Low GAS5 Levels as a Predictor of Poor Survival in Patients with Lower-Grade Gliomas

The Combination of Physiological and Transcriptomic Approaches Reveals New Insights into the Molecular Mechanisms of Leymus chinensis Growth Under Different Shading Intensities

Leymus chinensis is a grass species in the family Triticeae that is found in the Eurasian grassland region and is known for its outstanding ecological advantages and economic value. However, the increasing adoption of photovoltaic agriculture has modified the light environment for the grass, markedly inhibiting its photosynthesis, growth, and yield. This study used physiological and transcriptomic analyses to investigate the complex response mechanisms of two L. chinensis genotypes (Zhongke No. 3 [Lc3] and Zhongke No. 5 [Lc5]) under shading stress. Growth phenotype analysis revealed the superior growth performance of Lc3 under shading stress, evidenced by enhanced plant height and photosynthetic parameters. Additionally, differentially expressed genes (DEGs) were predominantly enriched in starch and sucrose metabolism and glycolysis/gluconeogenesis pathways, which were the most consistently enriched in both L. chinensis genotypes. However, the flavonoid biosynthesis and galactose metabolism pathways were more enriched in Lc3. Weighted gene co-expression network analysis identified the LcGolS2 gene, which encodes galactinol synthase, as a potential hub gene for resistance to shade stress in comparisons across different cultivars and shading treatments. The use of qRT-PCR analysis further validated the genes involved in these pathways, suggesting that they may play critical roles in regulating the growth and development of L. chinensis under shading conditions. These findings provide new insights into the molecular mechanisms underlying the growth and development of L. chinensis under different shading stress conditions.

Biomolecules to Biomarkers? U87MG Marker Evaluation on the Path towards Glioblastoma Multiforme Pathogenesis

The heterogeneity of the glioma subtype glioblastoma multiforme (GBM) challenges effective neuropathological treatment. The reliance on in vitro studies and xenografted animal models to simulate human GBM has proven ineffective. Currently, a dearth of knowledge exists regarding the applicability of cell line biomolecules to the realm of GBM pathogenesis. Our study's objectives were to address this preclinical issue and assess prominin-1, ICAM-1, PARTICLE and GAS5 as potential GBM diagnostic targets. The methodologies included haemoxylin and eosin staining, immunofluorescence, in situ hybridization and quantitative PCR. The findings identified that morphology correlates with malignancy in GBM patient pathology. Immunofluorescence confocal microscopy revealed prominin-1 in pseudo-palisades adjacent to necrotic foci in both animal and human GBM. Evidence is presented for an ICAM-1 association with degenerating vasculature. Significantly elevated nuclear PARTICLE expression from in situ hybridization and quantitative PCR reflected its role as a tumor activator. GAS5 identified within necrotic GBM validated this potential prognostic biomolecule with extended survival. Here we present evidence for the stem cell marker prominin-1 and the chemotherapeutic target ICAM-1 in a glioma animal model and GBM pathology sections from patients that elicited alternative responses to adjuvant chemotherapy. This foremost study introduces the long non-coding RNA PARTICLE into the context of human GBM pathogenesis while substantiating the role of GAS5 as a tumor suppressor. The validation of GBM biomarkers from cellular models contributes to the advancement towards superior detection, therapeutic responders and the ultimate attainment of promising prognoses for this currently incurable brain cancer.

5‑Aza‑dC suppresses melanoma progression by inhibiting GAS5 hypermethylation

The in‑depth study of melanoma pathogenesis has revealed that epigenetic modifications, particularly DNA methylation, is a universal inherent feature of the development and progression of melanoma. In the present study, the analysis of the tumor suppressor gene growth arrest‑specific transcript 5 (GAS5) demonstrated that its expression was downregulated in melanoma, and its expression level had a certain negative association with its methylation modification level. The promoter of GAS5 presented with detectable CpG islands, and methylation‑specific polymerase chain reaction analysis demonstrated that GAS5 was actually modified by methylation in melanoma tissues and cells; however, no methylation modification of GAS5 was detected in normal tissues. Following the treatment of melanoma cells with 5‑Aza‑2'‑deoxycytidine (5‑Aza‑dC), GAS5 methylation was significantly reversed. The analysis of melanoma cell proliferation revealed that 5‑Aza‑dC inhibited A375 and SK‑MEL‑110 cell proliferation in a time‑dependent manner. Further analysis of apoptosis demonstrated that 5‑Aza‑dC significantly increased the apoptosis level of the two cell lines. Moreover, migration analysis of melanoma cells revealed that 5‑Aza‑dC significantly reduced cell migration. Furthermore, 5‑Aza‑dC significantly decreased the invasive ability of the two cell lines. However, when the expression of GAS5 was silenced, the effects of 5‑Aza‑dC on cell proliferation, apoptosis, invasion and migration were not significant. Furthermore, the subcutaneous injection of A375 cells in nude mice successfully resulted in xenograft tumor formation. However, following an intraperitoneal injection of 5‑Aza‑dC, the volume and weight of xenograft tumors and Ki‑67 expression were significantly reduced, and caspase‑3 activity and GAS5 expression were enhanced; following the silencing of GAS5, the antitumor effect of 5‑Aza‑dC was significantly blocked. On the whole, the present study demonstrates that 5‑Aza‑dC inhibits the growth of melanoma, and its function may be related to the methylation modification of GAS5.

Tumor Suppressive Effects of GAS5 in Cancer Cells

In recent years, long non-coding RNAs (lncRNAs) have been shown to play important regulatory roles in cellular processes. Growth arrests specific transcript 5 (GAS5) is a lncRNA that is highly expressed during the cell cycle arrest phase but is downregulated in actively growing cells. Growth arrests specific transcript 5 was discovered to be downregulated in several cancers, primarily solid tumors, and it is known as a tumor suppressor gene that regulates cell proliferation, invasion, migration, and apoptosis via multiple molecular mechanisms. Furthermore, GAS5 polymorphism was found to affect GAS5 expression and functionality in a cell-specific manner. This review article focuses on GAS5’s tumor-suppressive effects in regulating oncogenic signaling pathways, cell cycle, apoptosis, tumor-associated genes, and treatment-resistant cells. We also discussed genetic polymorphisms of GAS5 and their association with cancer susceptibility.

A nomogram strategy for identifying the subclassification of IDH mutation and ATRX expression loss in lower-grade gliomas

OBJECTIVES

To construct a radiomics nomogram based on multiparametric MRI data for predicting isocitrate dehydrogenase 1 mutation (IDH +) and loss of nuclear alpha thalassemia/mental retardation syndrome X-linked expression (ATRX -) in patients with lower-grade gliomas (LrGG; World Health Organization [WHO] 2016 grades II and III).

METHODS

A total of 111 LrGG patients (76 mutated IDH and 35 wild-type IDH) were enrolled, divided into a training set (n = 78) and a validation set (n = 33) for predicting IDH mutation. IDH + LrGG patients were further stratified into the ATRX - (n = 38) and ATRX + (n = 38) subtypes. A total of 250 radiomics features were extracted from the region of interest of each tumor, including that from T₂ fluid-attenuated inversion recovery (T₂ FLAIR), contrast-enhanced T₁ WI, ASL-derived cerebral blood flow (CBF), DWI-derived ADC, and exponential ADC (eADC). A radiomics signature was selected using the Elastic Net regression model, and a radiomics nomogram was finally constructed using the age, gender information, and above features.

RESULTS

The radiomics nomogram identified LrGG patients for IDH mutation (C-index: training sets = 0.881, validation sets = 0.900) and ATRX loss (C-index: training sets = 0.863, validation sets = 0.840) with good calibration. Decision curve analysis further confirmed the clinical usefulness of the two nomograms for predicting IDH and ATRX status.

CONCLUSIONS

The nomogram incorporating age, gender, and the radiomics signature provided a clinically useful approach in noninvasively predicting IDH and ATRX mutation status for LrGG patients. The proposed method could facilitate MRI-based clinical decision-making for the LrGG patients.

KEY POINTS

• Non-invasive determination of IDH and ATRX gene status of LrGG patients can be obtained with a radiomics nomogram. • The proposed nomogram is constructed by radiomics signature selected from 250 radiomics features, combined with age and gender. • The proposed radiomics nomogram exhibited good calibration and discrimination for IDH and ATRX gene mutation stratification of LrGG patients in both training and validation sets.

Hidden Treasures: Macrophage Long Non-Coding RNAs in Lung Cancer Progression

Ever since RNA sequencing of whole genomes and transcriptomes became available, numerous RNA transcripts without having the classic function of encoding proteins have been discovered. Long non-coding RNAs (lncRNAs) with a length greater than 200 nucleotides were considered as "junk" in the beginning, but it has increasingly become clear that lncRNAs have crucial roles in regulating a variety of cellular mechanisms and are often deregulated in several diseases, such as cancer. Lung cancer is the leading cause of cancer-related deaths and has a survival rate of less than 10%. Immune cells infiltrating the tumor microenvironment (TME) have been shown to have a great effect on tumor development with macrophages being the major cell type within the TME. Macrophages can inherit an inflammatory M1 or an anti-inflammatory M2 phenotype. Tumor-associated macrophages, which are predominantly polarized to M2, favor tumor growth, angiogenesis, and metastasis. In this review, we aimed to describe the complex roles and functions of lncRNAs in macrophages and their influence on lung cancer development and progression through the TME.

Long Non-Coding RNAs in Diagnosis, Treatment, Prognosis, and Progression of Glioma: A State-of-the-Art Review

Glioma is the most common malignant central nervous system tumor with significant mortality and morbidity. Despite considerable advances, the exact molecular pathways involved in tumor progression are not fully elucidated, and patients commonly face a poor prognosis. Long non-coding RNAs (lncRNAs) have recently drawn extra attention for their potential roles in different types of cancer as well as non-malignant diseases. More than 200 lncRNAs have been reported to be associated with glioma. We aimed to assess the roles of the most investigated lncRNAs in different stages of tumor progression and the mediating molecular pathways in addition to their clinical applications. lncRNAs are involved in different stages of tumor formation, invasion, and progression, including regulating the cell cycle, apoptosis, autophagy, epithelial-to-mesenchymal transition, tumor stemness, angiogenesis, the integrity of the blood-tumor-brain barrier, tumor metabolism, and immunological responses. The well-known oncogenic lncRNAs, which are upregulated in glioma, are H19, HOTAIR, PVT1, UCA1, XIST, CRNDE, FOXD2-AS1, ANRIL, HOXA11-AS, TP73-AS1, and DANCR. On the other hand, MEG3, GAS5, CCASC2, and TUSC7 are tumor suppressor lncRNAs, which are downregulated. While most studies reported oncogenic effects for MALAT1, TUG1, and NEAT1, there are some controversies regarding these lncRNAs. Expression levels of lncRNAs can be associated with tumor grade, survival, treatment response (chemotherapy drugs or radiotherapy), and overall prognosis. Moreover, circulatory levels of lncRNAs, such as MALAT1, H19, HOTAIR, NEAT1, TUG1, GAS5, LINK-A, and TUSC7, can provide non-invasive diagnostic and prognostic tools. Modulation of expression of lncRNAs using antisense oligonucleotides can lead to novel therapeutics. Notably, a profound understanding of the underlying molecular pathways involved in the function of lncRNAs is required to develop novel therapeutic targets. More investigations with large sample sizes and increased focus on in-vivo models are required to expand our understanding of the potential roles and application of lncRNAs in glioma.

Prognostic Value of Immune-Related lncRNA SBF2-AS1 in Diffuse Lower-Grade Glioma

LncRNA SET-binding factor 2 (SBF2) antisense RNA1 (SBF2-AS1) has been proven to play an oncogenic role in various types of tumors, but the prognostic role of SBF2-AS1 in tumors, especially in diffuse lower-grade glioma (LGG), is still unclear. Here, we aimed to investigate the prognostic value of SBF2-AS1 in LGG. The LGG expression profiles from The Cancer Genome Atlas (TCGA, n = 524) and Chinese Glioma Genome Atlas (CGGA, n = 431) were mined by Kaplan-Meier analysis, Cox regression analysis, Chi-square test and GSEA analysis. Through Kaplan-Meier analysis, we found the prognosis of LGG patients with high expression of SBF2-AS1 were worse than that of patients with low expression (Log Rank P < 0.001). Cox analysis showed SBF2-AS1 was an independent prognostic factor for poorer overall survival in LGG (P < 0.05). SBF2-AS1 was found to be significantly related to IDH mutation status and SBF2-AS1 was highly expressed in IDH wildtype group. GSEA analysis obtained a total of 126 GO terms and 6 KEGG pathways that were significantly enriched in SBF2-AS1 high expression phenotype (NOM P value < 0.05). We found these 126 GO terms and KEGG pathways were mainly related to immunity. In conclusion, lncRNA SBF2-AS1 expression is an immune-related lncRNA associated with unfavorable overall survival in LGG. SBF2-AS1 could be a reliable prognostic biomarker for patients with LGG.

All-Trans Retinoic Acid Fosters the Multifarious U87MG Cell Line as a Model of Glioblastoma

Glioblastoma multiforme (GBM) is a primary brain cancer of poor prognosis, with existing treatments remaining essentially palliative. Current GBM therapy fails due to rapid reappearance of the heterogeneous neoplasm, with models suggesting that the recurrent growth is from treatment-resistant glioblastoma stem-like cells (GSCs). Whether GSCs depend on survival/proliferative cues from their surrounding microenvironmental niche, particularly surrounding the leading edge after treatment remains unknown. Simulating human GBM in the laboratory relies on representative cell lines and xenograft models for translational medicine. Due to U87MG source discrepancy and differential proliferation responses to retinoic acid treatment, this study highlights the challenges faced by laboratory scientists working with this representative GBM cell line. Investigating the response to all trans-retinoic acid (ATRA) revealed its sequestering of the prominin-1 stem cell marker. ICAM-1 universally present throughout U87MG was enhanced by ATRA, of interest for chemotherapy targeting studies. ATRA triggered diverse expression patterns of long non-coding RNAs PARTICLE and GAS5 in the leading edge and established monolayer growth zone microenvironment. Karyotyping confirmed the female origin of U87MG sourced from Europe. Passaging U87MG revealed the presence of chromosomal anomalies reflective of structural genomic alterations in this glioblastoma cell line. All evidence considered, this study exposes further phenotypic nuances of U87MG which may belie researchers seeking data contributing towards the elusive cure for GBM.

An independently validated survival nomogram for lower-grade glioma

BACKGROUND

Gliomas are the most common primary malignant brain tumor. Diffuse low-grade and intermediate-grade gliomas, which together compose the lower-grade gliomas (LGGs; World Health Organization [WHO] grades II and III), present a therapeutic challenge to physicians due to the heterogeneity of their clinical behavior. Nomograms are useful tools for individualized estimation of survival. This study aimed to develop and independently validate a survival nomogram for patients with newly diagnosed LGG.

METHODS

Data were obtained for newly diagnosed LGG patients from The Cancer Genome Atlas (TCGA) and the Ohio Brain Tumor Study (OBTS) with the following variables: tumor grade (II or III), age at diagnosis, sex, Karnofsky performance status (KPS), and molecular subtype (IDH mutant with 1p/19q codeletion [IDHmut-codel], IDH mutant without 1p/19q codeletion, and IDH wild-type). Survival was assessed using Cox proportional hazards regression, random survival forests, and recursive partitioning analysis, with adjustment for known prognostic factors. The models were developed using TCGA data and independently validated using the OBTS data. Models were internally validated using 10-fold cross-validation and externally validated with calibration curves.

RESULTS

A final nomogram was validated for newly diagnosed LGG. Factors that increased the probability of survival included grade II tumor, younger age at diagnosis, having a high KPS, and the IDHmut-codel molecular subtype.

CONCLUSIONS

A nomogram that calculates individualized survival probabilities for patients with newly diagnosed LGG could be useful to health care providers for counseling patients regarding treatment decisions and optimizing therapeutic approaches. Free online software for implementing this nomogram is provided: https://hgittleman.shinyapps.io/LGG_Nomogram_H_Gittleman/.

KEY POINTS

1. A survival nomogram for lower-grade glioma patients has been developed and externally validated.2. Free online software for implementing this nomogram is provided allowing for ease of use by practicing health care providers.

Non-coding RNAs regulation of macrophage polarization in cancer

Noncoding RNA (ncRNA) transcripts that did not code proteins but regulate their functions were extensively studied for the last two decades and the plethora of discoveries have instigated scientists to investigate their dynamic roles in several diseases especially in cancer. However, there is much more to learn about the role of ncRNAs as drivers of malignant cell evolution in relation to macrophage polarization in the tumor microenvironment. At the initial stage of tumor development, macrophages have an important role in directing Go/No-go decisions to the promotion of tumor growth, immunosuppression, and angiogenesis. Tumor-associated macrophages behave differently as they are predominantly induced to be polarized into M2, a pro-tumorigenic type when recruited with the tumor tissue and thereby favoring the tumorigenesis. Polarization of macrophages into M1 or M2 subtypes plays a vital role in regulating tumor progression, metastasis, and clinical outcome, highlighting the importance of studying the factors driving this process. A substantial number of studies have demonstrated that ncRNAs are involved in the macrophage polarization based on their ability to drive M1 or M2 polarization and in this review we have described their functions and categorized them into oncogenes, tumor suppressors, Juggling tumor suppressors, and Juggling oncogenes.

Novel insights into plasma biomarker candidates in patients with chronic mountain sickness based on proteomics

Chronic mountain sickness (CMS) is a progressive incapacitating syndrome induced by lifelong exposure to hypoxia. In the present study, proteomic analysis was used to identify the differentially expressed proteins (DEPs) and then evaluate the potential plasma biomarkers between CMS and non-CMS groups. A total of 145 DEPs were detected in CMS Han Chinese people who live in the plateau (CMS-HPu), among which 89 were significantly up-regulated and 56 were significantly down-regulated. GO enrichment analysis showed that various biological processes were enriched, including the hydrogen peroxide metabolic/catabolic process, reactive oxygen species (ROS) metabolic, and acute inflammatory response. Protein–protein interaction analysis showed that antioxidant activity, the hydrogen peroxide catabolic process and peroxidase activity were primarily mapped in interaction proteins. Nine modules showed significantly clustering based on WGCNA analysis, with two being the most significant, and GO analysis showed that proteins of both modules were primarily enriched in oxidative stress-related biological processes. Four DEPs increased in CMS patients were evaluated as the candidate biomarkers, and three showed significant AUC: hemoglobin β chain (HB-β), thioredoxin-1 (TRX1), and phosphoglycerate kinase 1 (PGK1). The present study provides insights into the pathogenesis of CMS and further evaluates the potentially biomarkers for its prevention and treatment of it.

The essential role of long non-coding RNA GAS5 in glioma: interaction with microRNAs, chemosensitivity and potential as a biomarker

Glioma is a malignant brain tumor with a generally poor prognosis. Dysregulation of a long non-coding RNA, GAS5, has been detected in numerous cancers, including glioma. Previous studies have suggested that GAS5 plays a significant functional role in glioma, affecting proliferation, metastasis, invasion, and apoptosis. In this review, we describe the roles and mechanisms of GAS5 in glioma. GAS5 may be a biomarker for diagnosis and prognosis, and even a potential target for glioma treatment, and therefore warrants further investigation.

The Non-Coding RNA GAS5 and Its Role in Tumor Therapy-Induced Resistance

The growth arrest-specific transcript 5 (GAS5) is a >200-nt lncRNA molecule that regulates several cellular functions, including proliferation, apoptosis, invasion and metastasis, across different types of human cancers. Here, we reviewed the current literature on the expression of GAS5 in leukemia, cervical, breast, ovarian, prostate, urinary bladder, lung, gastric, colorectal, liver, osteosarcoma and brain cancers, as well as its interaction with various miRNAs and its effect on therapy-related resistance in these malignancies. The general consensus is that GAS5 acts as a tumor suppressor across different tumor types and that its up-regulation results in tumor sensitization to chemotherapy or radiotherapy. GAS5 seems to play a previously unappreciated, but significant role in tumor therapy-induced resistance.

Prognostic and Predictive Value of a Long Non-coding RNA Signature in Glioma: A lncRNA Expression Analysis

The current histologically based grading system for glioma does not accurately predict which patients will have better outcomes or benefit from adjuvant chemotherapy. We proposed that combining the expression profiles of multiple long non-coding RNAs (lncRNAs) into a single model could improve prediction accuracy. We included 1,094 glioma patients from three different datasets. Using the least absolute shrinkage and selection operator (LASSO) Cox regression model, we built a multiple-lncRNA-based classifier on the basis of a training set. The predictive and prognostic accuracy of the classifier was validated using an internal test set and two external independent sets. Using this classifier, we classified patients in the training set into high- or low-risk groups with significantly different overall survival (OS, HR = 8.42, 95% CI = 4.99–14.2, p < 0.0001). The prognostic power of the classifier was then assessed in the other sets. The classifier was an independent prognostic factor and had better prognostic value than clinicopathological risk factors. The patients in the high-risk group were found to have a favorable response to adjuvant chemotherapy (HR = 0.4, 95% CI = 0.25–0.64, p < 0.0001). We built a nomogram that integrated the 10-lncRNA-based classifier and four clinicopathological risk factors to predict 3 and 5 year OS. Gene set variation analysis (GSVA) showed that pathways related to tumorigenesis, undifferentiated cancer, and epithelial–mesenchymal transition were enriched in the high-risk groups. Our classifier built on 10-lncRNAs is a reliable prognostic and predictive tool for OS in glioma patients and could predict which patients would benefit from adjuvant chemotherapy.

Comparative analysis of long noncoding RNAs in long-lived mammals provides insights into natural cancer-resistance

Mouse and rats are staple model organisms that have been traditionally used for oncological studies; however, their short lifespan and highly prone to cancers limit their utilizationsin understanding the mechanisms of cancer resistance. In recent years, several studies of the non-standard long-lived mammalian species like naked mole rat (NMR) have provided new insights of mechanisms in natural anti-cancer. How long-lived species genetically maintain longevity and cancer-resistance remains largely elusive. To better understand the underlying anti-cancer mechanisms in long-lived mammals, we genome widely identified long noncoding RNA (lncRNA) transcripts of two longevous mammals, bowhead whale (BW, Balaena mysticetus) and Brandt's bat (BB, Myotis brandtii) and featured their sequence traits, expression patterns, and their correlations with cancer-resistance. Similar with naked mole rat (NMR, Heterocephalus glaber), the most long-lived rodent, BW and BB lncRNAs show low sequence conservation and dynamic expressions among tissues and physiological stages. By utilizing k-mers clustering, 75-136 of BW, BB and NMR lncRNAs were found in close relation (Pearson's r ≥0.9, p < 0.01) with human ageing diseases related lncRNAs (HAR-Lncs). In addition, we observed thousands of BB and BW lncRNAs strongly co-expressed (r > 0.8 or r <-0.8, p < 0.01) with potential tumour suppressors, indicating that lncRNAs are potentially involved in anti-cancer regulation in long-lived mammals. Our study provides the basis for lncRNA researches in perspectives of evolution and anti-cancer studies. Abbreviations: BW: bowhead whale; BB: Brandt's bat; NMR: naked mole rat; LLM: long-lived mammal; HTS: human tumour-suppressors; PTS: potential tumour suppressor; ARD: ageing related diseases; HAR-Lncs: lncRNAs that related with human ageing diseases; Kmer-lncs: lncRNAs in long-lived mammal species that corelated (Pearson'sr ≥0.9, p < 0.01) with the 10 HAR-Lncs by k-mers clustering; All-lncs: all the lncRNAs in long-lived mammal species; SDE-lncs: significant differentially expressed lncRNAs.

A novel epigenetic signature for overall survival prediction in patients with breast cancer

BACKGROUND

Breast cancer is the most common malignancy in female patients worldwide. Because of its heterogeneity in terms of prognosis and therapeutic response, biomarkers with the potential to predict survival or assist in making treatment decisions in breast cancer patients are essential for an individualised therapy. Epigenetic alterations in the genome of the cancer cells, such as changes in DNA methylation pattern, could be a novel marker with an important role in the initiation and progression of breast cancer.

METHOD

DNA methylation and RNA-seq datasets from The Cancer Genome Atlas (TCGA) were analysed using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox model. Applying gene ontology (GO) and single sample gene set enrichment analysis (ssGSEA) an epigenetic signature associated with the survival of breast cancer patients was constructed that yields the best discrimination between tumour and normal breast tissue. A predictive nomogram was built for the optimal strategy to distinguish between high- and low-risk cases.

RESULTS

The combination of mRNA-expression and of DNA methylation datasets yielded a 13-gene epigenetic signature that identified subset of breast cancer patients with low overall survival. This high-risk group of tumor cases was marked by upregulation of known cancer-related pathways (e.g. mTOR signalling). Subgroup analysis indicated that this epigenetic signature could distinguish high and low-risk patients also in different molecular or histological tumour subtypes (by Her2-, EGFR- or ER expression or different tumour grades). Using Gene Expression Omnibus (GEO) the 13-gene signature was confirmed in four external breast cancer cohorts.

CONCLUSION

An epigenetic signature was discovered that effectively stratifies breast cancer patients into low and high-risk groups. Since its efficiency appears independent of other known classifiers (such as staging, histology, metastasis status, receptor status), it has a high potential to further improve likely individualised therapy in breast cancer.

Prognostic genes of triple-negative breast cancer identified by weighted gene co-expression network analysis

Triple-negative breast cancer (TNBC) is characterized by a deficiency in the estrogen receptor (ER), progesterone receptor (PR) and HER2/neu genes. Patients with TNBC have an increased likelihood of distant recurrence and mortality, compared with patients with other subtypes of breast cancer. The current study aimed to identify novel biomarkers for TNBC. Weighted gene co-expression network analysis (WGCNA) was applied to construct gene co-expression networks; these were used to explore the correlation between mRNA profiles and clinical data, thus identifying the most significant co-expression network associated with the American Joint Committee on Cancer-TNM stage of TNBC. Using RNAseq datasets from The Cancer Genome Atlas, downloaded from the University of California, Santa Cruz, WGCNA identified 23 modules via K-means clustering. The most significant module consisted of 248 genes, on which gene ontology analysis was subsequently performed. Differently Expressed Gene (DEG) analysis was then applied to determine the DEGs between normal and tumor tissues. A total of 42 genes were positioned in the overlap between DEGs and the most significant module. Following survival analysis, 5 genes [GIPC PDZ domain containing family member 1 (GIPC1), hes family bHLH transcription factor 6 (HES6), calmodulin-regulated spectrin-associated protein family member 3 (KIAA1543), myosin light chain kinase 2 (MYLK2) and peter pan homolog (PPAN)] were selected and their association with the American Joint Committee on Cancer-TNM diagnostic stage was investigated. The expression level of these genes in different pathological stages varied, but tended to increase in more advanced pathological stages. The expression of these 5 genes exhibited accurate capacity for the identification of tumor and normal tissues via receiver operating characteristic curve analysis. High expression of GIPC1, HES6, KIAA1543, MYLK2 and PPAN resulted in poor overall survival (OS) in patients with TNBC. In conclusion, via unsupervised clustering methods, a co-expressed gene network with high inter-connectivity was constructed, and 5 genes were identified as biomarkers for TNBC.

Development of a membrane lipid metabolism-based signature to predict overall survival for personalized medicine in ccRCC patients

Background

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma and is characterized by a dysregulation of changes in cellular metabolism. Altered lipid metabolism contributes to ccRCC progression and malignancy.

Method

Associations among survival potential and each gene ontology (GO) term were analyzed by univariate Cox regression. The results revealed that membrane lipid metabolism had the greatest hazard ratio (HR). Weighted gene co-expression network analysis (WGCNA) was applied to determine the key genes associated with membrane lipid metabolism. Consensus clustering was used to identify novel molecular subtypes based on the key genes. LASSO Cox regression was performed to build a membrane lipid metabolism-based signature. The random forest algorithm was applied to find the most important mutations associated with membrane lipid metabolism. Decision trees and nomograms were constructed to quantify risks for individual patients.

Result

Membrane lipid metabolism stratified ccRCC patients into high- and low-risk groups. Key genes were identified by WGCNA. Membrane lipid metabolism-based signatures exhibited higher prediction efficiency than other clinicopathological traits in both whole cohort and subgroup analyses. The random forest algorithm revealed high associations among the membrane lipid metabolism-based signature and BAP1, PBRM1 and VHL mutations. Decision trees and nomograms indicated high efficiency for risk stratification.

Conclusion

Our study might contribute to the optimization of risk stratification for survival and personalized management of ccRCC patients.

Immune Landscape of Invasive Ductal Carcinoma Tumor Microenvironment Identifies a Prognostic and Immunotherapeutically Relevant Gene Signature

Background: Invasive ductal carcinoma (IDC) is a clinically and molecularly distinct disease. Tumor microenvironment (TME) immune phenotypes play crucial roles in predicting clinical outcomes and therapeutic efficacy. Method: In this study, we depict the immune landscape of IDC by using transcriptome profiling and clinical characteristics retrieved from The Cancer Genome Atlas (TCGA) data portal. Immune cell infiltration was evaluated via single-sample gene set enrichment (ssGSEA) analysis and systematically correlated with genomic characteristics and clinicopathological features of IDC patients. Furthermore, an immune signature was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm. A random forest algorithm was applied to identify the most important somatic gene mutations associated with the constructed immune signature. A nomogram that integrated clinicopathological features with the immune signature to predict survival probability was constructed by multivariate Cox regression. Results: The IDC were clustered into low immune infiltration, intermediate immune infiltration, and high immune infiltration by the immune landscape. The high infiltration group had a favorable survival probability compared with that of the low infiltration group. The low-risk score subtype identified by the immune signature was characterized by T cell-mediated immune activation. Additionally, activation of the interferon-α response, interferon-γ response, and TNF-α signaling via the NFκB pathway was observed in the low-risk score subtype, which indicated T cell activation and may be responsible for significantly favorable outcomes in IDC patients. A random forest algorithm identified the most important somatic gene mutations associated with the constructed immune signature. Furthermore, a nomogram that integrated clinicopathological features with the immune signature to predict survival probability was constructed, revealing that the immune signature was an independent prognostic biomarker. Finally, the relationship of VEGFA, PD1, PDL-1, and CTLA-4 expression with the immune infiltration landscape and the immune signature was analyzed to interpret the responses of IDC patients to immunotherapy. Conclusion: Taken together, we performed a comprehensive evaluation of the immune landscape of IDC and constructed an immune signature related to the immune landscape. This analysis of TME immune infiltration landscape has shed light on how IDC respond to immunotherapy and may guide the development of novel drug combination strategies.