Single-cell analysis of human primary prostate cancer reveals the heterogeneity of tumor-associated epithelial cell states.

Prostate cancer is the second most common malignancy in men worldwide and consists of a mixture of tumor and non-tumor cell types. To characterize the prostate cancer tumor microenvironment, we perform single-cell RNA-sequencing on prostate biopsies, prostatectomy specimens, and patient-derived organoids from localized prostate cancer patients. We uncover heterogeneous cellular states in prostate epithelial cells marked by high androgen signaling states that are enriched in prostate cancer and identify a population of tumor-associated club cells that may be associated with prostate carcinogenesis. ERG-negative tumor cells, compared to ERG-positive cells, demonstrate shared heterogeneity with surrounding luminal epithelial cells and appear to give rise to common tumor microenvironment responses. Finally, we show that prostate epithelial organoids harbor tumor-associated epithelial cell states and are enriched with distinct cell types and states from their parent tissues. Our results provide diagnostically relevant insights and advance our understanding of the cellular states associated with prostate carcinogenesis.

Topographic mapping of the glioblastoma proteome reveals a triple-axis model of intra-tumoral heterogeneity.

Glioblastoma is an aggressive form of brain cancer with well-established patterns of intra-tumoral heterogeneity implicated in treatment resistance and progression. While regional and single cell transcriptomic variations of glioblastoma have been recently resolved, downstream phenotype-level proteomic programs have yet to be assigned across glioblastoma's hallmark histomorphologic niches. Here, we leverage mass spectrometry to spatially align abundance levels of 4,794 proteins to distinct histologic patterns across 20 patients and propose diverse molecular programs operational within these regional tumor compartments. Using machine learning, we overlay concordant transcriptional information, and define two distinct proteogenomic programs, MYC- and KRAS-axis hereon, that cooperate with hypoxia to produce a tri-dimensional model of intra-tumoral heterogeneity. Moreover, we highlight differential drug sensitivities and relative chemoresistance in glioblastoma cell lines with enhanced KRAS programs. Importantly, these pharmacological differences are less pronounced in transcriptional glioblastoma subgroups suggesting that this model may provide insights for targeting heterogeneity and overcoming therapy resistance.

Association of mutation signature effectuating processes with mutation hotspots in driver genes and non-coding regions.

Cancer driving mutations are difficult to identify especially in the non-coding part of the genome. Here, we present sigDriver, an algorithm dedicated to call driver mutations. Using 3813 whole-genome sequenced tumors from International Cancer Genome Consortium, The Cancer Genome Atlas Program, and a childhood pan-cancer cohort, we employ mutational signatures based on single-base substitution in the context of tri- and penta-nucleotide motifs for hotspot discovery. Knowledge-based annotations on mutational hotspots reveal enrichment in coding regions and regulatory elements for 6 mutational signatures, including APOBEC and somatic hypermutation signatures. APOBEC activity is associated with 32 hotspots of which 11 are known and 11 are putative regulatory drivers. Somatic single nucleotide variants clusters detected at hypermutation-associated hotspots are distinct from translocation or gene amplifications. Patients carrying APOBEC induced PIK3CA driver mutations show lower occurrence of signature SBS39. In summary, sigDriver uncovers mutational processes associated with known and putative tumor drivers and hotspots particularly in the non-coding regions of the genome.

TransLnc: a comprehensive resource for translatable lncRNAs extends immunopeptidome.

LncRNAs are not only well-known as non-coding elements, but also serve as templates for peptide translation, playing important roles in fundamental cellular processes and diseases. Here, we describe a database, TransLnc (http://bio-bigdata.hrbmu.edu.cn/TransLnc/), which aims to provide comprehensive experimentally supported and predicted lncRNA peptides in multiple species. TransLnc currently documents approximate 583 840 peptides encoded by 33 094 lncRNAs. Six types of direct and indirect evidences supporting the coding potential of lncRNAs were integrated, and 65.28% peptides entries were with at least one type of evidence. Considering the strong tissue-specific expression of lncRNAs, TransLnc allows users to access lncRNA peptides in any of the 34 tissues involved in. In addition, both the unique characteristic and homology relationship were also predicted and provided. Importantly, TransLnc provides computationally predicted tumour neoantigens from peptides encoded by lncRNAs, which would provide novel insights into cancer immunotherapy. There were 220 791 and 237 915 candidate neoantigens binding by major histocompatibility complex (MHC) class I or II molecules, respectively. Several flexible tools were developed to aid retrieve and analyse, particularly lncRNAs tissue expression patterns, clinical relevance across cancer types. TransLnc will serve as a valuable resource for investigating the translation capacity of lncRNAs and greatly extends the cancer immunopeptidome.

Cancer gene mutation frequencies for the U.S. population.

Mutations play a fundamental role in the development of cancer, and many create targetable vulnerabilities. There are both public health and basic science benefits from the determination of the proportion of all cancer cases within a population that include a mutant form of a gene. Here, we provide the first such estimates by combining genomic and epidemiological data. We estimate KRAS is mutated in only 11% of all cancers, which is less than PIK3CA (13%) and marginally higher than BRAF (8%). TP53 is the most commonly mutated gene (35%), and KMT2C, KMT2D, and ARID1A are among the ten most commonly mutated driver genes, highlighting the role of epigenetic dysregulation in cancer. Analysis of major cancer subclassifications highlighted varying dependencies upon individual cancer drivers. Overall, we find that cancer genetics is less dominated by high-frequency, high-profile cancer driver genes than studies limited to a subset of cancer types have suggested.

Dissecting esophageal squamous-cell carcinoma ecosystem by single-cell transcriptomic analysis.

Esophageal squamous-cell carcinoma (ESCC), one of the most prevalent and lethal malignant disease, has a complex but unknown tumor ecosystem. Here, we investigate the composition of ESCC tumors based on 208,659 single-cell transcriptomes derived from 60 individuals. We identify 8 common expression programs from malignant epithelial cells and discover 42 cell types, including 26 immune cell and 16 nonimmune stromal cell subtypes in the tumor microenvironment (TME), and analyse the interactions between cancer cells and other cells and the interactions among different cell types in the TME. Moreover, we link the cancer cell transcriptomes to the somatic mutations and identify several markers significantly associated with patients' survival, which may be relevant to precision care of ESCC patients. These results reveal the immunosuppressive status in the ESCC TME and further our understanding of ESCC.

Single-nuclei transcriptomes from human adrenal gland reveal distinct cellular identities of low and high-risk neuroblastoma tumors.

Childhood neuroblastoma has a remarkable variability in outcome. Age at diagnosis is one of the most important prognostic factors, with children less than 1 year old having favorable outcomes. Here we study single-cell and single-nuclei transcriptomes of neuroblastoma with different clinical risk groups and stages, including healthy adrenal gland. We compare tumor cell populations with embryonic mouse sympatho-adrenal derivatives, and post-natal human adrenal gland. We provide evidence that low and high-risk neuroblastoma have different cell identities, representing two disease entities. Low-risk neuroblastoma presents a transcriptome that resembles sympatho- and chromaffin cells, whereas malignant cells enriched in high-risk neuroblastoma resembles a subtype of TRKB+ cholinergic progenitor population identified in human post-natal gland. Analyses of these populations reveal different gene expression programs for worst and better survival in correlation with age at diagnosis. Our findings reveal two cellular identities and a composition of human neuroblastoma tumors reflecting clinical heterogeneity and outcome.

Clinical significance and molecular mechanism of angiotensin-converting enzyme 2 in hepatocellular carcinoma tissues.

During the pandemic of the coronavirus disease 2019, there exist quite a few studies on angiotensin-converting enzyme 2 (ACE2) and SARS-CoV-2 infection, while little is known about ACE2 in hepatocellular carcinoma (HCC). The detailed mechanism among ACE2 and HCC still remains unclear, which needs to be further investigated. In the current study with a total of 6,926 samples, ACE2 expression was downregulated in HCC compared with non-HCC samples (standardized mean difference = -0.41). With the area under the curve of summary receiver operating characteristic = 0.82, ACE2 expression showed a better ability to differentiate HCC from non-HCC. The mRNA expression of ACE2 was related to the age, alpha-fetoprotein levels and cirrhosis of HCC patients, and it was identified as a protected factor for HCC patients via Kaplan-Meier survival, Cox regression analyses. The potential molecular mechanism of ACE2 may be relevant to catabolic and cell division. In all, decreasing ACE2 expression can be seen in HCC, and its protective role for HCC patients and underlying mechanisms were explored in the study.

Higher TLR7 Gene Expression Predicts Poor Clinical Outcome in Advanced NSCLC Patients Treated with Immunotherapy.

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of lung cancer. However, their clinical benefit is limited to a minority of patients. To unravel immune-related factors that are predictive of sensitivity or resistance to immunotherapy, we performed a gene expression analysis by RNA-Seq using the Oncomine Immuno Response Assay (OIRRA) on a total of 33 advanced NSCLC patients treated with ICI evaluating the expression levels of 365 immune-related genes. We found four genes (CD1C, HLA-DPA1, MMP2, and TLR7) downregulated (p < 0.05) and two genes (IFNB1 and MKI67) upregulated (p < 0.05) in ICI-Responders compared to ICI-Non-Responders. The Bayesian enrichment computational analysis showed a more complex interaction network that involved 10 other genes (IFNA1, TLR4, CD40, TLR2, IL12A, IL12B, TLR9, CD1E, IFNG, and HLA-DPB1) correlated with different functional groups. Five main pathways were identified (FDR < 0.0001). High TLR7 expression levels were significantly associated with a lack of response to immunotherapy (p < 0.0001) and worse outcome in terms of both PFS (p < 0.001) and OS (p = 0.03). The multivariate analysis confirmed TLR7 RNA expression as an independent predictor for both poor PFS (HR = 2.97, 95% CI, 1.16-7.6, p = 0.023) and OS (HR = 2.2, 95% CI, 1-5.08, p = 0.049). In conclusion, a high TLR7 gene expression level was identified as an independent predictor for poor clinical benefits from ICI. These data could have important implications for the development of novel single/combinatorial strategies TLR-mediated for an efficient selection of "individualized" treatments for NSCLC in the era of immunotherapy.

Identification of prognostic lipid droplet-associated genes in pancreatic cancer patients via bioinformatics analysis.

BACKGROUND: Pancreatic cancer is the fourth leading cause of cancer deaths in the United States both in females and in males, and is projected to become the second deadliest cancer by 2030. The overall 5-year survival rate remains at around 10%. Cancer metabolism and specifically lipid metabolism plays an important role in pancreatic cancer progression and metastasis. Lipid droplets can not only store and transfer lipids, but also act as molecular messengers, and signaling factors. As lipid droplets are implicated in reprogramming tumor cell metabolism and in invasion and migration of pancreatic cancer cells, we aimed to identify lipid droplet-associated genes as prognostic markers in pancreatic cancer. METHODS: We performed a literature search on review articles related to lipid droplet-associated proteins. To select relevant lipid droplet-associated factors, bioinformatics analysis on the GEPIA platform (data are publicly available) was carried out for selected genes to identify differential expression in pancreatic cancer versus healthy pancreatic tissues. Differentially expressed genes were further analyzed regarding overall survival of pancreatic cancer patients. RESULTS: 65 factors were identified as lipid droplet-associated factors. Bioinformatics analysis of 179 pancreatic cancer samples and 171 normal pancreatic tissue samples on the GEPIA platform identified 39 deferentially expressed genes in pancreatic cancer with 36 up-regulated genes (ACSL3, ACSL4, AGPAT2, BSCL2, CAV1, CAV2, CAVIN1, CES1, CIDEC, DGAT1, DGAT2, FAF2, G0S2, HILPDA, HSD17B11, ICE2, LDAH, LIPE, LPCAT1, LPCAT2, LPIN1, MGLL, NAPA, NCEH1, PCYT1A, PLIN2, PLIN3, RAB5A, RAB7A, RAB8A, RAB18, SNAP23, SQLE, VAPA, VCP, VMP1) and 3 down-regulated genes (FITM1, PLIN4, PLIN5). Among 39 differentially expressed factors, seven up-regulated genes (CAV2, CIDEC, HILPDA, HSD17B11, NCEH1, RAB5A, and SQLE) and two down-regulation genes (BSCL2 and FITM1) were significantly associated with overall survival of pancreatic cancer patients. Multivariate Cox regression analysis identified CAV2 as the only independent prognostic factor. CONCLUSIONS: Through bioinformatics analysis, we identified nine prognostic relevant differentially expressed genes highlighting the role of lipid droplet-associated factors in pancreatic cancer.

Landscape of toll-like receptors expression in tumor microenvironment of triple negative breast cancer (TNBC): Distinct roles of TLR4 and TLR8.

TNBC is the most aggressive and hormone receptor-negative subtype of breast cancer with molecular heterogeneity in bulk tumors hindering effective treatment. Toll-like receptors (TLRs) have the potential to ignite diverse immune responses in the tumor microenvironment (TME). This encouraged us to screen their transcript expression in the publically available TCGA datasets. Reported molecular subtypes of TNBC may represent different TMEs and we observed differentially expressed TLRs (DETs) i.e. TLR3/4/6/8/9 have unique expression pattern in the TNBC subtypes, particularly in Immunomodulatory (IM) TNBC subtype. We then dissected expression of the DETs in immune and other components of the TME. TLR4 and TLR8 showed significant (p-value ≤ 0.05) negative partial correlation with tumor purity compared to other DETs. Interestingly, TLR4 and TLR8 expression showed a significant (adjusted p-value ≤ 0.05) correlation with different subsets of immune infiltrating cells having the highest correlation with monocytes/macrophage/dendritic cell populations mediating both innate and adaptive response in TNBC. The co-expression network identified genes correlated with these immune cells. Further, GSEA analysis of co-expressed genes showed a significant association of TLR8 partners with 'Peptide ligand binding', 'Gά-signaling', and 'Cytokine-cytokine interaction' while TLR4 associated genes correlated with 'Adaptive immune system' and 'Systemic lupus erythematosus' interactome. Finally, the expression of TLR4 protein was validated in a panel of TNBC cell lines. TLR4 expression in chemoresponsive TNBC was also validated in TNBC cell lines upon Paclitaxel (PTX) treatment. Collectively, the present study identified specific DETs in TNBC and discovered a prospective role of TLR4 and TLR8 in the maintenance of tumor-immune-microenvironment.

What we can learn from embryos to understand the mesenchymal-to-epithelial transition in tumor progression.

Epithelial plasticity involved the terminal and transitional stages that occur during epithelial-to-mesenchymal transition (EMT) and mesenchymal-to-epithelial transition (MET), both are essential at different stages of early embryonic development that have been co-opted by cancer cells to undergo tumor metastasis. These processes are regulated at multiple instances, whereas the post-transcriptional regulation of key genes mediated by microRNAs is gaining major attention as a common and conserved pathway. In this review, we focus on discussing the latest findings of the cellular and molecular basis of the less characterized process of MET during embryonic development, with special attention to the role of microRNAs. Although we take in consideration the necessity of being cautious when extrapolating the obtained evidence, we propose some commonalities between early embryonic development and cancer progression that can shed light into our current understanding of this complex event and might aid in the design of specific therapeutic approaches.

Bioassay-Guided Identification of the Antiproliferative Compounds of Cissus trifoliata and the Transcriptomic Effect of Resveratrol in Prostate Cancer Pc3 Cells.

The bioassay-guided fractionation of a CHCl3-MeOH extract from the stems of Cissus trifoliata identified an active fraction against PC3 prostate cancer cells. The treatment for 24 h showed an 80% reduction in cell viability (p ≤ 0.05) by a WST-1 assay at a concentration of 100 µg/mL. The HPLC-QTOF-MS analysis of the fraction showed the presence of coumaric and isoferulic acids, apigenin, kaempferol, chrysoeriol, naringenin, ursolic and betulinic acids, hexadecadienoic and octadecadienoic fatty acids, and the stilbene resveratrol. The exposure of PC3 cells to resveratrol (IC25 = 23 µg/mL) for 24 h induced significant changes in 847 genes (Z-score ≥ ±2). The functional classification tool of the DAVID v6.8 platform indicates that the underlying molecular mechanisms against the proliferation of PC3 cells were associated (p ≤ 0.05) with the process of differentiation and metabolism. These findings provide experimental evidence suggesting the potential of C. trifoliata as a promising natural source of anticancer compounds.

Identification of circulating biomarkers for differentiating patients with papillary thyroid cancers from benign thyroid tumors.

BACKGROUND: This study aimed to identify the potential circulating biomarkers of protein, mRNAs, and long non-coding RNAs (lncRNAs) to differentiate the papillary thyroid cancers from benign thyroid tumors. METHODS: The study population of 100 patients was classified into identification (10 patients with papillary thyroid cancers and 10 patients with benign thyroid tumors) and validation groups (45 patients with papillary thyroid cancers and 35 patients with benign thyroid tumors). The Sengenics Immunome Protein Array-combined data mining approach using the Open Targets Platform was used to identify the putative protein biomarkers, and their expression validated using the enzyme-linked immunosorbent assay. Next-generation sequencing by Illumina HiSeq was used for the detection of dysregulated mRNAs and lncRNAs. The website Timer v2.0 helped identify the putative mRNA biomarkers, which were significantly over-expressed in papillary thyroid cancers than in adjacent normal thyroid tissue. The mRNA and lncRNA biomarker expression was validated by a real-time polymerase chain reaction. RESULTS: Although putative protein and mRNA biomarkers have been identified, their serum expression could not be confirmed in the validation cohorts. In addition, seven lncRNAs (TCONS_00516490, TCONS_00336559, TCONS_00311568, TCONS_00321917, TCONS_00336522, TCONS_00282483, and TCONS_00494326) were identified and validated as significantly downregulated in patients with papillary thyroid cancers compared to those with benign thyroid tumors. These seven lncRNAs showed moderate accuracy based on the area under the curve (AUC = 0.736) of receiver operating characteristic in predicting the occurrence of papillary thyroid cancers. CONCLUSIONS: We identified seven downregulated circulating lncRNAs with the potential for predicting the occurrence of papillary thyroid cancers.

A first-generation pediatric cancer dependency map.

Exciting therapeutic targets are emerging from CRISPR-based screens of high mutational-burden adult cancers. A key question, however, is whether functional genomic approaches will yield new targets in pediatric cancers, known for remarkably few mutations, which often encode proteins considered challenging drug targets. To address this, we created a first-generation pediatric cancer dependency map representing 13 pediatric solid and brain tumor types. Eighty-two pediatric cancer cell lines were subjected to genome-scale CRISPR-Cas9 loss-of-function screening to identify genes required for cell survival. In contrast to the finding that pediatric cancers harbor fewer somatic mutations, we found a similar complexity of genetic dependencies in pediatric cancer cell lines compared to that in adult models. Findings from the pediatric cancer dependency map provide preclinical support for ongoing precision medicine clinical trials. The vulnerabilities observed in pediatric cancers were often distinct from those in adult cancer, indicating that repurposing adult oncology drugs will be insufficient to address childhood cancers.

Explainable drug sensitivity prediction through cancer pathway enrichment.

Computational approaches to predict drug sensitivity can promote precision anticancer therapeutics. Generalizable and explainable models are of critical importance for translation to guide personalized treatment and are often overlooked in favor of prediction performance. Here, we propose PathDSP: a pathway-based model for drug sensitivity prediction that integrates chemical structure information with enrichment of cancer signaling pathways across drug-associated genes, gene expression, mutation and copy number variation data to predict drug response on the Genomics of Drug Sensitivity in Cancer dataset. Using a deep neural network, we outperform state-of-the-art deep learning models, while demonstrating good generalizability a separate dataset of the Cancer Cell Line Encyclopedia as well as provide explainable results, demonstrated through case studies that are in line with current knowledge. Additionally, our pathway-based model achieved a good performance when predicting unseen drugs and cells, with potential utility for drug development and for guiding individualized medicine.

The mitotic checkpoint is a targetable vulnerability of carboplatin-resistant triple negative breast cancers.

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype, lacking effective therapy. Many TNBCs show remarkable response to carboplatin-based chemotherapy, but often develop resistance over time. With increasing use of carboplatin in the clinic, there is a pressing need to identify vulnerabilities of carboplatin-resistant tumors. In this study, we generated carboplatin-resistant TNBC MDA-MB-468 cell line and patient derived TNBC xenograft models. Mass spectrometry-based proteome profiling demonstrated that carboplatin resistance in TNBC is linked to drastic metabolism rewiring and upregulation of anti-oxidative response that supports cell replication by maintaining low levels of DNA damage in the presence of carboplatin. Carboplatin-resistant cells also exhibited dysregulation of the mitotic checkpoint. A kinome shRNA screen revealed that carboplatin-resistant cells are vulnerable to the depletion of the mitotic checkpoint regulators, whereas the checkpoint kinases CHEK1 and WEE1 are indispensable for the survival of carboplatin-resistant cells in the presence of carboplatin. We confirmed that pharmacological inhibition of CHEK1 by prexasertib in the presence of carboplatin is well tolerated by mice and suppresses the growth of carboplatin-resistant TNBC xenografts. Thus, abrogation of the mitotic checkpoint by CHEK1 inhibition re-sensitizes carboplatin-resistant TNBCs to carboplatin and represents a potential strategy for the treatment of carboplatin-resistant TNBCs.

Epigenetic Mechanisms of Therapy Resistance in Diffuse Large B Cell Lymphoma (DLBCL).

Diffuse large B cell lymphoma (DLBCL) is the most common histological subtype of non-Hodgkin B cell lymphoma (NHL), and manifests highly heterogeneous genetic/phenotypic characteristics as well as variable responses to conventional immunochemotherapy. Genetic profiling of DLBCL patients has revealed highly recurrent mutations of epigenetic regulator genes such as CREBBP, KMT2D, EZH2 and TET2. These mutations drive malignant transformation through aberrant epigenetic programming of B-cells and may influence clinical outcomes. These and other chromatin modifier genes also play critical roles in normal B-cells, as they undergo the various phenotypic transitions characteristic of the humoral immune response. Many of these functions have to do with impairing immune surveillance and may critically mediate resistance to immunotherapies. In this review, we describe how epigenetic dysfunction induces lymphomagenesis and discuss ways of implementing precision epigenetic therapies to reverse these immune resistant phenotypes.

Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression.

Glioblastoma (GBM) is a common malignant brain tumor which often presents as a comorbidity with central nervous system (CNS) disorders. Both CNS disorders and GBM cells release glutamate and show an abnormality, but differ in cellular behavior. So, their etiology is not well understood, nor is it clear how CNS disorders influence GBM behavior or growth. This led us to employ a quantitative analytical framework to unravel shared differentially expressed genes (DEGs) and cell signaling pathways that could link CNS disorders and GBM using datasets acquired from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA) datasets where normal tissue and disease-affected tissue were examined. After identifying DEGs, we identified disease-gene association networks and signaling pathways and performed gene ontology (GO) analyses as well as hub protein identifications to predict the roles of these DEGs. We expanded our study to determine the significant genes that may play a role in GBM progression and the survival of the GBM patients by exploiting clinical and genetic factors using the Cox Proportional Hazard Model and the Kaplan-Meier estimator. In this study, 177 DEGs with 129 upregulated and 48 downregulated genes were identified. Our findings indicate new ways that CNS disorders may influence the incidence of GBM progression, growth or establishment and may also function as biomarkers for GBM prognosis and potential targets for therapies. Our comparison with gold standard databases also provides further proof to support the connection of our identified biomarkers in the pathology underlying the GBM progression.

Proteomic Analysis of Extracellular Vesicles Derived from MDA-MB-231 Cells in Microgravity.

Patients with triple-negative breast cancer (TNBC) have a relatively poor prognosis and cannot benefit from endocrine and/or targeted therapy. Considerable effort has been devoted toward the elucidation of the molecular mechanisms and potential diagnostic/therapeutic targets. However, it is inefficient and often ineffective to study the biological nuances of TNBC in large-scale clinical trials. In contrast, the investigation of the association between molecular alterations induced through controlled variables and relevant physiochemical characteristics of TNBC cells in laboratory settings is simple, definite, and efficient in exploring the molecular mechanisms. In this study, microgravity was selected as the sole variable of study as it can inhibit cancer cell viability, proliferation, metastasis, and chemoresistance. Identifying the key molecules that shift cancer cells toward a less aggressive phenotype may facilitate future TNBC studies. We focused on extracellular vesicles (EV) derived from TNBC MDA-MB-231 cells in microgravity, which mediate intercellular communication by transporting signaling molecules between cells. Our results show that in comparison with cells in full gravity, EV release rate decreased in microgravity while average EV size increased. In addition, we found EVs may be superior to cells in analyzing differentially expressed proteins, especially those that are down-regulated ones and usually unidentified or neglected in analysis of intact cellular contents. Proteomic analysis of both EVs and cells further revealed a significant correlation with GTPases and proliferation of MDA-MB-231 cells in microgravity. Altogether, our findings would further inspire in-depth correlative cancer biological studies and subsequent clinical research.