A prognostic miRNA based signature in early-stage HER2-positive breast cancer patients.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e12600-e12600
Author(s):  
Anna Adam-Artigues ◽  
Miguel Angel Beltran ◽  
Juan Antonio Carbonell-Asins ◽  
Sheila Zuñiga ◽  
Santiago Moragon ◽  
...  

e12600 Background: In early-stage HER2+ breast cancer (BC), escalation or de-escalation of systemic treatment is an unmet need. Integration of promising biomarkers into risk scoring will further help progressing in the field. We aim to develop a prognostic signature that integrates two miRNAs (A and B) and quantitative and qualitative clinical variables in patients diagnosed with HER2+ BC. Methods: This study was conducted in a retrospective cohort of 45 HER2+ BC patients. Patients received standard treatment for localized disease. We calculated a prognostic signature for disease-free survival (DFS) using principal components analysis for mixed data combining clinicopathological data (Ki67 and axillary lymph node [pN0, pN1, pN2, pN3]) and expression of two microRNAs (we used mir-16 as housekeeping). Multiple DFS prognostic signatures were calculated and goodness of fit was evaluated by means of Akaike’s Information Criterion (AIC) to perform Cox model selection. Signature was then dichotomized into “high risk” and “low risk” using maximally selected Log-Rank statistics by Hothorn and Lausen, as method for optimal cut-off. Kaplan-Meier curves, Log-Rank test and Breslow test were used to ascertain statistical differences in the probability of DFS between high and low risk groups. MiRNA targeted genes were selected and used to perform functional enrichment analysis with the KEGG pathway database. To select significant terms/pathways, p-values were adjusted by the Benjamini-Hochberg method (p < 0.05). Results: MiR-A and miR-B expression was higher in primary tumor of patients who relapse compared to those free of disease after treatment (p = 0.018 and 0.004, respectively). Both miRNAs were strongly correlated (r = 0.84). This signature was significantly associated with relapse of the disease (HR 1.72; CI 95%: 1.243–2.382; p < 0.01, AIC = 114.02). The optimal cut-off of this score was obtained and patients were classified into high and low risk groups. Median DFS of the high-risk was 44 months while it has been not reached yet across the low risk after a median follow-up of 67 months (HR 8.39; p = 0.005, AIC = 111.784). Significant differences in survival between both groups were found (log rank test p < 0.001; Breslow test p = 0.002). miR-A and miR-B functional enrichment analysis returned 55 significant pathways. Interestingly, P53 pathway, apoptosis and cell cycle which are closely related to tumorigenesis and treatment response, were in the top 5 enriched pathways. Conclusions: Both miRNAs included in this signature are related to important biological pathways associated to BC progression. Our new prognostic signature identifies patients with early-stage, HER2+ BC who might be candidates for escalated or de-escalated systemic treatment. This signature was able to classify patients for DFS in high or low risk groups at the moment of BC diagnosis. Further investigations to validate the value of this new signature are on-going.

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yuntao Shi ◽  
Yingying Zhuang ◽  
Jialing Zhang ◽  
Mengxue Chen ◽  
Shangnong Wu

Objective. Although noncoding RNAs, especially the microRNAs, have been found to play key roles in CRC development in intestinal tissue, the specific mechanism of these microRNAs has not been fully understood. Methods. GEO and TCGA database were used to explore the microRNA expression profiles of normal mucosa, adenoma, and carcinoma. And the differential expression genes were selected. Computationally, we built the SVM model and multivariable Cox regression model to evaluate the performance of tumorigenic microRNAs in discriminating the adenomas from normal tissues and risk prediction. Results. In this study, we identified 20 miRNA biomarkers dysregulated in the colon adenomas. The functional enrichment analysis showed that MAPK activity and MAPK cascade were highly enriched by these tumorigenic microRNAs. We also investigated the target genes of the tumorigenic microRNAs. Eleven genes, including PIGF, TPI1, KLF4, RARS, PCBP2, EIF5A, HK2, RAVER2, HMGN1, MAPK6, and NDUFA2, were identified to be frequently targeted by the tumorigenic microRNAs. The high AUC value and distinct overall survival rates between the two risk groups suggested that these tumorigenic microRNAs had the potential of diagnostic and prognostic value in CRC. Conclusions. The present study revealed possible mechanisms and pathways that may contribute to tumorigenesis of CRC, which could not only be used as CRC early detection biomarkers, but also be useful for tumorigenesis mechanism studies.


2021 ◽  
Vol 11 ◽  
Author(s):  
Li-Chun Chang ◽  
Yi-Chiung Hsu ◽  
Han-Mo Chiu ◽  
Koji Ueda ◽  
Ming-Shiang Wu ◽  
...  

BackgroundPatient participation in colorectal cancer (CRC) screening via a stool test and colonoscopy is suboptimal, but participation can be improved by the development of a blood test. However, the suboptimal detection abilities of blood tests for advanced neoplasia, including advanced adenoma (AA) and CRC, limit their application. We aimed to investigate the proteomic landscape of small extracellular vesicles (sEVs) from the serum of patients with colorectal neoplasia and identify specific sEV proteins that could serve as biomarkers for early diagnosis.Materials and MethodsWe enrolled 100 patients including 13 healthy subjects, 12 non-AAs, 13 AAs, and 16 stage-I, 15 stage-II, 16 stage-III, and 15 stage-IV CRCs. These patients were classified as normal control, early neoplasia, and advanced neoplasia. The sEV proteome was explored by liquid chromatography-tandem mass spectrometry. Generalized association plots were used to integrate the clustering methods, visualize the data matrix, and analyze the relationship. The specific sEV biomarkers were identified by a decision tree via Orange3 software. Functional enrichment analysis was conducted by using the Ingenuity Pathway Analysis platform.ResultsThe sEV protein matrix was identified from the serum of 100 patients and contained 3353 proteins, of which 1921 proteins from 98 patients were finally analyzed. Compared with the normal control, subjects with early and advanced neoplasia exhibited a distinct proteomic distribution in the data matrix plot. Six sEV proteins were identified, namely, GCLM, KEL, APOF, CFB, PDE5A, and ATIC, which properly distinguished normal control, early neoplasia, and advanced neoplasia patients from each other. Functional enrichment analysis revealed that APOF+ and CFB+ sEV associated with clathrin-mediated endocytosis signaling and the complement system, which have critical implications for CRC carcinogenesis.ConclusionPatients with colorectal neoplasia had a distinct sEV proteome expression pattern in serum compared with those patients who were healthy and did not have neoplasms. Moreover, the six identified specific sEV proteins had the potential to discriminate colorectal neoplasia between early-stage and advanced neoplasia. Collectively, our study provided a six-sEV protein biomarker panel for CRC diagnosis at early or advanced stages. Furthermore, the implication of the sEV proteome in CRC carcinogenesis via specific signaling pathways was explored.


2021 ◽  
Vol 41 (1) ◽  
Author(s):  
Hang Tong ◽  
Tinghao Li ◽  
Shun Gao ◽  
Hubin Yin ◽  
Honghao Cao ◽  
...  

Abstract Bladder cancer is a common malignant tumour worldwide. Epithelial–mesenchymal transition (EMT)-related biomarkers can be used for early diagnosis and prognosis of cancer patients. To explore, accurate prediction models are essential to the diagnosis and treatment for bladder cancer. In the present study, an EMT-related long noncoding RNA (lncRNA) model was developed to predict the prognosis of patients with bladder cancer. Firstly, the EMT-related lncRNAs were identified by Pearson correlation analysis, and a prognostic EMT-related lncRNA signature was constructed through univariate and multivariate Cox regression analyses. Then, the diagnostic efficacy and the clinically predictive capacity of the signature were assessed. Finally, Gene set enrichment analysis (GSEA) and functional enrichment analysis were carried out with bioinformatics. An EMT-related lncRNA signature consisting of TTC28-AS1, LINC02446, AL662844.4, AC105942.1, AL049840.3, SNHG26, USP30-AS1, PSMB8-AS1, AL031775.1, AC073534.1, U62317.2, C5orf56, AJ271736.1, and AL139385.1 was constructed. The diagnostic efficacy of the signature was evaluated by the time-dependent receiver-operating characteristic (ROC) curves, in which all the values of the area under the ROC (AUC) were more than 0.73. A nomogram established by integrating clinical variables and the risk score confirmed that the signature had a good clinically predict capacity. GSEA analysis revealed that some cancer-related and EMT-related pathways were enriched in high-risk groups, while immune-related pathways were enriched in low-risk groups. Functional enrichment analysis showed that EMT was associated with abundant GO terms or signaling pathways. In short, our research showed that the 14 EMT-related lncRNA signature may predict the prognosis and progression of patients with bladder cancer.


2021 ◽  
Vol 11 ◽  
Author(s):  
JunJie Yu ◽  
WeiPu Mao ◽  
Si Sun ◽  
Qiang Hu ◽  
Can Wang ◽  
...  

PurposeThis study aimed to construct an m6A-related long non-coding RNAs (lncRNAs) signature to accurately predict the prognosis of kidney clear cell carcinoma (KIRC) patients using data obtained from The Cancer Genome Atlas (TCGA) database.MethodsThe KIRC patient data were downloaded from TCGA database and m6A-related genes were obtained from published articles. Pearson correlation analysis was implemented to identify m6A-related lncRNAs. Univariate, Lasso, and multivariate Cox regression analyses were used to identifying prognostic risk-associated lncRNAs. Five lncRNAs were identified and used to construct a prognostic signature in training set. Kaplan–Meier curves and receiver operating characteristic (ROC) curves were applied to evaluate reliability and sensitivity of the signature in testing set and overall set, respectively. A prognostic nomogram was established to predict the probable 1-, 3-, and 5-year overall survival of KIRC patients quantitatively. GSEA was performed to explore the potential biological processes and cellular pathways. Besides, the lncRNA/miRNA/mRNA ceRNA network and PPI network were constructed based on weighted gene co-expression network analysis (WGCNA). Functional Enrichment Analysis was used to identify the biological functions of m6A-related lncRNAs.ResultsWe constructed and verified an m6A-related lncRNAs prognostic signature of KIRC patients in TCGA database. We confirmed that the survival rates of KIRC patients with high-risk subgroup were significantly poorer than those with low-risk subgroup in the training set and testing set. ROC curves indicated that the prognostic signature had a reliable predictive capability in the training set (AUC = 0.802) and testing set (AUC = 0.725), respectively. Also, we established a prognostic nomogram with a high C-index and accomplished good prediction accuracy. The lncRNA/miRNA/mRNA ceRNA network and PPI network, as well as functional enrichment analysis provided us with new ways to search for potential biological functions.ConclusionsWe constructed an m6A-related lncRNAs prognostic signature which could accurately predict the prognosis of KIRC patients.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ke-zhi Li ◽  
Yi-xin Yin ◽  
Yan-ping Tang ◽  
Long Long ◽  
Ming-zhi Xie ◽  
...  

Abstract Background Cancers located on the right and left sides of the colon have distinct clinical and molecular characteristics. This study aimed to explore the regulatory mechanisms of location-specific long noncoding RNAs (lncRNAs) as competing endogenous RNAs (ceRNAs) in colon cancer and identify potential prognostic biomarkers. Method Differentially expressed lncRNAs (DELs), miRNAs (DEMs), and genes (DEGs) between right- and left-side colon cancers were identified by comparing RNA sequencing profiles. Functional enrichment analysis was performed for the DEGs, and a ceRNA network was constructed. Associations between DELs and patient survival were examined, and a DEL-based signature was constructed to examine the prognostic value of these differences. Clinical colon cancer tissues and Gene Expression Omnibus (GEO) datasets were used to validate the results. Results We identified 376 DELs, 35 DEMs, and 805 DEGs between right- and left-side colon cancers. The functional enrichment analysis revealed the functions and pathway involvement of DEGs. A ceRNA network was constructed based on 95 DEL–DEM–DEG interactions. Three DELs (LINC01555, AC015712, and FZD10-AS1) were associated with the overall survival of patients with colon cancer, and a prognostic signature was established based on these three DELs. High risk scores for this signature indicated poor survival, suggesting that the signature has prognostic value for colon cancer. Examination of clinical colon cancer tissues and GEO dataset analysis confirmed the results. Conclusion The ceRNA regulatory network suggests roles for location-specific lncRNAs in colon cancer and allowed the development of an lncRNA-based prognostic signature, which could be used to assess prognosis and determine treatment strategies in patients with colon cancer.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
XinJie Yang ◽  
Sha Niu ◽  
JiaQiang Liu ◽  
Jincheng Fang ◽  
ZeYu Wu ◽  
...  

AbstractGlioblastoma (GBM) is a strikingly heterogeneous and lethal brain tumor with very poor prognosis. LncRNAs play critical roles in the tumorigenesis of GBM through regulation of various cancer-related genes and signaling pathways. Here, we focused on the essential role of EMT and identified 78 upregulated EMT-related genes in GBM through differential expression analysis and Gene set enrichment analysis (GSEA). A total of 301 EMT-related lncRNAs were confirmed in GBM through Spearman correlation analysis and a prognostic signature consisting of seven EMT-related lncRNAs (AC012615.1, H19, LINC00609, LINC00634, POM121L9P, SNHG11, and USP32P3) was established by univariate and multivariate Cox regression analyses. Significantly, Kaplan–Meier analysis and receiver-operating-characteristic (ROC) curve validated the accuracy and efficiency of the signature to be satisfactory. Quantitative real-time (qRT)-PCR assay demonstrated the expression alterations of the seven lncRNAs between normal glial and glioma cell lines. Functional enrichment analysis revealed multiple EMT and metastasis-related pathways were associated with the EMT-related lncRNA prognostic signature. In addition, we observed the degree of immune cell infiltration and immune responses were significantly increased in high-risk subgroup compared with low-risk subgroup. In conclusion, we established an effective and robust EMT-related lncRNA signature which was expected to predict the prognosis and immunotherapy response for GBM patients.


Author(s):  
Shizhi Wang ◽  
Bo Ding ◽  
Mengjing Cui ◽  
Wenjing Yan ◽  
Qianqian Xia ◽  
...  

Fanconi anemia (FA) pathway is a typical and multienzyme-regulated DNA damage repairer that influences the occurrence and development of disease including cancers. Few comprehensive analyses were reported about the role of FA-related genes (FARGs) and their prognostic values in cancers. In this study, a comprehensive pan-cancer analysis on 79 FARGs was performed. According to the correlation analyses between HPV integration sites and FARGs, we found that FARGs played specific and critical roles in HPV-related cancers, especially in cervical cancer (CC). Based on this, a FARGs-associated prognostic risk score (FPS) model was constructed, and subsequently a nomogram model containing the FPS was developed with a good accuracy for CC overall survival (OS) and recurrence-free survival (RFS) outcome prediction. We also used the similar expression pattern of FARGs by consensus clustering analysis to separate the patients into three subgroups that exhibited significant differential OS but not RFS. Moreover, differential expressed genes (DEGs) between the two risk groups or three clusters were identified and immune pathways as well as cell adhesion processes were determined by functional enrichment analysis. Results indicated that FARGs might promote occurrence and development of CC by regulating the immune cells’ infiltration and cell adhesion. In addition, through the machine learning models containing decision tree, random forest, naïve bayes, and support vector machine models, screening of important variables on CC prognosis, we finally determined that ZBTB32 and CENPS were the main elements affecting CC OS, while PALB2 and BRCA2 were for RFS. Kaplan-Meier analysis revealed that bivariate prediction of CC outcome was reliable. Our study systematically analyzed the prognostic prediction values of FARGs and demonstrated their potential mechanism in CC aggressiveness. Results provided perspective in FA pathway-associated modification and theoretical basis for CC clinical treatments.


2021 ◽  
Author(s):  
Junqi Qin ◽  
Zhanyu Xu ◽  
Fanglu Qin ◽  
Jiangbo Wei ◽  
Liqiang Yuan ◽  
...  

Abstract Background: There are few studies on the role of iron metabolism genes in predicting the prognosis of lung adenocarcinoma (LUAD). Our research aims to screen key genes and to establish a prognostic signature that can predict the overall survival rate of lung adenocarcinoma patients. Methods: Genes related to iron metabolism were downloaded from the GeneCards database; in addition, RNA-Seq data and corresponding clinical materials of 594 adenocarcinoma patients from The Cancer Genome Atlas(TCGA) were downloaded. GSE42127 of Gene Expression Omnibus (GEO) database was also further verified. The multi-gene prognostic signature was constructed by the Cox regression model of the Least Absolute Shrinkage and Selection Operator (LASSO). The clinical applicability of the model and its connection with immune cell infiltration was then analyzed. Results: We constructed a prediction signature with 12 genes (HAVCR1, SPN, GAPDH, ANGPTL4, PRSS3, KRT8, LDHA, HMMR, SLC2A1, CYP24A1, LOXL2, TIMP1) in the TCGA test set, and counted the patient's risk value based on this 12-gene signature; patients were split into high and low-risk groups. The survival graph results revealed that the survival prognosis between the high and low-risk groups was significantly different (TCGA: P <0.001, GEO: P = 0.001). Univariate and multivariate Cox regression analysis confirmed that the risk value is a predictor of patient OS (P<0.001). The area under the time-dependent ROC curve (AUC) indicated that our signature had a relatively high true positive rate when predicting the 1-year, 3-year, and 5-year OS of the TCGA cohort, which was 0.735, 0.711, and 0.601, respectively. The analysis of the nomogram and calibration curve showed the predictive ability of the gene model. In addition, immune-related pathways were highlighted in the functional enrichment analysis, and immune response between the two risk groups was observed to be significantly different. All of the results proved the reliability of our iron metabolism-related gene risk prognostic model. Conclusion: We developed and verified a 12-gene prognostic signature, which can help predict the prognosis of lung adenocarcinoma and offer a variety of targeted options for the precise treatment of lung cancer.


Author(s):  
Jian Zhang ◽  
Rui Ding ◽  
Tianlong Wu ◽  
Jingyu Jia ◽  
Xigao Cheng

Osteosarcoma is a common malignant tumor that seriously threatens the lives of teenagers and children. Autophagy is an intracellular metabolic process mediated by autophagy-related genes (ARGs), which is known to be associated with the progression and drug resistance of osteosarcoma. In this study, RNA sequence data from TARGET and genotype-tissue expression (GTEx) databases were analyzed. A six autophagy-related long noncoding RNAs (ARLs) signature that accurately predicted the clinical outcomes of osteosarcoma patients was identified, and the relations between immune response and the ARLs prognostic signature were examined. In addition, we obtained 30 ARGs differentially expressed among osteosarcoma tissue and healthy tissue, and performed functional enrichment analysis on them. To screen for prognostic-related ARGs, univariate and LASSO Cox regression analyses were successively applied. Then, multivariate regression analysis was used to complete construction of the prognostic signature of ARGs. Based on the risk coefficient, we calculated the risk score and grouped the patients. Survival analysis showed that high-risk patients evolve with poor prognosis. And we verified the prognosis model in the GSE21257 cohort. Finally, verification was conducted by qRT-PCR and western blot to measure the expression of genes. The results show that autophagy-related marker models may provide a new therapeutic and diagnostic target for osteosarcoma.


Author(s):  
Jianming Wei ◽  
Bo Wang ◽  
Xibo Gao ◽  
Daqing Sun

BackgroundHepatitis C virus-induced genes (HCVIGs) play a critical role in regulating tumor development in hepatic cancer. The role of HCVIGs in hepatic cancer remains unknown. This study aimed to construct a prognostic signature and assess the value of the risk model for predicting the prognosis of hepatic cancer.MethodsDifferentially expressed HCVIGs were identified in hepatic cancer data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases using the library (“limma”) package of R software. The protein–protein interaction (PPI) network was constructed using the Cytoscape software. Functional enrichment analysis was performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Univariate and multivariate Cox proportional hazard regression analyses were applied to screen for prognostic HCVIGs. The signature of HCVIGs was constructed. Gene Set Enrichment Analysis (GSEA) compared the low-risk and high-risk groups. Finally, the International Cancer Genome Consortium (ICGC) database was used to validate this prognostic signature. Polymerase chain reaction (PCR) was performed to validate the expression of nine HCVIGs in the hepatic cancer cell lines.ResultsA total of 143 differentially expressed HCVIGs were identified in TCGA hepatic cancer dataset. Functional enrichment analysis showed that DNA replication was associated with the development of hepatic cancer. The risk score signature was constructed based on the expression of ZIC2, SLC7A11, PSRC1, TMEM106C, TRAIP, DTYMK, FAM72D, TRIP13, and CENPM. In this study, the risk score was an independent prognostic factor in the multivariate Cox regression analysis [hazard ratio (HR) = 1.433, 95% CI = 1.280–1.605, P &lt; 0.001]. The overall survival curve revealed that the high-risk group had a poor prognosis. The Kaplan–Meier Plotter online database showed that the survival time of hepatic cancer patients with overexpression of HCVIGs in this signature was significantly shorter. The prognostic signature-associated GO and KEGG pathways were significantly enriched in the risk group. This prognostic signature was validated using external data from the ICGC databases. The expression of nine prognostic genes was validated in HepG2 and LO-2.ConclusionThis study evaluates a potential prognostic signature and provides a way to explore the mechanism of HCVIGs in hepatic cancer.


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