Search

Ischemic stroke is the leading cause of long-term disability in adults, accounting for 80% of stroke cases. Diffusion weighted imaging (DWI) examination is the main test for acute ischemic stroke, but in recent years, several studies have shown that some patients show negative DWI examination after the onset of ischemic stroke with symptoms of significant neurological deficits. In this study, we investigated potential biomarkers related to immune metabolism in the peripheral blood of DWI-negative versus DWI-positive patients after ischemic stroke and explored their possible regulatory processes in ischemic stroke. The datasets related to ischemic stroke were downloaded from the GEO database, immune-related genes and metabolism-related genes were obtained from the ImmPort database and MSigDB database, respectively, and immune-related differential genes were obtained based on immune scores using the algorithm of the R software package “GSVA.” Candidate genes were selected based on intersections, hub genes were screened using the algorithm in Cytoscape software, and finally, GeneMANIA analysis, GSEA enrichment analysis, subcellular localization, gene transcription factor and gene-drug interaction networks, and disease correlation analyses were performed for the hub genes. Five hub genes (GART, TYMS, PPAT, CTPS1, and PAICS) were obtained by PPI network analysis and software analysis. Among them, PPAT and PAICS may be the real hub genes with consistent and significantly differentiated results from the discovery and validation sets. The functions of these hub genes may be related to pathways such as nucleotide biosynthetic processes. The constructed hub gene ceRNA network showed that hsa-10a-5p is the key miRNA connecting PAICS and multiple lncRNAs in this study. Differential genes related to immunity and metabolism in DWI-negative and DWI-positive patients after IS were identified using bioinformatics analysis, and their pathways and related TF-RNAs, miRNAs, and lncRNAs were identified. These genes may be considered effective targets for the diagnosis and treatment of ischemic stroke. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC), partly because of its complexity and high heterogeneity, has a poor prognosis and an extremely high mortality rate. In this study, mRNA sequencing expression profiles and relevant clinical data of HCC patients were gathered from different public databases. Kaplan–Meier survival curves as well as ROC curves validated that OLA1|CLEC3B was an independent predictor with better predictive capability of HCC prognosis compared to OLA1 and CLEC3B separately. Further, the cell transfection experiment verified that knockdown of OLA1 inhibited cell proliferation, facilitated apoptosis, and improved sensitivity of HCC cells to gemcitabine. In this study, the prognostic model of HCC composed of OLA1/CLEC3B genes was constructed and verified, and the prediction ability was favorable. A higher level of OLA1 along with a lower level of CEC3B is a sign of poor prognosis in HCC. We revealed a novel gene pair OLA1|CLEC3B overexpressed in HCC patients, which may serve as a promising independent predictor of HCC survival and an approach for innovative diagnostic and therapeutic strategies. [ABSTRACT FROM AUTHOR]

Yangjin Huang,* Jun Xu,* Chunming Xie, Yuejuan Liao, Rong Lin, Yuan Zeng, Fujun Yu Department of Gastroenterology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Fujun Yu; Yuan Zeng, Tel/Fax +86 577-555791922, Email yufujun@wmu.edu.cn; zengyuan121388@163.comBackground: Hepatocellular carcinoma (HCC), one of the commonest cancers at present, possesses elevated mortality. This study explored the predictive value of CSTF2/PDE2A for HCC prognosis.Methods: In this study, clinical information and RNA sequencing expression profiles of HCC patients were acquired from common databases. Kaplan–Meier curve compound with time-dependent ROC curve, nomogram model, and univariate/multivariate Cox analysis were carried out to access the prediction capacity of CSTF2/PDE2A. The immune status, tumor microenvironment, drug sensitivity, biological function and pathway between HCC and adjacent non-tumorous tissue were analyzed and compared. Finally, RT-qPCR, Western blot, and apoptosis assays were performed to verify the effect on HCC cells of CSTF2/PDE2A.Results: The optimal cut-off value of CSTF2, PDE2A and CSTF2/PDE2A was 6.95, 0.95 and 3.63, respectively. In TCGA and ICGC cohorts, the high group of CSTF2/PDE2A presented higher OS compared to low group. The area under the curve (AUC) for OS at 1-, 2-, and 3-years predicted by CSTF2/PDE2A were 0.731/0.695, 0.713/0.732 and 0.689/0.755, higher than the counterparts of the single gene CSTF2 and PDE2A. Multivariate Cox analysis revealed that CSTF2/PDE2A (HR = 1.860/3.236, 95% CI = 1.265– 2.733/1.575– 6.645) was an independent prognostic factor for HCC. The OS nomogram model created according to five independent factors including CSTF2/PDE2A showed excellent capacity for HCC prognosis. Furthermore, the immune status of the CSTF2/PDE2A high group was deleted, cell cycle-related genes and chemotherapy resistance were increased. Finally, cell experiments revealed distinct differences in the proliferation, apoptosis, protein and mRNA expression of HCC cells after si-CSTF2 transfection compared with the negative control.Conclusion: Taken together, the gene pair CSTF2/PDE2A is able to forecast the prognosis of HCC and regulates cell cycle, which is promising as a novel prognostic predictor of HCC.Keywords: hepatocellular carcinoma, gene pair, overall survival, cell cycle, immune status, prognosis

Objectives: There is increasing evidence for a close correlation between risk stratification, prognosis and the immune environment in colon adenocarcinoma (COAD). However, the efficacy of immunotherapy is different among different patients with COAD. Therefore, the current work tends to use immune-related gene to develop a gene-pair model to evaluate the COAD prognosis, and to develop a new method for risk stratification of COAD, which is conducive to better predict the immunotherapy effect of patients. Methods: Specifically, from the TCGA and GEO (GSE14333 and GSE39582) databases, we first collected gene expression profiles, associated survival follow-up information of COAD patients. Through systematic bioinformatics analysis, we established a prognosis-related model of colon cancer with three pairs of "immune gene pairs", with uni- and multivariate and lasso cox regression analyses verifying the model stability. Most immune cells showed markedly different levels of infiltration between the two risk subgroups calculated by the model. More, single-cell RNA-seq analyses were also performed to validate the selected genes in the immune gene-pair model. Results: A prognosis-related model of colon cancer with three pairs of "immune gene pairs" were built and validated by several datasets. The analysis of immune landscape of COAD revealed that low-risk subgroup obtained by the prognosis-related model for COAD can be further divided into three subclusters with different prognosis. Then, we applied the Tumor online Prognostic analyses Platform (ToPP) to construct a prognostic model using these five genes. Results show that APOD, ISG20 and STC2 are risk factors, while CXCL9 and IL7R are protection factors. We also found that only the five-gene model could also predict the prognosis of COAD patients, indicating the robustness of the gene-pair model. Among the five genes, including CXCL9, APOD, STC2, ISG20, and IL7R, in the gene-pair model, single-cell RNA sequencing reveals the high expression of CXCL9 and IL7R in inflammatory macrophages. Using cell–cell interaction and trajectory analysis, data indicate that CXCL9+/IL7R+ pro-inflammatory macrophages were capable of secreting and activating more anti-tumor pathways than CXCL9−/IL7R− pro-inflammatory macrophages. Conclusions: In short, we have successfully developed an "immune gene pair" related model that can judge the prognostic status of patients with COAD and may contribute to risk stratification and evaluate potential beneficiaries of immunotherapy, providing new ideas for the anti-COAD management and therapy. [ABSTRACT FROM AUTHOR]

Background: Metastasis remains the leading cause of mortality in patients diagnosed with colorectal cancer (CRC). The pivotal contribution of the immune microenvironment in the initiation and progression of CRC metastasis has gained significant attention. Methods: A total of 453 CRC patients from The Cancer Genome Atlas (TCGA) were included as the training set, and GSE39582, GSE17536, GSE29621, GSE71187 were included as the validation set. The single-sample gene set enrichment analysis (ssGSEA) was performed to assess the immune infiltration of patients. Least absolute shrinkage and selection operator (LASSO) regression analysis, Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier analysis were used to construct and validate risk models based on R package. CTSW and FABP4-knockout CRC cells were constructed via CRISPR-Cas9 system. Western-blot and Transwell assay were utilized to explore the role of fatty acid binding protein 4 (FABP4) / cathepsin W (CTSW) in CRC metastasis and immunity. Results: Based on the normal/tumor, high-/low-immune cell infiltration, and metastatic/non-metastatic group, we identified 161 differentially expressed genes. After random assignment and LASSO regression analysis, a prognostic model containing 3 metastasis- and immune-related gene pairs was constructed and represented good prognostic prediction efficiency in the training set and 4 independent CRC cohorts. According to this model, we clustered patients and found that the high-risk group was associated with stage, T and M stage. In addition, the high-risk group also shown higher immune infiltration and high sensitivity to PARP inhibitors. Further, FABP4 and CTSW derived from the constitutive model were identified to be involved in metastasis and immunity of CRC. Conclusion: In conclusion, a validated prognosis predictive model for CRC was constructed. CTSW and FABP4 are potential targets for CRC treatment. [ABSTRACT FROM AUTHOR]

Background: Ischemic stroke (IS) is a principal contributor to long-term disability in adults. A new cell death mediated by iron is ferroptosis, characterized by lethal aggregation of lipid peroxidation. However, a paucity of ferroptosis-related biomarkers early identify IS until now. This study investigated potential ferroptosis-related gene pair biomarkers in IS and explored their roles in immune infiltration. Results: In total, we identified 6 differentially expressed ferroptosis-related genes (DEFRGs) in the metadata cohort. Of these genes, 4 DEFRGs were incorporated into the competitive endogenous RNA (ceRNA) network, including 78 lncRNA-miRNA and 16 miRNA-mRNA interactions. Based on relative expression values of DEFRGs, we constructed gene pairs. An integrated scheme consisting of machine learning algorithms, ceRNA network, and gene pair was proposed to screen the key DEFRG biomarkers. The receiver operating characteristic (ROC) curve witnessed that the diagnostic performance of DEFRG pair CDKN1A/JUN was superior to that of single gene. Moreover, the CIBERSORT algorithm exhibited immune infiltration landscapes: plasma cells, resting NK cells, and resting mast cells infiltrated less in IS samples than controls. Spearman correlation analysis confirmed a significant correlation between plasma cells and CDKN1A/JUN (CDKN1A: r = − 0.503, P < 0.001, JUN: r = − 0.330, P = 0.025). Conclusions: Our findings suggested that CDKN1A/JUN could be a robust and promising gene-pair diagnostic biomarker for IS, regulating ferroptosis during IS progression via C9orf106/C9orf139-miR-22-3p-CDKN1A and GAS5-miR-139-5p/miR-429-JUN axes. Meanwhile, plasma cells might exert a vital interplay in IS immune microenvironment, providing an innovative insight for IS therapeutic target. [ABSTRACT FROM AUTHOR]

Abstract Background Ischemic stroke (IS) is a principal contributor to long-term disability in adults. A new cell death mediated by iron is ferroptosis, characterized by lethal aggregation of lipid peroxidation. However, a paucity of ferroptosis-related biomarkers early identify IS until now. This study investigated potential ferroptosis-related gene pair biomarkers in IS and explored their roles in immune infiltration. Results In total, we identified 6 differentially expressed ferroptosis-related genes (DEFRGs) in the metadata cohort. Of these genes, 4 DEFRGs were incorporated into the competitive endogenous RNA (ceRNA) network, including 78 lncRNA-miRNA and 16 miRNA-mRNA interactions. Based on relative expression values of DEFRGs, we constructed gene pairs. An integrated scheme consisting of machine learning algorithms, ceRNA network, and gene pair was proposed to screen the key DEFRG biomarkers. The receiver operating characteristic (ROC) curve witnessed that the diagnostic performance of DEFRG pair CDKN1A/JUN was superior to that of single gene. Moreover, the CIBERSORT algorithm exhibited immune infiltration landscapes: plasma cells, resting NK cells, and resting mast cells infiltrated less in IS samples than controls. Spearman correlation analysis confirmed a significant correlation between plasma cells and CDKN1A/JUN (CDKN1A: r = − 0.503, P

• The GMPS|RAMP3 can predict the prognosis and assess the immune heterogeneity of patients with HCC. • The GMPS|RAMP3 is closely related to cell cycle and tumor angiogenesis. • The GMPS|RAMP3 effectively overcomes the technical bias and biological heterogeneity arising from different platforms by utilizing relative gene expression levels. Hepatocellular carcinoma (HCC) is a highly fatal malignant worldwide. As different expression levels of specific genes can lead to different HCC outcomes, we aimed to develop a gene signature capable of predicting HCC prognosis. In this study, transcriptomic sequencing and relevant clinical data were extracted from public platforms. The guanine monophosphate synthase (GMPS)|receptor activity-modifying protein 3 (RAMP3) gene pair was developed based on the relative values of gene expression levels. Nomograms were developed using R software. Immune status was assessed through single‐sample gene set enrichment analysis. GMPS knockdown was achieved through siRNA transfection. Quantitative reverse transcription PCR, apoptosis assays, and cell proliferation were performed to verify the function of GMPS|RAMP3 in HCC cells. Here, a gene pair containing GMPS and RAMP3 was successfully constructed. We demonstrated that the GMPS|RAMP3 gene pair was an independent predictor with strong prognostic prediction power, based on which a nomogram was established. Functional analysis revealed that the enrichment of cell cycle-related pathways and immune status differed considerably between the two groups, with cell cycle-related genes highly expressed in the high GMPS|RAMP3 value group. Finally, cell experiments indicated that GMPS knockdown significantly repressed proliferation, promoted apoptosis, and enhanced the sensitivity of HCC cells to gemcitabine. The gene pair GMPS|RAMP3 is a novel prognostic predictor of HCC, providing a promising approach to the treatment and assessment of immune heterogeneity in HCC. [ABSTRACT FROM AUTHOR]

Lung adenocarcinoma is the most common histological subtype of lung cancer which causes the largest number of deaths worldwide. Exploring reliable prognostic biomarkers based on biological behaviors and molecular mechanisms is essential for predicting prognosis and individualized treatment strategies. Ferroptosis is a recently discovered type of regulated cell death. We downloaded ferroptosis-related genes from the literature and collected transcriptome profiles of lung adenocarcinoma from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to construct ferroptosis-related gene-pair matrixes. Then, we performed the least absolute shrinkage and selection operator regression to build our prognostic ferroptosis-related gene-pair index (FRGPI) in TCGA training matrix. Our study validated FRGPI through ROC curves, Kaplan–Meier methods, and Cox hazard analyses in TCGA and GEO cohorts. The optimal cut-off 0.081 stratified patients into low- and high-FRGPI groups. Also, the low-FRGPI group had a significantly better prognosis than the high-FRGPI group. For further study, we analyzed differentially expressed ferroptosis-related genes between high- and low-FRGPI groups. Gene set enrichment analysis (GSEA) enrichment maps indicated that "cell cycle," "DNA replication," "proteasome," and "the p53 signaling pathway" were significantly enriched in the high-FRGPI group. The high-FRGPI group also presented higher infiltration of M1 macrophages. Meanwhile, there were few differences in adaptive immune responses between high- and low-FRGPI groups. In conclusion, FRGPI was an independent prognostic biomarker which might be beneficial for guiding individualized tumor therapy. [ABSTRACT FROM AUTHOR]

Lung adenocarcinoma is the most common histological subtype of lung cancer which causes the largest number of deaths worldwide. Exploring reliable prognostic biomarkers based on biological behaviors and molecular mechanisms is essential for predicting prognosis and individualized treatment strategies. Ferroptosis is a recently discovered type of regulated cell death. We downloaded ferroptosis-related genes from the literature and collected transcriptome profiles of lung adenocarcinoma from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to construct ferroptosis-related gene-pair matrixes. Then, we performed the least absolute shrinkage and selection operator regression to build our prognostic ferroptosis-related gene-pair index (FRGPI) in TCGA training matrix. Our study validated FRGPI through ROC curves, Kaplan–Meier methods, and Cox hazard analyses in TCGA and GEO cohorts. The optimal cut-off 0.081 stratified patients into low- and high-FRGPI groups. Also, the low-FRGPI group had a significantly better prognosis than the high-FRGPI group. For further study, we analyzed differentially expressed ferroptosis-related genes between high- and low-FRGPI groups. Gene set enrichment analysis (GSEA) enrichment maps indicated that “cell cycle,” “DNA replication,” “proteasome,” and “the p53 signaling pathway” were significantly enriched in the high-FRGPI group. The high-FRGPI group also presented higher infiltration of M1 macrophages. Meanwhile, there were few differences in adaptive immune responses between high- and low-FRGPI groups. In conclusion, FRGPI was an independent prognostic biomarker which might be beneficial for guiding individualized tumor therapy.

Background: Recent evidence demonstrates that pyroptosis-derived long non-coding RNAs (lncRNAs) have profound impacts on the initiation, progression, and microenvironment of tumors. However, the roles of pyroptosis-derived lncRNAs (PDLs) in gastric cancer (GC) remain elusive.Methods: We comprehensively analyzed the multi-omics data of 839 GC patients from three independent cohorts. The previous gene set enrichment analysis embedding algorithm was utilized to identify PDLs. A gene pair pipeline was developed to facilitate clinical translation via qualitative relative expression orders. The LASSO algorithm was used to construct and validate a pyroptosis-derived lncRNA pair prognostics signature (PLPPS). The associations between PLPPS and multi-omics alteration, immune profile, and pharmacological landscape were further investigated.Results: A total of 350 PDLs and 61,075 PDL pairs in the training set were generated. Cox regression revealed 15 PDL pairs associated with overall survival, which were utilized to construct the PLPPS model via the LASSO algorithm. The high-risk group demonstrated adverse prognosis relative to the low-risk group. Remarkably, genomic analysis suggested that the lower tumor mutation burden and gene mutation frequency (e.g., TTN, MUC16, and LRP1B) were found in the high-risk group patients. The copy number variants were not significantly different between the two groups. Additionally, the high-risk group possessed lower immune cell infiltration abundance and might be resistant to a few chemotherapeutic drugs (including cisplatin, paclitaxel, and gemcitabine).Conclusion: PDLs were closely implicated in the biological process and prognosis of GC, and our PLPPS model could serve as a promising tool to advance prognostic management and personalized treatment of GC patients.

Oxidative stress (OS) irreversibly affects the pathogenesis of intervertebral disc degeneration (IDD). Certain non-coding RNAs act as competitive endogenous RNAs (ceRNAs) that regulate IDD progression. Analyzing the signatures of oxidative stress-related gene (OSRG) pairs and regulatory ceRNA mechanisms and immune-infiltration patterns associated with IDD may enable researchers to distinguish IDD and reveal the underlying mechanisms. In this study, OSRGs were downloaded and identified using the Gene Expression Omnibus database. Functional-enrichment analysis revealed the involvement of oxidative stress-related pathways and processes, and a ceRNA network was generated. Differentially expressed oxidative stress-related genes (De-OSRGs) were used to construct De-OSRG pairs, which were screened, and candidate De-OSRG pairs were identified. Immune cell-related gene pairs were selected via immune-infiltration analysis. A potential long non-coding RNA–microRNA–mRNA axis was determined, and clinical values were assessed. Eighteen De-OSRGs were identified that were primarily related to intricate signal-transduction pathways, apoptosis-related biological processes, and multiple kinase-related molecular functions. A ceRNA network consisting of 653 long non-coding RNA–microRNA links and 42 mRNA–miRNA links was constructed. Three candidate De-OSRG pairs were screened out from 13 De-OSRG pairs. The abundances of resting memory CD4+ T cells, resting dendritic cells, and CD8+ T cells differed between the control and IDD groups. CD8+ T cell infiltration correlated negatively with cyclin B1 (CCNB1) expression and positively with protein kinase D1 (PKD1) expression. CCNB1 – PKD1 was the only pair that was differentially expressed in IDD, was correlated with CD8+ T cells, and displayed better predictive accuracy compared to individual genes. The PKD1 – miR-20b-5p – AP000797 and CCNB1 – miR-212-3p – AC079834 axes may regulate IDD. Our findings indicate that the OSRG pair CCNB1 – PKD1 , which regulates oxidative stress during IDD development, is a robust signature for identifying IDD. This OSRG pair and increased infiltration of CD8+ T cells, which play important roles in IDD, were functionally associated. Thus, the OSRG pair CCNB1 – PKD1 is promising target for treating IDD. [ABSTRACT FROM AUTHOR]

Although chemotherapy is the first-line treatment for ovarian cancer (OCa) patients, chemoresistance (CR) decreases their progression-free survival. This paper investigates the genetic interaction (GI) related to OCa-CR. To decrease the complexity of establishing gene networks, individual signature genes related to OCa-CR are identified using a gradient boosting decision tree algorithm. Additionally, the genetic interaction coefficient (GIC) is proposed to measure the correlation of two signature genes quantitatively and explain their joint influence on OCa-CR. Gene pair that possesses high GIC is identified as signature pair. A total of 24 signature gene pairs are selected that include 10 individual signature genes and the influence of signature gene pairs on OCa-CR is explored. Finally, a signature gene pair-based prediction of OCa-CR is identified. The area under curve (AUC) is a widely used performance measure for machine learning prediction. The AUC of signature gene pair reaches 0.9658, whereas the AUC of individual signature gene-based prediction is 0.6823 only. The identified signature gene pairs not only build an efficient GI network of OCa-CR but also provide an interesting way for OCa-CR prediction. This improvement shows that our proposed method is a useful tool to investigate GI related to OCa-CR. [ABSTRACT FROM AUTHOR]

Oxidative stress (OS) irreversibly affects the pathogenesis of intervertebral disc degeneration (IDD). Certain non-coding RNAs act as competitive endogenous RNAs (ceRNAs) that regulate IDD progression. Analyzing the signatures of oxidative stress-related gene (OSRG) pairs and regulatory ceRNA mechanisms and immune-infiltration patterns associated with IDD may enable researchers to distinguish IDD and reveal the underlying mechanisms. In this study, OSRGs were downloaded and identified using the Gene Expression Omnibus database. Functional-enrichment analysis revealed the involvement of oxidative stress-related pathways and processes, and a ceRNA network was generated. Differentially expressed oxidative stress-related genes (De-OSRGs) were used to construct De-OSRG pairs, which were screened, and candidate De-OSRG pairs were identified. Immune cell-related gene pairs were selected via immune-infiltration analysis. A potential long non-coding RNA–microRNA–mRNA axis was determined, and clinical values were assessed. Eighteen De-OSRGs were identified that were primarily related to intricate signal-transduction pathways, apoptosis-related biological processes, and multiple kinase-related molecular functions. A ceRNA network consisting of 653 long non-coding RNA–microRNA links and 42 mRNA–miRNA links was constructed. Three candidate De-OSRG pairs were screened out from 13 De-OSRG pairs. The abundances of resting memory CD4+ T cells, resting dendritic cells, and CD8+ T cells differed between the control and IDD groups. CD8+ T cell infiltration correlated negatively with cyclin B1 (CCNB1) expression and positively with protein kinase D1 (PKD1) expression. CCNB1–PKD1 was the only pair that was differentially expressed in IDD, was correlated with CD8+ T cells, and displayed better predictive accuracy compared to individual genes. The PKD1–miR-20b-5p–AP000797 and CCNB1–miR-212-3p–AC079834 axes may regulate IDD. Our findings indicate that the OSRG pair CCNB1–PKD1, which regulates oxidative stress during IDD development, is a robust signature for identifying IDD. This OSRG pair and increased infiltration of CD8+ T cells, which play important roles in IDD, were functionally associated. Thus, the OSRG pair CCNB1–PKD1 is promising target for treating IDD.

Esophageal cancer (EC) is a common malignant tumor in the digestive system which is often diagnosed at the middle and late stages. Noninvasive diagnosis using circulating miRNA as biomarkers enables accurate detection of early-stage EC to reduce mortality. We built a diagnostic signature consisting of four miRNA pairs for the early detection of EC using individualized Pairwise Analysis of Gene Expression (iPAGE). Profiling of miRNA expression identified 496 miRNA pairs with significant relative expression change. Four miRNA pairs consistently selected from LASSO were used to construct the final diagnostic model. The performance of the signature was validated using two independent datasets, yielding both AUCs and PRCs over 0.99. Furthermore, precision, recall, and F-score were also evaluated for clinical application, when a fixed threshold is given, resulting in all the scores are larger than 0.92 in the training set, test set, and two validation sets. Our results suggested that the 4-miRNA signature is a new biomarker for the early diagnosis of patients with EC. The clinical use of this signature would have improved the detection of EC for earlier therapy and more favorite prognosis.

Esophageal cancer (EC) is a common malignant tumor in the digestive system which is often diagnosed at the middle and late stages. Noninvasive diagnosis using circulating miRNA as biomarkers enables accurate detection of early-stage EC to reduce mortality. We built a diagnostic signature consisting of four miRNA pairs for the early detection of EC using individualized Pairwise Analysis of Gene Expression (iPAGE). Profiling of miRNA expression identified 496 miRNA pairs with significant relative expression change. Four miRNA pairs consistently selected from LASSO were used to construct the final diagnostic model. The performance of the signature was validated using two independent datasets, yielding both AUCs and PRCs over 0.99. Furthermore, precision, recall, and F-score were also evaluated for clinical application, when a fixed threshold is given, resulting in all the scores are larger than 0.92 in the training set, test set, and two validation sets. Our results suggested that the 4-miRNA signature is a new biomarker for the early diagnosis of patients with EC. The clinical use of this signature would have improved the detection of EC for earlier therapy and more favorite prognosis. [ABSTRACT FROM AUTHOR]

Abstract Background Fast, complete, and ultimate removal of inhibitory compounds derived from lignocellulose pretreatment is the prerequisite for efficient production of cellulosic ethanol and biochemicals. Biodetoxification is the most promising method for inhibitor removal by its unique advantages. The biodetoxification mechanisms of a unique diploid fungus responsible for highly efficient biodetoxification in solid-state culture was extensively investigated in the aspects of cellular structure, genome sequencing, transcriptome analysis, and practical biodetoxification. Results The inborn heterozygous diploid structure of A. resinae ZN1 uniquely contributed to the enhancement of inhibitor tolerance and conversion. The co-expression of gene pairs contributed to the enhancement of the degradation of lignocellulose-derived model inhibitors. The ultimate inhibitors degradation pathways and sugar conservation were elucidated by microbial degradation experimentation as well as the genomic and transcriptomic sequencing analysis. Conclusions The finding of the heterozygous diploid structure in A. resinae ZN1 on biodetoxification took the first insight into the global overview of biodetoxification mechanism of lignocellulose-derived inhibitors. This study provided a unique and practical biodetoxification biocatalyst of inhibitor compounds for lignocellulose biorefinery processing, as well as the synthetic biology tools on biodetoxification of biorefinery fermenting strains.

The progression of cancer is accompanied by the acquisition of stemness features. Many stemness evaluation methods based on transcriptional profiles have been presented to reveal the relationship between stemness and cancer. However, instead of absolute stemness index values—the values with certain range—these methods gave the values without range, which makes them unable to intuitively evaluate the stemness. Besides, these indices were based on the absolute expression values of genes, which were found to be seriously influenced by batch effects and the composition of samples in the dataset. Recently, we have showed that the signatures based on the relative expression orderings (REOs) of gene pairs within a sample were highly robust against these factors, which makes that the REO-based signatures have been stably applied in the evaluations of the continuous scores with certain range. Here, we provided an absolute REO-based stemness index to evaluate the stemness. We found that this stemness index had higher correlation with the culture time of the differentiated stem cells than the previous stemness index. When applied to the cancer and normal tissue samples, the stemness index showed its significant difference between cancers and normal tissues and its ability to reveal the intratumor heterogeneity at stemness level. Importantly, higher stemness index was associated with poorer prognosis and greater oncogenic dedifferentiation reflected by histological grade. All results showed the capability of the REO-based stemness index to assist the assignment of tumor grade and its potential therapeutic and diagnostic implications. [ABSTRACT FROM AUTHOR]

BackgroundHigh-grade serous ovarian cancer (HGSOC) is a common cause of death from gynecological cancer, with an overall survival rate that has not significantly improved in decades. Reliable bio-markers are needed to identify high-risk HGSOC to assist in the selection and development of treatment options.MethodThe study included ten HGSOC cohorts, which were merged into four separate cohorts including a total of 1,526 samples. We used the relative expression of immune genes to construct the gene-pair matrix, and the least absolute shrinkage and selection operator regression was performed to build the prognosis model using the training set. The prognosis of the model was verified in the training set (363 cases) and three validation sets (of 251, 354, and 558 cases). Finally, the differences in immune cell infiltration and gene enrichment pathways between high and low score groups were identified.ResultsA prognosis model of HGSOC overall survival rate was constructed in the training set, and included data for 35 immune gene-related gene pairs and the regression coefficients. The risk stratification of HGSOC patients was successfully performed using the training set, with a p-value of Kaplan-Meier of < 0.001. A score from this model is an independent prognostic factor of HGSOC, and prognosis was evaluated in different clinical subgroups. This model was also successful for the other three validation sets, and the results of Kaplan-Meier analysis were statistically significant (p < 0.05). The model can also predict patient progression-free survival with HGSOC to reflect tumor growth status. There was a lower infiltration level of M1 macrophages in the high-risk group compared to that in the low-risk group (p < 0.001). Finally, the immune-related pathways were enriched in the low-risk group.ConclusionThe prognostic model based on immune-related gene pairs developed is a potential prognostic marker for high-grade serous ovarian cancer treated with platinum. The model has robust prognostic ability and wide applicability. More prospective studies will be needed to assess the practical application of this model for precision therapy.

Background: High-grade serous ovarian cancer (HGSOC) is a common cause of death from gynecological cancer, with an overall survival rate that has not significantly improved in decades. Reliable bio-markers are needed to identify high-risk HGSOC to assist in the selection and development of treatment options. Method: The study included ten HGSOC cohorts, which were merged into four separate cohorts including a total of 1,526 samples. We used the relative expression of immune genes to construct the gene-pair matrix, and the least absolute shrinkage and selection operator regression was performed to build the prognosis model using the training set. The prognosis of the model was verified in the training set (363 cases) and three validation sets (of 251, 354, and 558 cases). Finally, the differences in immune cell infiltration and gene enrichment pathways between high and low score groups were identified. Results: A prognosis model of HGSOC overall survival rate was constructed in the training set, and included data for 35 immune gene-related gene pairs and the regression coefficients. The risk stratification of HGSOC patients was successfully performed using the training set, with a p-value of Kaplan-Meier of < 0.001. A score from this model is an independent prognostic factor of HGSOC, and prognosis was evaluated in different clinical subgroups. This model was also successful for the other three validation sets, and the results of Kaplan-Meier analysis were statistically significant (p < 0.05). The model can also predict patient progression-free survival with HGSOC to reflect tumor growth status. There was a lower infiltration level of M1 macrophages in the high-risk group compared to that in the low-risk group (p < 0.001). Finally, the immune-related pathways were enriched in the low-risk group. Conclusion: The prognostic model based on immune-related gene pairs developed is a potential prognostic marker for high-grade serous ovarian cancer treated with platinum. The model has robust prognostic ability and wide applicability. More prospective studies will be needed to assess the practical application of this model for precision therapy. [ABSTRACT FROM AUTHOR]

Peng-fei Chen,1–3,* Fan Wang,1,2,* Zi-xiong Zhang,4,* Jia-yan Nie,1,2 Lan Liu,1,2 Jue-rong Feng,1,2 Rui Zhou,1,2 Hong-ling Wang,1,2 Jing Liu,1,2 Qiu Zhao1,2 1Department of Gastroenterology, Zhongnan Hospital of Wuhan University, Wuhan 430071, China; 2Hubei Clinical Center & Key Lab of Intestinal & Colorectal Diseases, Wuhan 430071, China; 3Department of Gastroenterology, The Central Hospital of Enshi Autonomous Prefecture, Enshi 445000, China; 4Department of Otolaryngology, The Central Hospital of Enshi Autonomous Prefecture, Enshi 445000, China *These authors contributed equally to this work Background: Colon cancer (CC) patients with early relapse usually have a poor prognosis. In this study, we aimed to identify a novel signature to improve the prediction of relapse-free survival (RFS) in CC. Methods: Four microarray datasets were merged into a training set (n=1,045), and one RNA-sequencing dataset was used as a validation set (n=384). In the training set, microarray meta-analysis screened out 596 common RFS-related genes across datasets, which were used to construct 177,310 gene pairs. Then, the LASSO penalized generalized linear model identified 16 RFS-related gene pairs, and a risk score was calculated for each sample according to the model coefficients. Results: The risk score demonstrated a good ability in predicting RFS (area under the curve [AUC] at 5 years: 0.724; concordance index [C-index]: 0.642, 95% CI: 0.615–0.669). High-risk patients showed a poorer prognosis than low-risk patients (HR: 3.519, 95% CI: 2.870–4.314). Subgroup analysis reached consistent results when considering multiple confounders. In the validation set, the risk score had a similar performance (AUC at 5 years: 0.697; C-index: 0.696, 95% CI: 0.627–0.766; HR: 2.926, 95% CI: 1.892–4.527). When compared with a 13-gene signature, a 15-gene signature, and TNM stage, the score showed a better performance (P5 years (n=314), the score demonstrated an excellent performance (C-index: 0.869, 95% CI: 0.816–0.922; HR: 13.55, 95% CI: 7.409–24.78). Conclusion: Our study identified a novel gene-pair signature for prediction of RFS in CC. Keywords: colon cancer, relapse-free survival, gene pair, prognosis

The development of multigene classifiers for cancer prognosis, treatment prediction, molecular subtypes or clinicopathological groups has been a cornerstone in transcriptomic analyses of human malignancies for nearly two decades. However, many reported classifiers are critically limited by different preprocessing needs like normalization and data centering. In response, a new breed of classifiers, single sample predictors (SSPs), has emerged. SSPs classify samples in an N-of-1 fashion, relying on, e.g. gene rules comparing expression values within a sample. To date, several methods have been reported, but there is a lack of head-to-head performance comparison for typical cancer classification problems, representing an unmet methodological need in cancer bioinformatics. To resolve this need, we performed an evaluation of two SSPs [k-top-scoring pair classifier (kTSP) and absolute intrinsic molecular subtyping (AIMS)] for two case examples of different magnitude of difficulty in non-small cell lung cancer: gene expression–based classification of (i) tumor histology and (ii) molecular subtype. Through the analysis of ~2000 lung cancer samples for each case example (n  = 1918 and n  = 2106, respectively), we compared the performance of the methods for different sample compositions, training data set sizes, gene expression platforms and gene rule selections. Three main conclusions are drawn from the comparisons: both methods are platform independent, they select largely overlapping gene rules associated with actual underlying tumor biology and, for large training data sets, they behave interchangeably performance-wise. While SSPs like AIMS and kTSP offer new possibilities to move gene expression signatures/predictors closer to a clinical context, they are still importantly limited by the difficultness of the classification problem at hand. [ABSTRACT FROM AUTHOR]

Abstract Background This article concerns the identification of gene pairs or combinations of gene pairs associated with biological phenotype or clinical outcome, allowing for building predictive models that are not only robust to normalization but also easily validated and measured by qPCR techniques. However, given a small number of biological samples yet a large number of genes, this problem suffers from the difficulty of high computational complexity and imposes challenges to the accuracy of identification statistically. Results In this paper, we propose a parsimonious model representation and develop efficient algorithms for identification. Particularly, we derive an equivalent model subject to a sum-to-zero constraint in penalized linear regression, where the correspondence between nonzero coefficients in these models is established. Most importantly, it reduces the model complexity of the traditional approach from the quadratic order to the linear order in the number of candidate genes, while overcoming the difficulty of model nonidentifiablity. Computationally, we develop an algorithm using the alternating direction method of multipliers (ADMM) to deal with the constraint. Numerically, we demonstrate that the proposed method outperforms the traditional method in terms of the statistical accuracy. Moreover, we demonstrate that our ADMM algorithm is more computationally efficient than a coordinate descent algorithm with a local search. Finally, we illustrate the proposed method on a prostate cancer dataset to identify gene pairs that are associated with pre-operative prostate-specific antigen. Conclusion Our findings demonstrate the feasibility and utility of using gene pairs as biomarkers.