Long non-coding RNA profile study identifies a metabolism-related signature for colorectal cancer

Establishment of disulfidptosis-related LncRNA signature as biomarkers in colon adenocarcinoma

PURPOSE

Metabolic reprogramming is a hallmark of cancer and plays a key role in precision oncology treatment. Long non-coding RNAs (lncRNAs) regulate cancer cell behavior, including metabolism. Disulfidptosis, a newly identified form of regulated cell death triggered by glucose starvation, has yet to be fully understood in colon adenocarcinoma (COAD). This study aimed to confirm the existence and role of disulfidptosis in COAD and identify disulfidptosis-related lncRNAs that may be targeted to induce disulfidptosis in COAD.

METHODS

PI and F-actin staining were used to observe disulfidptosis in COAD cell lines. Disulfidptosis-related lncRNAs were identified based on the expression of disulfidptosis-associated genes in the TCGA-COAD database. A four-lncRNA signature for disulfidptosis was established. Subsequently, loss-of-function assays explored the roles of AC013652.1 and MCM3AP-AS1 in disulfidptosis.

RESULTS

Disulfidptosis was observed in COAD cells under glucose starvation and could be reversed by agents that prevent disulfide stress, such as dithiothreitol (DTT) and tris-(2-carboxyethyl)-phosphine (TCEP). The prognostic value of disulfidptosis-associated genes in COAD patients was confirmed, with higher expression indicating longer survival. A disulfidptosis-related lncRNA signature comprising four lncRNAs was established based on the expression of these genes. Among these, AC013652.1 and MCM3AP-AS1 predicted worse prognoses. Furthermore, inhibiting AC013652.1 or MCM3AP-AS1 increased disulfidptosis-associated gene expression and cellular death, which could be reversed by DTT and TCEP.

CONCLUSIONS

This study provides hitherto undocumented evidence of the existence of disulfidptosis and the prognostic value of disulfidptosis-associated genes in COAD. Importantly, we identified lncRNAs AC013652.1 and MCM3AP-AS1, which suppress disulfidptosis and may serve as potential therapeutic targets for COAD.

The metabolism-related lncRNA signature predicts the prognosis of breast cancer patients

Long non-coding RNAs (lncRNAs) involved in metabolism are recognized as significant factors in breast cancer (BC) progression. We constructed a novel prognostic signature for BC using metabolism-related lncRNAs and investigated their underlying mechanisms. The training and validation cohorts were established from BC patients acquired from two public sources: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The prognostic signature of metabolism-related lncRNAs was constructed using the least absolute shrinkage and selection operator (LASSO) cox regression analysis. We developed and validated a new prognostic risk model for BC using the signature of metabolism-related lncRNAs (SIRLNT, SIAH2-AS1, MIR205HG, USP30-AS1, MIR200CHG, TFAP2A-AS1, AP005131.2, AL031316.1, C6orf99). The risk score obtained from this signature was proven to be an independent prognostic factor for BC patients, resulting in a poor overall survival (OS) for individuals in the high-risk group. The area under the curve (AUC) for OS at three and five years were 0.67 and 0.65 in the TCGA cohort, and 0.697 and 0.68 in the GEO validation cohort, respectively. The prognostic signature demonstrated a robust association with the immunological state of BC patients. Conventional chemotherapeutics, such as docetaxel and paclitaxel, showed greater efficacy in BC patients classified as high-risk. A nomogram with a c-index of 0.764 was developed to forecast the survival time of BC patients, considering their risk score and age. The silencing of C6orf99 markedly decreased the proliferation, migration, and invasion capacities in MCF-7 cells. Our study identified a signature of metabolism-related lncRNAs that predicts outcomes in BC patients and could assist in tailoring personalized prevention and treatment plans.

Decoding the regulatory landscape of lncRNAs as potential diagnostic and prognostic biomarkers for gastric and colorectal cancers

Colorectal cancer (CRC) and gastric cancer (GC) are major contributors to cancer-related mortality worldwide. Despite advancements in understanding molecular mechanisms and improved drug treatments, the overall survival rate for patients remains unsatisfactory. Metastasis and drug resistance are major challenges contributing to the high mortality rate in both CRC and GC. Recent research has shed light on the role of long noncoding RNAs (lncRNAs) in the development and progression of these cancers. LncRNAs regulate gene expression through various mechanisms, including epigenetic modifications and interactions with microRNAs (miRNAs) and proteins. They can serve as miRNA precursors or pseudogenes, modulating gene expression at transcriptional and post-transcriptional levels. Additionally, circulating lncRNAs have emerged as non-invasive biomarkers for the diagnosis, prognosis, and prediction of drug therapy response in CRC and GC. This review explores the intricate relationship between lncRNAs and CRC/GC, encompassing their roles in cancer development, progression, and chemoresistance. Furthermore, it discusses the potential of lncRNAs as therapeutic targets in these malignancies. The interplay between lncRNAs, miRNAs, and tumor microenvironment is also highlighted, emphasizing their impact on the complexity of cancer biology. Understanding the regulatory landscape and molecular mechanisms governed by lncRNAs in CRC and GC is crucial for the development of effective diagnostic and prognostic biomarkers, as well as novel therapeutic strategies. This review provides a comprehensive overview of the current knowledge and paves the way for further exploration of lncRNAs as key players in the management of CRC and GC.

Elevated Norepinephrine Stimulates Adipocyte Hyperplasia in Ovine Fetuses With Placental Insufficiency and IUGR

Prevailing hypoxemia and hypoglycemia in near-term fetuses with placental insufficiency-induced intrauterine growth restriction (IUGR) chronically increases norepinephrine concentrations, which lower adrenergic sensitivity and lipid mobilization postnatally, indicating a predisposition for adiposity. To determine adrenergic-induced responses, we examined the perirenal adipose tissue transcriptome from IUGR fetuses with or without hypercatecholaminemia. IUGR was induced in sheep with maternal hyperthermia, and hypercatecholaminemia in IUGR was prevented with bilateral adrenal demedullation. Adipose tissue was collected from sham-operated control (CON) and IUGR fetuses and adrenal-demedullated control (CAD) and IUGR (IAD) fetuses. Norepinephrine concentrations were lower in IAD fetuses than in IUGR fetuses despite both being hypoxemic and hypoglycemic. In IUGR fetuses, perirenal adipose tissue mass relative to body mass was greater compared with the CON, adrenal-demedullated control, and IAD groups. Transcriptomic analysis identified 581 differentially expressed genes (DEGs) in CON vs IUGR adipose tissue and 193 DEGs in IUGR vs IAD adipose tissue. Integrated functional analysis of these 2 comparisons showed enrichment for proliferator-activated receptor signaling and metabolic pathways and identified adrenergic responsive genes. Within the adrenergic-regulated DEGs, we identified transcripts that regulate adipocyte proliferation and differentiation: adipogenesis regulatory factor, C/CCAAT/enhancer binding protein α, and sterol carrier protein 2. DEGs associated with the metabolic pathway included pyruvate dehydrogenase kinase 4, 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 4, IGF-binding proteins (IGFBP-5 and IGFBP-7). Sex-specific expression differences were also found for adipogenesis regulatory factor, pyruvate dehydrogenase kinase 4, IGFBP5, and IGFBP7. These findings indicate that sustained adrenergic stimulation during IUGR leads to adipocyte hyperplasia with alterations in metabolism, proliferation, and preadipocyte differentiation pathways.

Long non-coding RNA PRR7-AS1 promotes osteosarcoma progression via binding RNF2 to transcriptionally suppress MTUS1

Introduction

Osteosarcoma is a common bone malignant tumor in adolescents with high mortality and poor prognosis. At present, the progress of osteosarcoma and effective treatment strategies are not clear. This study provides a new potential target for the progression and treatment of osteosarcoma.

Methods

The relationship between lncRNA PRR7-AS1 and osteosarcoma was analyzed using the osteosarcoma databases and clinical sample testing. Cell function assays and tumor lung metastasis were employed to study the effects of PRR7-AS1 on tumorigenesis in vivo and in vitro. Potential downstream RNF2 of PRR7-AS1 was identified and explored using RNA pulldown and RIP. The GTRD and KnockTF database were used to predict the downstream target gene, MTUS1, and ChIP-qPCR experiments were used to verify the working mechanismy. Rescue experiments were utilized to confirm the role of MTUS1 in the pathway.

Results

Deep mining of osteosarcoma databases combined with clinical sample testing revealed a positive correlation between lncRNA PRR7-AS1 and osteosarcoma progression. Knockdown of PRR7-AS1 inhibited osteosarcoma cell proliferation and metastasis in vitro and in vivo. Mechanistically, RNA pulldown and RIP revealed that PRR7-AS1 may bind RNF2 to play a cancer-promoting role. ChIP-qPCR experiments were utilized to validate the working mechanism of the downstream target gene MTUS1. RNF2 inhibited the transcription of MTUS1 through histone H2A lysine 119 monoubiquitin. Rescue experiments confirmed MTUS1 as a downstream direct target of PRR7-AS1 and RNF2.

Discussion

We identified lncRNA PRR7-AS1 as an important oncogene in osteosarcoma progression, indicating that it may be a potential target for diagnosis and prognosis of osteosarcoma.

Exploring GAS5's impact on prostate cancer: Recent discoveries and emerging paradigms

Novel treatment targets must be discovered to improve the results for patients with prostate cancer, which continues to be a significant worldwide health problem. Growth Arrest-Specific 5 (GAS5) is a long non-coding RNA (lncRNA) that has emerged as a promising target. GAS5 is a non-coding RNA that is a tumour suppressor in many different cancers by reducing cell proliferation and increasing apoptosis. GAS5 influences cell cycle control and apoptosis via interactions with important signalling pathways and microRNAs, as has been shown by recent studies. Furthermore, GAS5 has attracted interest for its diagnostic and prognostic potential in prostate cancer. GAS5 expression is a promising biomarker for disease classification and individualized treatment approaches because of its association with clinicopathological characteristics such as tumour stage, Gleason score, and metastatic potential. Preclinical models have revealed encouraging anticancer benefits from experimental techniques employing GAS5 overexpression or synthetic analogues, indicating the possibility of translational treatments. Whether GAS5 can be used as a diagnostic biomarker and therapeutic target might lead to more effective and individualized ways to fight prostate cancer, improving patient outcomes and quality of life. To utilize its potential for therapy and establish it as a useful addition to the clinical arsenal against this pervasive malignancy, more investigation into the complex molecular pathways of GAS5 in prostate cancer is essential. This review highlights the recent advancements and insights into the role of GAS5 in prostate cancer pathogenesis and progression.

Role of glycosphingolipid biosynthesis coregulators in malignant progression of thymoma

As the most common malignancy from mediastinum, the metabolic reprogramming of thymoma is important in its development. Nevertheless, the connection between the metabolic map and thymoma development is yet to be discovered. Thymoma was categorized into three subcategories by unsupervised clustering of molecular markers for metabolic pathway presentation in the TCGA dataset. Different genes and functions enriched were demonstrated through the utilization of metabolic Gene Ontology (GO) analysis. To identify the main contributors in the development of thymic malignancy, we utilized Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The prognosis of thymoma was evaluated by screening the essential pathways and genes using GSVA scores and machine learning classifiers. Furthermore, we integrated the transcriptomics findings with spectrum metabolomics investigation, detected through LC-MS/MS, in order to establish the essential controller network of metabolic reprogramming during thymoma progression. The thymoma prognosis is related to glycosphingolipid biosynthesis-lacto and neolacto series pathway, of what high B3GNT5 indicate poor survival. The investigation revealed that glycosphingolipid charts have a significant impact on metabolic dysfunction and could potentially serve as crucial targets in the clinical advancement of metabolic therapy.

Pan-cancer analysis of super enhancer-induced PRR7-AS1 as a potential prognostic and immunological biomarker

Introduction: Systematic pan-cancer analysis of the roles and regulatory mechanisms for PRR7-AS1 is currently not available. Methods: In the present study, a comprehensive bioinformatic approach was used to mine the underlying oncogenic effects of PRR7-AS1, including expression status, prognostic value and immune characteristics. Results: We discovered that PRR7-AS1 expression was remarkably upregulated in most cancer types and exhibited a negative correlation with the prognosis. Furthermore, PRR7-AS1 expression was inversely connected with the majority of tumor-infiltrating immune cells, immune scores and immune checkpoint gene expression in pancancer. There was also a significant correlation between PRR7-AS1 expression status and tumor mutational burden, microsatellite instability, and neoantigens in certain tumors. PRR7-AS1 had the best predictive power for immune checkpoint blockade efficacy compared to other well-recognized biomarkers. PRR7-AS1 overexpression could affect cytotoxic T cells-mediated antitumor responses. Functional enrichment analysis revealed that PRR7-AS1 might be involved in the metabolic pathways. Super enhancer activity might have participated in the regulation of PRR7-AS1 expression. And we constructed the competitive endogenous RNA networks for PRR7-AS1. Discussion: In general, PRR7-AS1 had the potential to be a diagnostic, prognostic and immune biomarker for pan cancer. PRR7-AS1 was correlated with an immunosuppressive microenvironment and was a new potential target for immunotherapy. Epigenetic factors were the driving forces for PRR7-AS1 overexpression in tumors.

Comprehensive analysis of ZNF family genes in prognosis, immunity, and treatment of esophageal cancer

BACKGROUND

As a common malignant tumor, esophageal carcinoma (ESCA) has a low early diagnosis rate and poor prognosis. This study aimed to construct the prognostic features composed of ZNF family genes to effectively predict the prognosis of ESCA patients.

METHODS

The mRNA expression matrix and clinical data were downloaded from TCGA and GEO database. Using univariate Cox analysis, lasso regression and multivariate Cox analysis, we screened six prognosis-related ZNF family genes to construct the prognostic model. We then used Kaplan-Meier plot, time-dependent receiver operating characteristic (ROC), multivariable Cox regression analysis of clinical information, and nomogram to evaluate the prognostic value within and across sets, separately and combined. We also validated the prognostic value of the six-gene signature using GSE53624 dataset. The different immune status was observed in the single sample Gene Set Enrichment Analysis (ssGSEA). Finally, real-time quantitative PCR was used to detect the expression of six prognostic ZNF genes in twelve pairs of ESCA and adjacent normal tissues.

RESULTS

A six prognosis-related ZNF family genes model consisted of ZNF91, ZNF586, ZNF502, ZNF865, ZNF106 and ZNF225 was identified. Multivariable Cox regression analysis revealed that six prognosis-related ZNF family genes were independent prognostic factors for overall survival of ESCA patients in TCGA and GSE53624. Further, a prognostic nomogram including the riskScore, age, gender, T, stage was constructed, and TCGA/GSE53624-based calibration plots indicated its excellent predictive performance. Drug Sensitivity and ssGSEA analysis showed that the six genes model was closely related to immune cells infiltration and could be used as a potential predictor of chemotherapy sensitivity.

CONCLUSION

We identified six prognosis-related ZNF family genes model of ESCA, which provide evidence for individualized prevention and 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.

Molecular subtypes based on metabolic genes are potential biomarkers for predicting prognosis and immune responses of clear cell renal cell carcinoma

Due to the existence of tumor molecular heterogeneity, even patients having similar clinicopathological features could have vastly different survival rates. Hence, we aimed to explore novel metabolism-associated genes (MAGs) related molecular subtypes for clear cell renal cell carcinoma (ccRCC) and their immune landscapes for predicting prognosis and immune responses. Gene matrices and clinical information were downloaded from TCGA and ICGC datasets. Consensus clustering was conducted by the R "ConsensusClusterPlus" package. ccRCC patients were successfully divided into three clusters (MC1, MC2, and MC3) based on MAGs in both TCGA and ICGC datasets. Our established three MAGs were significantly associated with chemokine/chemokine receptor, IFN, CYT, angiogenesis, immune checkpoint molecules, tumor-infiltrating immune cells, oncogenic pathways, pan-cancer immune subtypes, and tumor microenvironment (TME) scores or expressions. Moreover, these three metabolic ccRCC subtypes could predict immunotherapeutic responses. We further constructed a characteristic index (LDAscore) in three metabolic ccRCC subtypes and identified LDAscore-related modules by WGCNA. After deep data mining, 10 hub genes were obtained and seven genes (ATRX, BPTF, DHX9, EP300, POLR2B, SIN3A, UBE3A) were finally validated by qRT-PCR. Our results successfully established a novel ccRCC subtype based on MAGs, providing novel insights into metabolism-related ccRCC tumor heterogeneity and facilitating individualized therapy for future work.

A novel chromatin regulator signature predicts the prognosis, clinical features and immunotherapy of colon cancer

Aim: To elucidate the potential function and prognostic value of chromatin regulators (CRs) in colon cancer. Materials & methods: A comprehensive analysis of CR RNA expression data from public databases was conducted. Results: The authors successfully established and validated a 17 CR-based signature using public databases. Ten CRs of the signature were eventually verified at the protein level using the Human Protein Atlas database. Functional enrichment showed that CRs were significantly enriched in cancer-related pathways. This signature was remarkably relevant to immune cell infiltration, immune checkpoints, tumor immune dysfunction and exclusion (TIDE) score and drug sensitivity. Conclusion: The authors identified a novel, reliable prognostic signature for colon cancer. The study provided new insights into the function of CRs and has important clinical implications for immunotherapy for colon cancer.

LncRNA-miRNA axis in tumor progression and therapy response: An emphasis on molecular interactions and therapeutic interventions

Epigenetic factors are critical regulators of biological and pathological mechanisms and they could interact with different molecular pathways. Targeting epigenetic factors has been an idea approach in disease therapy, especially cancer. Accumulating evidence has highlighted function of long non-coding RNAs (lncRNAs) as epigenetic factors in cancer initiation and development and has focused on their association with downstream targets. microRNAs (miRNAs) are the most well-known targets of lncRNAs and present review focuses on lncRNA-miRNA axis in malignancy and therapy resistance of tumors. LncRNA-miRNA regulates cell death mechanisms such as apoptosis and autophagy in cancers. This axis affects tumor metastasis via regulating EMT and MMPs. Besides, lncRNA-miRNA axis determines sensitivity of tumor cells to chemotherapy, radiotherapy and immunotherapy. Based on the studies, lncRNAs can be affected by drugs and genetic tools in cancer therapy and this may affect expression level of miRNAs as their downstream targets, leading to cancer suppression/progression. LncRNAs have both tumor-promoting and tumor-suppressor functions in cancer and this unique function of lncRNAs has complicated their implication in tumor therapy. LncRNA-miRNA axis can also affect other signaling networks in cancer such as PI3K/Akt, STAT3, Wnt/β-catenin and EZH2 among others. Notably, lncRNA/miRNA axis can be considered as a signature for diagnosis and prognosis in cancers.

Prognostic Analysis of LncRNA MCM3AP-AS1 in Colorectal Cancer and the Mechanism of Its Effect on Tumor Cell Activity

To determine the clinical prognostic significance of lncRNA MCM3AP-AS1 in colorectal cancer (CRC) and its preliminary mechanism, 43 CRC patients and 48 healthy individuals were analyzed. Peripheral blood MCM3AP-AS1 was quantified via qRT–PCR in CRC patients at admission and 2 h after surgery and in healthy individuals. Human colon cancer cells (HCT116 and SW480) were transfected with shRNAs targeting upregulation of MCM3AP-AS1 expression (named as sh-MCM3AP-AS1 group) and corresponding negative RNAs (named as sh-MCM3AP-AS1 group). Additionally, the cells were then treated either with 50 mM of the VEGF-specific inhibitor PTK787 (Selleck, USA) (named as inhibition group) or normal saline as a control (named as control group). Before therapy, CRC patients presented a higher MCM3AP-AS1 level than healthy individuals (P < 0.05), and the sensitivity and specificity of MCM3AP-AS1 in predicting the occurrence of CRC were 65.12% and 83.33%, respectively (P < 0.001). After therapy, CRC patients presented a decrease in MCM3AP-AS1 levels, and recurrence was higher in patients who died (P < 0.05). Additionally, the high MCM3AP-AS1 expression group presented a higher mortality than the low MCM3AP-AS1 expression group (P < 0.05). In an in vitro assay, CRC cells showed a higher MCM3AP-AS1 level than CCD-18Co cells, and the sh-MCM3AP-AS1 group presented decreased cell proliferation and invasiveness, whereas the levels apoptosis-associated proteins were increased (P < 0.05). Moreover, the VEGF and VEGFR2 mRNA levels were increased in CRC cells, and VEGF/VEGFR2 pathway-associated proteins were inhibited in the sh-MCM3AP-AS1 group (P < 0.05). Moreover, treatment with PTK787 decreased cell proliferation and invasivness but increased the levels of apoptosis-associated proteins (P < 0.05).

PRR7-AS1 Correlates with Immune Cell Infiltration and Is a Diagnostic and Prognostic Marker for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a highly aggressive malignant disease, and numerous studies have shown that certain functional long noncoding RNAs (lncRNAs) are implicated in the progression of several cancers. The purpose of the research was to determine, using a database, bioinformatics, and statistical analysis, whether or not lncRNA PRR7-AS1 (PRR7-AS1) was related to HCC. TCGA datasets were used to conduct research on the PRR7-AS1 expression pattern in HCC. In order to evaluate the efficacy of GIHCG as a prognostic tool, both survival and Cox regression analyses were carried out. Furthermore, an investigation of the connection between the expression of PRR7-AS1 and immune infiltration in HCC was carried out. In this study, we identified 125 lncRNAs that were significantly dysregulated in HCC and were associated with long-term survival. Among the above 125 lncRNAs, our attention focused on PRR7-AS1. We found that PRR7-AS1 expressions were distinctly overexpressed in HCC samples compared with nontumor samples. ROC assays revealed that PRR7-AS1 effectively differentiated HCC specimens from normal tissues with an AUC of 0.875 (95% CI: 0.840 to 0.911). Moreover, the high PRR7-AS1 expression was associated with advanced clinical stage and poor prognosis of HCC patients. Importantly, the multivariate Cox proportional hazards model suggested that up-expression of PRR7-AS1 was an independent prognostic marker indicating shorter overall survival and disease-specific survival for HCC patients. Finally, we found that PRR7-AS1 expression was associated with the expression of NK CD56bright cells, Th2 cells, TFH, macrophages, Th1 cells, aDC, T helper cells, cytotoxic cells, DC, Tgd, neutrophils, and Th17 cells. Overall, the results of our study show that PRR7-AS1 was a biomarker that could be utilized to predict the prognosis of HCC patients and was linked to the infiltration of immune cells in HCC.

Identification of Metabolism-Related Gene-Based Subgroup in Prostate Cancer

Prostate cancer is still the main male health problem in the world. The role of metabolism in the occurrence and development of prostate cancer is becoming more and more obvious, but it is not clear. Here we firstly identified a metabolism-related gene-based subgroup in prostate cancer. We used metabolism-related genes to divide prostate cancer patients from The Cancer Genome Atlas into different clinical benefit populations, which was verified in the International Cancer Genome Consortium. After that, we analyzed the metabolic and immunological mechanisms of clinical beneficiaries from the aspects of functional analysis of differentially expressed genes, gene set variation analysis, tumor purity, tumor microenvironment, copy number variations, single-nucleotide polymorphism, and tumor-specific neoantigens. We identified 56 significant genes for non-negative matrix factorization after survival-related univariate regression analysis and identified three subgroups. Patients in subgroup 2 had better overall survival, disease-free interval, progression-free interval, and disease-specific survival. Functional analysis indicated that differentially expressed genes in subgroup 2 were enriched in the xenobiotic metabolic process and regulation of cell development. Moreover, the metabolism and tumor purity of subgroup 2 were higher than those of subgroup 1 and subgroup 3, whereas the composition of immune cells of subgroup 2 was lower than that of subgroup 1 and subgroup 3. The expression of major immune genes, such as CCL2, CD274, CD276, CD4, CTLA4, CXCR4, IL1A, IL6, LAG3, TGFB1, TNFRSF4, TNFRSF9, and PDCD1LG2, in subgroup 2 was almost significantly lower than that in subgroup 1 and subgroup 3, which is consistent with the results of tumor purity analysis. Finally, we identified that subgroup 2 had lower copy number variations, single-nucleotide polymorphism, and neoantigen mutation. Our systematic study established a metabolism-related gene-based subgroup to predict outcomes of prostate cancer patients, which may contribute to individual prevention and treatment.

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.

A robust immune-related lncRNA signature for the prognosis of human colorectal cancer

Background: Colorectal cancer (CRC) is one of the most prevalent malignant cancers worldwide. Immune-related long non-coding RNAs (IRlncRNAs) are proved to be essential in the development and progression of carcinoma. The purpose of the present study was to develop and validate a prognostic IRlncRNA signature for CRC patients.

Methods: Gene expression profiles of CRC samples were downloaded from The Cancer Genome Atlas (TCGA) database. Immune-related genes were obtained from the ImmPort database and were used to identify IRlncRNA by correlation analysis. Through LASSO Cox regression analyses, a prognostic signature was constructed. Functional enrichment analysis was performed by gene set enrichment analysis (GSEA). TIMER2.0 web server and tumor immune dysfunction and exclusion (TIDE) algorithm were employed to analyze the association between our model and tumor-infiltrating immune cells and immunotherapy response. The expression levels of IRlncRNAs in cell lines were detected by quantitative real-time PCR (qPCR).

Results: A 9-IRlncRNA signature was developed by a LASSO Cox proportional regression model. Based on the signature, CRC patients were divided into high- and low-risk groups with different prognoses. GSEA results indicated that patients in high-risk group were associated with cancer-related pathways. In addition, patients in low-risk group were found to have more infiltration of anti-tumor immune cells and might show a favorable response to immunotherapy. Finally, the result of qPCR revealed that most IRlncRNAs were differently expressed between normal and tumor cell lines.

Conclusion: The constructed 9-IRlncRNA signature has potential to predict the prognosis of CRC patients and may be helpful to guide personalized immunotherapy.

Comprehensive transcriptome profiling of BET inhibitor-treated HepG2 cells

Hepatocellular carcinoma (HCC) is the most common primary liver cancer and poor prognosis. Emerging evidence suggests that epigenetic alterations play a crucial role in HCC, suggesting epigenetic inhibition as a promising therapeutic approach. Indeed, the bromodomain and extra-terminal (BET) inhibitors inhibit the proliferation and invasion of various cancers but still lack a strong mechanistic rationale. Here, we identified the differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) in human HCC cell line HepG2 treated with the BET inhibitors, JQ1, OTX015, or ABBV-075. We analyzed the correlation between DEmRNAs and DElncRNAs in common for the three inhibitors based on their expression profiles and performed functional annotation pathway enrichment analysis. Most of these shared DEmRNAs and DElncRNAs, including some novel transcripts, were downregulated, indicating decreased proliferation/adhesion and increased apoptosis/inflammation. Our study suggests that BET proteins play a crucial role in regulating cancer progression-related genes and provide a valuable resource for novel putative biomarkers and therapeutic targets in HCC.

Construction of a new immune-related lncRNA model and prediction of treatment and survival prognosis of human colon cancer

Background

An increasing number of studies have shown that immune-related long noncoding RNAs (lncRNAs) do not require a unique expression level. This finding may help predict the survival and drug sensitivity of patients with colon cancer.

Methods

We retrieved original transcriptome and clinical data from The Cancer Genome Atlas (TCGA), sorted the data, differentiated mRNAs and lncRNAs, and then downloaded immune-related genes. Coexpression analysis predicted immune-related lncRNAs (irlncRNAs) and univariate analysis identified differentially expressed irlncRNAs (DEirlncRNAs). We have also amended the lasso pending region. Next, we compared the areas under the curve (AUCs), counted the Akaike information standard (AIC) value of the 3-year receiver operating characteristic (ROC) curve, and determined the cutoff point to establish the best model to differentiate the high or low disease risk group of colon cancer patients.

Results

We reevaluated the patients regarding the survival rate, clinicopathological features, tumor-infiltrating immune cells, immunosuppressive biomarkers, and chemosensitivity. A total of 155 irlncRNA pairs were confirmed, 31 of which were involved in the Cox regression model. After the colon cancer patients were regrouped according to the cutoff point, we could better distinguish the patients based on adverse survival outcomes, invasive clinicopathological features, the specific tumor immune cell infiltration status, high expression of immunosuppressive biomarkers, and low chemosensitivity.

Conclusions

In this study, we established a characteristic model by pairing irlncRNAs to better predict the survival rate, chemotherapy efficacy, and prognostic value of patients with colon cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02508-2.

A Prognostic Model of Bladder Cancer Based on Metabolism-Related Long Non-Coding RNAs

Background

Some studies have revealed a close relationship between metabolism-related genes and the prognosis of bladder cancer. However, the relationship between metabolism-related long non-coding RNAs (lncRNA) regulating the expression of genetic material and bladder cancer is still blank. From this, we developed and validated a prognostic model based on metabolism-associated lncRNA to analyze the prognosis of bladder cancer.

Methods

Gene expression, lncRNA sequencing data, and related clinical information were extracted from The Cancer Genome Atlas (TCGA). And we downloaded metabolism-related gene sets from the human metabolism database. Differential expression analysis is used to screen differentially expressed metabolism-related genes and lncRNAs between tumors and paracancer tissues. We then obtained metabolism-related lncRNAs associated with prognosis by correlational analyses, univariate Cox analysis, and logistic least absolute shrinkage and selection operator (LASSO) regression. A risk scoring model is constructed based on the regression coefficient corresponding to lncRNA calculated by multivariate Cox analysis. According to the median risk score, patients were divided into a high-risk group and a low-risk group. Then, we developed and evaluated a nomogram including risk scores and Clinical baseline data to predict the prognosis. Furthermore, we performed gene-set enrichment analysis (GSEA) to explore the role of these metabolism-related lncRNAs in the prognosis of bladder cancer.

Results

By analyzing the extracted data, our research screened out 12 metabolism-related lncRNAs. There are significant differences in survival between high and low-risk groups divided by the median risk scoring model, and the low-risk group has a more favorable prognosis than the high-risk group. Univariate and multivariate Cox regression analysis showed that the risk score was closely related to the prognosis of bladder cancer. Then we established a nomogram based on multivariate analysis. After evaluation, the modified model has good predictive efficiency and clinical application value. Furthermore, the GSEA showed that these lncRNAs affected bladder cancer prognosis through multiple links.

Conclusions

A predictive model was established and validated based on 12 metabolism-related lncRNAs and clinical information, and we found these lncRNA affected bladder cancer prognosis through multiple links.

Long noncoding RNA ADIRF antisense RNA 1 upregulates insulin receptor substrate 1 to decrease the aggressiveness of osteosarcoma by sponging microRNA-761

An increasing number of studies have supported the critical regulatory actions of long noncoding RNAs (lncRNAs) in osteosarcoma (OS). However, the detailed roles of adipogenesis regulatory factor-antisense RNA 1 (ADIRF-AS1) in OS have not been comprehensively described. Hence, we first detected ADIRF-AS1 expression in OS and evaluated its clinical significance. Functional experiments were then performed to determine the modulatory role of ADIRF-AS1 in OS progression. ADIRF-AS1 was found to be overexpressed in OS, and the overall survival of patients with OS who had high ADIRF-AS1 levels was shorter than that of those with low levels. ADIRF-AS1 knockdown led to restricted proliferation, migration, and invasiveness of OS cells and increased apoptosis. Additionally, ADIRF-AS1 downregulation impeded tumor growth in vivo. Mechanistically, ADIRF-AS1 acted as a competitive endogenous RNA for microRNA-761 (miR-761) that siphoned miR-761 away from its target, namely insulin receptor substrate 1 (IRS1), leading to IRS1 overexpression. Rescue experiments showed that low levels of miR-761 or restoration of IRS1 could neutralize the effects of ADIRF-AS1 ablation in OS cells. In summary, ADIRF-AS1 exacerbates the oncogenicity of the OS cells by targeting the miR-761/IRS1 axis. Our findings may aid in the advancement of lncRNA-directed therapeutics for OS.

A Radioresponse-Related lncRNA Biomarker Signature for Risk Classification and Prognosis Prediction in Non-Small-Cell Lung Cancer

Purpose

Radiotherapy resistance is now recognized as the major obstacle to the effective therapeutic management of non-small-cell lung cancer (NSCLC). As a single biomarker has limited effect in stratifying NSCLC patients, this research aimed to identify long non-coding RNAs (lncRNAs) correlated with radiotherapy response to ameliorate forecast of NSCLC prognosis.

Methods

In a cohort of NSCLC patients with radiotherapy history (n = 96) from TCGA, genetic data of lncRNA expression profiling were performed. To identify radioresponse-related lncRNA sets which dysregulated significantly between radiosensitive (RS) and radioresistant (RR) groups, differential expression analysis was carried out. Cox relative regression was implemented to set up a radioresponse-related risk model. Moreover, we adopted survival analysis to measure the predictive potentiality of the prognosis model.

Results

Four radioresponse-related lncRNAs (CASC19, LINC01977, LINC02471, and MAGI2-AS3) were screened to create a prognostic signature. Then, we described a lncRNA signature-based regulatory network and explored the correlation of the immune microenvironment and the signature. Additionally, in vitro assays uncovered inhibition of LINC01977 weakened radioresistance of NSCLC cells.

Conclusion

We provided a novel radioresponse-related lncRNAs signature with excellent clinical potency for an effective prognostic forecast of patients.