Tumor microbiota of renal cell carcinoma affects clinical prognosis by influencing the tumor immune microenvironment.

Despite reported influences of the intratumoral microbiome on cancer progression, its role in this subtype remains unclear. This study aimed to characterize the microbial landscape and signatures of kidney renal clear cell carcinoma using RNA-Seq data from The Cancer Genome Atlas. Following microbial decontamination, differential microbial analysis was conducted between tumorous and adjacent non-tumorous samples. Compared to non-tumorous samples, tumorous microbiota exhibited reduced α and ß diversity and distinct phylum-level communities. Differential microbial analysis between patients exhibiting long and short overall survival revealed ten significant differential microbial genera, with six genera correlating with a positive prognosis (Plasmodium, Babesia, Toxoplasma, Cytobacillus, Alicyclobacillus, Verrucomicrobium) and four with a negative prognosis (Colletotrichum, Leuconostoc, Gluconobacter, and Parabacteroides). Employing Cox regression analysis and support vector machines, a prognosis-related microbiome risk signature was developed, achieving an AUC of 0.809. Based on this risk signature, two microbiome-based subtypes were found to be significantly associated with distinct clinical prognoses and immune microenvironments. These findings were corroborated by significant correlations between prognostic-relevant microorganisms and 30 immune-related differentially expressed genes. Specifically, microbial genera associated with a negative prognosis were linked to a pro-tumor acute inflammatory immune response, whereas genera related to a positive prognosis were associated with an anti-tumor adaptive immune response. In conclusion, microbiome-based subtyping revealed correlations between tumor microbiome, clinical prognosis, and tumor microenvironment, indicating intratumoral microbiota as a promising prognostic biomarker for kidney renal clear cell carcinoma.

Developing Patient-Derived 3D-Bioprinting models of pancreatic cancer.

INTRODUCTION: Pancreatic cancer (PC) remains a challenging malignancy, and adjuvant chemotherapy is critical in improving patient survival post-surgery. However, the intrinsic heterogeneity of PC necessitates personalized treatment strategies, highlighting the need for reliable preclinical models. OBJECTIVES: This study aimed to develop novel patient-derived preclinical PC models using three-dimensional bioprinting (3DP) technology. METHODS: Patient-derived PC models were established using 3DP technology. Genomic and histological analyses were performed to characterize these models and compare them with corresponding patient tissues. Chemotherapeutic drug sensitivity tests were conducted on the PC 3DP models, and correlations with clinical outcomes were analyzed. RESULTS: The study successfully established PC 3DP models with a modeling success rate of 86.96%. These models preserved genomic and histological features consistent with patient tissues. Drug sensitivity testing revealed significant heterogeneity among PC 3DP models, mirroring clinical variability, and potential correlations with clinical outcomes. CONCLUSION: The PC 3DP models demonstrated their utility as reliable preclinical tools, retaining key genomic and histological characteristics. Importantly, drug sensitivity profiles in these models showed potential correlations with clinical outcomes, indicating their promise in customizing treatment strategies and predicting patient prognoses. Further validation with larger patient cohorts is warranted to confirm their potential clinical utility.

Intratumoral microbiota of pancreatic ductal adenocarcinoma impact patient prognosis by influencing tumor microenvironment.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is characterized by a highly metastatic potential and a heterogeneous tumor microenvironment. It exhibits limited sensitivity to conventional therapies, necessitating a deeper understanding of its pathogenesis. The role of the intratumoral microbiome in regulating cancer development in PDAC has been the subject of debate. Previous investigations into intra-tumor microbiomes have yielded uncertain results due to sample size limitations and insufficient decontamination procedures. Further research is imperative to elucidate the intricate relationship between intra-tumor microbiomes, the immune landscape of PDAC, and overall prognosis. RESULTS: Our findings revealed that the intratumor microbiota in PDAC tissue exhibited lower diversity and distinct communities compared to non-tumor tissues. The top microorganisms distinguishing between patients with long or short survival were used to construct the risk signature. We found that Stenotrophomonas is implicated in short survival of PDAC patients, while Neorickettia and Mediterraneibacter are correlated with long survival. This microbiome-based PDAC subtyping, grounded in prognosis-related signatures, exhibited significant correlations with distinct clinical prognoses and immune microenvironments. Microorganisms associated with negative prognoses were linked to pro-tumor immune activation, while those associated with positive prognoses were linked to anti-tumor immune response activation and a more favorable prognosis. CONCLUSIONS: Our PDAC subtyping approach, based on a microbiome-derived prognostic risk signature, unveiled compelling associations between the PDAC microbiota and disparities in both clinical prognosis and the tumor microenvironment. These findings suggest that microbiota may serve as a promising biomarker for predicting the prognosis of PDAC.

Prediction of Clinical Precision Chemotherapy by Patient-Derived 3D Bioprinting Models of Colorectal Cancer and Its Liver Metastases.

Methods accurately predicting the responses of colorectal cancer (CRC) and colorectal cancer liver metastasis (CRLM) to personalized chemotherapy remain limited due to tumor heterogeneity. This study introduces an innovative patient-derived CRC and CRLM tumor model for preclinical investigation, utilizing 3d-bioprinting (3DP) technology. Efficient construction of homogeneous in vitro 3D models of CRC/CRLM is achieved through the application of patient-derived primary tumor cells and 3D bioprinting with bioink. Genomic and histological analyses affirm that the CRC/CRLM 3DP tumor models effectively retain parental tumor biomarkers and mutation profiles. In vitro tests evaluating chemotherapeutic drug sensitivities reveal substantial tumor heterogeneity in chemotherapy responses within the 3DP CRC/CRLM models. Furthermore, a robust correlation is evident between the drug response in the CRLM 3DP model and the clinical outcomes of neoadjuvant chemotherapy. These findings imply a significant potential for the application of patient-derived 3DP cancer models in precision chemotherapy prediction and preclinical research for CRC/CRLM.

Advances in genetic abnormalities, epigenetic reprogramming, and immune landscape of intracranial germ cell tumors.

Intracranial germ cell tumors (IGCTs) are a rare subtype of central nervous system neoplasms that predominantly affect young individuals and exhibit a higher incidence in East Asia. IGCTs can be pathologically divided into two main categories: germinomas and non-germinomatous germ cell tumors (NGGCTs). Despite the scarcity of this disease, recent advancements in molecular biology techniques have facilitated the discovery of the inherent genetic and molecular characteristics of IGCTs. Somatic mutations that result in the activation of the KIT/RAS/MAPK and PI3K/AKT/mTOR pathways, chromosomal instability leading to characteristic changes in chromosomal fragments (notably 12p gain), and potentially diagnostic miRNAs (such as miR-371a-3p) may provide valuable insights for the efficient diagnosis, targeted therapy, and prognosis evaluation of IGCTs. Additionally, transcriptomic and methylomic analyses have provided new perspectives on the intrinsic development of IGCTs, further elucidating their equivalence with GCTs at other sites. The evaluation of the tumor immune landscape may guide prognosis prediction and immunotherapy for IGCT patients. Nevertheless, current research still faces challenges such as the absence of basic laboratory research systems, a single source of large sample research data, and a limited overall volume of research. The incorporation of larger sample sizes, the implementation of more innovative evaluation systems, and the employment of novel experimental methods are urgently required to become the focus of future research.

Interaction between intratumoral microbiota and tumor mediates the response of neoadjuvant therapy for rectal cancer.

Background: Previous observations have demonstrated that the response to neoadjuvant chemoradiotherapy (nCRT) is highly variable in patients with locally advanced rectal cancer (LARC). Recent studies focusing on the intratumoral microbiota of colorectal cancer have revealed its role in oncogenesis and tumor progression. However, limited research has focused on the influence of intratumoral microbiota on the nCRT of LARC. Methods: We explored the microbial profiles in the tumor microenvironment of LARC using RNA-seq data from a published European cohort. Microbial signatures were characterized in pathological complete response (pCR) and non-pCR groups. Multi-omics analysis was performed between intratumor microbiomes and transcriptomes. Results: Microbial α and ß diversity were significantly different in pCR and non-pCR groups. Twelve differential microbes were discovered between the pCR and non-pCR groups, six of which were related to subclusters of cancer-associated fibroblasts (CAFs) associated with extracellular matrix formation. A microbial risk score based on the relative abundance of seven differential microbes had predictive value for the nCRT response (AUC = 0.820, p < 0.001). Conclusion: Our study presents intratumoral microbes as potential independent predictive markers for the response of nCRT to LARC and demonstrates the underlying mechanism by which the interaction between intratumoral microbes and CAFs mediates the response to nCRT.

Intratumoural microbiome can predict the prognosis of hepatocellular carcinoma after surgery.

BACKGROUND: The dismal prognosis of hepatocellular carcinoma (HCC) is closely associated with characteristics of the tumour microenvironment (TME). Recent studies have confirmed the presence and potential influence of the microbiome in TME on cancer progression. Elucidating the relationship between microbes in the TME and cancer could provide valuable insights into novel diagnostic markers and therapeutic strategies for HCC and thus warrants a closer investigation of the role of intratumoural microbiome in the HCC TME. METHODS: We determined the presence of intratumoural microbiome using fluorescence in situ hybridisation, and explored the microbial community profiles in the HCC TME in paired tumour and adjacent normal tissues using 16S rDNA sequencing. Microbial signatures were characterised in the paired group, and their correlation with clinical characteristics was further investigated. We clustered the microbial signatures of tumour tissues by hepatotypes, and further analysis was performed to elucidate the independent prognostic value of the hepatotypes. RESULTS: This study revealed that microbial profiles and community networks differed notably between tumours and adjacent normal tissues. Proteobacteria and Actinobacteria were the most abundant phyla in the HCC TME. The TME microbial profiles also revealed heterogeneities between individuals and between multiple tumour lesions. Clustering of the microbial profiles into two hepatotypes revealed different microbial network patterns. Additionally, the hepatotypes were revealed to be independent prognostic factors in patients with resected HCC. CONCLUSIONS: Our study illuminates the microbial profiles in the TME of HCC and presents the hepatotype as a potential independent biomarker for the prognostic prediction of HCC after surgery.

Combining single-cell RNA sequencing of peripheral blood mononuclear cells and exosomal transcriptome to reveal the cellular and genetic profiles in COPD.

BACKGROUND: It has been a long-held consensus that immune reactions primarily mediate the pathology of chronic obstructive pulmonary disease (COPD), and that exosomes may participate in immune regulation in COPD. However, the relationship between exosomes and peripheral immune status in patients with COPD remains unclear. METHODS: In this study, we sequenced plasma exosomes and performed single-cell RNA sequencing on peripheral blood mononuclear cells (PBMCs) from patients with COPD and healthy controls. Finally, we constructed competing endogenous RNA (ceRNA) and protein-protein interaction (PPI) networks to delineate the interactions between PBMCs and exosomes within COPD. RESULTS: We identified 135 mRNAs, 132 lncRNAs, and 359 circRNAs from exosomes that were differentially expressed in six patients with COPD compared with four healthy controls. Functional enrichment analyses revealed that many of these differentially expressed RNAs were involved in immune responses including defending viral infection and cytokine-cytokine receptor interaction. We also identified 18 distinct cell clusters of PBMCs in one patient and one control by using an unsupervised cluster analysis called uniform manifold approximation and projection (UMAP). According to resultant cell identification, it was likely that the proportions of monocytes, dendritic cells, and natural killer cells increased in the COPD patient we tested, meanwhile the proportions of B cells, CD4 + T cells, and naïve CD8 + T cells declined. Notably, CD8 + T effector memory CD45RA + (Temra) cell and CD8 + effector memory T (Tem) cell levels were elevated in patient with COPD, which were marked by their lower capacity to differentiate due to their terminal differentiation state and lower reactive capacity to viral pathogens. CONCLUSIONS: We generated exosomal RNA profiling and single-cell transcriptomic profiling of PBMCs in COPD, described possible connection between impaired immune function and COPD development, and finally determined the possible role of exosomes in mediating local and systemic immune reactions.

Comprehensive analysis of coagulation indices for predicting survival in patients with biliary tract cancer.

BACKGROUND: Abnormal activation of the coagulation system has been reported in patients with malignancies, but its prognostic significance in biliary tract cancer (BTC) remains unclear. This study aims to analyze and compare the prognostic value of coagulation indices in patients with BTC. METHODS: The medical records of 450 patients with BTC who underwent surgical resection at our hospital between 2003 and 2017 were retrospectively analyzed. Time-dependent receiver operating characteristic curves were plotted to compare the predictive accuracy of coagulation indices. A predictive nomogram for overall survival (OS) was established based on the Cox regression analysis and validated in both the training and validation cohorts. A novel stratification model was created according to the total points of the nomogram. RESULTS: Fibrinogen and international normalized ratio (INR) had the best predictive accuracy among the coagulation indices considered and were also the independent prognostic factors for OS. The nomogram and the novel stratification model had satisfactory performance and outperformed TNM staging. CONCLUSIONS: The study demonstrated that coagulation indices are valuable in predicting OS in BTC, with fibrinogen and INR having the best predictive ability. The nomogram and the novel stratification model could be applied to predict survival for patients with BTC.

Prognostic Value of the Albumin-to-γ-glutamyltransferase Ratio for Gallbladder Cancer Patients and Establishing a Nomogram for Overall Survival.

Purpose: The albumin-to-γ-glutamyltransferase ratio (AGR), a novel inflammation-related index, has been reported to have prognostic importance in several malignancies but not yet in gallbladder cancer (GBC). This study intended to assess the prognostic value of AGR in GBC and to develop a nomogram based on AGR for predicting overall survival (OS) in GBC patients after surgery. Methods: Medical records of 140 qualified GBC patients between July 2003 and June 2017 were retrospectively analyzed. The function "surv_cutpoint" in the R package "survminer" was implemented to discover the optimal cut-off value of AGR. A nomogram on the fundamental of Cox model was established in the training cohort and was internally validated using calibration curves, Harrell's concordance index, time-dependent AUC plots and decisive curve analyses. Results: The optimal AGR cut-off value concerning overall survival was 2.050. Univariate and multivariate analyses demonstrated that AGR (HR=0.354, P=0.004), T stage (HR=3.114, P=0.004), R0 resection (HR=0.448, P=0.003), BMI (HR=0.470, P=0.002) and CA19-9 (HR=1.704, P=0.048) were independent predictors for OS. The nomogram combining these prognostic factors showed considerable prognostic performance in term of consistency, discrimination and net benefit. Conclusion: AGR has independent prognostic value for OS in GBC patients receiving surgery. A nomogram incorporating AGR, T stage, R0 resection, CA19-9 and BMI achieved enhanced prognostic ability.