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Abstract Background Submergence stress is a prevalent abiotic stress affecting plant growth and development and can restrict plant cultivation in areas prone to flooding. Research on plant submergence stress tolerance has been essential in managing plant production under excessive rainfall. Red clover (Trifolium pratense L.), a high-quality legume forage, exhibits low tolerance to submergence, and long-term submergence can lead to root rot and death. Results This study assessed the microstructure, physiological indicators, and the key genes and metabolic pathways under submergence stress in the root system of red clover HL(Hong Long) and ZY(Zi You) varieties under submergence stress at 0 h, 8 h, 24 h, 3 d, and 5 d. Based on 7740 transcripts identified in the leaves at 0 h, 8 h, and 24 h submergence stress, Weighted Gene Co-expression Network Analysis (WGCNA) was performed on the differentially expressed genes (DEGs) at 8 h and 24 h. Functional annotation of the DEGs in the four key modules was obtained. Based on the results, the red clover root system exhibited epidermal cell rupture, enlargement and rupture of cortical thin-walled cells, thickening of the mid-column, and a significant increase in the number of air cavities and air cavity area of aeration tissue with the prolongation of submergence stress. The malondialdehyde content, relative conductivity, peroxidase, and superoxide dismutase initially increased and decreased as submergence stress duration increased. Four specific modules (cyan, purple, light cyan, and ivory) closely correlated with each stress were identified by WGCNA. The 14 obtained Hub genes were functionally annotated, among which six genes, including gene51878, gene11315, and gene11848, were involved in glyoxylate and dicarboxylic acid metabolism, carbon fixation in photosynthetic organisms, carbon metabolism, biosynthesis of pantothenic acid and CoA, flavonoid biosynthesis. Conclusion In this study, using WGCNA, the molecular response mechanisms of red clover to submergence stress was proposed, and the core genes and metabolic pathways in response to submergence stress were obtained, providing a valuable data resource at the physiological and molecular levels for subsequent studies of submergence stress tolerance in plants.

IntroductionThe severity of flood disasters is increasing due to climate change, resulting in a significant reduction in the yield and quality of forage crops worldwide. This poses a serious threat to the development of agriculture and livestock. Hemarthria compressa is an important high-quality forage grass in southern China. In recent years, frequent flooding has caused varying degrees of impacts on H. compressa and their ecological environment.MethodsIn this study, we evaluated differences in flooding tolerance between the root systems of the experimental materials GY (Guang Yi, flood-tolerant) and N1291 (N201801291, flood-sensitive). We measured their morphological indexes after 7 d, 14 d, and 21 d of submergence stress and sequenced their transcriptomes at 8 h and 24 h, with 0 h as the control.ResultsDuring submergence stress, the number of adventitious roots and root length of both GY and N1291 tended to increase, but the overall growth of GY was significantly higher than that of N1291. RNA-seq analysis revealed that 6046 and 7493 DEGs were identified in GY-8h and GY-24h, respectively, and 9198 and 4236 DEGs in N1291-8h and N1291-24h, respectively, compared with the control. The GO and KEGG enrichment analysis results indicated the GO terms mainly enriched among the DEGs were oxidation-reduction process, obsolete peroxidase reaction, and other antioxidant-related terms. The KEGG pathways that were most significantly enriched were phenylpropanoid biosynthesis, plant hormone signal transduction etc. The genes of transcription factor families, such as C2H2, bHLH and bZIP, were highly expressed in the H. compressa after submergence, which might be closely related to the submergence adaptive response mechanisms of H. compressa.DiscussionThis study provides basic data for analyzing the molecular and morphological mechanisms of H. compressa in response to submergence stress, and also provides theoretical support for the subsequent improvement of submergence tolerance traits of H. compressa.

Thymic epithelial tumors (TETs) are a rare and diverse group of neoplasms characterized by distinct molecular signatures. This review delves into the complex molecular networks of TETs, highlighting key aspects such as chromosomal abnormalities, molecular subtypes, aberrant gene mutations and expressions, structural gene rearrangements, and epigenetic changes. Additionally, the influence of the dynamic tumor microenvironment on TET behavior and therapeutic responses is examined. A thorough understanding of these facets elucidates TET pathogenesis, offering avenues for enhancing diagnostic accuracy, refining prognostic assessments, and tailoring targeted therapeutic strategies. Our review underscores the importance of deciphering TETs’ unique molecular signatures to advance personalized treatment paradigms and improve patient outcomes. We also discuss future research directions and anticipated challenges in this intriguing field.

BackgroundCurrent paradigms of anti-tumor therapies are not qualified to evacuate the malignancy ascribing to cancer stroma’s functions in accelerating tumor relapse and therapeutic resistance. Cancer-associated fibroblasts (CAFs) has been identified significantly correlated with tumor progression and therapy resistance. Thus, we aimed to probe into the CAFs characteristics in esophageal squamous cancer (ESCC) and construct a risk signature based on CAFs to predict the prognosis of ESCC patients.MethodsThe GEO database provided the single-cell RNA sequencing (scRNA-seq) data. The GEO and TCGA databases were used to obtain bulk RNA-seq data and microarray data of ESCC, respectively. CAF clusters were identified from the scRNA-seq data using the Seurat R package. CAF-related prognostic genes were subsequently identified using univariate Cox regression analysis. A risk signature based on CAF-related prognostic genes was constructed using Lasso regression. Then, a nomogram model based on clinicopathological characteristics and the risk signature was developed. Consensus clustering was conducted to explore the heterogeneity of ESCC. Finally, PCR was utilized to validate the functions that hub genes play on ESCC.ResultsSix CAF clusters were identified in ESCC based on scRNA-seq data, three of which had prognostic associations. A total of 642 genes were found to be significantly correlated with CAF clusters from a pool of 17080 DEGs, and 9 genes were selected to generate a risk signature, which were mainly involved in 10 pathways such as NRF1, MYC, and TGF-Beta. The risk signature was significantly correlated with stromal and immune scores, as well as some immune cells. Multivariate analysis demonstrated that the risk signature was an independent prognostic factor for ESCC, and its potential in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the CAF-based risk signature and clinical stage was developed, which exhibited favorable predictability and reliability for ESCC prognosis prediction. The consensus clustering analysis further confirmed the heterogeneity of ESCC.ConclusionThe prognosis of ESCC can be effectively predicted by CAF-based risk signatures, and a comprehensive characterization of the CAF signature of ESCC may aid in interpreting the response of ESCC to immunotherapy and offer new strategies for cancer treatment.

BackgroundExtensive research has established the significant correlations between cancer-associated fibroblasts (CAFs) and various stages of cancer development, including initiation, angiogenesis, progression, and resistance to therapy. In this study, we aimed to investigate the characteristics of CAFs in lung adenocarcinoma (LUAD) and develop a risk signature to predict the prognosis of patients with LUAD.MethodsWe obtained single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the public database. The Seurat R package was used to process the scRNA-seq data and identify CAF clusters based on several biomarkers. CAF-related prognostic genes were further identified using univariate Cox regression analysis. To reduce the number of genes, Lasso regression was performed, and a risk signature was established. A novel nomogram that incorporated the risk signature and clinicopathological features was developed to predict the clinical applicability of the model. Additionally, we conducted immune landscape and immunotherapy responsiveness analyses. Finally, we performed in vitro experiments to verify the functions of EXO1 in LUAD.ResultsWe identified 5 CAF clusters in LUAD using scRNA-seq data, of which 3 clusters were significantly associated with prognosis in LUAD. A total of 492 genes were found to be significantly linked to CAF clusters from 1731 DEGs and were used to construct a risk signature. Moreover, our immune landscape exploration revealed that the risk signature was significantly related to immune scores, and its ability to predict responsiveness to immunotherapy was confirmed. Furthermore, a novel nomogram incorporating the risk signature and clinicopathological features showed excellent clinical applicability. Finally, we verified the functions of EXP1 in LUAD through in vitro experiments.ConclusionsThe risk signature has proven to be an excellent predictor of LUAD prognosis, stratifying patients more appropriately and precisely predicting immunotherapy responsiveness. The comprehensive characterization of LUAD based on the CAF signature can predict the response of LUAD to immunotherapy, thus offering fresh perspectives into the management of LUAD patients. Our study ultimately confirms the role of EXP1 in facilitating the invasion and growth of tumor cells in LUAD. Nevertheless, further validation can be achieved by conducting in vivo experiments.

BackgroundCuproptosis, a unique kind of cell death, has implications for cancer therapy, particularly lung adenocarcinoma (LUAD). Long non-coding RNAs (lncRNAs) have been demonstrated to influence cancer cell activity by binding to a wide variety of targets, including DNA, RNA, and proteins.MethodsCuproptosis-related lncRNAs (CRlncRNAs) were utilized to build a risk model that classified patients into high-and low-risk groups. Based on the CRlncRNAs in the model, Consensus clustering analysis was used to classify LUAD patients into different subtypes. Next, we explored the differences in overall survival (OS), the tumor immune microenvironment (TIME), and the mutation landscape between different risk groups and molecular subtypes. Finally, the functions of LINC00592 were verified through in vitro experiments.ResultsPatients in various risk categories and molecular subtypes showed statistically significant variations in terms of OS, immune cell infiltration, pathway activity, and mutation patterns. Cell experiments revealed that LINC00592 knockdown significantly reduced LUAD cell proliferation, invasion, and migration ability.ConclusionThe development of a trustworthy prediction model based on CRlncRNAs may significantly aid in the assessment of patient prognosis, molecular features, and therapeutic modalities and may eventually be used in clinical applications.

BackgroundLung adenocarcinoma (LUAD) is a heterogeneous disease with a dismal prognosis for advanced tumors. Immune-associated cells in the microenvironment substantially impact LUAD formation and progression, which has gained increased attention in recent decades. Sphingolipids have a profound impact on tumor formation and immune infiltration. However, few researchers have focused on the utilization of sphingolipid variables in the prediction of LUAD prognosis. The goal of this work was to identify the major sphingolipid-related genes (SRGs) in LUAD and develop a valid prognostic model based on SRGs.MethodsThe most significant genes for sphingolipid metabolism (SM) were identified using the AUCell and WGCNA algorithms in conjunction with single-cell and bulk RNA-seq. LASSO and COX regression analysis was used to develop risk models, and patients were divided into high-and low-risk categories. External nine provided cohorts evaluated the correctness of the models. Differences in immune infiltration, mutation landscape, pathway enrichment, immune checkpoint expression, and immunotherapy were also further investigated in distinct subgroups. Finally, cell function assay was used to verify the role of CACYBP in LUAD cells.ResultsA total of 334 genes were selected as being most linked with SM activity for further investigation, and a risk model consisting of 11 genes was established using lasso and cox regression. According to the median risk value, patients were split into high- and low-risk groups, and the high-risk group had a worse prognosis. The low-risk group had more immune cell infiltration and higher expression of immune checkpoints, which illustrated that the low-risk group was more likely to benefit from immunotherapy. It was verified that CACYBP could increase the ability of LUAD cells to proliferate, invade, and migrate.ConclusionThe eleven-gene signature identified in this research may help physicians create individualized care plans for LUAD patients. CACYBP may be a new therapeutic target for patients with advanced LUAD.