Robust transcriptional signatures for low-input RNA samples based on relative expression orderings

Carrier-based delivery system of phytohormones in plants: stepping outside of the ordinary

Climate change is increasing the stresses on crops, resulting in reduced productivity and further augmenting global food security issues. The dynamic climatic conditions are a severe threat to the sustainability of the ecosystems. The role of technology in enhancing agricultural produce with the minimum environmental impact is hence crucial. Active molecule/Plant growth regulators (PGRs) are molecules helping plants' growth, development, and tolerance to abiotic and biotic stresses. However, their degradation, leaching in surrounding soil and ground water, as well as the assessment of the correct dose of application etc., are some of the technical disadvantages faced. They can be resolved by encapsulation/loading of PGRs on polymer matrices. Micro/nanoencapsulation is a revolutionary tool to deliver bioactive compounds in an economically affordable and environmentally friendly way. Carrier-based smart delivery systems could be a better alternative to PGRs application in the agriculture field than conventional methods (e.g., spraying). The physiochemical properties and release kinetics of PGRs from the encapsulating system are being explored. Therefore, the present review emphasizes the current status of PGRs encapsulation approach and their potential benefits to plants. This review also addressed the mechanistic action of carrier-based delivery systems for release, which may aid in developing smart delivery systems with specific tailored properties in future research.

Identification of qualitative characteristics of immunosuppression in sepsis based on immune-related genes and immune infiltration features

Objective

Sepsis is linked to high morbidity and mortality rates. Consequently, early diagnosis is crucial for proper treatment, reducing hospitalization, and mortality rates. Additionally, over one-fifth of sepsis patients still face a risk of death. Hence, early diagnosis, and effective treatment play pivotal roles in enhancing the prognosis of patients with sepsis.

Method

The study analyzed whole blood data obtained from patients with sepsis and control samples sourced from three datasets (GSE57065, GSE69528, and GSE28750). Commonly dysregulated immune-related genes (IRGs) among these three datasets were identified. The differential characteristics of these common IRGs in the sepsis and control samples were assessed using the REO-based algorithm. Based on these differential characteristics, samples from eight Gene Expression Omnibus (GEO) databases (GSE57065, GSE69528, GSE28750, GSE65682, GSE69063, GSE95233, GSE131761, and GSE154918), along with three ArrayExpress databases (E-MTAB-4421, E-MTAB-4451, and E-MTAB-7581), were categorized and scored. The effectiveness of these differential characteristics in distinguishing sepsis samples from control samples was evaluated using the AUC value derived from the receiver operating characteristic curve (ROC) curve. Furthermore, the expression of IRGs was validated in peripheral blood samples obtained from patients with sepsis through qRT-PCR.

Results

Among the three training datasets, a total of 84 common dysregulated immune-related genes (IRGs) were identified. Utilizing a within-sample relative expression ordering (REOs)-based algorithm to analyze these common IRGs, differential characteristics were observed in three reverse stable pairs (ELANE-RORA, IL18RAP-CD247, and IL1R1-CD28). In the eight GEO datasets, the expression of ELANE, IL18RAP, and IL1R1 demonstrated significant upregulation, while RORA, CD247, and CD28 expression exhibited notable downregulation during sepsis. These three pairs of immune-related marker genes displayed accuracies of 95.89% and 97.99% in distinguishing sepsis samples among the eight GEO datasets and the three independent ArrayExpress datasets, respectively. The area under the receiver operating characteristic curve ranged from 0.81 to 1.0. Additionally, among these three immune-related marker gene pairs, mRNA expression levels of ELANE and IL1R1 were upregulated, whereas the levels of CD247 and CD28 mRNA were downregulated in blood samples from patients with sepsis compared to normal controls.

Conclusion

These three immune-related marker gene pairs exhibit high predictive performance for blood samples from patients with sepsis. They hold potential as valuable auxiliary clinical blood screening tools for sepsis.

Identifying individualized prognostic signature and unraveling the molecular mechanism of recurrence in early-onset colorectal cancer

BACKGROUND

The incidence and mortality of early-onset colorectal cancer (EOCRC; < 50 years old) is increasing worldwide, with a high recurrence rate. The inherent heterogeneity of EOCRC makes its treatment challenging. Hence, to further understand the biology and reveal the molecular mechanisms of EOCRC, a recurrence risk signature is needed to guide clinical management.

METHODS

Based on the relative expression orderings (REOs) of genes in each sample, a prognostic signature was developed and validated utilizing multiple independent datasets. The underlying molecular mechanisms between distinct prognostic groups were explored via integrative analysis of multi-omics data.

RESULTS

The prognostic signature consisting of 6 gene pairs (6-GPS) could predict the recurrence risk for EOCRC at the individual level. High-risk EOCRC classified by 6-GPS showed a poor prognosis but a good response to adjuvant chemotherapy. Moreover, high-risk EOCRC was characterized by epithelial-mesenchymal transition (EMT) and enriched angiogenesis, and had higher mutation burden, immune cell infiltration, and PD-1/PD-L1 expression. Furthermore, we identified four genes associated with relapse-free survival in EOCRC, including SERPINE1, PECAM1, CDH1, and ANXA1. They were consistently differentially expressed at the transcriptome and proteome levels between high-risk and low-risk EOCRCs. They were also involved in regulating cancer progression and immune microenvironment in EOCRC. Notably, the expression of SERPINE1 and ANXA1 positively correlated with M2-like macrophage infiltration.

CONCLUSION

Our results indicate that 6-GPS can robustly predict the recurrence risk of EOCRC, and that SERPINE1, PECAM1, CDH1, and ANXA1 may serve as potential therapeutic targets. This study provides valuable information for the precision treatment of EOCRC.

Cross-protection interactions in insect pests: Implications for pest management in a changing climate

Agricultural insect pests display an exceptional ability to adapt quickly to natural and anthropogenic stressors. Emerging evidence suggests that frequent and varied sources of stress play an important role in driving protective physiological responses; therefore, intensively managed agroecosystems combined with climatic shifts might be an ideal crucible for stress adaptation. Cross-protection, where responses to one stressor offers protection against another type of stressor, has been well documented in many insect species, yet the molecular and epigenetic underpinnings that drive overlapping protective responses in insect pests remain unclear. In this perspective, we discuss cross-protection mechanisms and provide an argument for its potential role in increasing tolerance to a wide range of natural and anthropogenic stressors in agricultural insect pests. By drawing from existing literature on single and multiple stressor studies, we outline the processes that facilitate cross-protective interactions, including epigenetic modifications, which are understudied in insect stress responses. Finally, we discuss the implications of cross-protection for insect pest management, focusing on the consequences of cross-protection between insecticides and elevated temperatures associated with climate change. Given the multiple ways that insect pests are intensively managed in agroecosystems, we suggest that examining the role of multiple stressors can be important in understanding the wide adaptability of agricultural insect pests. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Identification of cancer risk assessment signature in patients with chronic obstructive pulmonary disease and exploration of the potential key genes

It is essential to assess the cancer risk for patients with chronic obstructive pulmonary disease (COPD). Comparing gene expression data from patients with lung cancer (a total of 506 samples) and those with cancer-adjacent normal lung tissues (a total of 370 samples), we generated a qualitative transcriptional signature consisting of 2046 gene pairs. The signature was verified in an evaluation dataset comprising 18 subjects with severe disease and 52 subjects with moderate disease (Wilcoxon rank-sum test; p = 7.33 × 10^-5). Similar results were obtained in other independent datasets. Among the gene pairs in the signature, 326 COPD stage-related gene pairs were identified based on Spearman's rank correlation tests and those gene pairs comprised 368 unique genes. Of these 368 genes, 16 genes were significantly dysregulated in COPD rat model data compared with control data. Some of these genes (Dhx16, Upf2, Notch3, Sec61a1, Dyrk2, and Hmmr) were altered when the COPD rat model was treated with traditional Chinese medicines (TCM), including Bufei Yishen formula, Bufei Jianpi formula, and Yiqi Zishen formula. Overall, the signature could predict the cancer incidence-risk of COPD and the identified key genes might provide guidance regarding both the treatment of COPD using TCM and the prevention of cancer in patients with COPD. KEY MESSAGESA cancer risk assessment signature was identified in patients with COPD.The signature is insensitive to batch effects and is well verified.COPD key genes identified in this study might play a crucial role in TCM treatment and cancer prevention.

The Drosophila microRNA bantam regulates excitability in adult mushroom body output neurons to promote early night sleep

Sleep circuitry evolved to have both dedicated and context-dependent modulatory elements. Identifying modulatory subcircuits and understanding their molecular machinery is a major challenge for the sleep field. Previously, we identified 25 sleep-regulating microRNAs in Drosophila melanogaster, including the developmentally important microRNA bantam. Here we show that bantam acts in the adult to promote early nighttime sleep through a population of glutamatergic neurons that is intimately involved in applying contextual information to behaviors, the γ5β'2a/β'2mp/β'2mp_bilateral Mushroom Body Output Neurons (MBONs). Calcium imaging revealed that bantam inhibits the activity of these cells during the early night, but not the day. Blocking synaptic transmission in these MBONs rescued the effect of bantam knockdown. This suggests bantam promotes early night sleep via inhibition of the γ5β'2a/β'2mp/β'2mp_bilateral MBONs. RNAseq identifies Kelch and CCHamide-2 receptor as possible mediators, establishing a new role for bantam as an active regulator of sleep and neural activity in the adult fly.

Hierarchical identification of a transcriptional panel for the histological diagnosis of lung neuroendocrine tumors

Background: Lung cancer is a complex disease composed of neuroendocrine (NE) and non-NE tumors. Accurate diagnosis of lung cancer is essential in guiding therapeutic management. Several transcriptional signatures have been reported to distinguish between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) belonging to non-NE tumors. This study aims to identify a transcriptional panel that could distinguish the histological subtypes of NE tumors to complement the morphology-based classification of an individual.

Methods: A public dataset with NE subtypes, including 21 small-cell lung cancer (SCLC), 56 large-cell NE carcinomas (LCNECs), and 24 carcinoids (CARCIs), and non-NE subtypes, including 85 ADC and 61 SCC, was used as a training set. In the training set, consensus clustering was first used to filter out the samples whose expression patterns disagreed with their histological subtypes. Then, a rank-based method was proposed to develop a panel of transcriptional signatures for determining the NE subtype for an individual, based on the within-sample relative gene expression orderings of gene pairs. Twenty-three public datasets with a total of 3,454 samples, which were derived from fresh-frozen, formalin-fixed paraffin-embedded, biopsies, and single cells, were used for validation. Clinical feasibility was tested in 10 SCLC biopsy specimens collected from cancer hospitals via bronchoscopy.

Results: The NEsubtype-panel was composed of three signatures that could distinguish NE from non-NE, CARCI from non-CARCI, and SCLC from LCNEC step by step and ultimately determine the histological subtype for each NE sample. The three signatures achieved high average concordance rates with 97.31%, 98.11%, and 90.63%, respectively, in the 23 public validation datasets. It is worth noting that the 10 clinic-derived SCLC samples diagnosed via immunohistochemical staining were also accurately predicted by the NEsubtype-panel. Furthermore, the subtype-specific gene expression patterns and survival analyses provided evidence for the rationality of the reclassification by the NEsubtype-panel.

Conclusion: The rank-based NEsubtype-panel could accurately distinguish lung NE from non-NE tumors and determine NE subtypes even in clinically challenging samples (such as biopsy). The panel together with our previously reported signature (KRT5-AGR2) for SCC and ADC would be an auxiliary test for the histological diagnosis of lung cancer.

Predict ovarian cancer by pairing serum miRNAs: Construct of single sample classifiers

Objective

The accuracy of CA125 or clinical examination in ovarian cancer (OVC) screening is still facing challenges. Serum miRNAs have been considered as promising biomarkers for clinical applications. Here, we propose a single sample classifier (SSC) method based on within-sample relative expression orderings (REOs) of serum miRNAs for OVC diagnosis.

Methods

Based on the stable REOs within 4,965 non-cancer serum samples, we developed the SSC for OVC in the training cohort (GSE106817: OVC = 200, non-cancer = 2,000) by focusing on highly reversed REOs within OVC. The best diagnosis is achieved using a combination of reversed miRNA pairs, considering the largest evaluation index and the lowest number of miRNA pairs possessed according to the voting rule. The SSC was then validated in internal data (GSE106817: OVC = 120, non-cancer = 759) and external data (GSE113486: OVC = 40, non-cancer = 100).

Results

The obtained 13-miRPairs classifier showed high diagnostic accuracy on distinguishing OVC from non-cancer controls in the training set (sensitivity = 98.00%, specificity = 99.60%), which was reproducible in internal data (sensitivity = 98.33%, specificity = 99.21%) and external data (sensitivity = 97.50%, specificity = 100%). Compared with the published models, it stood out in terms of correct positive predictive value (PPV) and negative predictive value (NPV) (PPV = 96.08% and NPV=95.16% in training set, and both above 99% in validation set). In addition, 13-miRPairs demonstrated a classification accuracy of over 97.5% for stage I OVC samples. By integrating other non-OVC serum samples as a control, the obtained 17-miRPairs classifier could distinguish OVC from other cancers (AUC&gt;92% in training and validation set).

Conclusion

The REO-based SSCs performed well in predicting OVC (including early samples) and distinguishing OVC from other cancer types, proving that REOs of serum miRNAs represent a robust and non-invasive biomarker.

A novel insight into transcriptional and epigenetic regulation underlying sex expression and flower development in melon (Cucumis melo L.)

Melon (Cucumis melo L.) is an important cucurbit and has been considered as a model plant for studying sex determination. The four most common sexual morphotypes in melon are monoecious (A-G-M), gynoecious (--ggM-), andromonoecious (A-G-mm), and hermaphrodite (--ggmm). Sex expression in melons is complex, as the genes and associated networks that govern the sex expression are not fully explored. Recently, RNA-seq transcriptomic profiling, ChIP-qPCR analysis integrated with gene ontology annotation and Kyoto Encyclopedia of Genes and Genomes pathways predicted the differentially expressed genes including sex-specific ACS and ACO genes, in regulating the sex-expression, phytohormonal cross-talk, signal transduction, and secondary metabolism in melons. Integration of transcriptional control through genetic interaction in between the ACS7, ACS11, and WIP1 in epistatic or hypostatic manner, along with the recruitment of H3K9ac and H3K27me3, epigenetically, overall determine sex expression. Alignment of protein sequences for establishing phylogenetic evolution, motif comparison, and protein-protein interaction supported the structural conservation while presence of the conserved hydrophilic and charged residues across the diverged evolutionary group predicted the functional conservation of the ACS protein. Presence of the putative cis-binding elements or DNA motifs, and its further comparison with DAP-seq-based cistrome and epicistrome of Arabidopsis, unraveled strong ancestry of melons with Arabidopsis. Motif comparison analysis also characterized putative genes and transcription factors involved in ethylene biosynthesis, signal transduction, and hormonal cross-talk related to sex expression. Overall, we have comprehensively reviewed research findings for a deeper insight into transcriptional and epigenetic regulation of sex expression and flower development in melons.

Cardiorespiratory fitness in children with overweight/obesity: Insights into the molecular mechanisms

OBJECTIVES

High cardiorespiratory fitness (CRF) levels reduce the risk of developing cardiovascular disease (CVD) during adulthood. However, little is known about the molecular mechanisms underlying the health benefits of high CRF levels at the early stage of life. This study aimed to analyze the whole-blood transcriptome profile of fit children with overweight/obesity (OW/OB) compared to unfit children with OW/OB.

DESIGN

27 children with OW/OB (10.14 ± 1.3 years, 59% boys) from the ActiveBrains project were evaluated. VO₂ peak was assessed using a gas analyzer, and participants were categorized into fit or unfit according to the CVD risk-related cut-points. Whole-blood transcriptome profile (RNA sequencing) was analyzed. Differential gene expression analysis was performed using the limma R/Bioconductor software package (analyses adjusted by sex and maturational status), and pathways' enrichment analysis was performed with DAVID. In addition, in silico validation data mining was performed using the PHENOPEDIA database.

RESULTS

256 genes were differentially expressed in fit children with OW/OB compared to unfit children with OW/OB after adjusting by sex and maturational status (FDR < 0.05). Enriched pathway analysis identified gene pathways related to inflammation (eg, dopaminergic and GABAergic synapse pathways). Interestingly, in silico validation data mining detected a set of the differentially expressed genes to be related to CVD, metabolic syndrome, hypertension, inflammation, and asthma.

CONCLUSION

The distinct pattern of whole-blood gene expression in fit children with OW/OB reveals genes and gene pathways that might play a role in reducing CVD risk factors later in life.

Stratification of patients with clear cell renal cell carcinoma to facilitate drug repositioning

Clear cell renal cell carcinoma (ccRCC) is the most common histological type of kidney cancer and has high heterogeneity. Stratification of ccRCC is important since distinct subtypes differ in prognosis and treatment. Here, we applied a systems biology approach to stratify ccRCC into three molecular subtypes with different mRNA expression patterns and prognosis of patients. Further, we developed a set of biomarkers that could robustly classify the patients into each of the three subtypes and predict the prognosis of patients. Then, we reconstructed subtype-specific metabolic models and performed essential gene analysis to identify the potential drug targets. We identified four drug targets, including SOAT1, CRLS1, and ACACB, essential in all the three subtypes and GPD2, exclusively essential to subtype 1. Finally, we repositioned mitotane, an FDA-approved SOAT1 inhibitor, to treat ccRCC and showed that it decreased tumor cell viability and inhibited tumor cell growth based on in vitro experiments.

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Three consistent molecular ccRCC subtypes were found to guide patients' prognoses

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REOs-based biomarker was developed to robustly classify patients at individual level

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SOAT1 is identified as a common drug target for all ccRCC subtypes

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Mitotane was repositioned treatment of ccRCC via inhibiting SOAT1

Scrutinizing the impact of water deficit in plants: Transcriptional regulation, signaling, photosynthetic efficacy, and management

Suboptimal availability of water limits plant growth, development, and performance. Drought is one of the leading factors responsible for worldwide crop yield reduction. In the future, owing to climate changes, more agricultural land will be affected by prolonged periods of water deficit. Thus, understanding the fundamental mechanism of drought response is a major scientific concern for improvement of crop production. To combat drought stress, plants deploy varied mechanistic strategies and alter their morphological, physiochemical, and molecular attributes. This helps plant to enhance water uptake and storage, reduce water loss and avoid wilting. Induction of several transcription factors and drought responsive genes leads to synthesis of stress proteins, regulation of water channels i.e. aquaporins and production of osmolytes that are essential for maintenance of osmotic balance at the cellular level. Self- and hormone-regulated signaling pathways are often stimulated by plants after receiving drought stress signals via secondary messengers, mitogen-activated protein kinases, and stress hormones. These signaling cascades often leads to stomatal closure and reduction in transpiration rates. Reduced carbon dioxide diffusion in chloroplast, lowered efficacy of photosystems, and other metabolic constraints limits the key regulatory photosynthetic process during water deficit. The impact of these stomatal and nonstomatal limitations varies with stress intensity, superimposed stresses and plant species. A clear understanding of the drought resistance process is thus important before adopting strategies for imparting drought tolerance in plants. These management practices at present include exogenous hormone application, breeding, and genetic engineering techniques for combating the water deficit issues.

A manipulative interplay between positive and negative regulators of phytohormones: A way forward for improving drought tolerance in plants

Among different abiotic stresses, drought stress is the leading cause of impaired plant growth and low productivity worldwide. It is therefore essential to understand the process of drought tolerance in plants and thus to enhance drought resistance. Accumulating evidence indicates that phytohormones are essential signaling molecules that regulate diverse processes of plant growth and development under drought stress. Plants can often respond to drought stress through a cascade of phytohormones signaling as a means of plant growth regulation. Understanding biosynthesis pathways and regulatory crosstalk involved in these vital compounds could pave the way for improving plant drought tolerance while maintaining overall plant health. In recent years, the identification of phytohormones related key regulatory genes and their manipulation through state-of-the-art genome engineering tools have helped to improve drought tolerance plants. To date, several genes linked to phytohormones signaling networks, biosynthesis, and metabolism have been described as a promising contender for engineering drought tolerance. Recent advances in functional genomics have shown that enhanced expression of positive regulators involved in hormone biosynthesis could better equip plants against drought stress. Similarly, knocking down negative regulators of phytohormone biosynthesis can also be very effective to negate the negative effects of drought on plants. This review explained how manipulating positive and negative regulators of phytohormone signaling could be improvised to develop future crop varieties exhibiting higher drought tolerance. In addition, we also discuss the role of a promising genome editing tool, CRISPR/Cas9, on phytohormone mediated plant growth regulation for tackling drought stress.

Two novel qualitative transcriptional signatures robustly applicable to non-research-oriented colorectal cancer samples with low-quality RNA

Currently, due to the low quality of RNA caused by degradation or low abundance, the accuracy of gene expression measurements by transcriptome sequencing (RNA‐seq) is very challenging for non‐research‐oriented clinical samples, majority of which are preserved in hospitals or tissue banks worldwide with complete pathological information and follow‐up data. Molecular signatures consisting of several genes are rarely applied to such samples. To utilize these resources effectively, 45 stage II non‐research‐oriented samples which were formalin‐fixed paraffin‐embedded (FFPE) colorectal carcinoma samples (CRC) using RNA‐seq have been analysed. Our results showed that although gene expression measurements were significantly affected, most cancer features, based on the relative expression orderings (REOs) of gene pairs, were well preserved. We then developed two REO‐based signatures, which consisted of 136 gene pairs for early diagnosis of CRC, and 4500 gene pairs for predicting post‐surgery relapse risk of stage II and III CRC. The performance of our signatures, which included hundreds or thousands of gene pairs, was more robust for non‐research‐oriented clinical samples, compared to that of two published concise REO‐based signatures. In conclusion, REO‐based signatures with relatively more gene pairs could be robustly applied to non‐research‐oriented CRC samples.

A Qualitative Transcriptional Signature for the Risk Assessment of Precancerous Colorectal Lesions

It is meaningful to assess the risk of cancer incidence among patients with precancerous colorectal lesions. Comparing the within-sample relative expression orderings (REOs) of colorectal cancer patients measured by multiple platforms with that of normal colorectal tissues, a qualitative transcriptional signature consisting of 1,840 gene pairs was identified in the training data. Within an evaluation dataset of 16 active and 18 inactive (remissive) ulcerative colitis subjects, the median incidence risk score of colorectal carcinoma was 0.6402 in active ulcerative colitis subjects, significantly higher than that in remissive subjects (0.3114). Evaluation of two other independent datasets yielded similar results. Moreover, we found that the score significantly positively correlated with the degree of dysplasia in the case of colorectal adenomas. In the merged dataset, the median incidence risk score was 0.9027 among high-grade adenoma samples, significantly higher than that among low-grade adenomas (0.8565). In summary, the developed incidence risk score could well predict the incidence risk of precancerous colorectal lesions and has value in clinical application.

A Qualitative Transcriptional Signature for Predicting CpG Island Methylator Phenotype Status of the Right-Sided Colon Cancer

A part of colorectal cancer which is characterized by simultaneous numerous hypermethylation CpG islands sites is defined as CpG island methylator phenotype (CIMP) status. Stage II and III CIMP−positive (CIMP+) right-sided colon cancer (RCC) patients have a better prognosis than CIMP−negative (CIMP−) RCC treated with surgery alone. However, there is no gold standard available in defining CIMP status. In this work, we selected the gene pairs whose relative expression orderings (REOs) were associated with the CIMP status, to develop a qualitative transcriptional signature to individually predict CIMP status for stage II and III RCC. Based on the REOs of gene pairs, a signature composed of 19 gene pairs was developed to predict the CIMP status of RCC through a feature selection process. A sample is predicted as CIMP+ when the gene expression orderings of at least 12 gene pairs vote for CIMP+; otherwise the CIMP−. The difference of prognosis between the predicted CIMP+ and CIMP− groups was more significantly different than the original CIMP status groups. There were more differential methylation and expression characteristics between the two predicted groups. The hierarchical clustering analysis showed that the signature could perform better for predicting CIMP status of RCC than current methods. In conclusion, the qualitative transcriptional signature for classifying CIMP status at the individualized level can predict outcome and guide therapy for RCC patients.

Qualitative Transcriptional Signature for the Pathological Diagnosis of Pancreatic Cancer

It is currently difficult for pathologists to diagnose pancreatic cancer (PC) using biopsy specimens because samples may have been from an incorrect site or contain an insufficient amount of tissue. Thus, there is a need to develop a platform-independent molecular classifier that accurately distinguishes benign pancreatic lesions from PC. Here, we developed a robust qualitative messenger RNA signature based on within-sample relative expression orderings (REOs) of genes to discriminate both PC tissues and cancer-adjacent normal tissues from non-PC pancreatitis and healthy pancreatic tissues. A signature comprising 12 gene pairs and 17 genes was built in the training datasets and validated in microarray and RNA-sequencing datasets from biopsy samples and surgically resected samples. Analysis of 1,007 PC tissues and 257 non-tumor samples from nine databases indicated that the geometric mean of sensitivity and specificity was 96.7%, and the area under receiver operating characteristic curve was 0.978 (95% confidence interval, 0.947–0.994). For 20 specimens obtained from endoscopic biopsy, the signature had a diagnostic accuracy of 100%. The REO-based signature described here can aid in the molecular diagnosis of PC and may facilitate objective differentiation between benign and malignant pancreatic lesions.

A Qualitative Transcriptional Signature for Predicting Prognosis and Response to Bevacizumab in Metastatic Colorectal Cancer

Bevacizumab is the molecular-targeted agent used for the antiangiogenic therapy of metastatic colorectal cancer. But some patients are resistant to bevacizumab, it needs an effective biomarker to predict the prognosis and responses of metastatic colorectal cancer (mCRC) to bevacizumab therapy. In this work, we developed a qualitative transcriptional signature to individually predict the response of bevacizumab in patients with mCRC. First, using mCRC samples treated with bevacizumab, we detected differentially expressed genes between response and nonresponse groups. Then, the gene pairs, consisting of at least one differentially expressed gene, with stable relative expression orderings in the response samples but reversal stable relative expression orderings in the nonresponse samples were identified, denoted as pairs-bevacizumab. Similarly, we screened the gene pairs significantly associated with primary tumor locations, donated as pairs-LR. Among the overlapped gene pairs between the pairs-bevacizumab and pairs-LR, we adopted a feature selection process to extract gene pairs that reached the highest F-score for predicting bevacizumab response status in mCRC as the final gene pair signature (GPS), denoted as 64-GPS. In two independent datasets, the predicted response group showed significantly better overall survival than the nonresponse group (P = 6.00e-4 in GSE72970; P = 0.04 in TCGA). Genomic analyses showed that the predicted response group was characterized by frequent copy number alternations, whereas the nonresponse group was characterized by hypermutation. In conclusion, 64-GPS was an objective and robust predictive signature for patients with mCRC treated with bevacizumab, which could effectively assist in the decision of clinical therapy.

A qualitative transcriptional prognostic signature for patients with stage I-II pancreatic ductal adenocarcinoma

Accurately prognostic evaluation of patients with stage I-II pancreatic ductal adenocarcinoma (PDAC) is of importance to treatment decision and patient management. Most previously reported prognostic signatures were based on risk scores summarized from quantitative expression measurements of signature genes, which are susceptible to experimental batch effects and impractical for clinical applications. Based on the within-sample relative expression orderings of genes, we developed a robust qualitative transcriptional prognostic signature, consisting of 64 gene pairs (64-GPS), to predict the overall survival (OS) of 161 stage I-II PDAC patients in the training dataset who were treated with surgery only. Samples were classified into the high-risk group when at least 25 of 64 gene pairs suggested it was at high risk. The signature was successfully validated in 324 samples from 6 independent datasets produced by different laboratories. All samples in the low-risk group had significantly better OS than samples in the high-risk group. Multivariate Cox regression analyses showed that the 64-GPS remained significantly associated with the OS of patients after adjusting available clinical factors. Transcriptomic analysis of the 2 prognostic subgroups showed that the differential expression signals were highly reproducible in all datasets, whereas the differences between samples grouped by the TNM staging system were weak and irreproducible. The epigenomic analysis showed that the epigenetic alternations may cause consistently transcriptional changes between the 2 different prognostic groups. The genomic analysis revealed that mutation‑induced disturbances in several key genes, such as LRMDA, MAPK10, and CREBBP, might lead to poor prognosis for PDAC patients. Conclusively, the 64-GPS can robustly predict the prognosis of patients with stage I-II PDAC, which provides theoretical basis for clinical individualized treatment.

Qualitative Ras pathway signature for cetuximab therapy reveals resistant mechanism in colorectal cancer

Cetuximab therapy, which heavily relies on the activation of Ras pathway, has been used in KRAS, NRAS, BRAF, and PIK3CA wild-type colorectal cancer (CRC) (Ras-normal). However, the response rate only reached 60%, due to false-negative mutation detection and mutation-like transcriptome features in wild-type patients. Herein, by integrating RNA-seq, microarray, and mutation data, we developed a Ras pathway signature by characterizing KRAS/NRAS/BRAF/PIK3CA mutations to identify the hidden nonresponders from the Ras-normal patients by mutation detection. Using public and in-house data of CRC patients treated with cetuximab, discovery of the signature could identify cetuximab-resistant samples from the Ras-normal samples. Cetuximab resistance-related genes, such as PTEN, were significantly and frequently mutated in the identified Ras-activated samples, whereas two cetuximab sensitivity-related genes, APC and TP53, showed comutation and significantly higher mutation frequencies in the remaining Ras-normal samples. Furthermore, all the NF1- and BCL2L1-mutated samples were identified as Ras-activated from the Ras-normal samples by the Ras pathway signature with significantly under-regulated expression. Genes co-expressed with the two genes were both involved in Ras signaling pathway, the out-of-control of which could be attributed by the genes' loss-of-function mutations. To improve the treatment of cetuximab in CRC, NF1 and BCL2L1 could be used as complementary detection technique to those applied in clinical. In conclusion, the proposed Ras pathway signature could identify the hidden CRC patients resistant to cetuximab therapy and help to reveal resistance mechanisms.

A qualitative transcriptional signature to reclassify histological grade of ER-positive breast cancer patients

BACKGROUND

Histological grade (HG) is commonly adopted as a prognostic factor for ER-positive breast cancer patients. However, HG evaluation methods, such as the pathological Nottingham grading system, are highly subjective with only 50-85% inter-observer agreements. Specifically, the subjectivity in the pathological assignment of the intermediate grade (HG2) breast cancers, comprising of about half of breast cancer cases, results in uncertain disease outcomes prediction. Here, we developed a qualitative transcriptional signature, based on within-sample relative expression orderings (REOs) of gene pairs, to define HG1 and HG3 and reclassify pathologically-determined HG2 (denoted as pHG2) breast cancer patients.

RESULTS

From the gene pairs with significantly stable REOs in pathologically-determined HG1 (denoted as pHG1) samples and reversely stable REOs in pathologically-determined HG3 (denoted as pHG3) samples, concordantly identified from seven datasets, we extracted a signature which could determine the HG state of samples through evaluating whether the within-sample REOs match with the patterns of the pHG1 REOs or pHG3 REOs. A sample was classified into the HG3 group if at least a half of the REOs of the 10 gene pairs signature within this sample voted for HG3; otherwise, HG1. Using four datasets including samples of early stage (I-II) ER-positive breast cancer patients who accepted surgery only, we validated that this signature was able to reclassify pHG2 patients into HG1 and HG3 groups with significantly different survival time. For the original pHG1 and pHG3 patients, the signature could also more accurately and objectively stratify them into distinct prognostic groups. And the up-regulated and down down-regulated genes in HG1 compared with HG3 involved in cell proliferation and extracellular signal transduction pathways respectively. By comparing with existing signatures, 10-GPS was with prognostic significance and was more aligned with survival of patients especially for pHG2 samples.

CONCLUSIONS

The transcriptional qualitative signature can provide an objective assessment of HG states of ER-positive breast cancer patients, especially for reclassifying patients with pHG2, to assist decision making on clinical therapy.

A qualitative transcriptional signature for predicting the biochemical recurrence risk of prostate cancer patients after radical prostatectomy

BACKGROUND

The qualitative transcriptional characteristics, the within-sample relative expression orderings (REOs) of genes, are highly robust against batch effects and sample quality variations. Hence, we develop a qualitative transcriptional signature based on REOs to predict the biochemical recurrence risk of prostate cancer (PCa) patients after radical prostatectomy.

METHODS

Gene pairs with REOs significantly correlated with the biochemical recurrence-free survival (BFS) were identified from 131 PCa samples in the training data set. From these gene pairs, we selected a qualitative transcriptional signature based on the within-sample REOs of gene pairs which could predict the recurrence risk of PCa patients after radical prostatectomy.

RESULTS

A signature consisting of 74 gene pairs, named 74-GPS, was developed for predicting the recurrence risk of PCa patients after radical prostatectomy based on the majority voting rule that a sample was assigned as high risk when at least 37 gene pairs of the 74-GPS voted for high risk; otherwise, low risk. The signature was validated in six independent datasets produced by different platforms. In each of the validation datasets, the Kaplan-Meier survival analysis showed that the average BFS of the low-risk group was significantly better than that of the high-risk group. Analyses of multiomics data of PCa samples from TCGA suggested that both the epigenomic and genomic alternations could cause the reproducible transcriptional differences between the two different prognostic groups.

CONCLUSIONS

The proposed qualitative transcriptional signature can robustly stratify PCa patients after radical prostatectomy into two groups with different recurrence risk and distinct multiomics characteristics. Hence, 74-GPS may serve as a helpful tool for guiding the management of PCa patients with radical prostatectomy at the individual level.

A Qualitative Transcriptional Signature for Predicting Extreme Resistance of ER-Negative Breast Cancer to Paclitaxel, Doxorubicin, and Cyclophosphamide Neoadjuvant Chemotherapy

For estrogen receptor (ER)-negative breast cancer patients, paclitaxel (P), doxorubicin (A) and cyclophosphamide (C) neoadjuvant chemotherapy (NAC) is the standard therapeutic regimen. Pathologic complete response (pCR) and residual disease (RD) are common surrogate measures of chemosensitivity. After NAC, most patients still have RD; of these, some partially respond to NAC, whereas others show extreme resistance and cannot benefit from NAC but only suffer complications resulting from drug toxicity. Here we developed a qualitative transcriptional signature, based on the within-sample relative expression ordering (REO) of gene pairs, to identify extremely resistant samples to PAC NAC. Using gene expression data for ER-negative breast cancer patients including 113 pCR samples and 137 RD samples from four datasets, we selected 61 gene pairs with reversal REO patterns between the two groups as the resistance signature, denoted as NR61. Samples with more than 37 signature gene pairs that had the same REO patterns within the extremely resistant group were defined as having extreme resistance; otherwise, they were considered responders. In the GSE25055 and GSE25065 dataset, the NR61 signature could correctly identify 44 (97.8%) of the 45 pCR samples and 22 (95.7%) of the 23 pCR samples as responder samples, respectively; it also identified 13 (16.9%) of 77 RD samples and 8 (21.1%) of 38 RD samples as extremely resistant samples, respectively. Survival analysis showed that the distant relapse-free survival (DRFS) time of the 14 extremely resistant cases was significantly shorter than that of the 108 responders (P < 0.01; HR = 3.84; 95% CI = 1.91–7.70) in GSE25055. Similar results were obtained in GSE25065. Moreover, in the integrated data of the two datasets with 94 responders and 21 extremely resistant samples identified from RD patients, the former had significantly longer DRFS than the latter (P < 0.01; HR = 2.22; 95% CI = 1.26–3.90). In summary, our signature could effectively identify patients who completely respond to PAC NAC, as well as cases of extreme resistance, which can assist decision-making on the clinical therapy for these patients.

CLEAR: coverage-based limiting-cell experiment analysis for RNA-seq

Background

Direct cDNA preamplification protocols developed for single-cell RNA-seq have enabled transcriptome profiling of precious clinical samples and rare cell populations without the need for sample pooling or RNA extraction. We term the use of single-cell chemistries for sequencing low numbers of cells limiting-cell RNA-seq (lcRNA-seq). Currently, there is no customized algorithm to select robust/low-noise transcripts from lcRNA-seq data for between-group comparisons.

Methods

Herein, we present CLEAR, a workflow that identifies reliably quantifiable transcripts in lcRNA-seq data for differentially expressed genes (DEG) analysis. Total RNA obtained from primary chronic lymphocytic leukemia (CLL) CD5+ and CD5− cells were used to develop the CLEAR algorithm. Once established, the performance of CLEAR was evaluated with FACS-sorted cells enriched from mouse Dentate Gyrus (DG).

Results

When using CLEAR transcripts vs. using all transcripts in CLL samples, downstream analyses revealed a higher proportion of shared transcripts across three input amounts and improved principal component analysis (PCA) separation of the two cell types. In mouse DG samples, CLEAR identifies noisy transcripts and their removal improves PCA separation of the anticipated cell populations. In addition, CLEAR was applied to two publicly-available datasets to demonstrate its utility in lcRNA-seq data from other institutions. If imputation is applied to limit the effect of missing data points, CLEAR can also be used in large clinical trials and in single cell studies.

Conclusions

lcRNA-seq coupled with CLEAR is widely used in our institution for profiling immune cells (circulating or tissue-infiltrating) for its transcript preservation characteristics. CLEAR fills an important niche in pre-processing lcRNA-seq data to facilitate transcriptome profiling and DEG analysis. We demonstrate the utility of CLEAR in analyzing rare cell populations in clinical samples and in murine neural DG region without sample pooling.

Classification of clear cell renal cell carcinoma based on PKM alternative splicing

Clear cell renal cell carcinoma (ccRCC) accounts for 70-80% of kidney cancer diagnoses and displays high molecular and histologic heterogeneity. Hence, it is necessary to reveal the underlying molecular mechanisms involved in progression of ccRCC to better stratify the patients and design effective treatment strategies. Here, we analyzed the survival outcome of ccRCC patients as a consequence of the differential expression of four transcript isoforms of the pyruvate kinase muscle type (PKM). We first extracted a classification biomarker consisting of eight gene pairs whose within-sample relative expression orderings (REOs) could be used to robustly classify the patients into two groups with distinct molecular characteristics and survival outcomes. Next, we validated our findings in a validation cohort and an independent Japanese ccRCC cohort. We finally performed drug repositioning analysis based on transcriptomic expression profiles of drug-perturbed cancer cell lines and proposed that paracetamol, nizatidine, dimethadione and conessine can be repurposed to treat the patients in one of the subtype of ccRCC whereas chenodeoxycholic acid, fenoterol and hexylcaine can be repurposed to treat the patients in the other subtype.

Qualitative transcriptional signature for predicting pathological response of colorectal cancer to FOLFOX therapy

FOLFOX (5‐fluorouracil, leucovorin and oxaliplatin) is one of the main chemotherapy regimens for colorectal cancer (CRC), but only half of CRC patients respond to this regimen. Using gene expression profiles of 96 metastatic CRC patients treated with FOLFOX, we first selected gene pairs whose within‐sample relative expression orderings (REO) were significantly associated with the response to FOLFOX using the exact binomial test. Then, from these gene pairs, we applied an optimization procedure to obtain a subset that achieved the largest F‐score in predicting pathological response of CRC to FOLFOX. The REO‐based qualitative transcriptional signature, consisting of five gene pairs, was developed in the training dataset consisting of 96 samples with an F‐score of 0.90. In an independent test dataset consisting of 25 samples with the response information, an F‐score of 0.82 was obtained. In three other independent survival datasets, the predicted responders showed significantly better progression‐free survival than the predicted non‐responders. In addition, the signature showed a better predictive performance than two published FOLFOX signatures across different datasets and is more suitable for CRC patients treated with FOLFOX than 5‐fluorouracil‐based signatures. In conclusion, the REO‐based qualitative transcriptional signature can accurately identify metastatic CRC patients who may benefit from the FOLFOX regimen.

A qualitative transcriptional signature for the histological reclassification of lung squamous cell carcinomas and adenocarcinomas

Background

Targeted therapy for non-small cell lung cancer is histology dependent. However, histological classification by routine pathological assessment with hematoxylin-eosin staining and immunostaining for poorly differentiated tumors, particularly those from small biopsies, is still challenging. Additionally, the effectiveness of immunomarkers is limited by technical inconsistencies of immunostaining and lack of standardization for staining interpretation.

Results

Using gene expression profiles of pathologically-determined lung adenocarcinomas and squamous cell carcinomas, denoted as pADC and pSCC respectively, we developed a qualitative transcriptional signature, based on the within-sample relative gene expression orderings (REOs) of gene pairs, to distinguish ADC from SCC. The signature consists of two genes, KRT5 and AGR2, which has the stable REO pattern of KRT5 > AGR2 in pSCC and KRT5 < AGR2 in pADC. In the two test datasets with relative unambiguous NSCLC types, the apparent accuracy of the signature were 94.44 and 98.41%, respectively. In the other integrated dataset for frozen tissues, the signature reclassified 4.22% of the 805 pADC patients as SCC and 12% of the 125 pSCC patients as ADC. Similar results were observed in the clinical challenging cases, including FFPE specimens, mixed tumors, small biopsy specimens and poorly differentiated specimens. The survival analyses showed that the pADC patients reclassified as SCC had significantly shorter overall survival than the signature-confirmed pADC patients (log-rank p = 0.0123, HR = 1.89), consisting with the knowledge that SCC patients suffer poor prognoses than ADC patients. The proliferative activity, subtype-specific marker genes and consensus clustering analyses also supported the correctness of our signature.

Conclusions

The non-subjective qualitative REOs signature could effectively distinguish ADC from SCC, which would be an auxiliary test for the pathological assessment of the ambiguous cases.

A Qualitative Transcriptional Signature for Predicting Recurrence Risk for High-Grade Serous Ovarian Cancer Patients Treated With Platinum-Taxane Adjuvant Chemotherapy

Resistance to platinum and taxane adjuvant chemotherapy (ACT) is the main cause of the recurrence and poor prognosis of high-grade serous ovarian cancer (HGS-OvCa) patients receiving platinum-taxane ACT after surgery. However, currently reported quantitative transcriptional signatures, which are commonly based on risk scores summarized from gene expression, are unsuitable for clinical application because of their high sensitivity to experimental batch effects and quality uncertainties of clinical samples. Using 226 samples of HGS-OvCa patients receiving platinum-taxane ACT in TCGA, we developed a qualitative transcriptional signature, consisting of four gene pairs whose within-samples relative expression orderings could robustly predict patient recurrence-free survival (RFS). In two independent test datasets, the predicted non-responders had significantly shorter RFS than the predicted responders (log-rank p < 0.05). In a test dataset containing data for patient pathological response state, the signature reclassified 12 out of 22 pathological complete response patients as non-responders and two out of 16 pathological non-complete response patients as responders. Notably, the 12 predicted non-responders in the pathological complete response group had significantly shorter RFS than the predicted responders (log-rank p = 0.0122). This qualitative transcriptional signature, which is insensitive to experimental batch effects and quality uncertainties of clinical samples, can individually identify HGS-OvCa patients who are more likely to benefit from platinum-taxane adjuvant chemotherapy.

A qualitative transcriptional signature for predicting microsatellite instability status of right-sided Colon Cancer

Background

Microsatellite instability (MSI) accounts for about 15% of colorectal cancer and is associated with prognosis. Today, MSI is usually detected by polymerase chain reaction amplification of specific microsatellite markers. However, the instability is identified by comparing the length of microsatellite repeats in tumor and normal samples. In this work, we developed a qualitative transcriptional signature to individually predict MSI status for right-sided colon cancer (RCC) based on tumor samples.

Results

Using RCC samples, based on the relative expression orderings (REOs) of gene pairs, we extracted a signature consisting of 10 gene pairs (10-GPS) to predict MSI status for RCC through a feature selection process. A sample is predicted as MSI when the gene expression orderings of at least 7 gene pairs vote for MSI; otherwise the microsatellite stability (MSS). The classification performance reached the largest F-score in the training dataset. This signature was verified in four independent datasets of RCCs with the F-scores of 1, 0.9630, 0.9412 and 0.8798, respectively. Additionally, the hierarchical clustering analyses and molecular features also supported the correctness of the reclassifications of the MSI status by 10-GPS.

Conclusions

The qualitative transcriptional signature can be used to classify MSI status of RCC samples at the individualized level.

A qualitative transcriptional signature for the early diagnosis of colorectal cancer

Currently, using biopsy specimens for the early diagnosis of colorectal cancer (CRC) is not entirely reliable due to insufficient sampling amount and inaccurate sampling location. Thus, it is necessary to develop a signature that can accurately identify patients with CRC under these clinical scenarios. Based on the relative expression orderings of genes within individual samples, we developed a qualitative transcriptional signature to discriminate CRC tissues, including CRC adjacent normal tissues from non-CRC individuals. The signature was validated using multiple microarray and RNA sequencing data from different sources. In the training data, a signature consisting of 7 gene pairs was identified. It was well validated in both biopsy and surgical resection specimens from multiple datasets measured by different platforms. For biopsy specimens, 97.6% of 42 CRC tissues and 94.5% of 163 non-CRC (normal or inflammatory bowel disease) tissues were correctly classified. For surgically resected specimens, 99.5% of 854 CRC tissues and 96.3% of 81 CRC adjacent normal tissues were correctly identified as CRC. Notably, we additionally measured 33 CRC biopsy specimens by the Affymetrix platform and 13 CRC surgical resection specimens, with different proportions of tumor epithelial cells, ranging from 40% to 100%, by the RNA sequencing platform, and all these samples were correctly identified as CRC. The signature can be used for the early diagnosis of CRC, which is also suitable for minimum biopsy specimens and inaccurately sampled specimens, and thus has potential value for clinical application.

A Qualitative Transcriptional Signature for Predicting Recurrence Risk of Stage I-III Bladder Cancer Patients After Surgical Resection

Background: Previously reported transcriptional signatures for predicting the prognosis of stage I-III bladder cancer (BLCA) patients after surgical resection are commonly based on risk scores summarized from quantitative measurements of gene expression levels, which are highly sensitive to the measurement variation and sample quality and thus hardly applicable under clinical settings. It is necessary to develop a signature which can robustly predict recurrence risk of BLCA patients after surgical resection.

Methods: The signature is developed based on the within-sample relative expression orderings (REOs) of genes, which are qualitative transcriptional characteristics of the samples.

Results: A signature consisting of 12 gene pairs (12-GPS) was identified in training data with 158 samples. In the first validation dataset with 114 samples, the low-risk group of 54 patients had a significantly better overall survival than the high-risk group of 60 patients (HR = 3.59, 95% CI: 1.34~9.62, p = 6.61 × 10⁻⁰³). The signature was also validated in the second validation dataset with 57 samples (HR = 2.75 × 10⁰⁸, 95% CI: 0~Inf, p = 0.05). Comparison analysis showed that the transcriptional differences between the low- and high-risk groups were highly reproducible and significantly concordant with DNA methylation differences between the two groups.

Conclusions: The 12-GPS signature can robustly predict the recurrence risk of stage I-III BLCA patients after surgical resection. It can also aid the identification of reproducible transcriptional and epigenomic features characterizing BLCA metastasis.

CLEC3A, MMP7, and LCN2 as novel markers for predicting recurrence in resected G1 and G2 pancreatic neuroendocrine tumors

Although the postoperative recurrence rate for pancreatic neuroendocrine tumors (PNETs) is reported to be 13.5%-30%, the paucity of valuable biomarkers to predict recurrence poses a problem for the early detection of relapse. Hence, this study aimed to identify new biomarkers to predict the recurrence of PNETs. We performed RNA sequencing (RNA-Seq) on RNA isolated from frozen primary tumors sampled from all localized G1/G2 PNETs resected curatively from 1998 to 2015 in our institution. We calculated differentially expressed genes (DEGs) in tumor with and without recurrence (≥3 years) for the propensity-matched cohort. Gene ontology analysis for the identified DEGs was also performed. Furthermore, we evaluated the expression levels of candidate genes as recurrence predictors via immunostaining. Comparison of transcriptional levels in tumors with and without recurrence identified 166 DEGs. Up- and downregulated genes with high significance in these tumors were mainly related to extracellular organization and cell adhesion, respectively. We observed the top three upregulated genes, C-type lectin domain family 3 member A (CLEC3A), matrix metalloproteinase-7 (MMP7), and lipocalin2 (LCN2) immunohistochemically and compared their levels in recurrent and nonrecurrent tumors. Significantly higher recurrence rate was shown in patients with positive expression of CLEC3A (P = 0.028), MMP7 (P = 0.003), and LCN2 (P = 0.040) than that with negative expression. We identified CLEC3A, MMP7, and LCN2 known to be associated with the phosphatidylinositol-3-kinase/Akt pathway, as potential novel markers to predict the postoperative recurrence of PNETs.

A robust qualitative transcriptional signature for the correct pathological diagnosis of gastric cancer

BACKGROUND

Currently, pathological examination of gastroscopy biopsy specimens is the gold standard for gastric cancer (GC) diagnosis. However, it has a false-negative rate of 10-20% due to inaccurate sampling locations and/or insufficient sampling amount. A signature should be developed to aid the early diagnosis of GC using biopsy specimens even when they are sampled from inaccurate locations.

METHODS

We extracted a robust qualitative transcriptional signature, based on the within-sample relative expression orderings (REOs) of gene pairs, to discriminate both GC tissues and adjacent-normal tissues from non-GC gastritis, intestinal metaplasia and normal gastric tissues.

RESULTS

A signature consisting of two gene pairs for GC diagnosis was identified and validated in data of both biopsy specimens and surgical resection specimens pooled from publicly available datasets measured by different laboratories with different platforms. For gastroscopy biopsy specimens, 96.20% of 79 non-GC tissues were correctly identified as non-GC, and 96.84% of 158 GC tissues and six of seven adjacent-normal tissues were correctly identified as GC. For surgical resection specimens, 98.37% of 2560 GC tissues and 97.28% of 221 adjacent-normal tissues were correctly identified as GC. Especially, 97.67% of the 257 GC patients at stage I were exactly diagnosed as GC. We additionally measured 21 GC tissues from seven different GC patients, each with three specimens sampled from three tumor locations with different proportions of the tumor epithelial cell. All these GC tissues were correctly identified as GC, even when the proportion of the tumor epithelial cell was as low as 14%.

CONCLUSIONS

The qualitative transcriptional signature can distinguish both GC and adjacent-normal tissues from normal, gastritis and intestinal metaplasia tissues of non-GC patients even using inaccurately sampled biopsy specimens, which can be applied robustly at the individual level to aid the early GC diagnosis.

A qualitative signature for early diagnosis of hepatocellular carcinoma based on relative expression orderings

BACKGROUND & AIMS

Currently, using biopsy specimens to confirm suspicious liver lesions of early hepatocellular carcinoma are not entirely reliable because of insufficient sampling amount and inaccurate sampling location. It is necessary to develop a signature to aid early hepatocellular carcinoma diagnosis using biopsy specimens even when the sampling location is inaccurate.

METHODS

Based on the within-sample relative expression orderings of gene pairs, we identified a simple qualitative signature to distinguish both hepatocellular carcinoma and adjacent non-tumour tissues from cirrhosis tissues of non-hepatocellular carcinoma patients.

RESULTS

A signature consisting of 19 gene pairs was identified in the training data sets and validated in 2 large collections of samples from biopsy and surgical resection specimens. For biopsy specimens, 95.7% of 141 hepatocellular carcinoma tissues and all (100%) of 108 cirrhosis tissues of non-hepatocellular carcinoma patients were correctly classified. Especially, all (100%) of 60 hepatocellular carcinoma adjacent normal tissues and 77.5% of 80 hepatocellular carcinoma adjacent cirrhosis tissues were classified to hepatocellular carcinoma. For surgical resection specimens, 99.7% of 733 hepatocellular carcinoma specimens were correctly classified to hepatocellular carcinoma, while 96.1% of 254 hepatocellular carcinoma adjacent cirrhosis tissues and 95.9% of 538 hepatocellular carcinoma adjacent normal tissues were classified to hepatocellular carcinoma. In contrast, 17.0% of 47 cirrhosis from non-hepatocellular carcinoma patients waiting for liver transplantation were classified to hepatocellular carcinoma, indicating that some patients with long-lasting cirrhosis could have already gained hepatocellular carcinoma characteristics.

CONCLUSIONS

The signature can distinguish both hepatocellular carcinoma tissues and tumour-adjacent tissues from cirrhosis tissues of non-hepatocellular carcinoma patients even using inaccurately sampled biopsy specimens, which can aid early diagnosis of hepatocellular carcinoma.

Quantitative or qualitative transcriptional diagnostic signatures? A case study for colorectal cancer

Background

Due to experimental batch effects, the application of a quantitative transcriptional signature for disease diagnoses commonly requires inter-sample data normalization, which would be hardly applicable under common clinical settings. Many cancers might have qualitative differences with the non-cancer states in the gene expression pattern. Therefore, it is reasonable to explore the power of qualitative diagnostic signatures which are robust against experimental batch effects and other random factors.

Results

Firstly, using data of technical replicate samples from the MicroArray Quality Control (MAQC) project, we demonstrated that the low-throughput PCR-based technologies also exist large measurement variations for gene expression even when the samples were measured in the same test site. Then, we demonstrated the critical limitation of low stability for classifiers based on quantitative transcriptional signatures in applications to individual samples through a case study using a support vector machine and a naïve Bayesian classifier to discriminate colorectal cancer tissues from normal tissues. To address this problem, we identified a signature consisting of three gene pairs for discriminating colorectal cancer tissues from non-cancer (normal and inflammatory bowel disease) tissues based on within-sample relative expression orderings (REOs) of these gene pairs. The signature was well verified using 22 independent datasets measured by different microarray and RNA_seq platforms, obviating the need of inter-sample data normalization.

Conclusions

Subtle quantitative information of gene expression measurements tends to be unstable under current technical conditions, which will introduce uncertainty to clinical applications of the quantitative transcriptional diagnostic signatures. For diagnosis of disease states with qualitative transcriptional characteristics, the qualitative REO-based signatures could be robustly applied to individual samples measured by different platforms.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4446-y) contains supplementary material, which is available to authorized users.