Integrative analysis of single-cell and bulk RNA sequencing reveals the oncogenic role of ANXA5 in gastric cancer and its association with drug resistance

BackgroundGastric cancer (GC) remains a leading cause of cancer-related mortality, with over one million new cases and 769,000 deaths reported in 2020. Despite advancements in chemotherapy, surgery, and targeted therapies, delayed diagnosis due to overlooked early symptoms leads to poor prognosis.MethodsWe integrated bulk RNA sequencing and single-cell RNA sequencing datasets from TCGA, GEO, and OMIX001073, employing normalization, batch effect correction, and dimensionality reduction methods to identify key cell populations associated with GC invasion and epithelial-mesenchymal transition (EMT), as well as analyze the tumor immune microenvironment.ResultsOur analysis identified the MUC5AC+ malignant epithelial cell cluster as a significant player in GC invasion and EMT. Cluster 1, representing this cell population, exhibited higher invasion and EMT scores compared to other clusters. Survival analysis showed that high abundance in cluster 0 correlated with improved survival rates (P=0.012), whereas cluster 1 was associated with poorer outcomes (P=0.045). A prognostic model highlighted ANXA5 and GABARAPL2 as two critical genes upregulated in GC tumors. High-risk patients demonstrated increased immune cell infiltration and worse prognosic. Analysis of tumor mutation burden (TMB) indicated that patients with low TMB in the high-risk group had the worst prognosis. Wet-lab validation experiments confirmed the oncogenic role of ANXA5, showing its facilitation of cell proliferation, invasion, and migration while suppressing apoptosis.ConclusionThis study offers novel insights into the subpopulations of malignant epithelial cells in GC and their roles in tumor progression. It provides a prognostic model and potential therapeutic targets to combat GC, contributing crucial understanding to the fundamental mechanisms of drug resistance in gastrointestinal cancers

Prognosis and Characterization of Immune Microenvironment in Acute Myeloid Leukemia Through Identification of an Autophagy-Related Signature

Acute myeloid leukemia (AML) is one of the most common hematopoietic malignancies that has an unfavorable outcome and a high rate of relapse. Autophagy plays a vital role in the development of and therapeutic responses to leukemia. This study identifies a potential autophagy-related signature to monitor the prognoses of patients of AML. Transcriptomic profiles of AML patients (GSE37642) with the relevant clinical information were downloaded from Gene Expression Omnibus (GEO) as the training set while TCGA-AML and GSE12417 were used as validation cohorts. Univariate regression analyses and multivariate stepwise Cox regression analysis were respectively applied to identify the autophagy-related signature. The univariate Cox regression analysis identified 32 autophagy-related genes (ARGs) that were significantly associated with the overall survival (OS) of the patients, and were mainly rich in signaling pathways for autophagy, p53, AMPK, and TNF. A prognostic signature that comprised eight ARGs (BAG3, CALCOCO2, CAMKK2, CANX, DAPK1, P4HB, TSC2, and ULK1) and had good predictive capacity was established by LASSO–Cox stepwise regression analysis. High-risk patients were found to have significantly shorter OS than patients in low-risk group. The signature can be used as an independent prognostic predictor after adjusting for clinicopathological parameters, and was validated on two external AML sets. Differentially expressed genes analyzed in two groups were involved in inflammatory and immune signaling pathways. An analysis of tumor-infiltrating immune cells confirmed that high-risk patients had a strong immunosuppressive microenvironment. Potential druggable OS-related ARGs were then investigated through protein–drug interactions. This study provides a systematic analysis of ARGs and develops an OS-related prognostic predictor for AML patients. Further work is needed to verify its clinical utility and identify the underlying molecular mechanisms in AML

A Novel Fatty Acid Metabolism Signature Predicts Prognoses, Tumor Immune Microenvironment, and Immunotherapy Response In Lung Adenocarcinoma

Abstract Background: Lung adenocarcinoma (LUAD) is the most common and aggressive subtype of non-small cell lung cancer. Aberrant fatty acid metabolism (FAM) has been demonstrated to play an essential role in the tumorigenesis of human cancers, yet limited studies in LUAD. Methods: The RNA-sequencing dataset of LUAD patients with clinical features from the TCGA database was used as the training set. Six independent LUAD cohorts totaling 1,368 encompassing diverse platforms from the GEO database were employed as validation sets. The prognostic signature was constructed by multivariate Cox regression analysis with the Akaike information criterion. The tumor immune microenvironment (TIME) was analyzed by ESTIMATE and infiltrated immune cell subsets were calculated using multiple deconvolution algorithms. Tumor characteristics such as T cell receptors richness and diversity, and tumor mutation burden (TMB) were assessed. The implication of the signature in predicting immunotherapy response was also investigated. Results: Overall survival (OS) related FAMGs were identified. A robust prognostic signature for OS prediction was developed. Patients were divided into high- and low-risk groups and decreased OS was observed in low-risk patients. Furthermore, the signature could be an independent prognostic indicator after adjusting for clinicopathological features. Receiver operating characteristic curve analysis indicated the validity of the signature. The predictive power was validated using six LUAD validation cohorts. The signature also has strong risk stratification utility for patients’ disease relapse. TIME analysis showed increased immune activity in low-risk patients, which was convinced by higher infiltrated CD8+ T, natural killer, and B cells, as well as lower tumor purity, stemness index, TMB, and cell proliferation. Additionally, elevated activated and less senescence of immune cells were observed in low-risk patients. Differentially expressed pathways that related to resistance to immune checkpoint blockades such as DNA repair, hypoxia, cell cycle, epithelial-mesenchymal-transition, and oxidative phosphorylation were enriched in high-risk patients. T cell receptor richness and diversity were higher in low-risk patients. Responders had lower risk scores in contrast to non-responders for LUAD patients receiving anti-PD-1 treatment. Conclusions: The study was the first time to establish a novel FAMGs-based signature in recognition of the prognosis for LUAD patients and evaluation of the possibility of immunotherapy response in personalized treatment.</jats:p

Prognosis and Novel Drug Targets for Key lncRNAs of Epigenetic Modification in Colorectal Cancer

Background. Colorectal cancer (CRC) has been the 3rd most commonly malignant tumor of the gastrointestinal tract in the world. 5-Methylcytosine (m5C) and long noncoding RNAs (lncRNAs) have an essential role in predicting the prognosis and immune response for CRC patients. Therefore, we built a m5C-related lncRNA (m5CRlncRNA) model to investigate the prognosis and treatment methods for CRC patients. Methods. Firstly, we secured the transcriptome and clinical data for CRC from The Cancer Genome Atlas (TCGA). Then, m5CRlncRNAs were recognized by coexpression analysis. Then, univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were utilized to build m5C-related prognostic characteristics. Besides, Kaplan-Meier analysis, ROC, PCA, C-index, enrichment analysis, and nomogram were performed to investigate the model. Additionally, immunotherapy responses and antitumor medicines were explored for CRC patients. Results. A total of 8 m5C-related lncRNAs (AC093157.1, LINC00513, AC025171.4, AC090948.2, ZEB1-AS1, AC109449.1, AC009041.3, and LINC02516) were adopted to construct a risk model to investigate survival and prognosis for CRC patients. CRC samples were separated into low- and high-risk groups, with the latter having a worse prognosis. The m5C-related lncRNA model helps us to better distinguish immunotherapy responses and IC50 of antitumor medicines in different groups of CRC patients. Conclusion. The research may give new perspectives on tailored therapy approaches as well as novel theories for forecasting the prognosis of CRC patients

Identification of survival-related alternative splicing signatures in acute myeloid leukemia

Abstract Aberrant RNA alternative splicing (AS) variants play critical roles in tumorigenesis and prognosis in human cancers. Here, we conducted a comprehensive profiling of aberrant AS events in acute myeloid leukemia (AML). RNA AS profile, including seven AS types, and the percent spliced in (PSI) value for each patient were generated by SpliceSeq using RNA-seq data from TCGA. Univariate followed by multivariate Cox regression analysis were used to identify survival-related AS events and develop the AS signatures. A nomogram was developed, and its predictive efficacy was assessed. About 27,892 AS events and 3,178 events were associated with overall survival (OS) after strict filtering. Parent genes of survival-associated AS events were mainly enriched in leukemia-associated processes including chromatin modification, autophagy, and T-cell receptor signaling pathway. The 10 AS signature based on seven types of AS events showed better efficacy in predicting OS of patients than those built on a single AS event type. The area under curve (AUC) value of the 10 AS signature for 3-year OS was 0.91. Gene set enrichment analysis (GSEA) confirmed that these survival-related AS events contribute to AML progression. Moreover, the nomogram showed good predictive performance for patient's prognosis. Finally, the correlation network of AS variants with splicing factor genes found potential important regulatory genes in AML. The present study presented a systematic analysis of survival-related AS events and developed AS signatures for predicting the patient’s survival. Further studies are needed to validate the signatures in independent AML cohorts and might provide a promising perspective for developing therapeutic targets.</jats:p

Molecular subtyping of acute myeloid leukemia through ferroptosis signatures predicts prognosis and deciphers the immune microenvironment

Acute myeloid leukemia (AML) is one of the most aggressive hematological malignancies with a low 5-year survival rate and high rate of relapse. Developing more efficient therapies is an urgent need for AML treatment. Accumulating evidence showed that ferroptosis, an iron-dependent form of programmed cell death, is closely correlated with cancer initiation and clinical outcome through reshaping the tumor microenvironment. However, understanding of AML heterogeneity based on extensive profiling of ferroptosis signatures remains to be investigated yet. Herein, five independent AML transcriptomic datasets (TCGA-AML, GSE37642, GSE12417, GSE10358, and GSE106291) were obtained from the GEO and TCGA databases. Then, we identified two ferroptosis-related molecular subtypes (C1 and C2) with distinct prognosis and tumor immune microenvironment (TIME) by consensus clustering. Patients in the C1 subtype were associated with favorable clinical outcomes and increased cytotoxic immune cell infiltration, including CD8+/central memory T cells, natural killer (NK) cells, and non-regulatory CD4+ T cells while showing decreased suppressive immune subsets such as M2 macrophages, neutrophils, and monocytes. Functional enrichment analysis of differentially expressed genes (DEGs) implied that cell activation involved in immune response, leukocyte cell–cell adhesion and migration, and cytokine production were the main biological processes. Phagosome, antigen processing and presentation, cytokine–cytokine receptor interaction, B-cell receptor, and chemokine were identified as the major pathways. To seize the distinct landscape in C1 vs. C2 subtypes, a 5-gene prognostic signature (LSP1, IL1R2, MPO, CRIP1, and SLC24A3) was developed using LASSO Cox stepwise regression analysis and further validated in independent AML cohorts. Patients were divided into high- and low-risk groups, and decreased survival rates were observed in high- vs. low-risk groups. The TIME between high- and low-risk groups has a similar scenery in C1 vs. C2 subtypes. Single-cell-level analysis verified that LSP1 and CRIP1 were upregulated in AML and exhausted CD8+ T cells. Dual targeting of these two markers might present a promising immunotherapeutic for AML. In addition, potential effective chemical drugs for AML were predicted. Thus, we concluded that molecular subtyping using ferroptosis signatures could characterize the TIME and provide implications for monitoring clinical outcomes and predicting novel therapies

Green Process: Improved Semi-Continuous Fermentation of Pichia pastoris Based on the Principle of Vitality Cell Separation

The large-scale fermentation of Pichia pastoris for recombinant protein production would be time consuming and produce a large amount of waste yeast. Here we introduce a novel semi-continuous fermentation process for P. pastoris GS115 that can separate vitality cells from broth and recycle the cells to produce high-secretory recombinant pectate lyase. It is based on differences in cell sedimentation coefficients with the formation of salt bridges between metal ions and various cell states. Compared to batch-fed cultivation and general semi-continuous culture, the novel process has significant advantages, such as consuming fewer resources, taking less time, and producing less waste yeast. Sedimentation with the addition of Fe3+ metal ions consumed 14.8 ± 0.0% glycerol, 97.8 ± 1.3% methanol, 55.0 ± 0.9 inorganic salts, 81.5 ± 0.0% time cost, and 77.0 ± 0.1% waste yeast versus batch-fed cultivation to produce an equal amount of protein; in addition, the cost of solid–liquid separation was lower for cells in the collected fermentation broth. The process is economically and environmentally efficient for producing recombinant proteins.</jats:p