Immune-Related lncRNAs to Construct Novel Signatures and Predict the Prognosis of Rectal Cancer

LncRNAs: Emerging biomarkers and therapeutic targets in rectal cancer

According to findings, long non-coding RNAs (lncRNAs) have an important function in the onset and growth of various cancers, including rectal cancer (RC). RC offers unique issues in terms of diagnosis, treatment, and results, needing a full understanding of the cellular mechanisms that cause it to develop. This thorough study digs into the various functions that lncRNAs perform in RC, giving views into their multiple roles as well as possible therapeutic consequences. The function of lncRNAs in RC cell proliferation, apoptosis, migratory and infiltrating capacities, epithelial-mesenchymal shift, and therapy tolerance are discussed. Various lncRNA regulatory roles are investigated in depth, yielding information on their effect on essential cell functions such as angiogenesis, death, immunity, and growth. Systemic lncRNAs are currently acknowledged as potential indications for the initial stages of identification of cancer, with the ability to diagnose as well as forecast. Besides adding to their diagnostic utility, lncRNAs offer therapeutic opportunities as actors, contributing to the expanding landscape of cancer research. Moreover, the investigation looks into the assessment and predictive utility of lncRNAs as RC markers. The article also offers insight into lncRNAs as chemoresistance and drug resistance facilitators in the setting of RC.

Transcriptome-wide N6-methyladenosine (m6A) methylation profiling of long non-coding RNAs in ovarian endometriosis

N6-methyladenosine (m6A) methylation is the most prevalent internal epigenetic posttranscriptional mechanism for regulating mammalian RNA. Despite recent advances in determining the biological functions of m6A methylation, its association with the pathology of ovarian endometriosis remains uncertain. Herein, we performed m6A transcriptome-wide profiling to identify key lncRNAs with m6A modification involved in ovarian endometriosis development by bioinformatics analysis. We found the total m6A level was lower in ovarian endometriosis than in normal endometrium samples, with 9663 m6A peaks associated with 8989 lncRNAs detected in ovarian endometriosis and 9902 m6A peaks associated with 9210 lncRNAs detected in normal endometrium samples. These m6A peaks were primarily enriched within AAACU motifs. Functional enrichment analysis indicated that pathways involving the regulation of adhesion and development were significantly enriched in these differentially methylated lncRNAs. The regulatory relationships among lncRNAs, microRNAs (miRNAs), and mRNAs were identified by competing endogenous RNA (ceRNA) analysis and determination of the network regulating lncRNA-mRNA expression. Several specific lncRNA, including LINC00665, LINC00937, FZD10-AS1, DIO3OS and GATA2-AS1 which were differently expressed and modified by m6A, were validated using qRT-PCR and its interaction with infiltrating immune cells was explored. Furthermore, we found LncRNA DIO3OS promotes the invasion and migration of Human endometrial stromal cells (THESCs) and ALKBH5 regulates the expression of the lncRNA DIO3OS through m6A modification in vitro. Our study firstly revealed the transcriptome-wide map of m6A modification in lncRNAs of ovarian endometriosis. These findings may enable the determination of the underlying mechanism governing the pathogenesis of ovarian endometriosis and provide theoretical basis for further deeper research on the role of m6A in the development of ovarian endometriosis.

Prediction Model for Therapeutic Responses in Ovarian Cancer Patients using Paclitaxel-resistant Immune-related lncRNAs

BACKGROUND

Ovarian cancer (OC) is the deadliest malignant tumor in women with a poor prognosis due to drug resistance and lack of prediction tools for therapeutic responses to anti- cancer drugs.

OBJECTIVE

The objective of this study was to launch a prediction model for therapeutic responses in OC patients.

METHODS

The RNA-seq technique was used to identify differentially expressed paclitaxel (PTX)- resistant lncRNAs (DE-lncRNAs). The Cancer Genome Atlas (TCGA)-OV and ImmPort database were used to obtain immune-related lncRNAs (ir-lncRNAs). Univariate, multivariate, and LASSO Cox regression analyses were performed to construct the prediction model. Kaplan- meier plotter, Principal Component Analysis (PCA), nomogram, immune function analysis, and therapeutic response were applied with Genomics of Drug Sensitivity in Cancer (GDSC), CIBERSORT, and TCGA databases. The biological functions were evaluated in the CCLE database and OC cells.

RESULTS

The RNA-seq defined 186 DE-lncRNAs between PTX-resistant A2780-PTX and PTXsensitive A2780 cells. Through the analysis of the TCGA-OV database, 225 ir-lncRNAs were identified. Analyzing 186 DE-lncRNAs and 225 ir-lncRNAs using univariate, multivariate, and LASSO Cox regression analyses, 9 PTX-resistant immune-related lncRNAs (DEir-lncRNAs) acted as biomarkers were discovered as potential biomarkers in the prediction model. Single-cell RNA sequencing (scRNA-seq) data of OC confirmed the relevance of DEir-lncRNAs in immune responsiveness. Patients with a low prediction score had a promising prognosis, whereas patients with a high prediction score were more prone to evade immunotherapy and chemotherapy and had poor prognosis.

CONCLUSION

The novel prediction model with 9 DEir-lncRNAs is a valuable tool for predicting immunotherapeutic and chemotherapeutic responses and prognosis of patients with OC.

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.

A review of literature: role of long noncoding RNA TPT1-AS1 in human diseases

Human diseases are multifactorial processes mainly driven by the intricate interactions of genetic and environmental factors. Long noncoding RNAs (lncRNAs) represent a type of non-coding RNAs with more than 200 nucleotides. Multiple studies have demonstrated that the dysregulation of lncRNAs is associated with complex biological as well as pathological processes through various mechanism, especially the regulation of gene transcription and related signal transduction pathways. Moreover, an increasing number of studies have explored lncRNA-based clinical applications in different diseases. For instance, the lncRNA Tumor Protein Translationally Controlled 1 (TPT1) Antisense RNA 1 (TPT1-AS1) was found to be dysregulated in several types of disease and strongly associated with patient prognosis and diverse clinical features. Recent studies have also documented that TPT1-AS1 modulates numerous biological processes through multiple mechanisms, including cell proliferation, apoptosis, autophagy, invasion, migration, radiosensitivity, chemosensitivity, stemness, and extracellular matrix (ECM) synthesis. Furthermore, TPT1-AS1 was regarded as a promising biomarker for the diagnosis, prognosis and treatment of several human diseases. In this review, we summarize the role of TPT1-AS1 in human diseases with the aspects of its expression, relevant clinical characteristics, molecular mechanisms, biological functions, and subsequent clinical applications.

Establishing a prognostic model of ferroptosis- and immune-related signatures in kidney cancer: A study based on TCGA and ICGC databases

Background

Kidney cancer (KC) is one of the most challenging cancers due to its delayed diagnosis and high metastasis rate. The 5-year survival rate of KC patients is less than 11.2%. Therefore, identifying suitable biomarkers to accurately predict KC outcomes is important and urgent.

Methods

Corresponding data for KC patients were obtained from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) databases. Systems biology/bioinformatics/computational approaches were used to identify suitable biomarkers for predicting the outcome and immune landscapes of KC patients.

Results

We found two ferroptosis- and immune-related differentially expressed genes (FI-DEGs) (Klotho (KL) and Sortilin 1 (SORT1)) independently correlated with the overall survival of KC patients. The area under the curve (AUC) values of the prognosis model using these two FI-DEGs exceeded 0.60 in the training, validation, and entire groups. The AUC value of the 1-year receiver operating characteristic (ROC) curve reached 0.70 in all the groups.

Conclusions

Our present study indicated that KL and SORT1 could be prognostic biomarkers for KC patients. Whether this model can be used in clinical settings requires further validation.

Comprehensive Analysis of Ferroptosis- and Immune-Related Signatures to Improve the Prognosis and Diagnosis of Kidney Renal Clear Cell Carcinoma

Background

Almost 40% of patients with kidney renal clear cell carcinoma (KIRC) with advanced cancers eventually develop to metastases, and their 5-year survival rates are approximately 10%. Aberrant DNA methylations are significantly associated with the development of KIRC. The aim of our present study was to identify suitable ferroptosis- and immune-related (FI) biomarkers correlated with aberrant methylations to improve the prognosis and diagnosis of KIRC.

Methods

ChAMP and DESeq2 in R (3.6.2) were used to screen the differentially expressed methylation probes and differentially expressed genes, respectively. Univariate and multivariate Cox regression were used to identify the overall survival (OS)-related biomarkers.

Results

We finally identified five FI biomarkers (CCR4, CMTM3, IFITM1, MX2, and NR3C2) that were independently correlated with the OS of KIRC. The area under the curve value of the receiver operating characteristic value of prognosis model was 0.74, 0.68, and 0.72 in the training, validation, and entire cohorts, respectively. The sensitivity and specificity of the diagnosis model were 0.8698 and 0.9722, respectively. In addition, the prognosis model was also significantly correlated with several immune cells and factors.

Conclusion

Our present study suggested that these five FI-DEGs (CCR4, CMTM3, IFITM1, MX2, and NR3C2) could be used as prognosis and diagnosis biomarkers for patients with KIRC, but further cross-validation clinical studies are still needed to confirm them.

Development and Validation of Ferroptosis- and Immune-Related lncRNAs Signatures for Breast Infiltrating Duct and Lobular Carcinoma

Background

Heterogeneity of breast cancer (BRCA) is significantly correlated with its prognosis. Target therapy for ferroptosis and immunity is a new cancer treatment option discovered in recent years. In the present study, we aimed to identify ferroptosis- and immune-related long non-coding RNAs (lncRNAs) to accurately predict the prognosis and diagnosis of patients with breast infiltrating duct and lobular carcinoma by integrated analyses.

Methods

The corresponding data for the patients with breast infiltrating duct and lobular carcinoma by integrated analyses were obtained from The Cancer Genome Atlas (TCGA). Analyses of univariate and multivariate Cox regressions were used to identify the suitable candidate biomarkers.

Results

We found that seven ferroptosis- and immune-related differentially expressed lncRNAs (FI-DELs) (AC007686.3, AC078883.1, ADAMTS9-AS1, AL035661.1, CBR3-AS1, FTX, and TMEM105) were correlated with the overall survival of patients with breast infiltrating duct and lobular carcinoma. The areas under the receiver operating characteristic (AUCs) value of the prognosis model were all over 0.6 in training, validation, and entire groups. The sensitivity and specificity of the diagnosis model was 87.84% and 97.06%, respectively.

Conclusions

Through a series of bioinformatics analyses, we found that the seven FI-DELs could serve as prognostic and diagnostic biomarkers for patients with breast infiltrating duct and lobular carcinoma. However, whether these seven biomarkers could be really applied to the clinic requires further investigations.

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.

LncRNA GATA2-AS1 suppresses esophageal squamous cell carcinoma progression via the mir-940/PTPN12 axis

Esophageal squamous cell carcinoma (ESCC) is a common malignant tumor worldwide. Long noncoding RNAs (lncRNAs) exhibit a regulatory role in the progression of ESCC. Our research was performed to investigate the potential molecular mechanism of lncRNA GATA2-AS1 in ESCC.

METHODS

The expression of GATA2-AS1 was identified by qRT-PCR. Cell function assays explored the potential effect of GATA2-AS1 on ESCC progression. The subcellular hierarchical localization method was executed to identify the subcellular localization of GATA2-AS1 in ESCC cells. A prediction website was utilized to discover the relationships among GATA2-AS1, miR-940 and PTPN12. Dual luciferase reporter gene, pull-down assays and RIP assays were executed to verify the binding activity among GATA2-AS1, miR-940 and PTPN12. Xenograft tumor experiments were performed to evaluate ESCC cell growth in vivo.

RESULTS

The expression of GATA2-AS1 and PTPN12 was reduced, while miR-940 expression was enhanced in ESCC tissues and cell lines. In vivo experiments showed that GATA2-AS1 inhibited the progression of ESCC cells toward malignancy. Bioinformatics analysis, dual luciferase and RIP assays revealed that GATA2-AS1 upregulated PTPN12 expression by competitively targeting miR-940. miR-940 reversed the inhibitory effect of GATA2-AS1 on the biological behavior of ESCC cells.

CONCLUSION

Our findings suggested that GATA2-AS1, expressed at low levels in ESCC, plays a crucial role in the progression of ESCC by targeting the miR-940/PTPN12 axis and could be a potential drug target to treat ESCC patients.

Identification of Five Ferroptosis-Related LncRNAs as Novel Prognosis and Diagnosis Signatures for Renal Cancer

Background: Ferroptosis is a novel regulated cell death that is characterized by iron-dependent oxidative damage. Renal cancer is the second most common cancer of the urinary system, which is highly correlated with iron metabolism. The aim of our present study was to identify suitable ferroptosis-related prognosis signatures for renal cancer.

Methods: We downloaded the RNA-seq count data of renal cancer from The Cancer Genome Atlas database and used the DESeq2, Survival, and Cox regression analyses to screen the prognosis signatures.

Results: We identified 5 ferroptosis-related differentially expressed lncRNAs (FR-DELs) (DOCK8-AS1, SNHG17, RUSC1-AS1, LINC02609, and LUCAT1) to be independently correlated with the overall survival (OS) of patients with renal cancer. The risk assessment model and diagnosis model constructed by those 5 FR-DELs could well predict the outcome and the diagnosis of renal cancer.

Conclusion: Our present study not only suggested those 5 FR-DELs could be used as prognosis and diagnosis signatures for renal cancer but also provided strategies for other cancers in the screening of ferroptosis-related biomarkers.

Identification of Novel Tumor Microenvironment-Related Long Noncoding RNAs to Determine the Prognosis and Response to Immunotherapy of Hepatocellular Carcinoma Patients

Introduction: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with poor prognosis. The tumor microenvironment (TME) plays a vital role in HCC progression. Thus, this research was designed to analyze the correlation between the TME and the prognosis of HCC patients and to construct a TME-related long noncoding RNA (lncRNA) signature to determine HCC patients’ prognosis and response to immunotherapy.

Methods: We assessed the stromal–immune–estimate scores within the HCC microenvironment using the ESTIMATE (Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data) algorithm based on The Cancer Genome Atlas database, and their associations with survival and clinicopathological parameters were also analyzed. Thereafter, differentially expressed lncRNAs were filtered out according to the immune and stromal scores. Cox regression analysis was performed to build a TME-related lncRNA risk signature. Kaplan–Meier analysis was used to explore the prognostic value of the risk signature. Furthermore, we explored the biological functions and immune microenvironment features in the high- and low-risk groups. Lastly, we probed the association of the risk model with treatment responses to immune checkpoint inhibitors (ICIs) in HCC.

Results: The stromal, immune, and estimate scores were obtained utilizing the ESTIMATE algorithm for patients with HCC. Kaplan–Meier analysis showed that high scores were significantly correlated with better prognosis in HCC patients. Six TME-related lncRNAs were screened to construct the prognostic model. The Kaplan–Meier curves suggested that HCC patients with low risk had better prognosis than those with high risk. Receiver operating characteristic (ROC) curve and Cox regression analyses indicated that the risk model could predict HCC survival exactly and independently. Functional enrichment analysis revealed that some tumor- and immune-related pathways were activated in the high-risk group. We also revealed that some immune cells, which were important in enhancing immune responses toward cancer, were significantly increased in the low-risk group. In addition, there was a close correlation between ICIs and the risk signature, which can be used to predict the treatment responses of HCC patients.

Conclusion: We analyzed the influence of the stromal, immune, and estimate scores on the prognosis of HCC patients. A novel TME-related lncRNA risk model was established, which could be effectively applied as an independent prognostic biomarker and predictor of ICIs for HCC patients.