Computational identification of mutator-derived lncRNA signatures of genome instability for improving the clinical outcome of cancers: a case study in breast cancer

2019 ◽  
Vol 21 (5) ◽  
pp. 1742-1755 ◽  
Author(s):  
Siqi Bao ◽  
Hengqiang Zhao ◽  
Jian Yuan ◽  
Dandan Fan ◽  
Zicheng Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Genomic Instability ◽  
Genome Instability ◽  
Risk Group ◽  
Expression Profiles ◽  
Risk Groups ◽  
Low Risk ◽  
A Genome ◽  
Significant Difference

Abstract Emerging evidence revealed the critical roles of long non-coding RNAs (lncRNAs) in maintaining genomic instability. However, identification of genome instability-associated lncRNAs and their clinical significance in cancers remain largely unexplored. Here, we developed a mutator hypothesis-derived computational frame combining lncRNA expression profiles and somatic mutation profiles in a tumor genome and identified 128 novel genomic instability-associated lncRNAs in breast cancer as a case study. We then identified a genome instability-derived two lncRNA-based gene signature (GILncSig) that stratified patients into high- and low-risk groups with significantly different outcome and was further validated in multiple independent patient cohorts. Furthermore, the GILncSig correlated with genomic mutation rate in both ovarian cancer and breast cancer, indicating its potential as a measurement of the degree of genome instability. The GILncSig was able to divide TP53 wide-type patients into two risk groups, with the low-risk group showing significantly improved outcome and the high-risk group showing no significant difference compared with those with TP53 mutation. In summary, this study provided a critical approach and resource for further studies examining the role of lncRNAs in genome instability and introduced a potential new avenue for identifying genomic instability-associated cancer biomarkers.

An Autophagy-related Long Non-coding RNA Signature for Breast Cancer

2021 ◽  
Vol 24 ◽  
Author(s):  
Feng Jiang ◽  
Chuyan Wu ◽  
Ming Wang ◽  
Ke Wei ◽  
Jimei Wang
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Risk Groups ◽  
Low Risk ◽  
Optimal Cutoff ◽  
Non Coding Rna ◽  
Long Non Coding Rna

Background: The most prevalent malignant tumor in women is breast cancer (BC). Autophagic therapies have been identified for their contribution in BC cell death. Therefore, the potential prognostic role of long non-coding RNA (lncRNA) related to autophagy in patients with BC was examined. Methods: The lncRNAs expression profiles were derived from The Cancer Genome Atlas (TCGA) database. Throughout univariate Cox regression and multivariate Cox regression test, lncRNA with BC prognosis have been differentially presented. We then defined the optimal cutoff point between high and low-risk groups. The receiver operating characteristic (ROC) curves were drawn to test this signature. In order to examine possible signaling mechanisms linked to these lncRNAs, the Gene Set Enrichment Analysis (GSEA) has been carried out. Results: Based on the lncRNA expression profiles for BC, a 9 lncRNA signature associated with autophagy was developed. The optimal cutoff value for high-risk and low-risk groups was used. The high-risk group had less survival time than the low-risk group. The result of this lncRNA signature was highly sensitive and precise. GSEA study found that the gene sets have been greatly enriched in many cancer pathways. Conclusions: Our signature of 9 lncRNAs related to autophagy has prognostic value for BC, and these lncRNAs related to autophagy may play an important role in BC biology.

Proliferation-, estrogen-, and T-cell-related metagenes to predict outcome after adjuvant/neoadjuvant chemotherapy for operable breast cancer in the ECTO trial.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 1014-1014 ◽  
Author(s):  
Giampaolo Bianchini ◽  
Vera Cappelletti ◽  
Maurizio Callari ◽  
Maria Luisa Carcangiu ◽  
Wolfgang Eiermann ◽  
...  
Keyword(s):  
Breast Cancer ◽  
T Cell ◽  
High Risk ◽  
Risk Group ◽  
Molecular Subtype ◽  
Expression Profiles ◽  
Risk Groups ◽  
Operable Breast Cancer ◽  
Low Risk ◽  
Phase Iii

1014 Background: Predicting recurrence in operable breast cancer (BC) despite optimal chemotherapy would be relevant to new drug development and tailored treatments. Methods: A large series (n=3,154) of public Affymetrix gene-expression profiles (GEP) was used to define prognostic/predictive metagenes in different BC subtypes. In ER+/HER2- a proliferation and an ER-related metagene were combined to predict low, intermediate and high risk of recurrence. In TN and in HER2+ a T cell metagene was used to predict low, intermediate and high risk (higher expression associated with lower risk). The metagenes were validated in patients enrolled in the phase III ECTO trial (Gianni L. JCO 2009) and treated with the same taxane-anthracycline-CMF regimen as neoadjuvant or adjuvant therapy before endocrine therapy if indicated. The outcome was distant event free survival (DEFS). Results: 283 good quality GEPs were obtained (neoadjuvant n=121; adjuvant n=162) from 464 retrospectively collected samples. Median follow-up was 8.9 years. In ER+/HER2- tumors the 10-yrs DEFS was 92.3, 81.2 and 66.6% in low, intermediate and high risk groups, respectively [high vs low HR 4.38 (1.01-19.1) p=.048] according to proliferation and ER-related metagenes. In HER2+ and TN subgroup the 10-yrs DEFS was 97.2, 75.6 and 78.8% in low, intermediate and high risk groups, respectively [high vs low HR 8.73 (1.09-69.8) p=.041]. In TN tumors, the pCR rate was 20% in the high and 61.5% in the low risk group. By combining the predicted risk group in each molecular subtype the 10-yrs DEFS was 95.3, 79.2 and 71.5% in low (24.2%), intermediate (42.7%) and high (33.1%) risk group, respectively [logrank p=0.003; high vs low HR 6.22 (1.87-20.6) p=.002]. ER, PGR, Ki67 and lymphocyte infiltration (LI) by IHC underperformed compared to genomic predictors. Conclusions: BC patients at higher risk of relapse despite optimal standard treatment can be identified who should be spared ineffective and toxic therapy and considered for investigational new strategies. In TN and HER2+, high T cell metagene and to a lesser extent LI are prognostic/predictive and associated with an extremely low risk of DEFS after chemotherapy.

Proteomic Pattern-Based Risk Stratification of Outcome Shows Significant Higher Accuracy Compared to HCT-CI in Patients with AML and MDS,

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 4128-4128
Author(s):  
Christiane E Dobbelstein ◽  
Jochen Metzger ◽  
Elke Dammann ◽  
Uwe Borchert ◽  
Stefanie Buchholz ◽  
...  
Keyword(s):  
Risk Stratification ◽  
Cell Transplantation ◽  
Hematopoietic Cell Transplantation ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
The Other ◽  
Unrelated Donor ◽  
Intermediate Risk ◽  
Significant Difference

Abstract Abstract 4128 Objectives: Allogeneic stem cell transplantation (SCT) is an established treatment for many severe disorders of hematopoiesis. Although SCT has considerable curative potential, its application is limited by transplant-related complications such as infections and graft-versus host disease (GvHD) which could lead to high mortality rates especially in older or less fit patients. Therefore, a careful pre-SCT assessment of risk and benefit is mandatory and different scores have recently emerged as helpful tools. We have previously applied proteomics to identify a specific urinary polypeptide patterns (PP) predictive for developing acute GvHD (aGvHD) (Weissinger EM et al, Blood 2007;109:5511–5519). The aim of this study was to investigate whether the PPs can predict overall outcome after allo-SCT and to compare these findings to those of the hematopoietic cell transplantation comorbidity index (HCT-CI) (Sorror M et al, Blood 2005;106:2912–2919). Methods: In this retrospective analysis from Hannover Medical School, the datasets from all patients (pts) with acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS), who were allo-transplanted from a fully matched donor (matched related/unrelated donor (MRD/MUD)) between 2003–2008 and for whom relevant PP data were available, were included. Pts with a pt-donor HLA-mismatch constellation were excluded from this study. PP data from urine samples which were prospectively collected by day ≥ +7 after allo-SCT were correlated with overall survival (OS), aGvHD, non-relapse mortality (NRM), relapse rate and mortality (RM), and compared to the predictive value of the HCT-CI. Results: PP data were available from 111 pts (97 pts with AML, 14 with MDS; median age 52y; median EBMT score 4; 59 male/52 female; 69 MUD/42 MRD). They were grouped in high (PP-HRG), low (PP-LRG) or intermediate risk groups (PP-IRG). Forty-three pts (39%) belonged to the PP-LRG for aGvHD compared to 47 pts (42%) who were classified PP-HRG. Patient characteristics of PP-LRG and PP-HRG were similar in terms of age, sex and EBMT score (median 4 in both groups). OS compared favorably for the PP-LRG with an OS of 72% vs. 49% for the PP-HRG (p=0.03), also if only reduced intensity conditioning (RIC) was considered (73% vs 42%; p=0.01), respectively. There was a trend for higher incidence of NRM in the PP-HRG compared to PP-LRG (30% vs 14%, p=0.07) for the whole cohort, and a significant higher NRM rate, if only RIC was evaluated (35% vs 11%, p=0.01). However, if risk stratification was based on the HCT-CI, there was no significant difference between high risk (S-HRG) and low risk group (S-LRG) in terms of OS and NRM regardless of intensity of conditioning (OS for whole cohort: 57% vs 45%, p=0.4; OS for RIC: 56% vs 36%, p=0.2; NRM for whole cohort: 20% vs 23%, p=0.8; NRM for RIC: 18% vs 29%, p=0.4). Concerning the PP-IRG, there was a difference in OS between PP-IRG and PP-LRG (38% vs 73%, p=0.02). However, there was no significant difference in OS of the PP-IRG compared to the other PP-based risk groups nor between the HCT-CI based risk groups. Further, NRM did not show a significant difference neither for PP-based nor HCT-CI-based intermediate risk group compared to the other risk groups. Thirty vs 15 pts developed aGvHD in PP-HRG and PP-LRG (64% vs 35%, p<0.01) compared to 48% vs 64% (p=0.2) for S-HRG and S-LRG of the whole cohort, respectively. Incidence of aGvHD differed also significantly in the RIC cohort for PP-HRG and PP-LRG (65% vs 32%, p=0.01), but not for HCT-CI-based risk groups (47% vs 64%, p=0.1). Relapse rates and RM were not significantly different between high and low risk groups, neither for PP-based nor HCT-CI based (whole cohort and RIC subgroup), respectively. Conclusion: Risk stratification according to GvHD-match based PP, which has previously been shown to predict aGvHD, now also allows the identification of patient groups with significantly different OS and NRM. In comparison to the HCT-CI, PP-based prediction shows significantly higher accuracy in this rather homogeneous cohort of patients. Since proteomics is a new method which has been available only at a few centers, further multicenter analyses are essential to determinate the value of PP-based prediction of complications and outcome in SCT. Disclosures: Metzger: Mosaiques Diagnostics GmbH: Employment.

scholarly journals Complete Identification of Autophagy-Related lncRNA Prognostic Signature for Breast Cancer

2021 ◽  
Author(s):  
Wenxi Wang ◽  
Na Li ◽  
Lin Shen ◽  
Qin Zhou ◽  
Zhanzhan Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prognostic Model ◽  
Immune Cell ◽  
Risk Group ◽  
Pearson Correlation ◽  
Risk Groups ◽  
Cell Types ◽  
High Risk Group ◽  
Low Risk ◽  
High Morbidity

Abstract Purpose: Breast cancer (BC) has a relatively high morbidity and mortality for women. The research about BC prognosis is significant. Autophagy is an essential process for tumor progression, which could play its role with lncRNA, a kind of ncRNA that have regulatory roles in multiple tumors. Therefore, constructing an autophagy-related prognostic model for breast cancer is meaningful.Methods: We download data from the TCGA and HADb. Pearson correlation analysis was performed to excavate autophagy-related lncRNA. Then with gene expression difference analysis, etc. we explored the relationship between clinical features and the signature. We applied Cytoscape as well as KEGG, etc. to explore expression condition. And the autophagy status of our signature was investigated by GSEA, etc. We also searched the immune distinction by CIBERSORTx to extend our study and preliminarily verified our study in the end.Results: Firstly, we got an independent autophagy-related lncRNA prognostic model, by which BC patients were divided into high- and low-risk groups. We found that the OS of high-risk group is significantly lower than that of low-risk group, which was identical to those within various clinical subgroups. Then, the KEGG and GO analysis enriched several pathways including autophagy. PCA and GSEA analysis demonstrated the autophagy status. Several distinguishing immune cell types in separated groups were revealed by immunity analysis. Then the verification in the end proved the feasibility of our signature.Conclusion: In this study, we acquired an independent autophagy-related lncRNA signature involving 12 lncRNAs, which contributes to the prediction of prognosis of BC patients.

scholarly journals Hypoxia-Related Long Non-Coding RNA Signature Predicts the Prognosis of Breast Cancer

2021 ◽  
Author(s):  
Jinlong Huo ◽  
Shuang Shen ◽  
Chen Chen ◽  
Rui Qu ◽  
Youming Guo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
T Cell ◽  
High Risk ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Cytolytic Activity ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas

Abstract Background: Breast cancer(BC) is the most common tumour in women. Hypoxia stimulates metastasis in cancer and is linked to poor patient prognosis.Methods: We screened prognostic-related lncRNAs(Long Non-Coding RNAs) from the Cancer Genome Atlas (TCGA) data and constructed a prognostic signature based on hypoxia-related lncRNAs in BC.Results: We identified 21 differentially expressed lncRNAs associated with BC prognosis. Kaplan Meier survival analysis indicated a significantly worse prognosis for the high-risk group(P<0.001). Moreover, the ROC-curve (AUC) of the lncRNAs signature was 0.700, a performance superior to other traditional clinicopathological characteristics. Gene set enrichment analysis (GSEA) showed many immune and cancer-related pathways and in the low-risk group patients. Moreover, TCGA revealed that functions including activated protein C (APC)co-inhibition, Cinnamoyl CoA reductase(CCR),check-point pathways, cytolytic activity, human leukocyte antigen (HLA), inflammation-promotion, major histocompatibility complex(MHC) class1, para-inflammation, T cell co-inhibition, T cell co-stimulation, and Type Ⅰ and Ⅱ Interferons (IFN) responses were significantly different in the low-risk and high-risk groups. Immune checkpoint molecules such as ICOS, IDO1, TIGIT, CD200R1, CD28, PDCD1(PD-1), were also expressed differently between the two risk groups. The expression of m6A-related mRNA indicated that YTHDC1, RBM15, METTL3, and FTO were significantly between the high and low-risk groups.Additionally, immunotherapy in patients with BC from the low-risk group yielded a higher frequency of clinical responses to anti-PD-1/PD-L1 therapy or a combination of anti-PD-1/PD-L1and anti-CTLA4 therapies.Except for lapatinib, the results also show that a high-risk score is related to a higher half-maximal inhibitory concentration (IC50) of chemotherapy drugs.Conclusion: A novel hypoxia-related lncRNAs signature may serve as a prognostic model for BC.

scholarly journals Risk-Adapted Postmastectomy Radiotherapy Decision Based on Prognostic Nomogram for pT1-2N1M0 Breast Cancer: A Multicenter Study

2020 ◽  
Vol 10 ◽  
Author(s):  
Ming Li ◽  
Jinbo Yue ◽  
Xiangbo Wan ◽  
Bin Hua ◽  
Qiuan Yang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Locoregional Recurrence ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Moderate Risk ◽  
Primary Tumor Site ◽  
Free Survival ◽  
Significant Difference

PurposeThe aim of this study was to develop a widely accepted prognostic nomogram and establish a risk-adapted PMRT strategy based on locoregional recurrence for pT1-2N1M0 breast cancer.Methods and MaterialsA total of 3,033 patients with pT1-2N1M0 breast cancer treated at 6 participating institutions between 2000 and 2016 were retrospectively reviewed. A nomogram was developed to predicted locoregional recurrence-free survival (LRFS). A propensity score-matched (PSM) analyses was performed in risk-adapted model.ResultsWith the median follow-up of 65.0 months, the 5-year overall survival (OS), disease free survival (DFS) and LRFS were 93.0, 84.8, and 93.6%, respectively. There was no significant difference between patients who received PMRT or not for the entire group. A nomogram was developed and validated to estimate the probability of 5-year LRFS based on five independent factors including age, primary tumor site, positive lymph nodes number, pathological T stage, and molecular subtype that were selected by a multivariate analysis of patients who did not receive PMRT in the primary cohort. According to the total nomogram risk scores, the entire patients were classified into low- (40.0%), moderate- (42.4%), and high-risk group (17.6%). The 5-year outcomes were significantly different among these three groups (P&lt;0.001). In low-risk group, patients who received PMRT or not both achieved a favorable OS, DFS, and LRFS. In moderate-risk group, no differences in OS, DFS, and LRFS were observed between PMRT and no PMRT patients. In high-risk group, compared with no PMRT, PMRT resulted in significantly different OS (86.8 vs 83.9%, P = 0.050), DFS (77.2 vs 70.9%, P = 0.049), and LRFS (90.8 vs. 81.6%, P = 0.003). After PSM adjustment, there were no significant differences in OS, DFS, and LRFS in low-risk and moderate-risk groups. However, in the high-risk group, PMRT still resulted in significantly better OS, DFS and improved LRFS.ConclusionsThe proposed nomogram provides an individualized risk estimate of LRFS in patients with pT1-2N1M0 breast cancer. Risk-adapted PMRT for high-risk patients is a viable effective strategy.

scholarly journals Identification of an Exosome-Related Signature Associated with Prognosis and Immune Infiltration in Breast Cancer

2022 ◽  
Author(s):  
Qiaonan Guo ◽  
Pengjun Qiu ◽  
Kelun Pan ◽  
Jianpeng Chen ◽  
Jianqing Lin
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
High Risk ◽  
Immune Cell ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Immune Cell Infiltration ◽  
Immune Microenvironment ◽  
Expression Levels

Abstract Background: Exosomes are nanosized vesicles, play a vital role in breast cancer (BC) occurrence, development, invasion, metastasis, and drug resistance. Nevertheless, studies about exosome-related genes in breast cancer are limited. Besides, the interaction between the exosomes and tumor immune microenvironment (TIME) in BC are still unclear. Hence, we procced to study the potential prognostic value of exosome-related genes and their relationship to immune microenvironment in BC. Methods: 121 exosome-related genes were provided by ExoBCD database and 7 final genes were selected from the intersection of 33 differential expression genes (DEGs) and 19 prognostic genes in BC. Based on the expression levels of the 7 genes, downloaded from The Cancer Genome Atlas (TCGA) database, as well as the regression coefficients, the exosome-related signature was constructed. As a result, the patients in TCGA and GEO database were separated into low- and high- risk groups, respectively. Subsequently, R clusterProfiler package was applied to identify the distinct enrichment pathways between high-risk group and low-risk group. The ESTIMATE method was used to calculate ESTIMATE Score and CIBERSORT was applied to evaluate the immune cell infiltration. Eventually, the different expression levels of immune checkpoint related genes were analyzed between the two risk groups. Results: Results of BC prognosis vary from different risk groups. The low-risk groups were identified with higher survival rate both in TCGA and GEO cohort. The DEGs between high- and low- risk groups were found to enrich in immunity, biological processes, and inflammation pathways. The BC patients with higher ESTIMATE scores were revealed to have better overall survival (OS). Subsequently, CD8+ T cells, naive B cells, CD4+ resting memory T cells, monocytes, and neutrophils were upregulated, while M0 macrophages and M2 macrophages were downregulated in the low-risk group. At last, 4 genes reported as the targets of immune checkpoint inhibitors were further analyzed. The low-risk groups in TCGA and GEO cohorts were indicated with higher expression levels of LAG-3, CD274, TIGIT and CTLA-4. Conclusion: According to this study, exosomes are closely associated with the prognosis and immune cell infiltration of BC patients. These findings may make contributions to improve immunotherapy and bring a new sight for BC treatment strategies.

scholarly journals Oseltamivir, Lopinavir/ritonavir and Reduning May Improve Survival of COVID-19 Patients With High-risk

2020 ◽  
Author(s):  
zhiyong zeng ◽  
Chaohui Wu ◽  
Zhenlv Lin ◽  
Yong Ye ◽  
Shaodan Feng ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Low Risk ◽  
Lasso Regression ◽  
Kaplan Meier ◽  
Significant Difference ◽  
Reduning Injection ◽  
Total Point

Abstract Background No therapeutics have demonstrated specific efficacy for patients with COVID-19. Methods We retrospectively evaluated 351 patients with COVID-19 admitted to the Third People's Hospital of Yichang from 9 January to 25 March, 2020.Univariate logistic regression and least absolute shrinkage and selection operator (LASSO) regression were employed to identify risk factors associated with progression, which were then incorporated into the nomogram. Survival of patients between high-risk and low-risk groups was compared by kaplan-Meier analysis. Moreover, we assessed the effects of existing common drugs on survival of patients with high-risk. Results Based on the LASSO, four variables (white blood cell, C-reactive protein, whether lymphocyte ≥ 0.8 × 109/L, and whether lactate dehydrogenase ≥ 400 U/L) were selected for construction of the nomogram. Patients in the total cohort were stratified into low-risk group (total point < 160) and high-risk group (total point ≥ 160). Kaplan-Meier analysis demonstrated that there was significant difference in survival of patients between high-risk and low-risk groups (8-week survival rate: 71.41% vs 100%, P < 0.0001). Among the common drugs, we found that patients with high-risk received oseltamivir, lopinavir/ritonavir or Reduning injection exhibited better survival. The combination of these three drugs showed the effect of improving survival, although single drug may have no effect in different grouping analysis. Conclusions The combination of oseltamivir, lopinavir/ritonavir and Reduning injection may improve survival of COVID-19 patients with high-risk identified by our simple-to-use nomogram.

scholarly journals Risk Evaluation of Early-stage Hormone Receptor-positive and Human Epidermal Growth Factor Receptor 2-negative Breast Cancer Patients: a Population-based Study From Taiwan

2021 ◽  
Author(s):  
Lei Lei ◽  
Han-Ching Chan ◽  
Tzu-Pin Lu ◽  
Hung-Chun Skye Cheng
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Prognostic Value ◽  
Risk Group ◽  
Early Stage ◽  
Growth Factor Receptor ◽  
Risk Groups ◽  
Low Risk ◽  
Hormone Receptor Positive ◽  
Epidermal Growth

Abstract PURPOSE: To assess the prognostic value of the Dutch criteria for patients with early-stage hormone receptor-positive and human epidermal growth factor receptor 2-negative breast cancer from the Taiwan Cancer Database. PATIENTS AND METHODS:We included 8,295 patients with early-stage node-negative breast cancer who underwent surgery during January 2008–December 2012. Patients were stratified into low- and high-risk groups based on the Dutch criteria. The Kaplan–Meier method and log-rank test were used to estimate the difference in breast cancer-specific survival (BCSS) and overall survival (OS) between groups. Multivariable analysis was used to evaluate the prognostic value of the Dutch criteria.RESULTS: Overall, the low-risk and high-risk groups comprised 5,375 and 2,920 patients, respectively. In the low- and high-risk groups, the 5-year BCSS rate was 99.6% and 98.2% (P<0.0001) and the 5-year OS rate was 98.3% and 96.8% (P<0.0001), respectively. The hazard ratio for BCSS was 4.18 (95% confidence interval [CI], 2.63–6.63, P<0.0001), and the hazard ratio for OS was 1.94 (95% CI, 1.48–2.55); both were significantly poorer in the high-risk group than in the low-risk group. In the low-risk group, the 5-year BCSS and OS of patients who did and did not receive adjuvant chemotherapy were similar (99.5% versus 99.6% [P=0.927] and 98.8% and 98.1% [P=0.0683], respectively). CONCLUSIONS: The prognosis of low-risk patients as classified using the Dutch criteria is excellent with or without adjuvant chemotherapy. The benefit of multi-gene testing for chemotherapy decision-making might be minimal in these patients.

scholarly journals Bioinformatic identification of genomic instability-associated lncRNAs signatures for improving the clinical outcome of cervical cancer by a prognostic model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jian Zhang ◽  
Nan Ding ◽  
Yongxing He ◽  
Chengbin Tao ◽  
Zhongzhen Liang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Cancer Patients ◽  
Genomic Instability ◽  
Prognostic Model ◽  
Risk Group ◽  
Expression Profiles ◽  
Risk Groups ◽  
Training Set

AbstractThe research is executed to analyze the connection between genomic instability-associated long non-coding RNAs (lncRNAs) and the prognosis of cervical cancer patients. We set a prognostic model up and explored different risk groups' features. The clinical datasets and gene expression profiles of 307 patients have been downloaded from The Cancer Genome Atlas database. We established a prognostic model that combined somatic mutation profiles and lncRNA expression profiles in a tumor genome and identified 35 genomic instability-associated lncRNAs in cervical cancer as a case study. We then stratified patients into low-risk and high-risk groups and were further checked in multiple independent patient cohorts. Patients were separated into two sets: the testing set and the training set. The prognostic model was built using three genomic instability-associated lncRNAs (AC107464.2, MIR100HG, and AP001527.2). Patients in the training set were divided into the high-risk group with shorter overall survival and the low-risk group with longer overall survival (p < 0.001); in the meantime, similar comparable results were found in the testing set (p = 0.046), whole set (p < 0.001). There are also significant differences in patients with histological grades, FIGO stages, and different ages (p < 0.05). The prognostic model focused on genomic instability-associated lncRNAs could predict the prognosis of cervical cancer patients, paving the way for further research into the function and resource of lncRNAs, as well as a key approach to customizing individual care decision-making.

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