A Novel Prognostic Prediction Model for Colorectal Cancer Based on Nine Autophagy-Related Long Noncoding RNAs

Prognostic Analysis of a Hypoxia-Associated lncRNA Signature in Glioblastoma and its Pan-Cancer Landscape

BACKGROUND

 Hypoxia is an important clinical feature of glioblastoma (GBM), which regulates a variety of tumor processes and is inseparable from radiotherapy. Accumulating evidence suggests that long noncoding RNAs (lncRNAs) are strongly associated with survival outcomes in GBM patients and modulate hypoxia-induced tumor processes. Therefore, the aim of this study was to establish a hypoxia-associated lncRNAs (HALs) prognostic model to predict survival outcomes in GBM patients.

METHODS

 LncRNAs in GBM samples were extracted from The Cancer Genome Atlas database. Hypoxia-related genes were downloaded from the Molecular Signature Database. Co-expression analysis of differentially expressed lncRNAs and hypoxia-related genes in GBM samples was performed to determine HALs. Six optimal lncRNAs were selected for building HALs models by univariate Cox regression analysis.

RESULTS

 The prediction model has a good predictive effect on the prognosis of GBM patients. Meanwhile, LINC00957 among the six lncRNAs was selected and subjected to pan-cancer landscape analysis.

CONCLUSION

 Taken together, our findings suggest that the HALs assessment model can be used to predict the prognosis of GBM patients. In addition, LINC00957 included in the model may be a useful target to study the mechanism of cancer development and design individualized treatment strategies.

Roles, underlying mechanisms and clinical significances of LINC01503 in human cancers

Long intergenic non-coding RNA 01503 (LINC01503) is a long non-coding RNA (lncRNA) located on human chromosome 9q34.11. There is compelling evidence indicating that LINC01503 is upregulated in multiple types of tumors and functions as a tumor stimulator. The upregulation of LINC01503 was significantly associated with the risk of 12 tumors and showed a strong correlation with clinicopathological characteristics and poor prognosis in 9 tumors. The expression of LINC01503 is regulated by transcription factors such as TP63, EGR1, c-MYC, GATA1 and AR. The downstream regulatory mechanisms of LINC01503 are complex and multifaceted. LINC01503, as a competing endogenous RNA (ceRNA), regulates gene expression by competitively inhibiting miRNA. LINC01503 may also regulate gene expression via interacting with biomolecules or recruiting chromatin-modifying complexes. In addition, LINC01503 can abnormally activate the ERK/MAPK, PI3K/AKT and Wnt/β-catenin signaling pathways to enhance tumor progression. Here, this review presents an overview of the latest research progress of LINC01503 in the field of oncology, summarizes its comprehensive network involved in multiple cancer molecular mechanisms, and explores its potential applications in cancer diagnosis, prognosis, and treatment.

Identification of necroptosis-related lncRNAs for prognosis prediction and screening of potential drugs in patients with colorectal cancer

BACKGROUND

Tumor recurrence and metastasis lead to a poor prognosis in colorectal cancer (CRC). Necroptosis is closely related to the tumor microenvironment (TME) and affects tumor recurrence and metastasis. We aimed to stratify CRC patients according to necroptosis-related long noncoding RNAs (lncRNAs), which can be used to not only evaluate prognosis and improve precision medicine in clinical practice but also screen potential immunotherapy drugs.

AIM

To stratify CRC patients according to necroptosis-related lncRNAs (NRLs), which can be used to not only evaluate prognosis and improve precision medicine in clinical practice but also screen potential immunotherapy drugs.

METHODS

LncRNA expression profiles were collected from The Cancer Genome Atlas. NRLs were identified by coexpression analysis. Cox regression analysis identified a NRL signature. Then, the value of this signature was comprehensively and multidimensionally evaluated, and its reliability for CRC prognosis prediction was assessed with clinical CRC data and compared with that of six other lncRNA signatures. Gene set enrichment analysis, TME analysis and half-maximal inhibitory concentration (IC₅₀) prediction were also performed according to the risk score (RS) of the signature.

RESULTS

An 8-lncRNA signature significantly associated with overall survival (OS) was constructed, and its reliability was validated with clinical CRC data. Most of the areas under the receiver operating characteristic curves (AUCs) values for 1-, 3- and 5-year OS for this signature were higher than those for the other six lncRNA signatures. OS, disease-specific survival and the progression-free interval were all significantly poorer in the high-risk group. The RS of the signature showed good concordance with the predicted prognosis, with AUCs for 1-, 3- and 5-year OS of 0.79, 0.81 and 0.77, respectively. Additionally, the calibration plots for this signature combined with clinical factors showed that this combination could effectively improve the ability to predict OS. The RS was correlated with tumor stage, lymph node metastasis and distant metastasis. Most of the enriched Kyoto Encyclopedia of Genes and Genomes and Gene Ontology terms were tumor metastasis-related pathways in the high-risk group; these patients showed greater infiltration of immunosuppressive cells, such as cancer-associated fibroblasts, hematopoietic stem cells and M2 macrophages, but less infiltration of infiltrating antitumor effector immune cells, such as cluster of differentiation 8⁺ T cells and regulatory T cells (Tregs). We explored additional potential immune checkpoint genes and potential immunotherapeutic and chemotherapeutic drugs with relatively low IC₅₀ values.

CONCLUSION

We identified an NRL signature with strong fidelity that could stably predict prognosis and might be an indicator of the TME of CRC. Furthermore, additional potential immunotherapeutic and chemotherapeutic drugs were explored.

Unravelling the complexity of lncRNAs in autophagy to improve potential cancer therapy

Autophagy is well-known as an internal catabolic process that is evolutionarily conserved and performs the key biological function in maintaining cellular homeostasis. It is tightly controlled by several autophagy-related (ATG) proteins, which are closely associated with many types of human cancers. However, what has remained controversial is the janus roles of autophagy in cancer progression. Interestingly, the biological function of long non-coding RNAs (lncRNAs) in autophagy has been gradually understood in different types of human cancers. More recently, numerous studies have demonstrated that several lncRNAs may regulate some ATG proteins and autophagy-related signaling pathways to either activate or inhibit the autophagic process in cancer. Thus, in this review, we summarize the latest advance in the knowledge of the complicated relationships between lncRNAs and autophagy in cancer. Also, the in-depth dissection of the lncRNAs-autophagy-cancers axis involved in this review would shed new light on discovery of more potential cancer biomarkers and therapeutic targets in the future.

Expression of LINC00174 in different cancers: Review of the literature and bioinformatics analyses

LINC00174 is an example of long intergenic non-coding RNAs with important functions in the development of human cancers. The gene encoding this lincRNA is located on 7q11.21. LINC00174 has been demonstrated to play an oncogenic role in a variety of cancers, including colorectal carcinoma, thymic carcinoma, glioma, glioblastoma, hepatocellular carcinoma, kidney renal clear cell carcinoma, breast cancer and non-functioning pituitary adenoma. In lung cancer, there is an obvious discrepancy between different studies regarding the role of this lincRNA. This lincRNA is also involved in the determination of prognosis of different cancers, particularly colorectal cancer. In the current review, we discuss the role of this lincRNA in human carcinogenesis based on the available data in the literature and bioinformatics tools.

Cuprotosis Programmed-Cell-Death-Related lncRNA Signature Predicts Prognosis and Immune Landscape in PAAD Patients

In terms of mortality and survival, pancreatic cancer is one of the worst malignancies. Known as a unique type of programmed cell death, cuprotosis contributes to tumor cell growth, angiogenesis, and metastasis. Cuprotosis programmed-cell-death-related lncRNAs (CRLs) have been linked to PAAD, although their functions in the tumor microenvironment and prognosis are not well understood. This study included data from the TCGA-PAAD cohort. Random sampling of PAAD data was conducted, splitting the data into two groups for use as a training set and test set (7:3). We searched for differentially expressed genes that were substantially linked to prognosis using univariate Cox and Lasso regression analysis. Through the use of multivariate Cox proportional risk regression, a risk-rating system for prognosis was developed. Correlations between the CRL signature and clinicopathological characteristics, tumor microenvironment, immunotherapy response, and chemotherapy sensitivity were further evaluated. Lastly, qRT-PCR was used to compare CRL expression in healthy tissues to that in tumors. Some CRLs are thought to have strong correlations with PAAD outcomes. These CRLs include AC005332.6, LINC02041, LINC00857, and AL117382.1. The CRL-based signature construction exhibited outstanding predictive performance and offers a fresh approach to evaluating pre-immune effectiveness, paving the way for future studies in precision immuno-oncology.

Long Noncoding RNAs and Circular RNAs Regulate AKT and Its Effectors to Control Cell Functions of Cancer Cells

AKT serine-threonine kinase (AKT) and its effectors are essential for maintaining cell proliferation, apoptosis, autophagy, endoplasmic reticulum (ER) stress, mitochondrial morphogenesis (fission/fusion), ferroptosis, necroptosis, DNA damage response (damage and repair), senescence, and migration of cancer cells. Several lncRNAs and circRNAs also regulate the expression of these functions by numerous pathways. However, the impact on cell functions by lncRNAs and circRNAs regulating AKT and its effectors is poorly understood. This review provides comprehensive information about the relationship of lncRNAs and circRNAs with AKT on the cell functions of cancer cells. the roles of several lncRNAs and circRNAs acting on AKT effectors, such as FOXO, mTORC1/2, S6K1/2, 4EBP1, SREBP, and HIF are explored. To further validate the relationship between AKT, AKT effectors, lncRNAs, and circRNAs, more predicted AKT- and AKT effector-targeting lncRNAs and circRNAs were retrieved from the LncTarD and circBase databases. Consistently, using an in-depth literature survey, these AKT- and AKT effector-targeting database lncRNAs and circRNAs were related to cell functions. Therefore, some lncRNAs and circRNAs can regulate several cell functions through modulating AKT and AKT effectors. This review provides insights into a comprehensive network of AKT and AKT effectors connecting to lncRNAs and circRNAs in the regulation of cancer cell functions.

N7-methylguanosine-related lncRNAs: Distinction between hot and cold tumors and construction of predictive models in colon adenocarcinoma

Colon adenocarcinoma (COAD) is a prevalent malignant tumor that severely threatens human health across the globe. Immunotherapy is an essential need for patients with COAD. N7-methylguanosine (m7G) has been associated with human diseases, and non-coding RNAs (lncRNAs) regulate various tumor-related biological processes. Nonetheless, the m7G-related lncRNAs involved in COAD regulation are limited. This study aims to construct the clustering features and prognostic model of m7G-related lncRNAs in COAD. First, The Cancer Genome Atlas (TCGA) database was used to identify m7G-related differentially expressed lncRNAs (DELs), based on which COAD cases could be classified into two subtypes. Subsequently, univariate Cox analysis was used to identify 9 prognostic m7G-related lncRNAs. Further, Five candidates were screened by LASSO-Cox regression to develop new models. The patients were divided into high-risk and low-risk groups based on the median risk score. Consequently, the Kaplan-Meier survival curve demonstrated a statistically significant overall survival (OS) between the high- and low-risk groups (P<0.001). Multivariate Cox regression analysis revealed that risk score is an independent prognostic factor in COAD patients (P<0.001). This confirms the clinical applicability of the model. Additionally, we performed Gene Set Enrichment Analysis (GSEA), which uncovered the biological and functional differences between risk subgroups, i.e., enrichment of immune-related diseases in the high-risk group and enrichment of metabolic-related pathways in the low-risk group. In a drug sensitivity analysis, high-risk group were more sensitive to some chemotherapeutics and targeted drugs than low-risk group. Eventually, the stability of the model was confirmed by qRT-PCR. Our study unraveled the features of different immune states of COAD and established a prognostic model, including five m7G-related lncRNAs for COAD patients. These results will bolster clinical treatment and survival prediction of COAD.

N7-methylguanosine-related lncRNAs: Predicting the prognosis and diagnosis of colorectal cancer in the cold and hot tumors

Background: 7-Methylguanosine(m7G) contributes greatly to its pathogenesis and progression in colorectal cancer. We proposed building a prognostic model of m7G-related LncRNAs. Our prognostic model was used to identify differences between hot and cold tumors.

Methods: The study included 647 colorectal cancer patients (51 cancer-free patients and 647 cancer patients) from The Cancer Genome Atlas (TCGA). We identified m7G-related prognostic lncRNAs by employing the univariate Cox regression method. Assessments were conducted using univariate Cox regression, multivariate Cox regression, receiver operating characteristics (ROC), nomogram, calibration curves, and Kaplan-Meier analysis. All of these procedures were used with the aim of confirming the validity and stability of the model. Besides these two analyses, we also conducted half-maximal inhibitory concentration (IC50), immune analysis, principal component analysis (PCA), and gene set enrichment analysis (GSEA). The entire set of m7G-related (lncRNAs) with respect to cold and hot tumors has been divided into two clusters for further discussion of immunotherapy.

Results: The risk model was constructed with 17 m7G-related lncRNAs. A good correlation was found between the calibration plots and the prognosis prediction in the model. By assessing IC50 in a significant way across risk groups, systemic treatment can be guided. By using clusters, it may be possible to distinguish hot and cold tumors effectively and to aid in specific therapeutic interventions. Cluster 1 was identified as having the highest response to immunotherapy drugs and thus was identified as the hot tumor.

Conclusion: This study shows that 17 m7G-related lncRNA can be used in clinical settings to predict prognosis and use them to determine whether a tumor is cold or hot in colorectal cancer and improve the individualization of treatment.

Identification of an m6A-Related Long Noncoding RNA Risk Model for Predicting Prognosis and Directing Treatments in Patients With Colon Adenocarcinoma

N6-methyladenosine (m6A) and lncRNAs have been implicated in the development of colon cancer, including tumorigenesis, migration, and invasion. However, the specific effect of m6A regulators on lncRNAs is not clear, and m6A-related lncRNAs may be new prognostic biomarkers and may help direct treatment and medication. We identified 29 prognostic m6A-related lncRNAs and constructed a risk model using 12 lncRNAs. The model was an independent prognostic factor and could accurately predict the prognosis. A stable and robust nomogram that combined the model and pathologic stage was constructed. A total of 2,424 differentially expressed genes (DEGs) were identified based on the model. Functional analysis of the DEGs showed that they were associated with tumor progression, helping investigate the underlying biological functions and signaling pathways of the risk model. In addition, the low-risk group based on the risk model had more sensitivity to afatinib, metformin, and GW.441756, and patients with low risk would more likely respond to immunotherapy. Moreover, patients with higher risk were more sensitive to olaparib, bexarotene, and doxorubicin.