Identification and Validation of Six Autophagy-related Long Non-coding RNAs as Prognostic Signature in Colorectal Cancer

MIR210HG Accelerates the Progression of Colorectal Cancer and Affects the Function of Colorectal Cancer Cells by Downregulating miR-1226-3p

Background/Aims

Colorectal cancer (CRC) is a widespread cancerous disease with an unfavorable prognosis. MIR210HG appears to have a significant connection with the development of CRC, but the precise regulatory mechanism remains obscure.

Materials and Methods

Quantitative real-time polymerase chain reaction was utilized to determine expression quantities of MIR210HG and miR-1226-3p. The proliferative capacity of CRC cells was measured by cell counting kit-8. The apoptosis rate of cells was examined using flow cytometry. The invasive capability was assessed through the transwell experiment. The targeted regulation of MIR210HG and miR-1226-3p was validated through dual-luciferase reporter gene experiments.

Results

In carcinoma tissues and blood serum of colorectal cancer patients and cell lines, MIR210HG expression was upregulated, while the miR-1226-3p expression was downregulated. MIR210HG had a diagnostic and prognostic value for CRC patients. MIR210HG may target and regulate miR-1226-3p. MIR210HG may have the capacity to augment the vitality and invasion of CRC cells and suppress cell apoptosis, and this influence is counteracted by miR-1226-3p.

Conclusion

lncRNA MIR210HG accelerated the progression of colorectal cancer by controlling miR-1226-3p. lncRNA MIR210HG/miR1226-3p may potentially serve as therapeutic targets for addressing colorectal cancer.

Autophagy-related lncRNAs and exosomal lncRNAs in colorectal cancer: focusing on lncRNA-targeted strategies

Autophagy is a cellular process that involves the degradation and recycling of cellular components, including damaged proteins and organelles. It is an important mechanism for maintaining cellular homeostasis and has been implicated in various diseases, including cancer. Long non-coding RNAs (lncRNAs) are a class of RNA molecules that do not code for proteins but instead play regulatory roles in gene expression. Emerging evidence suggests that lncRNAs can influence autophagy and contribute to the development and progression of colorectal cancer (CRC). Several lncRNAs have been identified as key players in modulating autophagy in CRC. The dysregulation of autophagy and non-coding RNAs (ncRNAs) in CRC suggests a complex interplay between these two factors in the pathogenesis of the disease. Modulating autophagy may sensitize cancer cells to existing therapies or improve the efficacy of new treatment approaches. Additionally, targeting specific lncRNAs involved in autophagy regulation could potentially be used as a therapeutic intervention to inhibit tumor growth, metastasis, and overcome drug resistance in CRC. In this review, a thorough overview is presented, encompassing the functions and underlying mechanisms of autophagy-related lncRNAs in a range of critical areas within tumor biology. These include cell proliferation, apoptosis, migration, invasion, drug resistance, angiogenesis, and radiation resistance.

Navigating the labyrinth of long non-coding RNAs in colorectal cancer: From chemoresistance to autophagy

Long non-coding RNAs (lncRNAs), with transcript lengths exceeding 200 nucleotides and little or no protein-coding capacity, have been found to impact colorectal cancer (CRC) through various biological processes. LncRNA expression can regulate autophagy, which plays dual roles in the initiation and progression of cancers, including CRC. Abnormal expression of lncRNAs is associated with the emergence of chemoresistance. Moreover, it has been confirmed that targeting autophagy through lncRNA regulation could be a viable approach for combating chemoresistance. Two recent studies titled "Human β-defensin-1 affects the mammalian target of rapamycin pathway and autophagy in colon cancer cells through long non-coding RNA TCONS_00014506" and "Upregulated lncRNA PRNT promotes progression and oxaliplatin resistance of colorectal cancer cells by regulating HIPK2 transcription" revealed novel insights into lncRNAs associated with autophagy and oxaliplatin resistance in CRC, respectively. In this editorial, we particularly focus on the regulatory role of lncRNAs in CRC-related autophagy and chemoresistance since the regulation of chemotherapeutic sensitivity by intervening with the lncRNAs involved in the autophagy process has become a promising new approach for cancer treatment.

Long non-coding RNAs in ferroptosis and cuproptosis impact on prognosis and treatment in hepatocellular carcinoma

Ferroptosis and cuproptosis are recently discovered forms of cell death that have gained interest as potential cancer treatments, particularly for hepatocellular carcinoma. Long non-coding RNAs (lncRNAs) influence cancer cell activity by interacting with various nucleic acids and proteins. However, the role of ferroptosis and cuproptosis-related lncRNAs (FCRLs) in cancer remains underexplored. Ferroptosis and cuproptosis scores for each sample were assessed using Gene Set Variation Analysis (GSVA). Weighted correlation network analysis identified the FCRLs most relevant to our study. A risk model based on FCRLs was developed to categorize patients into high-risk and low-risk groups. We then compared overall survival (OS), tumor immune microenvironment, and clinical characteristics between these groups. The IPS score and ImmuCellAI webpage were used to predict the association between FCRL-related signatures and immunotherapy response. Finally, we validated the accuracy of FCRLs in hepatocellular carcinoma cell lines using induction agents (elesclomol and erastin). Patients in different risk subgroups showed significant differences in OS, immune cell infiltration, pathway activity, and clinical characteristics. Cellular assays revealed significant changes in the expression of AC019080.5, AC145207.5, MIR210HG, and LINC01063 in HCC cell lines following the addition of ferroptosis and cuproptosis inducers. We created a signature of four FCRLs that accurately predicted survival in HCC patients, laid the foundation for basic research related to ferroptosis and cuproptosis in hepatocellular carcinoma, and provided therapeutic recommendations for HCC patients.

Biomarker Profile of Colorectal Cancer: Current Findings and Future Perspective

OBJECTIVE

Breakthroughs in omics technology have led to a deeper understanding of the fundamental molecular changes that play a critical role in the development and progression of cancer. This review delves into the hidden molecular drivers of colorectal cancer (CRC), offering potential for clinical translation through novel biomarkers and personalized therapies.

METHODS

We summarizes recent studies utilizing various omics approaches, including genomics, transcriptomics, proteomics, epigenomics, metabolomics and data integration with computational algorithms, to investigate CRC.

RESULTS

Integrating multi-omics data in colorectal cancer research unlocks hidden biological insights, revealing new pathways and mechanisms. This powerful approach not only identifies potential biomarkers for personalized prognosis, diagnosis, and treatment, but also predicts patient response to specific therapies, while computational tools illuminate the landscape by deciphering complex datasets.

CONCLUSIONS

Future research should prioritize validating promising biomarkers and seamlessly translating them into clinical practice, ultimately propelling personalized CRC management to new heights.

Unlocking the predictive potential of long non-coding RNAs: a machine learning approach for precise cancer patient prognosis

The intricate web of cancer biology is governed by the active participation of long non-coding RNAs (lncRNAs), playing crucial roles in cancer cells' proliferation, migration, and drug resistance. Pioneering research driven by machine learning algorithms has unveiled the profound ability of specific combinations of lncRNAs to predict the prognosis of cancer patients. These findings highlight the transformative potential of lncRNAs as powerful therapeutic targets and prognostic markers. In this comprehensive review, we meticulously examined the landscape of lncRNAs in predicting the prognosis of the top five cancers and other malignancies, aiming to provide a compelling reference for future research endeavours. Leveraging the power of machine learning techniques, we explored the predictive capabilities of diverse lncRNA combinations, revealing their unprecedented potential to accurately determine patient outcomes.

A new marker constructed from immune-related lncRNA pairs can be used to predict clinical treatment effects and prognosis: in-depth exploration of underlying mechanisms in HNSCC

BACKGROUND

Long non-coding RNA (lncRNA) plays a vital role in tumor proliferation, migration, and treatment. Since it is challenging to standardize the gene expression levels detected by different platforms, the signatures composed of many immune-related single lncRNAs are still inaccurate. Utilizing a gene pair formed of two immune-related lncRNAs and strategically assigning values can effectively meet the demand for a higher-accuracy dual biomarker combination.

METHODS

Co-expression and differential expression analyses were performed on immune genes and lncRNAs data from The Cancer Genome Atlas and the ImmPort database to obtain differentially expressed immune-related lncRNAs for pairwise pairing. The prognostic-related differentially expressed immune-related lncRNAs (PR-DE-irlncRNAs) pairs were then identified by univariate Cox regression and used for lasso regression to construct a prognostic model. Various methods were used to validate the predictive prognostic performance of the model. Additionally, we explored the potential guiding value of the model in immunotherapy and chemotherapy and constructed a nomogram suitable for efficient prognosis prediction. Mechanistic exploration of anti-tumor immunity and mutational perspectives are also included. We also analyzed the correlation between the model and immune checkpoint inhibitors (ICIs)-related, N6-methyadenosine (m6A)-related, and multidrug resistance genes.

RESULTS

We used a total of 20 pairs of PR-DE-irlncRNAs to create a prognosis model. Quantitative real-time polymerase chain reaction experiments further verified the abnormal expression of 11 lncRNAs in HNSCC cells. Various methods have confirmed the excellent performance of the model in predicting patient prognosis. We reasoned that lncRNAs/TP53 mutation might play a positive/negative anti-tumor role through the immune system by multi-perspective analyses. Finally, it was found that the prognostic model was closely related to immunotherapy and chemotherapy as well as the expression of ICIs/m6A/multidrug resistance-related genes.

CONCLUSION

The prognostic model performs excellently in predicting the prognosis of patients and provides the potential value of practical guidance for treatment.

Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer

The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies.

Identification and Characterization of an Ageing-Associated 13-lncRNA Signature That Predicts Prognosis and Immunotherapy in Hepatocellular Carcinoma

Background

In cancer pathology, cell senescence not only alters cell function but also reshapes the immune microenvironments in tumours. However, the association between cell senescence, tumour microenvironment, and disease progression of hepatocellular carcinoma (HCC) is yet to be fully understood. Therefore, the role of cell senescence-related genes and long noncoding RNAs (lncRNAs) in evaluating the clinical prognosis and immune cell infiltration (ICI) of HCC patients requires further investigation.

Methods

The limma R package was utilised to investigate differentially expressed genes according to the multiomics data. The CIBERSORT R package was utilised to assess ICI, and unsupervised cluster analysis was conducted using the R software's ConsensusClusterPlus package. A polygenic prognostic model of lncRNAs was constructed by conducting univariate and least absolute shrinkage and selection operator (Lasso) cox proportional-hazards regression analyses. The time-dependent receiver operating characteristic (ROC) curves were used for validation. We utilised the survminer R package to evaluate the tumour mutational burden (TMB). Moreover, the gene set enrichment analysis (GSEA) helped in pathway enrichment analysis, and the immune infiltration level of the model was evaluated using the IMvigor210 cohort.

Results

The identification of 36 prognosis-related genes was achieved based on their differential expression between healthy and liver cancer tissues. Liver cancer individuals were categorised into 3 independent senescence subtypes using the gene list, revealing considerable survival differences (variations). We observed that the prognosis of patients in the ARG-ST2 subtype was substantially better as compared to that in the ARG-ST3 subtype. Differences were observed in gene expression profiles among the three subtypes, with the differentially expressed genes predominantly associated with cell cycle control. The enrichment of upregulated genes in the ARG-ST3 subtype was observed in pathways related to biological processes, for instance, organelle fission, nuclear division, and chromosome recombination. ICI in the ARG-ST1 and ARG-ST2 subtypes, with relatively better prognosis, was substantially higher as compared to the ARG-ST3 subtype. Furthermore, a risk-score model, which can be employed as a reliable prognostic factor in an independent manner for individuals suffering from liver cancer, was constructed based on 13 cell senescence-related lncRNAs (MIR99AHG, LINC01224, LINC01138, SLC25A30AS1, AC006369.2, SOCS2AS1, LINC01063, AC006037.2, USP2AS1, FGF14AS2, LINC01116, KIF25AS1, and AC002511.2). The individuals with higher risk scores had noticeably poor prognoses in contrast with those having low-risk scores. Moreover, increased levels of TMB and ICI were observed in individuals with low-risk scores and gaining more benefit from immune checkpoint therapy.

Conclusion

Cell senescence is an essential factor in HCC onset and progression. We identified 13 senescence-related lncRNAs as HCC prognostic markers, which can help understand their function in the onset and progression of HCC and guide clinical diagnosis and treatment.

The crucial role of LncRNA MIR210HG involved in the regulation of human cancer and other disease

Long noncoding RNAs (lncRNAs) have evoked considerable interest in recent years due to their critical functions in the regulation of disease processes. Abnormal expression of lncRNAs is found in multiple diseases, and lncRNAs have been exploited for diverse medical applications. The lncRNA MIR210HG is a recently discovered lncRNA that is widely dysregulated in human disease. MIR210HG was described to have biological functions with potential roles in disease development, including cell proliferation, invasion, migration, and energy metabolism. And MIR210HG dysregulation was confirmed to have promising clinical values in disease diagnosis, treatment, and prognosis. In this review, we systematically summarize the expression profiles, roles, underlying mechanisms, and clinical applications of MIR210HG in human disease.

Identifying enhancer-driven subtype-specific prognostic markers in breast cancer based on multi-omics data

Breast cancer is a cancer of high complexity and heterogeneity, with differences in prognosis and survival among patients of different subtypes. Copy number variations (CNVs) within enhancers are crucial drivers of tumorigenesis by influencing expression of their targets. In this study, we performed an integrative approach to identify CNA-driven enhancers and their effect on expression of target genes in four breast cancer subtypes by integrating expression data, copy number data and H3K27ac data. We identified 672, 555, 531, 361 CNA-driven enhancer-gene pairs and 280, 189, 113 and 98 CNA-driven enhancer-lncRNA pairs in the Basal-like, Her2, LumA and LumB subtypes, respectively. We then reconstructed a CNV-driven enhancer-lncRNA-mRNA regulatory network in each subtype. Functional analysis showed CNA-driven enhancers play an important role in the progression of breast cancer subtypes by influencing P53 signaling pathway, PPAR signaling pathway, systemic lupus erythematosus and MAPK signaling pathway in the Basal-like, Her2, LumA and LumB subtypes, respectively. We characterized the potentially prognostic value of target genes of CNV-driven enhancer and lncRNA-mRNA pairs in the subtype-specific network. We identified MUM1 and AC016876.1 as prognostic biomarkers in LumA and Basal-like subtypes, respectively. Higher expression of MUM1 with an amplified enhancer exhibited poorer prognosis in LumA patients. Lower expression of AC016876.1 with a deleted enhancer exhibited poorer survival outcomes of Basal-like patients. We also identified enhancer-related lncRNA-mRNA pairs as prognostic biomarkers, including AC012313.2-MUM1 in the LumA, AC026471.4-PLK5 in the LumB, AC027307.2-OAZ1 in the Basal-like and AC022431.1-HCN2 in the Her2 subtypes. Finally, our results highlighted target genes of CNA-driven enhancers and enhancer-related lncRNA-mRNA pairs could act as prognostic markers and potential therapeutic targets in breast cancer subtypes.

Prognostic prediction of head and neck squamous cell carcinoma: Construction of cuproptosis-related long non-coding RNA signature

BACKGROUND

Recently, a new type of programmed cell death, cuproptosis, has been identified to play important role in the progression of tumors. We constructed a cuproptosis-related long non-coding RNA (lncRNA) signature to predict the prognostic significance for head and neck squamous cell carcinoma (HNSCC).

METHODS

The risk model was developed based on differentially expressed lncRNAs associated with cuproptosis. Principal component analysis was used to assess the validity. The Kaplan-Meier curves were analyzed to compare the overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS) values. The multivariate and univariate Cox regression analyses were used to evaluate the prognostic efficiency. Furthermore, the functional enrichment, immune cell infiltration, tumor mutation burden (TMB), and sensitivity toward chemotherapy were also explored.

RESULTS

Six cuproptosis-related lncRNAs (AL109936.2, CDKN2A-DT, AC090587.1, KLF3-AS1, AL133395.1, and LINC01063) were identified to construct the independent prognostic predictor for HNSCC. The area under the curve and C-index values obtained using the risk model were higher than the values corresponding to the clinical factors. Analysis of Kaplan-Meier curves indicated that the OS, PFS, and DSS time recorded for the patients in the low-risk group were higher than the corresponding values recorded for the patients belonging to the high-risk group. By functional enrichment analysis, we observed that differentially expressed genes were enriched in the immune response and tumor-associated pathways. The patients characterized by a low-risk score exhibited better immune cell infiltration than the patients belonging to the other group. We also observed that the sensitivity of the individuals belonging to the low-risk group to chemotherapeutic agents (cisplatin, docetaxel, and paclitaxel) was higher than the sensitivity of those in the other group.

CONCLUSIONS

A cuproptosis-related lncRNA-based signature that functioned as an independent prognosis predictor for HNSCC patients was constructed. The chemosensitivity of individual patients can be potentially predicted using this signature.

Glycosyltransferase-related long non-coding RNA signature predicts the prognosis of colon adenocarcinoma

Purpose

Colon adenocarcinoma (COAD) is the most common type of colorectal cancer (CRC) and is associated with poor prognosis. Emerging evidence has demonstrated that glycosylation by long noncoding RNAs (lncRNAs) was associated with COAD progression. To date, however, the prognostic values of glycosyltransferase (GT)-related lncRNAs in COAD are still largely unknown.

Methods

We obtained the expression matrix of mRNAs and lncRNAs in COAD from The Cancer Genome Atlas (TCGA) database. Then, the univariate Cox regression analysis was conducted to identify 33 prognostic GT-related lncRNAs. Subsequently, LASSO and multivariate Cox regression analysis were performed, and 7 of 33 GT-related lncRNAs were selected to conduct a risk model. Gene set enrichment analysis (GSEA) was used to analyze gene signaling pathway enrichment of the risk model. ImmuCellAI, an online tool for estimating the abundance of immune cells, and correlation analysis were used to explore the tumor-infiltrating immune cells in COAD. Finally, the expression levels of seven lncRNAs were detected in colorectal cancer cell lines by reverse transcription-quantitative polymerase chain reaction (RT-qPCR).

Results

A total of 1,140 GT-related lncRNAs were identified, and 7 COAD-specific GT-related lncRNAs (LINC02381, MIR210HG, AC009237.14, AC105219.1, ZEB1-AS1, AC002310.1, and AC020558.2) were selected to conduct a risk model. Patients were divided into high- and low-risk groups based on the median of risk score. The prognosis of the high-risk group was worse than that of the low-risk group, indicating the good reliability and specificity of our risk model. Additionally, a nomogram based on the risk score and clinical traits was built to help clinical decisions. GSEA showed that the risk model was significantly enriched in metabolism-related pathways. Immune infiltration analysis revealed that five types of immune cells were significantly different between groups, and two types of immune cells were negatively correlated with the risk score. Besides, we found that the expression levels of these seven lncRNAs in tumor cells were significantly higher than those in normal cells, which verified the feasibility of the risk model.

Conclusion

The efficient risk model based on seven GT-related lncRNAs has prognostic potential for COAD, which may be novel biomarkers and therapeutic targets for COAD patients.

Adipose-derived stem cells-derived exosomes facilitate cutaneous wound healing by delivering XIST and restoring discoidin domain receptor 2

BACKGROUND

Adipose-derived stem cells (ADSCs) and their derived exosomes (ADSC-Exos) have shown potential functions in tissue repair. This study focuses on the effects of ADSCs-Exos on cutaneous wound healing and the potential involvement of the long non-coding RNA (lncRNA) XIST/microRNA-96-5p (miR-96-5p)/discoidin domain receptor 2 (DDR2) axis.

METHODS

Exos were isolated from the ADSCs and identified. A mouse model of full-thickness skin wounds was established. The mice were treated with ADSC-Exos to evaluate the function of ADSC-Exos in wound healing. Mouse dermal fibroblasts (MDFs) were co-cultured with the ADSC-Exos for in vitro experiments. The most differentially expressed lncRNAs in mouse skin tissues after ADSC-Exo treatment were screened by microarray analysis. The downstream molecules were analyzed by bioinformatics tools. Gain- and loss-of-function studies were performed to examine the functions of the XIST/miR-96-5p/DDR2 axis in wound healing.

RESULTS

ADSC-Exos facilitated wound healing in mice, reduced inflammatory infiltration, and increased collagen deposition in the wound skin tissues. In vitro, the ADSC-Exos promoted proliferation, migration of the MDFs. XIST was the most upregulated lncRNA in MDFs after ADSC-Exo treatment. Downregulation of XIST suppressed the promoting role of ADSC-Exos in wound healing. XIST bound to miR-96-5p to restore the expression of DDR2 mRNA. Either silencing of miR-96-5p or overexpression of DDR2 restored the promoting functions of ADSC-Exos in proliferation and migration of MDFs.

CONCLUSION

This study demonstrates that ADSC-Exos-carried XIST accelerates cutaneous wound healing through suppressing miR-96-5p and restoring the DDR2 expression.

Identification of a Genomic Instability-Related Long Noncoding RNA Prognostic Model in Colorectal Cancer Based on Bioinformatic Analysis

Background. In recent years, a growing body of research has revealed that long noncoding RNAs (lncRNAs) participate in regulating genomic instability. Materials and Methods. We obtained RNA expression profiles, somatic mutation profiles, clinical information, and pathological features of colorectal cancer (CRC) from The Cancer Genome Atlas project. We divided the cohort into two groups based on mutation frequency and identified genomic instability-related lncRNAs (GI-lncRNAs) using R software. We further analyzed the function of identified GI-lncRNAs and established a prognostic model through Cox regression. Using the established prognostic model, we divided the cohort into the high- and low-risk groups and further verified the prognostic differences between the two groups as well as the predictive power of prognosis-related lncRNAs in the genomic instability of CRC. Results. We identified a total of 143 GI-lncRNAs that were differentially expressed between the higher mutation frequency group and the lower mutation frequency group. According to Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology analyses, a series of cancer-associated terms were enriched. We further constructed a prognostic model that included five GI-lncRNAs (lncRNA PTPRD-AS1, lncRNA AC009237.14, lncRNA LINC00543, lncRNA AP003555.1, and lncRNA AL109615.3). We confirmed that the expression of the five GI-lncRNAs was associated with prognosis and the mutation of critical genes in the CRC patient cohort. Conclusions. The present research further confirmed the vital function of GI-lncRNAs in the genomic instability of CRC. The five GI-lncRNAs identified in our study are potential biomarkers and need to be studied in more depth.

Use of Omics Technologies for the Detection of Colorectal Cancer Biomarkers

Colorectal cancer (CRC) is one of the most frequently diagnosed cancers with high mortality rates, especially when detected at later stages. Early detection of CRC can substantially raise the 5-year survival rate of patients, and different efforts are being put into developing enhanced CRC screening programs. Currently, the faecal immunochemical test with a follow-up colonoscopy is being implemented for CRC screening. However, there is still a medical need to describe biomarkers that help with CRC detection and monitor CRC patients. The use of omics techniques holds promise to detect new biomarkers for CRC. In this review, we discuss the use of omics in different types of samples, including breath, urine, stool, blood, bowel lavage fluid, or tumour tissue, and highlight some of the biomarkers that have been recently described with omics data. Finally, we also review the use of extracellular vesicles as an improved and promising instrument for biomarker detection.

Identification and Validation of Three Autophagy-Related Long Noncoding RNAs as Prognostic Signature in Cholangiocarcinoma

Cholangiocarcinoma (CCA) is featured by common occurrence and poor prognosis. Autophagy is a biological process that has been extensively involved in the progression of tumors. Long noncoding RNAs (lncRNAs) have been discovered to be critical in diagnosing and predicting various tumors. It may be valuable to elaborate autophagy-related lncRNAs (ARlncRNAs) in CCA, and indeed, there are still few studies concerning the role of ARlncRNAs in CCA. Here, a prognostic ARlncRNA signature was constructed to predict the survival outcome of CCA patients. Through identification, three differentially expressed ARlncRNAs (DEARlncRNAs), including CHRM3.AS2, MIR205HG, and LINC00661, were screened and were considered predictive signatures. Furthermore, the overall survival (OS) of patients with high-risk scores was significantly lower than that of patients with low scores. Interestingly, the risk score was an independent factor for the OS of patients with CCA. Moreover, receiver operating characteristic (ROC) curve analysis showed that the screened and constructed prognosis signature for 1 year (AUC = 0.884), 3 years (AUC =0.759), and 5 years (AUC = 0.788) presented a high score of accuracy in predicting OS of CCA patients. Gene set enrichment analysis (GSEA) revealed that the three DEARlncRNAs were significantly enriched in CCA-related signaling pathways, including “pathways of basal cell carcinoma”, “glycerolipid metabolism”, etc. Quantitative real-time PCR (qRT-PCR) showed that expressions of CHRM3.AS2, MIR205HG, and LINC00661 were higher in CCA tissues than those in normal tissues, similar to the trends detected in the CCA dataset. Furthermore, Pearson’s analysis reported an intimate correlation of the risk score with immune cell infiltration, indicating a predictive value of the signature for the efficacy of immunotherapy. In addition, the screened lncRNAs were found to have the ability to modulate the expression of mRNAs by interacting with miRNAs based on the established lncRNA-miRNA-mRNA network. In conclusion, our study develops a novel nomogram with good reliability and accuracy to predict the OS of CCA patients, providing a significant guiding value for developing tailored therapy for CCA patients.

Hypoxia-Induced LncRNA-MIR210HG Promotes Cancer Progression By Inhibiting HIF-1α Degradation in Ovarian Cancer

LncRNA-MIR210HG plays crucial roles in the progression of diverse cancers. However, the expression and function of MIR210HG in ovarian cancer remains unclear. In the present study, we aimed to determine the expression and function of lncRNA-MIR210HG in ovarian cancer under hypoxic conditions. MIR210HG expression in ovarian cancer cells under hypoxic conditions was determined by qPCR analysis, and the distribution was determined by FISH and qPCR analysis based on cell nucleus and cytosol RNA extraction. Epithelial-Mesenchymal Transition (EMT) assay and human umbilical vein endothelial cell-based tube formation and migration assays were employed to determine the potential function of MIR210HG in vitro, followed by establishment of a subcutaneous tumor model in mice. The direct target of MIR210HG was determined by RNA pull-down and western blotting. Furthermore, the expression and clinical correlation of MIR210HG was determined based on malignant tissues from ovarian cancer patients. Our results indicated that MIR210HG was induced by hypoxia, which is HIF-1α dependent and mainly located in the cytosol of ovarian cancer cells. Knockdown of MIR210HG significantly inhibited EMT and tumor angiogenesis in vitro and impaired tumor growth in mice. Molecular investigations indicated that MIR210HG directly targets HIF-1α protein and inhibits VHL-dependent HIF-1α protein degradation in ovarian cancer. Further results demonstrated that MIR210HG was upregulated in ovarian cancer tissues and correlated with tumor progression and poor prognosis of ovarian cancer patients. Our study suggests that hypoxia-induced MIR210HG promotes cancer progression by inhibiting HIF-1α degradation in ovarian cancer, which could be a therapeutic target for ovarian cancer.

Identification of an Autophagy-Related Gene Signature for the Prediction of Prognosis in Early-Stage Colorectal Cancer

Purpose: A certain number of early-stage colorectal cancer (CRC) patients suffer tumor recurrence after initial curative resection. In this context, an effective prognostic biomarker model is constantly in need. Autophagy exhibits a dual role in tumorigenesis. Our study aims to develop an autophagy-related gene (ATG) signature-based on high-throughput data analysis for disease-free survival (DFS) prognosis of patients with stage I/II CRC.

Methods: Gene expression profiles and clinical information of CRC patients extracted from four public datasets were distributed to discovery and training cohort (GSE39582), validation cohort (TCGA CRC, n = 624), and meta-validation cohort (GSE37892 and GSE14333, n = 420). Autophagy genes significantly associated with prognosis were identified.

Results: Among 655 autophagy-related genes, a 10-gene ATG signature, which was significantly associated with DFS in the training cohort (HR, 2.76[1.56–4.82]; p = 2.06 × 10–4), was constructed. The ATG signature, stratifying patients into high and low autophagy risk groups, was validated in the validation (HR, 2.29[1.15–4.55]; p = 1.5 × 10–2) and meta-validation cohorts (HR, 2.5[1.03–6.06]; p = 3.63 × 10–2) and proved to be prognostic in a multivariate analysis. Functional analysis revealed enrichment of several immune/inflammatory pathways in the high autophagy risk group, where increased infiltration of T regulatory cells (Tregs) and decreased infiltration of M1 macrophages were observed.

Conclusion: Our study established a prognostic ATG signature that effectively predicted DFS for early-stage CRC patients. Meanwhile, the study also revealed the possible relationship among autophagy process, immune/inflammatory response, and tumorigenesis.

Six mutator-derived lncRNA signature of genome instability for predicting the clinical outcome of colon cancer

Background

Colon adenocarcinoma (COAD) is one of the most common malignancies worldwide. Genomic instability is one of the hallmarks of colon cancer and is associated with prognosis. Nevertheless, the impact of genome instability-associated long non-coding RNAs (lncRNAs) along with their clinical significance in cancers has remained mostly unexplored.

Methods

In this study, a mutator hypothesis-derived computational frame integrating the somatic mutation profiles and lncRNA expression profiles in a tumor genome was developed, which enabled the identification of 137 novel genomic instability-associated lncRNAs in colon cancer. Subsequently, a genome instability-derived lncRNA signature (GILncSig) segregated the patients into low- and high-risk groups with prominent differences in outcomes.

Results

Combined with the overall survival data, we established 6 six lncRNA-based signature to predict prognosis, which were LINC00896, AC007996.1, NKILA, AP003555.2, MIRLET7BHG, and AC009237.14. We found that the expression level of PD-L1 (CD274) and somatic mutations in the high-risk group were higher than those in the low-risk group. This suggests that high-risk patients may be sensitive to immunotherapy. We further found that the prognosis of patients in the high-risk group was significantly lower than that of patients in the low-risk group, and that patients' prognosis was likely to be worse as the patient's risk score increased.

Conclusions

In conclusion, this study explores the role of lncRNAs in genomic instability and cancer prognosis and provides a new idea for the prognostic prediction of colon cancer.

Over Fifty Years of Life, Death, and Cannibalism: A Historical Recollection of Apoptosis and Autophagy

Research in biomedical sciences has changed dramatically over the past fifty years. There is no doubt that the discovery of apoptosis and autophagy as two highly synchronized and regulated mechanisms in cellular homeostasis are among the most important discoveries in these decades. Along with the advancement in molecular biology, identifying the genetic players in apoptosis and autophagy has shed light on our understanding of their function in physiological and pathological conditions. In this review, we first describe the history of key discoveries in apoptosis with a molecular insight and continue with apoptosis pathways and their regulation. We touch upon the role of apoptosis in human health and its malfunction in several diseases. We discuss the path to the morphological and molecular discovery of autophagy. Moreover, we dive deep into the precise regulation of autophagy and recent findings from basic research to clinical applications of autophagy modulation in human health and illnesses and the available therapies for many diseases caused by impaired autophagy. We conclude with the exciting crosstalk between apoptosis and autophagy, from the early discoveries to recent findings.

The Role of non-coding RNAs in colorectal cancer, with a focus on its autophagy

Colorectal cancer (CRC) is one of malignant afflictions burdening people worldwide, mainly caused by shortages of effective medical intervention and poorly mechanistic understanding of the pathogenesis of CRC. Non-coding RNAs (ncRNAs) are a type of heterogeneous transcripts without the capability of coding protein, but have the potency of regulating protein-coding gene expression. Autophagy is an evolutionarily conserved catabolic process in which cytoplasmic contents are delivered to cellular lysosomes for degradation, resulting in the turnover of cellular components and producing energy for cell functions. A growing body of evidence reveals that ncRNAs, autophagy, and the crosstalks of ncRNAs and autophagy play intricate roles in the initiation, progression, metastasis, recurrence and therapeutic resistance of CRC, which confer ncRNAs and autophagy to serve as clinical biomarkers and therapeutic targets for CRC. In this review, we sought to delineate the complicated roles of ncRNAs, mainly including miRNAs, lncRNAs and circRNAs, in the pathogenesis of CRC, particularly focus on the regulatory role of ncRNAs in CRC-related autophagy, attempting to shed light on the complex pathological mechanisms, involving ncRNAs and autophagy, responsible for CRC tumorigenesis and development, so as to underpin the ncRNAs- and autophagy-based therapeutic strategies for CRC in clinical setting.

Prognostic Significance of Autophagy-Relevant Gene Markers in Colorectal Cancer

Background

Colorectal cancer (CRC) is a common malignant solid tumor with an extremely low survival rate after relapse. Previous investigations have shown that autophagy possesses a crucial function in tumors. However, there is no consensus on the value of autophagy-associated genes in predicting the prognosis of CRC patients. This work screens autophagy-related markers and signaling pathways that may participate in the development of CRC, and establishes a prognostic model of CRC based on autophagy-associated genes.

Methods

Gene transcripts from the TCGA database and autophagy-associated gene data from the GeneCards database were used to obtain expression levels of autophagy-associated genes, followed by Wilcox tests to screen for autophagy-related differentially expressed genes. Then, 11 key autophagy-associated genes were identified through univariate and multivariate Cox proportional hazard regression analysis and used to establish prognostic models. Additionally, immunohistochemical and CRC cell line data were used to evaluate the results of our three autophagy-associated genes EPHB2, NOL3, and SNAI1 in TCGA. Based on the multivariate Cox analysis, risk scores were calculated and used to classify samples into high-risk and low-risk groups. Kaplan-Meier survival analysis, risk profiling, and independent prognosis analysis were carried out. Receiver operating characteristic analysis was performed to estimate the specificity and sensitivity of the prognostic model. Finally, GSEA, GO, and KEGG analysis were performed to identify the relevant signaling pathways.

Results

A total of 301 autophagy-related genes were differentially expressed in CRC. The areas under the 1-year, 3-year, and 5-year receiver operating characteristic curves of the autophagy-based prognostic model for CRC were 0.764, 0.751, and 0.729, respectively. GSEA analysis of the model showed significant enrichment in several tumor-relevant pathways and cellular protective biological processes. The expression of EPHB2, IL-13, MAP2, RPN2, and TRAF5 was correlated with microsatellite instability (MSI), while the expression of IL-13, RPN2, and TRAF5 was related to tumor mutation burden (TMB). GO analysis showed that the 11 target autophagy genes were chiefly enriched in mRNA processing, RNA splicing, and regulation of the mRNA metabolic process. KEGG analysis showed enrichment mainly in spliceosomes. We constructed a prognostic risk assessment model based on 11 autophagy-related genes in CRC.

Conclusion

A prognostic risk assessment model based on 11 autophagy-associated genes was constructed in CRC. The new model suggests directions and ideas for evaluating prognosis and provides guidance to choose better treatment strategies for CRC.