Genetic analysis and QTL mapping of seed hardness trait in a soybean (Glycine max) recombinant inbred line (RIL) population.

Seed hardness is a critical quality trait impacting both the suitability of soybeans for consumption and their processing. The primary objective of this study was to explore the genetic foundations underlying seed hardness in soybeans. A 234 recombinant inbred line (RIL) population was evaluated for seed hardness across three years (2015 in Gansu, 2016, and 2017 in Hainan). Notably, the parent varieties, Zhonghuang35 and Jindou21, displayed significant differences in seed hardness. Also, the RIL population exhibited a wide range of genetic variation in seed hardness, with coefficients of variation between 70.53 % and 94.94 %. The frequency distribution of this trait conformed to a relatively normal distribution, making it suitable for QTL analysis. Six QTLs associated with seed hardness were identified with three located on chromosome 2 and three on chromosome 16. The major QTL, qHS-2-1, consistently exhibited the highest percentage of PVE and LOD in Gansu 2015, Hainan 2016, and Hainan 2017, suggesting its central role in determining seed hardness. Further investigation revealed four genes within the qHS-2-1 interval potentially related to seed hardness. GO enrichment analysis provided insights into their functions, including factors such as Glyma.02G307000, a pectin lyase-like superfamily protein, which could influence seed hardness through its role in pectin lyase enzyme activity. Expression analysis of these candidate genes demonstrated significant differences between the two parent varieties, further highlighting their potential role in seed coat hardness. This study offers valuable insights into the genetic mechanisms governing soybean seed coat hardness, providing a foundation for future research and crop improvement efforts.

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.

SEPTIN9-SDC2-VIM methylation signature as a biomarker for the early diagnosis of colorectal cancer.

The accurate detection of colorectal cancer (CRC) at its initial stage can reduce mortality. However, the broad application of endoscopy has been limited due to the invasive procedure and patient noncompliance. Liquid biopsy with subsequent mapping of methylation in specific cell-free DNA (cfDNA) may represent an alternative approach for early diagnosis. In this study, we have developed a minimal-invasive blood-based test for detection of precancerous lesions and early-stage CRC. Using TCGA M450K methylation data, we identified candidate methylation sites with the highest Fold Change (FC) for three genes (SEPTIN9, SDC2 and VIM), which were selected from previous studies. Based on logistic regression models, we developed a 3-gene methylation signature for CRC diagnosis with high accuracy (Sensitivity =0.959, Specificity =1, AUC =0.997). Using independent public databases and data from blood samples, this model has demonstrated superior performance. The AUC was 0.919-1 and 0.905-0.916 in public tissue database for CRC and blood sample data, respectively. Thus, our proposed 3-gene methylation signature has a more reliable performance than other methods. Furthermore, signal enhancement effect of 3-gene methylation signature can improve the accuracy of early diagnosis for CRC, which demonstrates the potential for clinical application.

StemSC: a cross-dataset human stemness index for single-cell samples.

BACKGROUND: Stemness is defined as the potential of cells for self-renewal and differentiation. Many transcriptome-based methods for stemness evaluation have been proposed. However, all these methods showed low negative correlations with differentiation time and can't leverage the existing experimentally validated stem cells to recognize the stem-like cells. METHODS: Here, we constructed a stemness index for single-cell samples (StemSC) based on relative expression orderings (REO) of gene pairs. Firstly, we identified the stemness-related genes by selecting the genes significantly related to differentiation time. Then, we used 13 RNA-seq datasets from both the bulk and single-cell embryonic stem cell (ESC) samples to construct the reference REOs. Finally, the StemSC value of a given sample was calculated as the percentage of gene pairs with the same REOs as the ESC samples. RESULTS: We validated the StemSC by its higher negative correlations with differentiation time in eight normal datasets and its higher positive correlations with tumor dedifferentiation in three colorectal cancer datasets and four glioma datasets. Besides, the robust of StemSC to batch effect enabled us to leverage the existing experimentally validated cancer stem cells to recognize the stem-like cells in other independent tumor datasets. And the recognized stem-like tumor cells had fewer interactions with anti-tumor immune cells. Further survival analysis showed the immunotherapy-treated patients with high stemness had worse survival than those with low stemness. CONCLUSIONS: StemSC is a better stemness index to calculate the stemness across datasets, which can help researchers explore the effect of stemness on other biological processes.

Integrated multi-omics analysis reveals miR-20a as a regulator for metabolic colorectal cancer.

Single-driver molecular events specific to the metabolic colorectal cancer (CRC) have not been clearly elucidated. Herein, we identified 12 functional miRNAs linked to activated metabolism by integrating multi-omics features in metabolic CRC. These miRNAs exhibited significantly enriched CRC driver miRNAs, significant impacts on CRC cell growth and significantly correlated metabolites. Importantly, miR-20a is minimally expressed in normal colorectal tissues but highly expressed in metabolic CRC, suggesting the potential therapeutic target. Bioinformatics analyses further revealed miR-20a as the most powerful determinant that regulates a cascade of dysregulated events, including Wnt signaling pathway, core enzymes involved in FA metabolism program and triacylglycerol abundances. In vitro assays demonstrated that elevated miR-20a up-regulated FA synthesis enzymes via Wnt/ß-catenin signaling, and finally promoted proliferative and migration of metabolic CRC cells. Overall, our study revealed that miR-20a promoted progression of metabolic CRC by regulating FA metabolism and served as a potential target for preventing tumor metastasis.

IndGOterm: a qualitative method for the identification of individually dysregulated GO terms in cancer.

Individual pathway analysis can dissect heterogeneities among different cancer patients and provide efficient guidelines for individualized therapy. However, the existence of the batch effect brings extensive limitations for the application of many individual methods for pathway analysis. Previously, researchers proposed that methods based on within-sample relative expression ordering (REO) of the genes are notably insensitive to 'batch effects'. In this article, we focus on the Gene Ontology (GO) database and propose an individual qualitative GO term analysis method (IndGOterm) based on the REO of genes. Compared with some current widely used single-sample enrichment analysis methods, such as ssGSEA and GSVA, IndGOterm has a predominance of ignoring the batch effects caused by diverse technologies. Through the survival and drug responses analysis, we found IndGOterm could capture more terms connected to cancer than other single-sample enrichment analysis methods. Furthermore, through the application of IndGOterm, we found some terms that present different dysregulation models that manifest heterogenetic in homologous patients. Collectively, these results attested that IndGOterm could capture useful information from patients and be a useful tool to reveal the intrinsic characteristic of cancer. An open-source R statistical analysis package 'IndGOterm' is available at https://github.com/robert19960424/IndGOterm.

A Cancer-Specific Qualitative Method for Estimating the Proportion of Tumor-Infiltrating Immune Cells.

Tumor-infiltrating immune cells are important components in the tumor microenvironment (TME) and different types of these cells exert different effects on tumor development and progression; these effects depend upon the type of cancer involved. Several methods have been developed for estimating the proportion of immune cells using bulk transcriptome data. However, there is a distinct lack of methods that are capable of predicting the immune contexture in specific types of cancer. Furthermore, the existing methods are based on absolute gene expression and are susceptible to experimental batch effects, thus resulting in incomparability across different datasets. In this study, we considered two common neoplasms as examples (colorectal cancer [CRC] and melanoma) and introduced the Tumor-infiltrating Immune Cell Proportion Estimator (TICPE), a cancer-specific qualitative method for estimating the proportion of tumor-infiltrating immune cells. The TICPE was based on the relative expression orderings (REOs) of gene pairs within a sample and is notably insensitive to batch effects. Performance evaluation using public expression data with mRNA mixtures, single-cell RNA-Seq (scRNA-Seq) data, immunohistochemistry data, and simulated bulk RNA-seq samples, indicated that the TICPE can estimate the proportion of immune cells with levels of accuracy that are clearly superior to other methods. Furthermore, we showed that the TICPE could effectively detect prognostic signals in patients with tumors and changes in the fractions of immune cells during immunotherapy in melanoma. In conclusion, our work presented a unique novel method, TICPE, to estimate the proportion of immune cells in specific cancer types and explore the effect of the infiltration of immune cells on the efficacy of immunotherapy and the prognosis of cancer. The source code for TICPE is available at https://github.com/huitingxiao/TICPE.