Development of a TMErisk model based on immune infiltration in tumour microenvironment to predict prognosis of immune checkpoint inhibitor treatment in hepatocellular carcinoma.

Immune checkpoint inhibitor (ICI) treatment has created the opportunity of improved outcome for patients with hepatocellular carcinoma (HCC). However, only a minority of HCC patients benefit from ICI treatment owing to poor treatment efficacy and safety concerns. There are few predictive factors that precisely stratify HCC responders to immunotherapy. In this study, we developed a tumour microenvironment risk (TMErisk) model to divide HCC patients into different immune subtypes and evaluated their prognosis. Our results indicated that virally mediated HCC patients who had more common tumour protein P53 (TP53) alterations with lower TMErisk scores were appropriate for ICI treatment. HCC patients with alcoholic hepatitis who more commonly harboured catenin beta 1 (CTNNB1) alterations with higher TMErisk scores could benefit from treatment with multi-tyrosine kinase inhibitors. The developed TMErisk model represents the first attempt to anticipate tumour tolerance of ICIs in the TME through the degree of immune infiltration in HCCs.

All is not lost: learning from 9p21 loss in cancer.

The cancer research community continues to search for additional biomarkers of response and resistance to immune checkpoint treatment (ICT). The ultimate goal is to direct the use of ICT in patients whose tumors are most likely to benefit to achieve a refinement that is equivalent to that of a genotype-matched targeted treatment. Dissecting the mechanisms of ICT resistance can help us characterize ICT nonresponders more efficiently. In this opinion, we argue that there may be additional knowledge gained about immune evasion in cancer by analyzing the loss of the human 9p21.3 locus; as an example, we highlight findings of 9p21.3 loss from the investigator-initiated, pan-cancer INSPIRE study, in which patients were treated with pembrolizumab (anti-PD-1 antibody) ICT.

TP53 mutations in urothelial carcinoma: not all one and the same†.

Systemic therapy options for urothelial carcinoma have expanded in recent years, with both immunotherapy and cytotoxic chemotherapy being widely available. However, we lack biomarkers to select which drug is likely to work best in individual patients. A new article in this journal by Jin, Xu, Su, et al reports that disruptive versus non-disruptive TP53 mutations may guide these personalised therapy choices. Intriguingly, patients with disruptive TP53 tumour mutations had poor overall survival versus those with non-disruptive TP53 mutations or wild type TP53 but responded particularly well to immunotherapy. Of relevance, an increased tumour mutational burden and increased effector CD8+ T-cell infiltration was seen in tumours with disruptive mutations. The impact of different TP53 mutations on prognosis and therapy choices appears to be tumour- and therapy-type specific, with no clear consensus on overall tumour phenotype according to type of mutation. Nonetheless, profiling of specific types of TP53 mutation is increasingly clinically feasible with targeted sequencing or immunohistochemistry. There is an urgent need for additional studies in urothelial cancer clarifying how the type of TP53 mutation present within a tumour can best be used as a predictive biomarker. Further important remaining questions include the impact of TP53 mutations on other clinically important aspects of the tumour microenvironment, including cancer-associated fibroblasts. Furthermore, the impact of gain-of-function mutations in TP53 and other related genes signalling upstream or downstream of TP53 is of wide interest. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Changes in the tumour microenvironment mark the transition from serous borderline tumour to low-grade serous carcinoma.

Low-grade serous ovarian carcinoma (LGSC) is a rare and lethal subtype of ovarian cancer. LGSC is pathologically, biologically, and clinically distinct from the more common high-grade serous ovarian carcinoma (HGSC). LGSC arises from serous borderline ovarian tumours (SBTs). The mechanism of transformation for SBTs to LGSC remains poorly understood. To better understand the biology of LGSC, we performed whole proteome profiling of formalin-fixed, paraffin-embedded tissue blocks of LGSC (n = 11), HGSC (n = 19), and SBTs (n = 26). We identified that the whole proteome is able to distinguish between histotypes of the ovarian epithelial tumours. Proteins associated with the tumour microenvironment were differentially expressed between LGSC and SBTs. Fibroblast activation protein (FAP), a protein expressed in cancer-associated fibroblasts, is the most differentially abundant protein in LGSC compared with SBT. Multiplex immunohistochemistry (IHC) for immune markers (CD20, CD79a, CD3, CD8, and CD68) was performed to determine the presence of B cells, T cells, and macrophages. The LGSC FAP+ stroma was associated with greater abundance of Tregs and M2 macrophages, features not present in SBTs. Our proteomics cohort reveals that there are changes in the tumour microenvironment in LGSC compared with its putative precursor lesion, SBT. These changes suggest that the tumour microenvironment provides a supportive environment for LGSC tumourigenesis and progression. Thus, targeting the tumour microenvironment of LGSC may be a viable therapeutic strategy. © 2024 The Pathological Society of Great Britain and Ireland.

Stroma modulation of radiation response in head and neck squamous cell carcinoma: Insights from zebrafish larvae xenografts.

BACKGROUND: The tumour microenvironment (TME) of head and neck squamous cell carcinoma (HNSCC) consists of different subtypes of cells that interact with the tumour or with each other. This study investigates the possibility of co-culturing HNSCC cells with different stroma cells in a zebrafish xenograft model, focusing on the effect of stroma cells on HNSCC growth and response to irradiation. MATERIAL AND METHOD: HNSCC metastatic cell line HSC-3 was used along with five types of stroma cells: normal gingival fibroblasts (NOF), cancer associated fibroblasts (CAF), macrophages, CD4+ T cells, and human umbilical vein endothelial cells (HUVEC). The mixture of HSC-3 cells and each-stroma cell type-was injected into 2-day post-fertilization zebrafish embryos, and the effect of stroma cells on tumour growth was tested. The study also aimed to mimic the HNSCC tumour by injecting a mixture of HSC-3 cells, CAFs, macrophages, and HUVECs into zebrafish embryos and testing the effect of these stroma cells on the cancer cells' response to irradiation compared to HSC-3-only tumours. RESULTS: CAFs had a significant inducement effect on tumour size, while HUVECs showed the opposite effect. The irradiated group of HSC-3-only tumour had a significantly smaller tumor cell area compared to the control, while the group with stroma cells and HSC-3 cells showed cancer cells being resistant to irradiation. CONCLUSION: This is the first report of co-culturing cancer cells with several types of stroma cells using a zebrafish xenograft model. This study also highlighted the role of stroma cells in turning the cancer cells from radioresponsive to radioresistant.

Machine learning-derived identification of prognostic signature for improving prognosis and drug response in patients with ovarian cancer.

Clinical assessments relying on pathology classification demonstrate limited effectiveness in predicting clinical outcomes and providing optimal treatment for patients with ovarian cancer (OV). Consequently, there is an urgent requirement for an ideal biomarker to facilitate precision medicine. To address this issue, we selected 15 multicentre cohorts, comprising 12 OV cohorts and 3 immunotherapy cohorts. Initially, we identified a set of robust prognostic risk genes using data from the 12 OV cohorts. Subsequently, we employed a consensus cluster analysis to identify distinct clusters based on the expression profiles of the risk genes. Finally, a machine learning-derived prognostic signature (MLDPS) was developed based on differentially expressed genes and univariate Cox regression genes between the clusters by using 10 machine-learning algorithms (101 combinations). Patients with high MLDPS had unfavourable survival rates and have good prediction performance in all cohorts and in-house cohorts. The MLDPS exhibited robust and dramatically superior capability than 21 published signatures. Of note, low MLDIS have a positive prognostic impact on patients treated with anti-PD-1 immunotherapy by driving changes in the level of infiltration of immune cells. Additionally, patients suffering from OV with low MLDIS were more sensitive to immunotherapy. Meanwhile, patients with low MLDIS might benefit from chemotherapy, and 19 compounds that may be potential agents for patients with low MLDIS were identified. MLDIS presents an appealing instrument for the identification of patients at high/low risk. This could enhance the precision treatment, ultimately guiding the clinical management of OV.

Pathway-based stratification of gliomas uncovers four subtypes with different TME characteristics and prognosis.

Increasing evidences have found that the interactions between hypoxia, immune response and metabolism status in tumour microenvironment (TME) have clinical importance of predicting clinical outcomes and therapeutic efficacy. This study aimed to develop a reliable molecular stratification based on these key components of TME. The TCGA data set (training cohort) and two independent cohorts from CGGA database (validation cohort) were enrolled in this study. First, the enrichment score of 277 TME-related signalling pathways was calculated by gene set variation analysis (GSVA). Then, consensus clustering identified four stable and reproducible subtypes (AFM, CSS, HIS and GLU) based on TME-related signalling pathways, which were characterized by differences in hypoxia and immune responses, metabolism status, somatic alterations and clinical outcomes. Among the four subtypes, HIS subtype had features of immunosuppression, oxygen deprivation and active energy metabolism, resulting in a worst prognosis. Thus, for better clinical application of this acquired stratification, we constructed a risk signature by using the LASSO regression model to identify patients in HIS subtype accurately. We found that the risk signature could accurately screen out the patients in HIS subtype and had important reference value for individualized treatment of glioma patients. In brief, the definition of the TME-related subtypes was a valuable tool for risk stratification in gliomas. It might serve as a reliable prognostic classifier and provide rational design of individualized treatment, and follow-up scheduling for patients with gliomas.

Pan-cancer analysis identifies the IRF family as a biomarker for survival prognosis and immunotherapy.

IRF family genes have been shown to be crucial in tumorigenesis and tumour immunity. However, information about the role of IRF in the systematic assessment of pan-cancer and in predicting the efficacy of tumour therapy is still unknown. In this work, we performed a systematic analysis of IRF family genes in 33 tumour samples, including expression profiles, genomics and clinical characteristics. We then applied Single-Sample Gene-Set Enrichment Analysis (ssGSEA) to calculate IRF-scores and analysed the impact of IRF-scores on tumour progression, immune infiltration and treatment efficacy. Our results showed that genomic alterations, including SNPs, CNVs and DNA methylation, can lead to dysregulation of IRFs expression in tumours and participate in regulating multiple tumorigenesis. IRF-score expression differed significantly between 12 normal and tumour samples and the impact on tumour prognosis and immune infiltration depended on tumour type. IRF expression was correlated to drug sensitivity and to the expression of immune checkpoints and immune cell infiltration, suggesting that dysregulation of IRF family expression may be a critical factor affecting tumour drug response. Our study comprehensively characterizes the genomic and clinical profile of IRFs in pan-cancer and highlights their reliability and potential value as predictive markers of oncology drug efficacy. This may provide new ideas for future personalized oncology treatment.

Unravelling tumour cell diversity and prognostic signatures in cutaneous melanoma through machine learning analysis.

Melanoma, a highly malignant tumour, presents significant challenges due to its cellular heterogeneity, yet research on this aspect in cutaneous melanoma remains limited. In this study, we utilized single-cell data from 92,521 cells to explore the tumour cell landscape. Through clustering analysis, we identified six distinct cell clusters and investigated their differentiation and metabolic heterogeneity using multi-omics approaches. Notably, cytotrace analysis and pseudotime trajectories revealed distinct stages of tumour cell differentiation, which have implications for patient survival. By leveraging markers from these clusters, we developed a tumour cell-specific machine learning model (TCM). This model not only predicts patient outcomes and responses to immunotherapy, but also distinguishes between genomically stable and unstable tumours and identifies inflamed ('hot') versus non-inflamed ('cold') tumours. Intriguingly, the TCM score showed a strong association with TOMM40, which we experimentally validated as an oncogene promoting tumour proliferation, invasion and migration. Overall, our findings introduce a novel biomarker score that aids in selecting melanoma patients for improved prognoses and targeted immunotherapy, thereby guiding clinical treatment decisions.

A novel ARHGAP family gene signature for survival prediction in glioma patients.

ARHGAP family genes are often used as glioma oncogenic factors, and their mechanism of action remains unexplained. Our research entailed a thorough examination of the immune microenvironment and enrichment pathways across various glioma subtypes. A distinctive 6-gene signature was developed employing the CGGA cohort, leading to insights into the disparities in clinical characteristics, mutation patterns, and immune cell infiltration among distinct risk categories. Additionally, a unique nomogram was established, grounded on ARHGAPs, with DCA curves illustrating the model's prospective clinical utility in guiding therapeutic strategies. Emphasizing the role of ARHGAP30, integral to our model, its impact on glioma severity and the credibility of our risk assessment model were substantiated through RT-qPCR, Western blot analysis, and cellular functional assays. We identified 6 ARHGAP family genes associated with glioma prognosis. Analysis using the Kaplan-Meier method indicated a correlation between elevated risk levels and adverse outcomes in glioma patients. The risk score, linked with tumour staging and IDH mutation status, emerged as an independent factor predicting prognosis. Patients in the high-risk category exhibited increased immune cell infiltration, enhanced tumour mutational burden, more pronounced expression of immune checkpoint genes, and a better response to ICB therapy. A nomogram, integrating the risk score with the pathological features of glioma patients, was developed. DCA analysis and cellular studies confirmed the model's potential to improve clinical treatment outcomes for patients. A novel ARHGAP family gene signature reveals the prognosis of glioma.

Troponin T1 in tumorigenesis and immune modulation: Insights into multiple cancers and kidney renal clear cell carcinoma.

Troponin T1 (TNNT1) plays a crucial role in muscle contraction but its role in cancer, particularly in kidney renal clear cell carcinoma (KIRC), is not well-understood. This study explores the expression, clinical significance and biological functions of TNNT1 in various cancers, with an emphasis on its involvement in KIRC. We analysed TNNT1 expression in cancers using databases like TCGA and GTEx, assessing its prognostic value, mutation patterns, methylation status and functional implications. The study also examined TNNT1's effect on the tumour microenvironment and drug sensitivity in KIRC, complemented by in vitro TNNT1 knockdown experiments in KIRC cells. TNNT1 is overexpressed in several cancers and linked to adverse outcomes, showing frequent upregulation mutations and abnormal methylation. Functionally, TNNT1 connects to muscle and cancer pathways, affects immune infiltration and drug responses, and its overexpression in KIRC is associated with advanced disease and reduced survival. Knocking down TNNT1 curbed KIRC cell growth. TNNT1's aberrant expression plays a significant role in tumorigenesis and immune modulation, highlighting its value as a prognostic biomarker and a potential therapeutic target in KIRC and other cancers. Further studies are essential to understand TNNT1's oncogenic mechanisms in KIRC.

A novel mitochondria-related core gene signature to predict the prognosis and evaluate tumour microenvironment in CESC single-cell validation.

Mitochondria and their related genes (MTRGs) are pivotal in the tumour microenvironment (TME) of cervical cancer, influencing prognosis and treatment response. This study developed a prognostic model using MTRGs to predict overall survival (OS) in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), aiming for personalized therapy. Analysing 14 MTRGs like ISCU and NDUFA11 through techniques such as univariate Cox regression, we found that a low mitochondrial (MT) score is associated with better survival, while a high MT score predicts poorer outcomes. The TME score, particularly influenced by CD8 T cells, also correlates with prognosis, with a high score indicating favourable outcomes. The interplay between MT and TME subtypes revealed that the best prognosis is seen in patients with a low MT and high TME score. Our findings highlight the role of MTRGs as potential biomarkers and therapeutic targets in cervical cancer, offering a novel approach to improving patient outcomes through a more nuanced understanding of mitochondrial function and immune interactions within the TME. This model presents a promising avenue for enhancing the precision of prognostic assessments in CESC.

Deciphering the tumour microenvironment of clear cell renal cell carcinoma: Prognostic insights from programmed death genes using machine learning.

Clear cell renal cell carcinoma (ccRCC), a prevalent kidney cancer form characterised by its invasiveness and heterogeneity, presents challenges in late-stage prognosis and treatment outcomes. Programmed cell death mechanisms, crucial in eliminating cancer cells, offer substantial insights into malignant tumour diagnosis, treatment and prognosis. This study aims to provide a model based on 15 types of Programmed Cell Death-Related Genes (PCDRGs) for evaluating immune microenvironment and prognosis in ccRCC patients. ccRCC patients from the TCGA and arrayexpress cohorts were grouped based on PCDRGs. A combination model using Lasso and SuperPC was constructed to identify prognostic gene features. The arrayexpress cohort validated the model, confirming its robustness. Immune microenvironment analysis, facilitated by PCDRGs, employed various methods, including CIBERSORT. Drug sensitivity analysis guided clinical treatment decisions. Single-cell data enabled Programmed Cell Death-Related scoring, subsequent pseudo-temporal and cell-cell communication analyses. A PCDRGs signature was established using TCGA-KIRC data. External validation in the arrayexpress cohort underscored the model's superiority over traditional clinical features. Furthermore, our single-cell analysis unveiled the roles of PCDRG-based single-cell subgroups in ccRCC, both in pseudo-temporal progression and intercellular communication. Finally, we performed CCK-8 assay and other experiments to investigate csf2. In conclusion, these findings reveal that csf2 inhibit the growth, infiltration and movement of cells associated with renal clear cell carcinoma. This study introduces a PCDRGs prognostic model benefiting ccRCC patients while shedding light on the pivotal role of programmed cell death genes in shaping the immune microenvironment of ccRCC patients.

Prognostic heterogeneity of Ki67 in non-small cell lung cancer: A comprehensive reappraisal on immunohistochemistry and transcriptional data.

In the present study, the debatable prognostic value of Ki67 in patients with non-small cell lung cancer (NSCLC) was attributed to the heterogeneity between lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC). Based on meta-analyses of 29 studies, a retrospective immunohistochemical cohort of 1479 patients from our center, eight transcriptional datasets and a single-cell datasets with 40 patients, we found that high Ki67 expression suggests a poor outcome in LUAD, but conversely, low Ki67 expression indicates worse prognosis in LUSC. Furthermore, low proliferation in LUSC is associated with higher metastatic capacity, which is related to the stronger epithelial-mesenchymal transition potential, immunosuppressive microenvironment and angiogenesis. Finally, nomogram model incorporating clinical risk factors and Ki67 expression outperformed the basic clinical model for the accurate prognostic prediction of LUSC. With the largest prognostic assessment of Ki67 from protein to mRNA level, our study highlights that Ki67 also has an important prognostic value in NSCLC, but separate evaluation of LUAD and LUSC is necessary to provide more valuable information for clinical decision-making in NSCLC.

The cell death-related genes machine learning model for precise therapy and clinical drug selection in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the prevailing subtype of hepatocellular malignancy. While previous investigations have evidenced a robust link with programmed cell death (PCD) and tumorigenesis, a comprehensive inquiry targeting the relationship between multiple PCDs and HCC remains scant. Our aim was to develop a predictive model for different PCD patterns in order to investigate their impact on survival rates, prognosis and drug response rates in HCC patients. We performed functional annotation and pathway analysis on identified PCD-related genes (PCDRGs) using multiple bioinformatics tools. The prognostic value of these PCDRGs was verified through a dataset obtained from GEO. Consensus clustering analysis was utilized to elucidate the correlation between diverse PCD clusters and pertinent clinical characteristics. To comprehensively uncover the distinct PCD regulatory patterns, our analysis integrated gene expression profiling, immune cell infiltration and enrichment analysis. To predict survival differences in HCC patients, we established a PCD model. To enhance the clinical applicability for the model, we developed a highly accurate nomogram. To address the treatment of HCC, we identified several promising chemotherapeutic agents and novel targeted drugs. These drugs may be effective in treating HCC and could improve patient outcomes. To develop a cell death feature for HCC patients, we conducted an analysis of 12 different PCD mechanisms using eligible data obtained from public databases. Through this analysis, we were able to identify 1254 PCDRGs likely to contribute to cell death on HCC. Further analysis of 1254 PCDRGs identified 37 genes with prognostic value in HCC patients. These genes were then categorized into two PCD clusters A and B. The categorization was based on the expression patterns of the genes in the different clusters. Patients in PCD cluster B had better survival probabilities. This suggests that PCD mechanisms, as represented by the genes in cluster B, may have a protective effect against HCC progression. Furthermore, the expression of PCDRGs was significantly higher in PCD cluster A, indicating that this cluster may be more closely associated with PCD mechanisms. Furthermore, our observations indicate that patients exhibiting elevated tumour mutation burden (TMB) are at an augmented risk of mortality, in comparison to those displaying low TMB and low-risk statuses, who are more likely to experience prolonged survival. In addition, we have investigated the potential distinctions in the susceptibility of diverse risk cohorts towards emerging targeted therapies, designed for the treatment of HCC. Moreover, our investigation has shown that AZD2014, SB505124, LJI308 and OSI-207 show a greater efficacy in patients in the low-risk category. Conversely, for the high-risk group patients, PD173074, ZM447439 and CZC24832 exhibit a stronger response. Our findings suggest that the identification of risk groups and personalized treatment selection could lead to better clinical outcomes for patients with HCC. Furthermore, significant heterogeneity in clinical response to ICI therapy was observed among HCC patients with varying PCD expression patterns. This novel discovery underscores the prospective usefulness of these expression patterns as prognostic indicators for HCC patients and may aid in tailoring targeted treatment for those of distinct risk strata. Our investigation introduces a novel prognostic model for HCC that integrates diverse PCD expression patterns. This innovative model provides a novel approach for forecasting prognosis and assessing drug sensitivity in HCC patients, driving a more personalized and efficacious treatment paradigm, elevating clinical outcomes. Nonetheless, additional research endeavours are required to confirm the model's precision and assess its potential to inform clinical decision-making for HCC patients.

Machine learning-based identification of a cell death-related signature associated with prognosis and immune infiltration in glioma.

Accumulating evidence suggests that a wide variety of cell deaths are deeply involved in cancer immunity. However, their roles in glioma have not been explored. We employed a logistic regression model with the shrinkage regularization operator (LASSO) Cox combined with seven machine learning algorithms to analyse the patterns of cell death (including cuproptosis, ferroptosis, pyroptosis, apoptosis and necrosis) in The Cancer Genome Atlas (TCGA) cohort. The performance of the nomogram was assessed through the use of receiver operating characteristic (ROC) curves and calibration curves. Cell-type identification was estimated by using the cell-type identification by estimating relative subsets of known RNA transcripts (CIBERSORT) and single sample gene set enrichment analysis methods. Hub genes associated with the prognostic model were screened through machine learning techniques. The expression pattern and clinical significance of MYD88 were investigated via immunohistochemistry (IHC). The cell death score represents an independent prognostic factor for poor outcomes in glioma patients and has a distinctly superior accuracy to that of 10 published signatures. The nomogram performed well in predicting outcomes according to time-dependent ROC and calibration plots. In addition, a high-risk score was significantly related to high expression of immune checkpoint molecules and dense infiltration of protumor cells, these findings were associated with a cell death-based prognostic model. Upregulated MYD88 expression was associated with malignant phenotypes and undesirable prognoses according to the IHC. Furthermore, high MYD88 expression was associated with poor clinical outcomes and was positively related to CD163, PD-L1 and vimentin expression in the in-horse cohort. The cell death score provides a precise stratification and immune status for glioma. MYD88 was found to be an outstanding representative that might play an important role in glioma.

Multifaceted role of FAM210B in hepatocellular carcinoma: Implications for tumour progression, microenvironment modulation and therapeutic selection.

Hepatocellular carcinoma (HCC) is a common and lethal liver cancer characterized by complex aetiology and limited treatment options. FAM210B, implicated in various cancers, is noteworthy for its potential role in the progression and treatment response of HCC. Yet, its expression patterns, functional impacts and correlations with patient outcomes and resistance to therapy are not well understood. We employed a comprehensive methodology to explore the role of FAM210B in HCC, analysing its expression across cancers, subcellular localization and impacts on cell proliferation, invasion, migration, biological enrichment and the immune microenvironment. Additionally, we investigated its expression in single cells, drug sensitivity and relationships with genomic instability, immunotherapy efficacy and key immune checkpoints. While FAM210B expression varied across cancers, there was no notable difference between HCC and normal tissues. Elevated levels of FAM210B were associated with improved survival outcomes. Subcellular analysis located FAM210B in the plasma membrane and cytosol. FAM210B was generally downregulated in HCC, and its suppression significantly enhanced cell proliferation, invasion and migration. Biological enrichment analysis linked FAM210B to metabolic and immune response pathways. Moreover, its expression modified the immune microenvironment of HCC, affecting drug responsiveness and immunotherapy outcomes. High expression levels of FAM202B correlated with increased resistance to sunitinib and enhanced responsiveness to immunotherapy, as evidenced by associations with tumour mutation burden, PDCD1, CTLA4 and TIDE scores. FAM210B exerts a complex influence on HCC, affecting tumour cell behaviour, metabolic pathways, the immune microenvironment and responses to therapy.

Single-cell transcriptomic analysis in clear cell renal cell carcinoma: Deciphering the role of APP within the tumour microenvironment.

Clear cell renal cell carcinoma (ccRCC) represents a significant challenge in oncology, primarily due to its resistance to conventional therapies. Understanding the tumour microenvironment (TME) is crucial for developing new treatment strategies. This study focuses on the role of amyloid precursor protein (APP) in tumour-associated macrophages (TAMs) within the ccRCC TME, exploring its potential as a prognostic biomarker. Basing TAM-related genes, the prognostic model was important to constructed. Employing advanced single-cell transcriptomic analysis, this research dissects the TME of ccRCC at an unprecedented cellular resolution. By isolating and examining the gene expression profiles of individual cells, particularly focusing on TAMs, the study investigates the expression levels of APP and their association with the clinical outcomes of ccRCC patients. The analysis reveals a significant correlation between the expression of APP in TAMs and patient prognosis in ccRCC. Patients with higher APP expression in TAMs showed differing clinical outcomes compared to those with lower expression. This finding suggests that APP could serve as a novel prognostic biomarker for ccRCC, providing insights into the disease progression and potential therapeutic targets. This study underscores the importance of single-cell transcriptomics in understanding the complex dynamics of the TME in ccRCC. The correlation between APP expression in TAMs and patient prognosis highlights APP as a potential prognostic biomarker. However, further research is needed to validate these findings and explore the regulatory mechanisms and therapeutic implications of APP in ccRCC.

Unravelling the heterogeneity of oral squamous cell carcinoma by integrative analysis of single-cell and bulk transcriptome data.

Oral squamous cell carcinoma (OSCC) is a prevalent malignancy of the head and neck with rising global incidence. Despite advances in treatment modalities, OSCC prognosis remains diverse due to the complex molecular and cellular heterogeneity within tumours, as well as the heterogeneity in tumour microenvironment (TME). In this study, we utilized single-cell RNA sequencing (scRNA-seq) analysis to explore distinct subpopulations of tumour cells in OSCC tissues and their interaction with components in TME. We identified four major tumour cell subpopulations (C0, C1, C2 and C3) with unique molecular characteristics and functional features. Pathway enrichment analysis revealed that C0 primarily expressed genes involved in extracellular matrix interactions and C1 showed higher proliferation levels, suggesting that the two cell subpopulations exhibited tumour aggressiveness. Conversely, C2 and C3 displayed features associated with keratinization and cornified envelope formation. Accordingly, C0 and C1 subpopulations were associated with shorter overall and disease-free survival times, while C2 and C3 were weakly correlated with longer survival. Genomic analysis showed that C1 demonstrated a positive correlation with tumour mutation burden. Furthermore, C0 exhibited resistant to cisplatin treatment, while C1 showed more sensitive to cisplatin treatment, indicating that C0 might exhibit more aggressive compared to C1. Additionally, C0 had a higher level of communication with fibroblasts and endothelial cells in TME via integrin-MAPK signalling, suggesting that the function of C0 was maintained by that pathway. In summary, this study provided critical insights into the molecular and cellular heterogeneity of OSCC, with potential implications for prognosis prediction and personalized therapeutic approaches.

Exploring the current landscape of single-cell RNA sequencing applications in gastric cancer research.

Gastric cancer (GC) represents a major global health burden and is responsible for a significant number of cancer-related fatalities. Its complex nature, characterized by heterogeneity and aggressive behaviour, poses considerable challenges for effective diagnosis and treatment. Single-cell RNA sequencing (scRNA-seq) has emerged as an important technique, offering unprecedented precision and depth in gene expression profiling at the cellular level. By facilitating the identification of distinct cell populations, rare cells and dynamic transcriptional changes within GC, scRNA-seq has yielded valuable insights into tumour progression and potential therapeutic targets. Moreover, this technology has significantly improved our comprehension of the tumour microenvironment (TME) and its intricate interplay with immune cells, thereby opening avenues for targeted therapeutic strategies. Nonetheless, certain obstacles, including tumour heterogeneity and technical limitations, persist in the field. Current endeavours are dedicated to refining protocols and computational tools to surmount these challenges. In this narrative review, we explore the significance of scRNA-seq in GC, emphasizing its advantages, challenges and potential applications in unravelling tumour heterogeneity and identifying promising therapeutic targets. Additionally, we discuss recent developments, ongoing efforts to overcome these challenges, and future prospects. Although further enhancements are required, scRNA-seq has already provided valuable insights into GC and holds promise for advancing biomedical research and clinical practice.