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Two mechanisms lead to drug resistance in tumors: inherent drug resistance and acquired drug resistance
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Malignant tumor is a largely harmful disease worldwide. The cure rate of malignant tumors increases with the continuous discovery of anti-tumor drugs and the optimisation of chemotherapy options. However, drug resistance of tumor cells remains a massive obstacle in the treatment of anti-tumor drugs. The heterogeneity of malignant tumors makes study...
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Circulating tumor cells (CTCs) represent a subset of heterogeneous cells, which, once released from a tumor site, have the potential to give rise to metastasis in secondary sites. Recent research focused on the attempt to detect and characterize these rare cells in the circulation, and advancements in defining their molecular profile have been repo...
Single-cell sequencing (SCS) is an emerging high-throughput technology that can be used to study the genomics, transcriptomics, and epigenetics at a single cell level. SCS is widely used in the diagnosis and treatment of various diseases, including cancer. Over the years, SCS has gradually become an effective clinical tool for the exploration of tu...
Epithelial ovarian cancer (EOC) is the deadliest gynecological malignancy worldwide. Despite the latest advances, a major clinical issue in EOC is the disappointing prognosis related to chemoresistance in almost one-third of cases. Drug resistance relies on heterogeneous cancer stem cells (CSCs), endowed with tumor-initiating potential, leading to...
Epithelial ovarian cancer (EOC) is the deadliest gynecological malignancy worldwide. Despite the latest advances, a major clinical issue in EOC is the disappointing prognosis related to chemoresistance in almost one-third of cases. Drug resistance relies on heterogeneous cancer stem cells (CSCs), endowed with tumor-initiating potential, leading to...
Purpose
Liquid biopsy refers to the detection and analysis of the components from biological fluids non-invasively, including circulating tumor cells, nucleic acids, and extracellular vesicles (EVs). It is necessary to review the clinical value of liquid biopsy assays in PC and explore its potential application.
Materials and methods
We systematic...
Citations
... New advances in multi-omics techniques powered by AI h now enable researchers to integrate genomic, transcriptomic, epigenomic, and other related data to gain the most accurate information on the activity state of individual genes and proteins to reveal the novel cancer drivers and genetic vulnerabilities for prevention and cure [35,36]. The emerging field of single-cell technology thus provides an unprecedented insight into the complex genetic and epigenetic heterogeneity within individual tumors for advanced precision oncology-based treatment and is likely to streamline future research directions [37,38]. ...
Cancer is a fatal genetic disease with different aspects of complexity, including cancer immune evasion, treatment resistance and recurrence, requiring optimized treatment for proper cure. Molecular studies have revealed that tumors are hugely heterogeneous in nature, leading to the complexity of cancer progression that is ultimately linked to its genetic machinery. In recent decades, there has been a deluge in the large-scale production of anticancer agents, primarily due to advances in genomic technologies enabling precise targeting of oncogenic pathways involved in disease development. It is important to note that patients with the same types of cancer respond differently to cancer treatments, indicating the need for patient-specific treatment options. An in-depth genomic study of tumors will be needed to fully understand the driving factors of cancer initiation and progression for effective targeted therapy. Precision oncology has evolved as a form of cancer therapy focused on genetic profiling of patients’ tumors to identify molecular alterations involved in cancer manifestation for tailored individualized treatment of the disease. This article aims to briefly explain the foundations and frontiers of precision oncology and review the tools and techniques involved in the process to assess its scope and importance in achieving effective cures against cancer.
... New advances in multi-omics techniques powered by AI h now enable researchers to integrate genomic, transcriptomic, epigenomic, and other related data to gain the most accurate information on the activity state of individual genes and proteins to reveal the novel cancer drivers and genetic vulnerabilities for prevention and cure [35,36]. The emerging field of single-cell technology thus provides an unprecedented insight into the complex genetic and epigenetic heterogeneity within individual tumors for advanced precision oncology-based treatment and is likely to streamline future research directions [37,38]. ...
In recent decades, there has been a deluge in the large-scale production of anticancer agents, primarily due to advances in genomic technologies enabling precise targeting of oncogenic pathways involved in disease progression. This initiated a paradigm shift in cancer research and therapeutics based on the ability to study molecular changes throughout the genome. It provided a unique opportunity in the field of translational cancer research and have led to the concept of precision medicine in cancer therapy, raising hopes of developing better diagnostic and therapeutic means for the management of cancer. The purpose of this article is to briefly review the tools and techniques involved in precision oncology research and their applications in the field of cancer treatment.
... Resistance of tumor cells to chemotherapeutic drugs is a considerable challenge in clinical treatment. SCS has the ability to uncover the internal drug resistance mechanisms of tumor cells, thus providing a basis for individualized drug therapy (Dai et al., 2020). For example, an earlier study using SCS to characterize chemotherapy drugresistant cells in colon cancer patients showed specific gene expression patterns in these cells, which could serve as an important indicator for predicting drug resistance and developing new therapeutic strategies (Wang et al., 2022). ...
Background: Single-cell sequencing (SCS) is a technique used to analyze the genome, transcriptome, epigenome, and other genetic data at the level of a single cell. The procedure is commonly utilized in multiple fields, including neurobiology, immunology, and microbiology, and has emerged as a key focus of life science research. However, a thorough and impartial analysis of the existing state and trends of SCS-related research is lacking. The current study aimed to map the development trends of studies on SCS during the years 2010–2022 through bibliometric software.
Methods: Pertinent papers on SCS from 2010 to 2022 were obtained using the Web of Science Core Collection. Research categories, nations/institutions, authors/co-cited authors, journals/co-cited journals, co-cited references, and keywords were analyzed using VOSviewer, the R package “bibliometric”, and CiteSpace.
Results: The bibliometric analysis included 9,929 papers published between 2010 and 2022, and showed a consistent increase in the quantity of papers each year. The United States was the source of the highest quantity of articles and citations in this field. The majority of articles were published in the periodical Nature Communications. Butler A was the most frequently quoted author on this topic, and his article “Integrating single-cell transcriptome data across diverse conditions, technologies, and species” has received numerous citations to date. The literature and keyword analysis showed that studies involving single-cell RNA sequencing (scRNA-seq) were prominent in this discipline during the study period.
Conclusion: This study utilized bibliometric techniques to visualize research in SCS-related domains, which facilitated the identification of emerging patterns and future directions in the field. Current hot topics in SCS research include COVID-19, tumor microenvironment, scRNA-seq, and neuroscience. Our results are significant for scholars seeking to identify key issues and generate new research ideas.
... Circulating tumor cells (CTCs) are vital components of liquid biopsies for the diagnosis of residual cancer, monitoring of therapy response, and prediction of recurrence [115]. Transcriptomics of CTCs represents an attractive opportunity to bridge the knowledge gap and develop novel biomarkers, and analysis of CTCs collected from patient blood may provide a new perspective for understanding the drug resistance of tumors and reveal a broad range of targets for use in the field of precision oncology [116][117][118]. Kozuka et al. [119] conducted a study in which CTCs were collected from metastatic colorectal cancer (mCRC) patients without relying on any traditional CTC markers, such as epithelial and mesenchymal cell antigens, and were subjected to scRNA-seq using SMART-seq v4. ...
Cancers are a group of heterogeneous diseases characterized by the acquisition of functional capabilities during the transition from a normal to a neoplastic state. Powerful experimental and computational tools can be applied to elucidate the mechanisms of occurrence, progression, metastasis, and drug resistance; however, challenges remain. Bulk RNA sequencing techniques only reflect the average gene expression in a sample, making it difficult to understand tumor heterogeneity and the tumor microenvironment. The emergence and development of single-cell RNA sequencing (scRNA-seq) technologies have provided opportunities to understand subtle changes in tumor biology by identifying distinct cell subpopulations, dissecting the tumor microenvironment, and characterizing cellular genomic mutations. Recently, scRNA-seq technology has been increasingly used in cancer studies to explore tumor heterogeneity and the tumor microenvironment, which has increased the understanding of tumorigenesis and evolution. This review summarizes the basic processes and development of scRNA-seq technologies and their increasing applications in cancer research and clinical practice.
... Numerous studies have found that tumour heterogeneity plays a crucial role in the development of drug resistance. 45 Single-cell sequencing studies of OS patients before and after conventional chemotherapy can offer further evidence and insights into this concept. Zeng et al. analyzed six pre-chemotherapy OS samples and categorized OS cells into 10 clusters, with specific clusters representing proliferative osteoblast cells, stromal tumour cells with osteogenic functions, and senescent tumour cells with weaker osteogenic functions. ...
Background
Osteosarcoma (OS) is a prevalent bone cancer characterized by its aggressive nature, which often metastasizes to the lungs and contributes to a poor prognosis. Treatment progress has been hindered by drug resistance, distant metastases, and significant patient heterogeneity. However, recent advancements in technologies such as sequencing and multi‐omics have enabled more in‐depth investigations at the micro level.
Aim
By undertaking a critical analysis of related studies, the present work intends to provide an overview of the microenvironmental features of osteosarcoma discovered through single‐cell RNA sequencing (scRNA‐seq) analysis. The articles included in this review were from several medical and health databases.
Results and discussion
We also discuss drug resistance traits, taking into account the heterogeneity of OS cells within the tumor microenvironment (TME). This includes inherent and acquired resistance in both pre‐ and post‐chemotherapy osteosarcoma, as well as the close association between cancer stem cells (CSCs) and drug resistance. Additionally, we examine the intercellular communication between OS cells and other cell types, as revealed by both traditional and single‐cell studies, and suggest several promising therapeutic targets.
Conclusion
Single‐cell RNA sequencing (scRNA‐seq) provides a detailed view of the tumor microenvironment, revealing intercellular communication between osteosarcoma cells and other cells, and aiding in the identification of potential therapeutic targets.
... Advancements in bioinformatic analysis provide a great convenience for researchers in investigating the underlying biological mechanisms of diseases [10]. In our study, we quantifed the CAFs infltration using the ssGSEA algorithm and comprehensively explored its role in GC. ...
Background:
Cancer-associated fibroblasts (CAFs) have reported widely involved in cancer progression. However, its underlying mechanism in gastric cancer is still not clarified.
Methods:
The data used in this study were all downloaded from the Cancer Genome Atlas database. R software and the R packages were used for all the analyses.
Results:
In our study, we first quantified the CAFs infiltration using the ssGSEA algorithm. The clinical correlation result showed that CAFs were associated with a worse prognosis and clinical features. Pathway enrichment also indicated several oncogenic pathways in GC patients with high CAFs infiltration, including epithelial-mesenchymal transition (EMT), myogenesis, allograft rejection, the inflammatory response, and IL2/STAT5 signaling. Furthermore, FNDC1 and RSPO3 were identified as the characteristic genes of CAFs through two machine learning algorithms, LASSO logistic regression and SVM-RFE. The following analysis showed that FNDC1 and RSPO3 were associated with more progressive clinical features and had a good prediction efficiency of the CAFs infiltration status in GC patients. Pathway enrichment and genomic instability were performed to explore the underlying mechanisms of FNDC1 and RSPO3. Immune infiltration analysis showed that CAFs were positively correlated with M2 macrophages. Moreover, we found that the GC patients with low CAFs infiltration were more sensitive to immunotherapy. Also, the CAFs, FNDC1, and RSPO3 could generate a certain effect on the sensitivity of doxorubicin, mitomycin, and paclitaxel.
Conclusions:
In summary, our study comprehensively investigated the role of CAFs in GC, which might be associated with immunotherapy sensitivity. Meanwhile, FNDC1 and RSPO3 were identified as the underlying targets of GC.
... Researchers have great convenience to investigate further with the rapid development of bioinformatics technology (9). In our study, we performed the TIDE analysis to evaluate the immunotherapy response rate of OC patients. ...
Background
Preclinical trials of immunotherapy in ovarian cancer (OC) have shown promising results. This makes it meaningful to prospectively examine the biological mechanisms explaining the differences in response performances to immunotherapy among OC patients.
Methods
Open-accessed data was obtained from the Cancer Genome Atlas and Gene Expression Omnibus database. All the analysis was conducted using the R software.
Results
We firstly performed the TIDE analysis to evaluate the immunotherapy response rate of OC patients. The machine learning algorithm LASSO logistic regression and SVM-RFE were used to identify the characteristic genes. The genes DPT, RUNX1T1, PTPRN, LSAMP, FDCSP and COL6A6 were selected for molecular typing. Our result showed that the patients in Cluster1 might have a better prognosis and might be more sensitive to immunotherapy, including PD-1 and CTLA4 therapy options. Pathway enrichment analysis showed that in Cluster2, the pathway of EMT, TNFα/NF-kB signaling, IL2/STAT5 signaling, inflammatory response, KRAS signaling, apical junction, complement, interferon-gamma response and allograft rejection were significantly activated. Also, genomic instability analysis was performed to identify the underlying genomic difference between the different Cluster patients. Single-cell analysis showed that the DPT, COL6A6, LSAMP and RUNX1T1 were mainly expressed in the fibroblasts. We then quantified the CAFs infiltration in the OC samples. The result showed that patients with low CAFs infiltration might have a lower TIDE score and a higher proportion of immunotherapy responders. Also, we found all the characteristic genes DPT, RUNX1T1, PTPRN, LSAMP, FDCSP and COL6A6 were upregulated in the patients with high CAFs infiltration. Immune infiltration analysis showed that the patients in Cluster2 might have a higher infiltration of naive B cells, activated NK cells and resting Dendritic cells.
Conclusions
In summary, our study provides new insights into ovarian cancer immunotherapy. Meanwhile, specific targets DPT, RUNX1T1, PTPRN, LSAMP, FDCSP, COL6A6 and CAFs were identified for OC immunotherapy.
... Nowadays, the advancement of bioinformatics analysis can effectively help researchers find novel molecules involved in disease development (14). In our study, through comprehensive bioinformatic analysis, we explored the metabolism pathway in NPC. ...
Background
Tumor metabolism is important for cancer progression. Nevertheless, the role of the metabolism pathway and related molecules in nasopharyngeal carcinoma (NPC) is limited.
Methods
Open-accessed data was downloaded from The Cancer Genome Atlas database. All the analysis was performed using the R software and the package in R environments.
Results
In our study, we firstly explored the role of 21 metabolism-related pathways in NPC patients. We found that the steroid biosynthesis and biosynthesis of unsaturated fatty acids were risk factors, while the alpha linolenic acid metabolism was a protective factor. Then, the alpha linolenic acid metabolism aroused our interest. A total of 128 differentially expressed genes (DEGs) were identified, including 71 downregulated and 57 upregulated genes identified between high and low alpha linolenic acid metabolism level. Based on these DEGs, we constructed a prognosis model including DEFB4B, FOXL2NB, MDGA2, RTL1, SLURP2, TMEM151B and TSPAN19, which showed great prediction efficiency in both training and validation cohorts. Clinical correlation analysis showed that high-risk patients might have worse clinical pathology parameters. Pathway enrichment analysis showed that riskscore was positively correlated with angiogenesis, DNA repair, G2/M checkpoints, IL6/JAK/STAT3 signaling, KRAS signaling up, WNT beta-catenin signaling, PI3K/AKT/mTOR signaling, yet positively correlated with inflammatory response, xenobiotic metabolism, TNF-α signaling via NFKB and interferon-gamma response. Immune infiltration analysis showed that the riskscore was positively correlated with the M2 and M0 macrophages, but negatively correlated with neutrophils, plasma cells, follicular helper T cells and resting dendritic cells Moreover, we found that the low-risk patients might be more sensitive to immunotherapy and lapatinib.
Conclusions
In all, our study identified the genes associated with alpha linolenic acid metabolism and constructed an effective prognosis model which could robustly predict NPC patients prognosis.
... Therefore, to optimize medical care, it is necessary to identify biomarkers that address intratumoral heterogeneity and help decide which patients to treat and which therapy is most likely to be effective. Recent developments in single-cell sequencing technology have provided more profound insights into how therapeutic responses differ across heterogeneous genomic and transcriptomic cell states [7][8][9]. However, static single-cell omics measurements lack the ability to decode highly dynamic cellular and molecular behaviors, like single-cell response to different stimuli [10]. ...
When several life-prolonging drugs are indicated for cancer treatment, predictive drug-response tumor biomarkers are essential to guide management. Most conventional biomarkers are based on bulk tissue analysis, which cannot address the complexity of single-cell heterogeneity responsible for drug resistance. Therefore, there is a need to develop alternative drug response predictive biomarker approaches that could directly interrogate single-cell and whole population cancer cell drug sensitivity. In this study, we report a novel method exploiting bioluminescence microscopy to detect single prostate cancer (PCa) cell response to androgen receptor (AR)-axis-targeted therapies (ARAT) and predict cell population sensitivity.
Methods: We have generated a new adenovirus-delivered biosensor, PCA3-Cre-PSEBC-ITSTA, which combines an integrated two-step transcriptional amplification system (ITSTA) and the activities of the prostate cancer antigen 3 (PCA3) and modified prostate-specific antigen (PSEBC) gene promoters as a single output driving the firefly luciferase reporter gene. This system was tested on PCa cell lines and on primary PCa cells. Single cells, exposed or not to ARAT, were dynamically imaged by bioluminescence microscopy. A linear discriminant analysis (LDA)-based method was used to determine cell population sensitivities to ARAT.
Results: We show that the PCA3-Cre-PSEBC-ITSTA biosensor is PCa-specific and can dynamically monitor single-cell AR transcriptional activity before and after ARAT by bioluminescence microscopy. After biosensor transduction and bioluminescence microscopy single-cell luminescence dynamic quantification, LDA analysis could discriminate the cell populations overall ARAT sensitivity despite heterogeneous single-cell responses. Indeed, the biosensor could detect a significant decrease in AR activity following exposure to conventional ARAT in hormone-naive primary PCa cells, while in castration-resistant PCa patients, treatment response correlated with the observed clinical ARAT resistance.
Conclusion: The exploitation of bioluminescence microscopy and multi-promoter transcriptionally-regulated biosensors can aptly define the overall treatment response of patients by monitoring live single cell drug response from primary cancer tissue. This approach can be used to develop predictive biomarkers for drug response in order to help clinicians select the best drug combinations or sequences for each patient.
... This constitutes heterogeneity of gene expression, which is caused by differences in the genome, cell cycle, and microenvironment. Single cell transcriptome sequencing can dynamically represent the total RNA produced by strains or a particular cell at a certain functional stage, and is thus better for defining the cell type [20]. However, only 1-10 pg of RNA is contained in each cell, which does not meet the minimum sample requirement of the existing sequencers. ...
Cancer is an intricate disease with inherent intra-tumor heterogeneity at the cellular level because of genetic changes and environmental differences. Cellular heterogeneity exists even within the same tumor type. Small deviations in a genome or transcriptome can lead to significant differences in function. Conventional bulk population sequencing, which produces admixed populations of cells, can only provide an average expression signal for one cell population, ignoring differences between individual cells. Important advances in sequencing have been made in recent years. Single cell sequencing starts in a single cell, thereby increasing our capability to characterize intratumor heterogeneity. This technology has been used to analyze genetic variation, specific metabolic activity, and evolutionary processes in tumors, which may help us understand tumor occurrence and development and improve our understanding of the tumor microenvironment. In addition, it provides a theoretical basis for the development of clinical treatments, especially for personalized medicine. In this article, we briefly introduce Single cell sequencing technology, summarize the application of Single cell sequencing to study the tumor microenvironment, as well as its therapeutic application in different clinical procedures.
