Ferroptosis-Related Long Non-Coding RNAs and the Roles of LASTR in Stomach Adenocarcinoma

Abstract Background: Ferroptosis is a form of cell death involved in diverse physiological context. Increasing evidence suggests that there is a closely regulatory relationship between ferroptosis and long noncoding RNAs (lncRNAs).Method: RNA-sequencing data from The Cancer Genome Atlas (TCGA) data resource and ferroptosis-related genes from FerrDb (http://www.zhounan.org/ferrdb/) data resource were employed to select differentially expressed lncRNAs. We performed Univariate Cox regression and multivariate Cox analyses analysis on these differentially expressed lncRNAs to screen independent predictive factors. Subsequently, we established two signatures for predicting overall survival (OS) and progression-free survival (PFS). Finally, experiments were conducted to verify the roles of LASTR in gastric cancer (GC).Results: We identified 12 differentially expressed lncRNAs linked with OS and 13 associated with PFS. Kaplan-Meier(K-M) analyses exhibited that the high-risk group was related to a poor prognosis of stomach adenocarcinoma (STAD). The AUCs of the OS, as well as PFS signatures of lncRNAs were 0.734 and 0.771, respectively, indicating their excellent efficacy in predicting STAD prognosis. Our experimental results illustrated that the inhibition of LASTR inhibited tumor proliferation and migration in GC.Conclusion: This comprehensive evaluation of the ferroptosis-related lncRNA landscape in STAD unearthed novel lncRNAs related to carcinogenesis. In addition, we also experimentally confirmed the effects of LASTR on proliferation, migration and ferroptosis. These results provide potential novel targets for tumor treatment and promote personalized medicine.

Download Full-text

Identification of the angiogenesis related genes for predicting prognosis of patients with gastric cancer

BMC Gastroenterology ◽

10.1186/s12876-021-01734-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Sheng Zheng ◽

Zizhen Zhang ◽

Ning Ding ◽

Jiawei Sun ◽

Yifeng Lin ◽

...

Keyword(s):

Gastric Cancer ◽

Overall Survival ◽

High Efficiency ◽

Cox Regression ◽

Risk Group ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Lasso Regression ◽

Sequencing Data ◽

Cox Regression Analysis

Abstract Introduction Angiogenesis is a key factor in promoting tumor growth, invasion and metastasis. In this study we aimed to investigate the prognostic value of angiogenesis-related genes (ARGs) in gastric cancer (GC). Methods mRNA sequencing data with clinical information of GC were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The differentially expressed ARGs between normal and tumor tissues were analyzed by limma package, and then prognosis‑associated genes were screened using Cox regression analysis. Nine angiogenesis genes were identified as crucially related to the overall survival (OS) of patients through least absolute shrinkage and selection operator (LASSO) regression. The prognostic model and corresponding nomograms were establish based on 9 ARGs and verified in in both TCGA and GEO GC cohorts respectively. Results Eighty-five differentially expressed ARGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that ARGs-related signaling pathway genes were highly related to tumor angiogenesis development. Kaplan–Meier analysis revealed that patients in the high-risk group had worse OS rates compared with the low-risk group in training cohort and validation cohort. In addition, RS had a good prognostic effect on GC patients with different clinical features, especially those with advanced GC. Besides, the calibration curves verified fine concordance between the nomogram prediction model and actual observation. Conclusions We developed a nine gene signature related to the angiogenesis that can predict overall survival for GC. It’s assumed to be a valuable prognosis model with high efficiency, providing new perspectives in targeted therapy.

Download Full-text

A 13-Gene Metabolic Prognostic Signature Is Associated With Clinical and Immune Features in Stomach Adenocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.612952 ◽

2021 ◽

Vol 11 ◽

Author(s):

Zaisheng Ye ◽

Miao Zheng ◽

Yi Zeng ◽

Shenghong Wei ◽

He Huang ◽

...

Keyword(s):

Risk Score ◽

Molecular Mechanisms ◽

Cancer Metabolism ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Stomach Adenocarcinoma ◽

Prognostic Prediction

Patients with advanced stomach adenocarcinoma (STAD) commonly show high mortality and poor prognosis. Increasing evidence has suggested that basic metabolic changes may promote the growth and aggressiveness of STAD; therefore, identification of metabolic prognostic signatures in STAD would be meaningful. An integrative analysis was performed with 407 samples from The Cancer Genome Atlas (TCGA) and 433 samples from Gene Expression Omnibus (GEO) to develop a metabolic prognostic signature associated with clinical and immune features in STAD using Cox regression analysis and least absolute shrinkage and selection operator (LASSO). The different proportions of immune cells and differentially expressed immune-related genes (DEIRGs) between high- and low-risk score groups based on the metabolic prognostic signature were evaluated to describe the association of cancer metabolism and immune response in STAD. A total of 883 metabolism-related genes in both TCGA and GEO databases were analyzed to obtain 184 differentially expressed metabolism-related genes (DEMRGs) between tumor and normal tissues. A 13-gene metabolic signature (GSTA2, POLD3, GLA, GGT5, DCK, CKMT2, ASAH1, OPLAH, ME1, ACYP1, NNMT, POLR1A, and RDH12) was constructed for prognostic prediction of STAD. Sixteen survival-related DEMRGs were significantly related to the overall survival of STAD and the immune landscape in the tumor microenvironment. Univariate and multiple Cox regression analyses and the nomogram proved that a metabolism-based prognostic risk score (MPRS) could be an independent risk factor. More importantly, the results were mutually verified using TCGA and GEO data. This study provided a metabolism-related gene signature for prognostic prediction of STAD and explored the association between metabolism and the immune microenvironment for future research, thereby furthering the understanding of the crosstalk between different molecular mechanisms in human STAD. Some prognosis-related metabolic pathways have been revealed, and the survival of STAD patients could be predicted by a risk model based on these pathways, which could serve as prognostic markers in clinical practice.

Download Full-text

Features of alternative splicing in stomach adenocarcinoma and their clinical implication: A research based on massive sequencing data

10.21203/rs.2.20011/v3 ◽

2020 ◽

Author(s):

Yuanyuan Zhang ◽

Qian Niu ◽

Yun Han ◽

Xingyu Liu ◽

Jie Jiang ◽

...

Keyword(s):

Alternative Splicing ◽

High Efficiency ◽

Cox Regression ◽

Area Under The Curve ◽

The Cancer Genome Atlas ◽

Regulation Mechanism ◽

Sequencing Data ◽

Cox Regression Analysis ◽

Molecular Events ◽

Stomach Adenocarcinoma

Abstract Background: Alternative splicing (AS) offers a main mechanism to form protein polymorphism. A growing body of evidence indicates the correlation between splicing disorders and carcinoma. Nevertheless, an overall analysis of AS signatures in stomach adenocarcinoma (STAD) is absent and urgently needed.Methods: Within this work, genetic expression and clinical data of STAD were queried from The Cancer Genome Atlas (TCGA), and profiles of AS events were searched from the SpliceSeq database. Cox regression analysis found survival associated AS events. Finally, the splicing network was constructed to reflect the correlation between survival associated AS events and splicing factors (SF).Results: 2042 splicing events were confirmed as prognostic molecular events. Furthermore, the final prognostic signature constructed by 10 AS events gave good result with an area under the curve (AUC) of receiver operating characteristic (ROC) curve up to 0.902 for 5 years, showing high potency in predicting patient outcome. We built the splicing regulatory network to show the internal regulation mechanism of splicing events in STAD. QKI may play a significant part in the prognosis induced by splicing events.Conclusions: In our study, a high-efficiency prognostic prediction model was built for STAD patients, and the results showed that AS events could become potential prognostic biomarkers for STAD. Meanwhile, QKI may become an important target for drug design in the future.

Download Full-text

Features of alternative splicing in stomach adenocarcinoma and their clinical implication: A research based on massive sequencing data

10.21203/rs.2.20011/v2 ◽

2020 ◽

Author(s):

Yuanyuan Zhang ◽

Qian Niu ◽

Yun Han ◽

Xingyu Liu ◽

Jie Jiang ◽

...

Keyword(s):

Alternative Splicing ◽

High Efficiency ◽

Cox Regression ◽

Area Under The Curve ◽

The Cancer Genome Atlas ◽

Regulation Mechanism ◽

Sequencing Data ◽

Cox Regression Analysis ◽

Molecular Events ◽

Stomach Adenocarcinoma

Download Full-text

Differentially expressed liver exosome-related genes as a candidate prognostic biomarker for hepatocellular carcinoma

10.21203/rs.3.rs-105334/v1 ◽

2020 ◽

Author(s):

Bangyou Zuo ◽

Haitao Zhao ◽

Jin Bian ◽

Junyun Long ◽

Xu Yang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Cell Communication ◽

High Risk Group ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Differentially Expressed

Abstract Background The function of exosome includes cell-to-cell communication, neovascularization, and metastasis of cancer cell and drug resistance, which plays an important part in the occurrence and progression of hepatocellular carcinoma (HCC). Because the mechanism in this area is less studied, our goal is to identify exosome-related genes in HCC, establish a reliable prognostic model for liver cancer patients, and explore its underlying mechanisms. Methods The exoRbase database and The Cancer Genome Atlas (TCGA) database were used to analyze differentially expressed genes (DEGs). Cox regression and LASSO analysis were applied to determine DEGs closely related to overall survival (OS). Then the exosome-related prognostic model was constructed in TCGA and validated in the database of International Cancer Genome Consortium (ICGC). Nomogram graph was performed to predict the survival. CIBERSORT was used to estimate the score of different type of immune cells. DEGs related to immunotherapy are used to predict the effect of immunotherapy. Results 48 exosome-related DEGs were obtained and five genes (XPO1, IFI30, FBXO16, CALM1, MORC3) among them were selected to construct predictive model. Then we divided the HCC patients into low-risk and high-risk groups by the best cut-off value according to the X-tile software. The high-risk related to exosome were significantly associated with a poor prognosis. Moreover, the features related to exosome could positively regulate immune response. At the same time, the proportion of T cell regulatory factors (Tregs) and macrophages M2 is higher in the high-risk group, and high-risk group exhibited higher expression of immune checkpoint molecular including PD-L1, PD-L2, TIGIT, and IDO1. Conclusions Overall, our research showed that markers related to exosomes were potential biomarkers for the prognosis of HCC, providing an immunological perspective for the development of precision treatment.

Download Full-text

Features of alternative splicing in stomach adenocarcinoma and their clinical implication: A research based on massive sequencing data

10.21203/rs.2.20011/v1 ◽

2020 ◽

Author(s):

Yuanyuan Zhang ◽

Qian Niu ◽

Yun Han ◽

Xingyu Liu ◽

Jie Jiang ◽

...

Keyword(s):

Alternative Splicing ◽

High Efficiency ◽

Cox Regression ◽

Area Under The Curve ◽

The Cancer Genome Atlas ◽

Regulation Mechanism ◽

Sequencing Data ◽

Cox Regression Analysis ◽

Molecular Events ◽

Stomach Adenocarcinoma

Abstract Background: Alternative splicing (AS) offers a main mechanism to form protein polymorphism. A growing body of evidence indicates the correlation between splicing disorders and carcinoma. Nevertheless, an overall analysis of AS signatures in stomach adenocarcinoma (STAD) is absent and urgently needed. Methods: Within this work, genetic expression and clinical data of STAD were queried from The Cancer Genome Atlas (TCGA), and profiles of AS events were searched from the SpliceSeq database. Cox regression analysis found survival associated AS events. Finally, the splicing network was constructed to reflect the correlation between survival associated AS events and splicing factors (SF). Results: 2042 splicing events were confirmed as prognostic molecular events. Furthermore, the final prognostic signature constructed by 10 AS events gave good result with an area under the curve (AUC) of receiver operating characteristic (ROC) curve up to 0.902 for 5 years, showing high potency in predicting patient outcome. We built the splicing regulatory network to show the internal regulation mechanism of splicing events in STAD. QKI may play a significant part in the prognosis induced by splicing events. Conclusions: In our study, a high-efficiency prognostic prediction model was built for STAD patients, and the results showed that AS events could become potential prognostic biomarkers for STAD. Meanwhile, QKI may become an important target for drug design in the future.

Download Full-text

Effect of Different Expression of Immune-Related lncRNA on Colon Adenocarcinoma and Its Relation to Prognosis

BioMed Research International ◽

10.1155/2020/6942740 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Meiwei Mu ◽

Yi Tang ◽

Zheng Yang ◽

Yuling Qiu ◽

Xiaohong Li ◽

...

Keyword(s):

Cox Regression ◽

Colon Adenocarcinoma ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Future Research ◽

Sequencing Data ◽

Tissue Samples ◽

Gene Sets ◽

Cancer Genome Atlas ◽

Immune Related Genes

Objective. To explore the expression of immune-related lncRNAs in colon adenocarcinoma and find out the effect on how these lncRNAs influence the development and prognosis of colon adenocarcinoma. Method. Transcriptome data of colon adenocarcinoma from The Cancer Genome Atlas (TCGA) were downloaded, and gene sets “IMMUNE RESPONSE” and “IMMUNE SYSTEM PROCESS” were sought from the Molecular Signatures Database (MSigDB). The expression of immune-related genes was extracted that were immune-related mRNAs. Then, the immune-related lncRNAs were sought out by utilizing of the above data. Clinical traits were combined with immune-related lncRNAs, so that prognostic-related lncRNAs were identified by Cox regression. Multivariate Cox regression was built to calculate risk scores. Relationships between clinical traits and immune-related lncRNAs were also calculated. Result. A total of 480 colorectal adenocarcinoma patients and 41 normal control patients’ transcriptome sequencing data of tissue samples were obtained from TCGA database. 918 immune-related lncRNAs were screened. Cox regression showed that 34 immune-related lncRNAs were associated with colon adenocarcinoma prognosis. Seven lncRNAs were independent risk factors. Conclusion. This study revealed that some lncRNAs can affect the development and prognosis of colon adenocarcinoma. It may provide new theory evidence of molecular mechanism for the future research and molecular targeted therapy of colon adenocarcinoma.

Download Full-text

Immune Characteristics-Related Typing of Colorectal Cancer and the Establishment of 14-Gene Prognostic Model

10.21203/rs.3.rs-1093264/v1 ◽

2021 ◽

Author(s):

Jianxing Ma ◽

Chen Wang

Keyword(s):

Colorectal Cancer ◽

Risk Model ◽

Cox Regression ◽

Nonnegative Matrix ◽

Test Group ◽

Progression Free Survival ◽

Risk Groups ◽

The Cancer Genome Atlas ◽

Lasso Regression ◽

Immune Related Genes

Abstract This study is to establish NMF (nonnegative matrix factorization) typing related to the tumor microenvironment (TME) of colorectal cancer (CRC) and to construct a gene model related to prognosis to be able to more accurately estimate the prognosis of CRC patients. NMF algorithm was used to classify samples merged clinical data of differentially expressed genes (DEGs) of TCGA that are related to the TME shared in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, and survival differences between subtype groups were compared. By using createData Partition command, TCGA database samples were randomly divided into train group and test group. Then the univariate Cox analysis, Lasso regression and multivariate Cox regression models were used to obtain risk model formula, which is used to score the samples in the train group, test group and GEO database, and to divide the samples of each group into high-risk and low-risk groups, according to the median score of the train group. After that, the model was validated. Patients with CRC were divided into 2, 3, 5 subtypes respectively. The comparison of patients with overall survival (OS) and progression-free survival (PFS) showed that the method of typing with the rank set to 5 was the most statistically significant (p=0.007, p<0.001, respectively). Moreover, the model constructed containing 14 immune-related genes (PPARGC1A, CXCL11, PCOLCE2, GABRD, TRAF5, FOXD1, NXPH4, ALPK3, KCNJ11, NPR1, F2RL2, CD36, CCNF, DUSP14) can be used as an independent prognostic factor, which is superior to some previous models in terms of patient prognosis. The 5-type typing of CRC patients and the 14 immune-related genes model constructed by us can accurately estimate the prognosis of patients with CRC.

Download Full-text

A Risk Signature of Nine Autophagy-Related Genes Associated With Immune Microenvironment and Clinical Prognosis in Wilms Tumor

10.21203/rs.3.rs-892534/v1 ◽

2021 ◽

Author(s):

Shaopei Ye ◽

Wenbin Tang ◽

Ke Huang

Keyword(s):

Regression Analysis ◽

High Risk ◽

Risk Score ◽

Cox Regression ◽

Wilms Tumor ◽

Risk Group ◽

High Risk Group ◽

Differentially Expressed ◽

Clinical Prognosis ◽

Cox Regression Analysis

Abstract Background: Autophagy is a biological process to eliminate dysfunctional organelles, aggregates or even long-lived proteins. . Nevertheless, the potential function and prognostic values of autophagy in Wilms Tumor (WT) are complex and remain to be clarifed. Therefore, we proposed to systematically examine the roles of autophagy-associated genes (ARGs) in WT.Methods: Here, we obtained differentially expressed autophagy-related genes (ARGs) between healthy and Wilms tumor from Therapeutically Applicable Research To Generate Effective Treatments(TARGET) and The Cancer Genome Atlas (TCGA) database. The functionalities of the differentially expressed ARGs were analyzed using Gene Ontology. Then univariate COX regression analysis and multivariate COX regression analysis were performed to acquire nine autophagy genes related to WT patients’ survival. According to the risk score, the patients were divided into high-risk and low-risk groups. The Kaplan-Meier curve demonstrated that patients with a high-risk score tend to have a poor prognosis.Results: Eighteen DEARGs were identifed, and nine ARGs were fnally utilized to establish the FAGs based signature in the TCGA cohort. we found that patients in the high-risk group were associated with mutations in TP53. We further conducted CIBERSORT analysis, and found that the infiltration of Macrophage M1 was increased in the high-risk group. Finally, the expression levels of crucial ARGs were verifed by the experiment, which were consistent with our bioinformatics analysis.Conclusions: we emphasized the clinical significance of autophagy in WT, established a prediction system based on autophagy, and identified a promising therapeutic target of autophagy for WT.

Download Full-text

Integration of mRNA and miRNA Analysis Reveals the Molecular Mechanism of Cotton Response to Salt Stress

Frontiers in Plant Science ◽

10.3389/fpls.2021.767984 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jingjing Zhan ◽

Yangyang Diao ◽

Guo Yin ◽

Muhammad Sajjad ◽

Xi Wei ◽

...

Keyword(s):

Salt Stress ◽

Differential Expression ◽

Signal Transduction Pathway ◽

Differentially Expressed ◽

Sequencing Data ◽

Regulatory Relationship ◽

Significant Differential Expression ◽

Amplification Bias ◽

Before And After ◽

Increased Sensitivity

To identify the regulatory network of known and novel microRNAs (miRNAs) and their targets responding to salt stress, a combined analysis of mRNA libraries, small RNA libraries, and degradome libraries were performed. In this study, we used unique molecular identifiers (UMIs), which are more sensitive, accurate, and reproducible than traditional methods of sequencing, to quantify the number of molecules and correct for amplification bias. We identified a total of 312 cotton miRNAs using seedlings at 0, 1, 3, and 6 h after NaCl treatment, including 80 known ghr-miRNAs and 232 novel miRNAs and found 155 miRNAs that displayed significant differential expression under salt stress. Among them, fifty-nine differentially expressed miRNAs were simultaneously induced in two or three tissues, while 66, 11, and 19 were specifically expressed in the roots, leaves, and stems, respectively. It is indicated there were different populations of miRNAs against salt stress in roots, leaves and stems. 399 candidate targets of salt-induced miRNAs showed significant differential expression before and after salt treatment, and 72 targets of 25 miRNAs were verified by degradome sequencing data. Furthermore, the regulatory relationship of miRNA-target gene was validated experimentally via 5′RLM-RACE, proving our data reliability. Gene ontology and KEGG pathway analysis found that salt-responsive miRNA targets among the differentially expressed genes were significantly enriched, and mainly involved in response to the stimulus process and the plant hormone signal transduction pathway. Furthermore, the expression levels of newly identified miRNA mir1 and known miRNAs miR390 and miR393 gradually decreased when subjected to continuous salt stress, while overexpression of these miRNAs both increased sensitivity to salt stress. Those newly identified miRNAs and mRNA pairs were conducive to genetic engineering and better understanding the mechanisms responding to salt stress in cotton.

Download Full-text