncRNA2919 as a new therapeutic target in animal wool production and human hair-related disease treatment.

Potentials of long non-coding RNAs as biomarkers of colorectal cancer

Colorectal cancer (CRC) is the third most common malignant tumor worldwide and the fourth major cause of cancer-related death, with high morbidity and increased mortality year by year. Although significant progress has been made in the therapy strategies for CRC, the great difficulty in early diagnosis, feeble susceptibility to radiotherapy and chemotherapy, and high recurrence rates have reduced therapeutic efficacy resulting in poor prognosis. Therefore, it is urgent to understand the pathogenesis of CRC and unravel novel biomarkers to improve the early diagnosis, treatment and prediction of CRC recurrence. Long non-coding RNAs (lncRNAs) are non-coding RNAs with a length of more than 200 nucleotides, which are abnormally expressed in tumor tissues and cell lines, activating or inhibiting specific genes through multiple mechanisms including transcription and translation. A growing number of studies have shown that lncRNAs are important regulators of microRNAs (miRNAs, miRs) expression in CRC and may be promising biomarkers and potential therapeutic targets in the research field of CRC. This review mainly summarizes the potential application value of lncRNAs as novel biomarkers in CRC diagnosis, radiotherapy, chemotherapy and prognosis. Additionally, the significance of lncRNA SNHGs family and lncRNA-miRNA networks in regulating the occurrence and development of CRC is mentioned, aiming to provide some insights for understanding the pathogenesis of CRC and developing new diagnostic and therapeutic strategies.

LINC01210 promotes malignant phenotypes of colorectal cancer through epigenetically upregulating SRSF3

Long non-coding RNAs (lncRNAs) have been linked to tumorigenesis. However, the role of LINC01210 in colorectal cancer (CRC) remains unclear. Relative levels of LINC01210 in CRC tissues and adjacent tissues were determined. Proliferative, migratory, and invasive abilities were examined in HCT116 cells and LoVo cells after silencing or overexpressing LINC01210. The interaction between LINC01210 and SRSF3 was explored by ChIP-PCR. Upregulated LINC01210 was associated with metastasis and advanced stage of CRC. Silencing LINC01210 attenuated proliferative, migratory, and invasive abilities in LoVo cells, while overexpressing LINC01210 promoted proliferative, migratory, and invasive abilities in HCT116 cells. Mechanism study revealed that LINC01210 increased the expression of SRSF3 by recruiting mixed lineage leukaemia protein-1, which upregulated the trimethylation of H3K4 me3 on SRSF3 promoter. Silencing SRSF3 reversed the effects of LINC01210 on CRC cells. In conclusions, LINC01210 accelerated proliferation and invasion in CRC cells through epigenetically upregulating SRSF3, and may be a potential therapeutic target for CRC treatment.

Identifying the hub genes in non-small cell lung cancer by integrated bioinformatics methods and analyzing the prognostic values

BACKGROUND

Lung cancer, a malignant tumor, has the highest mortality and second most common morbidity worldwide. Non-small cell lung cancer (NSCLC) is the most common pathological subtype of lung cancer. This study aimed to identify the gene signature associated with the NSCLC prognosis using bioinformatics analysis.

MATERIALS AND METHODS

The dataset GSE103512 was utilized to construct co-expression networks using weighted gene co-expression network analysis (WGCNA). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed using Database for Annotation, Visualization, and Integrated Discovery. Gene set enrichment analysis was conducted to ascertain the function of the hub genes more accurately. The relationship between the hub genes and immune infiltration was investigated using a single sample gene set enrichment analysis. Hub genes were screened and validated by other datasets and online websites.

RESULTS

The results of WGCNA demonstrated that the blue module was most significantly related to tumor progression in NSCLC. Functional enrichment analysis showed that the blue module was associated with DNA replication, cell division, mitotic nuclear division, and cell cycle. A total of five hub genes (RFC5, UBE2S, CHAF1A, FANCI, and TMEM194A) were chosen to be identified and validated at transcriptional and translational levels. Receiver operating characteristic curve verified that the mRNA levels of these five genes can excellently discriminate between normal and tumor tissues. Survival analysis was also performed. Additionally, the protein levels of these five genes were also significantly different between tumor and normal tissues. Immune infiltration analysis showed that the expression levels of the hub genes had a negative correlation with the infiltration levels of many cells related to innate immune response, antigen-presenting process, humoral immune response, or T cell-mediated immune responses.

CONCLUSIONS

We identified five hub genes associated with the NSCLC tumorigenesis. NSCLC patients with higher expressions of each hub gene had a worse prognosis than those with lower expressions. Moreover, the hub genes might serve as biomarkers and therapeutic targets for precise diagnosis, target therapy, and immunotherapy of NSCLC in the future.

LncRNA MCF2L-AS1 aggravates the malignant development of colorectal cancer via targeting miR-105-5p/RAB22A axis

BACKGROUND

Colorectal cancer (CRC) represents one of the major malignant cancers in the world. It has been demonstrated that long non-coding RNAs (lncRNAs) can cause great influences on various human cancers. Though MCF.2 cell line derived transforming sequence like antisense RNA 1 (MCF2L-AS1) and its carcinogenic effect in CRC has been elucidated by several previous researches, the underlying mechanism remains unknown.

AIM

We aimed at exploring the function and regulatory mechanism of MCF2L-AS1 in CRC.

METHODS

MCF2L-AS1 expression in CRC cells was tested via RT-qPCR assay. The effects of MCF2L-AS1 on the biological properties of CRC cells were testified through functional experiments. The molecular mechanism of MCF2L-AS1 was verified through mechanism experiments.

RESULTS

MCF2L-AS1 was highly expressed in CRC cells, and it could enhance the proliferation, migration, invasion and epithelial-mesenchymal transition (EMT) process of CRC cells. MiR-105-5p was sponged by MCF2L-AS1 in CRC cells and Ras-related protein Rab-22A (RAB22A) was verified to be the downstream target of miR-105-5p. It was verified through rescue assays that RAB22A overexpression or miR-105-5p silencing could reverse the repressive impact of MCF2L-AS1 silencing on CRC progression.

CONCLUSION

MCF2L-AS1 accelerated the malignant development of CRC cells by targeting the miR-105-5p/RAB22A axis.

Identification of hub genes and functional modules in colon adenocarcinoma based on public databases by bioinformatics analysis

Background

Colon adenocarcinoma (COAD) is one of the most common cancers in the world. Although an extensive effort has been made to elucidate its pathogenesis, the underlying molecular mechanisms and genetic characteristics remain elusive.

Methods

In this study, protein-coding transcript expression profiles of COAD were downloaded from the Cancer RNA-Seq Nexus (CRN) database. They were then integrated to identify the overlapping transcripts expressed in every COAD RNA sequencing (RNA-seq) subset. The functional annotation of these overlapping genes (OLGs) involved noting their biological process (BP), cellular components (CC), molecular function (MF) for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway in the Database for Annotation, Visualization and Integrated Discovery (DAVID). Protein-protein interaction (PPI) networks were then constructed and analyzed using Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and Cytoscape 3.8.2.

Results

A total of 10 hub genes and 3 functional modules were screened by the plugin cytoHubba and MCODE, respectively. The plugin ClueGO and DAVID were used for the functional enrichment analyses of both hub genes and modules. The expression of hub genes was verified through the gene expression profiling interactive analysis (GEPIA) database. Survival analysis of the hub genes revealed that low expressions of ADCY5, GNG2, and PTPRC were significantly associated with an improved COAD prognosis. Furthermore, the expression level of ADCY5 in stages I/II was lower than that in stages III/IV, which seems to explain why the low expression of ADCY5 results in a better prognosis.

Conclusions

The identification of hub genes, functional modules, and pathways have the potential to improve our understanding of the causes and underlying molecular events of COAD. The hub gene ADCY5 could also be a prognostic monitoring indicator or therapeutic target in the treatment of COAD.

Separating the Wheat from the Chaff: The Use of Upstream Regulator Analysis to Identify True Differential Expression of Single Genes within Transcriptomic Datasets

The development of DNA microarray and RNA-sequencing technology has led to an explosion in the generation of transcriptomic differential expression data under a wide range of biologic systems including those recapitulating the monogenic muscular dystrophies. Data generation has increased exponentially due in large part to new platforms, improved cost-effectiveness, and processing speed. However, reproducibility and thus reliability of data remain a central issue, particularly when resource constraints limit experiments to single replicates. This was observed firsthand in a recent rare disease drug repurposing project involving RNA-seq-based transcriptomic profiling of primary cerebrocortical cultures incubated with clinic-ready blood-brain penetrant drugs. Given the low validation rates obtained for single differential expression genes, alternative approaches to identify with greater confidence genes that were truly differentially expressed in our dataset were explored. Here we outline a method for differential expression data analysis in the context of drug repurposing for rare diseases that incorporates the statistical rigour of the multigene analysis to bring greater predictive power in assessing individual gene modulation. Ingenuity Pathway Analysis upstream regulator analysis was applied to the differentially expressed genes from the Care4Rare Neuron Drug Screen transcriptomic database to identify three distinct signaling networks each perturbed by a different drug and involving a central upstream modulating protein: levothyroxine (DIO3), hydroxyurea (FOXM1), dexamethasone (PPARD). Differential expression of upstream regulator network related genes was next assessed in in vitro and in vivo systems by qPCR, revealing 5× and 10× increases in validation rates, respectively, when compared with our previous experience with individual genes in the dataset not associated with a network. The Ingenuity Pathway Analysis based gene prioritization may increase the predictive value of drug-gene interactions, especially in the context of assessing single-gene modulation in single-replicate experiments.

Four-lncRNA immune prognostic signature for triple-negative breast cancer Running title: Immune lncRNAs predict prognosis of TNBC

OBJECTIVE

We aimed to explore key immune-related long non-coding RNAs (lncRNAs) and their effect in predicting of prognosis of triple-negative breast cancer (TNBC).

METHODS

Four datasets of TNBC were downloaded from TCGA and GEO databases. ImmPort database was utilized to acquire immune-related mRNAs. Single sample gene set enrichment analysis (ssGSEA) and correlation analysis were utilized to screen immune-related lncRNAs. Univariate and multivariate Cox regression analyses were utilized to screen independent prognostic lncRNAs to establish prognostic risk model, and the model was evaluated by survival analysis and nomogram. Differential functions and immune cells infiltration in high and low risk group were analyzed by Gene set variation analysis and ssGSEA. Finally, competitive endogenous RNAs was constructed.

RESULTS

We revealed 62 immune-related lncRNAs, of which four lncRNAs (RP11-890B15.3, RP11-1024P17.1, MFI2-AS1 and RP11-180N14.1) had independent prognostic value. These four lncRNAs-based prognostic risk model could stratify the TNBC patients into high and low risk groups, and patients with high risk displayed unfavorable outcomes. Nomogram indicated that the prognostic model could indicate TNBC patients survival very well. We further found that high risk group showed significantly enriched immune response to tumor cell, humoral immune response and high infiltrating abundance of regulatory T cell, Type 2 T helper cell, eosinophil, etc. LncRNAs RP11-180N14.1, RP11-1024P17.1 and RP11-890B15.3 regulated more mRNAs by targeting various miRNAs. While MFI2-AS1 regulated three mRNAs by sponging miR-3150a-3p.

CONCLUSION

These four lncRNAs were prognostic biomarkers and could be possible therapeutic targets in TNBC.

Comprehensive analysis of lncRNA-associated ceRNA network reveals the novel potential of lncRNA, miRNA and mRNA biomarkers in human rectosigmoid junction cancer

Although accumulating evidence has confirmed the potential biological functions of long non-coding RNAs (lncRNAs) as competitive endogenous RNAs (ceRNAs) in colorectal tumorigenesis and progression, few studies have focused on rectosigmoid junction cancer. In the present study, a comprehensive analysis was conducted to explore lncRNA-mediated ceRNA implications and their potential value for prognosis. lncRNA, microRNA (miR/miRNA) and mRNA expression profiles were downloaded from The Cancer Genome Atlas database. Subsequently, a lncRNA-miRNA-mRNA regulatory network was constructed to evaluate the functions of these differentially expressed genes on overall survival (OS) for rectosigmoid junction cancer. As a result, a rectosigmoid junction cancer-specific ceRNA network was successfully constructed with 7 differentially expressed (DE)lncRNAs, 16 DEmiRNAs and 71 DEmRNAs. Among the network, one DElncRNA (small nucleolar RNA host gene 20) and three mRNAs (sodium- and chloride-dependent taurine transporter, fibroblast growth factor 13 and tubulin polyglutamylase TTLL7) were significantly associated with OS (P<0.05). Additionally, two lncRNAs (KCNQ1OT1 and MIR17HG) interacted with most of the DEmiRNAs. Notably, two top-ranked miRNAs (hsa-miR-374a-5p and hsa-miR-374b-5p) associated networks were identified to be markedly associated with the pathogenesis. Furthermore, four DEmRNAs (caveolin-1, MET, filamin-A and AKT3) were enriched in the Kyoto Encylopedia of Gene and Genomes pathway analysis, as well as being included in the ceRNA network. In summary, the present results revealed that a specific lncRNA-miRNA-mRNA network was associated with rectosigmoid junction cancer, providing several molecules that may be used as novel prognostic biomarkers and therapeutic targets.