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.

Multicellular 3D bioprinted human gallbladder carcinoma forin vitromimicry of tumor microenvironment and intratumoral heterogeneity.

Gallbladder carcinoma (GBC) is a malignant hepatobiliary cancer characterized by an intricate tumor microenvironments (TME) and heterogeneity. The traditional GBC 2D culture models cannot faithfully recapitulate the characteristics of the TME. Three-dimensional (3D) bioprinting enables the establishment of high-throughput and high-fidelity multicellular GBC models. In this study, we designed a concentric cylindrical tetra-culture model to reconstitute the spatial distribution of cells in tumor tissue, with the inner portion containing GBC cells, and the outer ring containing a mixture of endothelial cells, fibroblasts, and macrophages. We confirmed the survival, proliferation, biomarker expression and gene expression profiles of GBC 3D tetra-culture models. Hematoxylin-eosin (HE) and immunofluorescence staining verified the morphology and robust expression of GBC/endothelial/fibroblast/macrophage biomarkers in GBC 3D tetra-culture models. Single-cell RNA sequencing revealed two distinct subtypes of GBC cells within the model, glandular epithelial and squamous epithelial cells, suggesting the mimicry of intratumoral heterogeneity. Comparative transcriptome profile analysis among variousin vitromodels revealed that cellular interactions and the TME in 3D tetra-culture models reshaped the biological processes of tumor cells to a more aggressive phenotype. GBC 3D tetra-culture models restored the characteristics of the TME as well as intratumoral heterogeneity. Therefore, this model is expected to have future applications in tumor biology research and antitumor drug development.

Co-evolution of vaginal microbiome and cervical cancer.

BACKGROUND: Exploration of adaptive evolutionary changes at the genetic level in vaginal microbial communities during different stages of cervical cancer remains limited. This study aimed to elucidate the mutational profile of the vaginal microbiota throughout the progression of cervical disease and subsequently establish diagnostic models. METHODS: This study utilized a metagenomic dataset consisting of 151 subjects classified into four categories: invasive cervical cancer (CC) (n = 42), cervical intraepithelial neoplasia (CIN) (n = 43), HPV-infected (HPVi) patients without cervical lesions (n = 34), and healthy controls (n = 32). The analysis focused on changes in microbiome abundance and extracted information on genetic variation. Consequently, comprehensive multimodal microbial signatures associated with CC, encompassing taxonomic alterations, mutation signatures, and enriched metabolic functional pathways, were identified. Diagnostic models for predicting CC were established considering gene characteristics based on single nucleotide variants (SNVs). RESULTS: In this study, we screened and analyzed the abundances of 18 key microbial strains during CC progression. Additionally, 71,6358 non-redundant mutations were identified, predominantly consisting of SNVs that were further annotated into 25,773 genes. Altered abundances of SNVs and mutation types were observed across the four groups. Specifically, there were 9847 SNVs in the HPV-infected group and 14,892 in the CC group. Furthermore, two distinct mutation signatures corresponding to the benign and malignant groups were identified. The enriched metabolic pathways showed limited similarity with only two overlapping pathways among the four groups. HPVi patients exhibited active nucleotide biosynthesis, whereas patients with CC demonstrated a significantly higher abundance of signaling and cellular-associated protein families. In contrast, healthy controls showed a distinct enrichment in sugar metabolism. Moreover, biomarkers based on microbial SNV abundance displayed stronger diagnostic capability (cc.AUC = 0.87) than the species-level biomarkers (cc.AUC = 0.78). Ultimately, the integration of multimodal biomarkers demonstrated optimal performance for accurately identifying different cervical statuses (cc.AUC = 0.86), with an acceptable performance (AUC = 0.79) in the external testing set. CONCLUSIONS: The vaginal microbiome exhibits specific SNV evolution in conjunction with the progression of CC, and serves as a specific biomarker for distinguishing between different statuses of cervical disease.

A solid-state pulse power sub-nanosecond SiC DSRD-based generator with high-voltage and high repetition frequency for pulse discharge water treatment.

Water treatment is one of the most important issues for all walks of life around the world. The unique advantages of the solid-state power electronic pulses in water treatment make it attractive and promising in practical applications. The output voltage, rising time, repetition rate, and peak power of output pulses have a significant impact on the effectiveness of water treatment. Especially in pulse electric field treatment and pulse discharge treatment, the pulse with fast rising time achieves the advantage of generating plasma without corona, which can avoid water heating effect and greatly improve the efficiency of the pulse generator. High repetition rate can significantly reduce the peak power requirement of the pulse in water treatment application, making the equipment smaller and improving the power density. Therefore, the study developed a high-voltage high frequency sub-nanosecond pulse power generator (PPG) system for wastewater treatment. It adopts SiC DSRD (Drift Step Recovery Diode) solid-state switches and realize modular design, which can achieve high performance and can be flexible expanded according to the requirements of water treatment capacity. Finally, an expandable high-voltage PPG for water treatment is built. The output parameters of the PPG include output pulse voltage range from 1 to 5.28 kV, rise time <600 ps (20%-90%), repetition up to 1 MHz. The experiment results of PPG application for pulse discharge water treatment is presented. The results indicate that the proposed generator achieves high-efficiency degradation of 4-Chlorophenol (4-CP), which is one of the most common chlorophenol compounds in wastewater. From experiment, the homemade system can degrade 450 mL waste water containing 500 mg/L 4-CP in 35 min, with a degradation rate of 98%. Thereby, the requirement for electric field intensity decreased. Through the further quantitative analysis, the impact of frequency, voltage, and electrode spacing on the degradation effect of 4-CP is confirmed.

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.

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.

Study on drug screening multicellular model for colorectal cancer constructed by three-dimensional bioprinting technology.

The existing in vitro models for antitumor drug screening have significant limitations. Many compounds that inhibit two-dimensional (2D) cultured cells do not exhibit the same pharmacological effects in vivo, thereby wasting human and material resources and time during drug development. Therefore, it is crucial to develop new models. Three-dimensional (3D) bioprinting technology has greater advantages in constructing human tissues than sandwich culture and organoid construction. We used 3D bioprinting technology to construct a 3D multicellular model of SW480 cells, tumor-associated macrophages, and endothelial cells. The biological activities of the model were evaluated by immunofluorescence, hematoxylin and eosin staining of frozen pathological sections, and transcriptome sequencing. Compared with 3D bioprinted single-cell model (3D printing-S), 3D bioprinted multicellular models (3D printing-M) showed significantly improved expression of tumor-related genes, including hub genes IL1B, FCGR2A, FCGR3A, CYBB, SPI1, CCL2, ITGAM, and ITGB2. Antitumor drug screening experiment showed that the IC50 values of 5-FU, oxaliplatin, and irinotecan in 3D printing-S group/2D culture group were 31.13 µM/12.79 µM, 26.79 µM/0.80 µM, and 16.73 µM/10.45 µM, respectively. Compared with the 3D printing-S group, 3D printing-M group was significantly more resistant to chemotherapy.

3D bioprinted liver tissue and disease models: Current advances and future perspectives.

Three-dimensional (3D) bioprinting is a promising technology for fabricating complex tissue constructs with biomimetic biological functions and stable mechanical properties. In this review, the characteristics of different bioprinting technologies and materials are compared, and development in strategies for bioprinting normal and diseased hepatic tissue are summarized. In particular, features of bioprinting and other bio-fabrication strategies, such as organoids and spheroids are compared to demonstrate the strengths and weaknesses of 3D printing technology. Directions and suggestions, such as vascularization and primary human hepatocyte culture, are provided for the future development of 3D bioprinting.

Characterization of immune features and immunotherapy response in subtypes of hepatocellular carcinoma based on mitophagy.

Mitophagy is suggested to be involved in tumor initiation and development; however, mitophagy heterogeneity in hepatocellular carcinoma (HCC) and its association with immune status and prognosis remain unclear. Differentially expressed genes (DEGs) were identified using expression profiles acquired from The Cancer Genome Atlas (TCGA). Mitophagy-related subtypes were identified using the ConsensusClusterPlus software. The differences in prognosis, clinical characteristics, and immune status, including immune cell infiltration, immune function, immune-checkpoint gene expression, and response to immunotherapy, were compared between subtypes. A mitophagy-related gene signature was constructed by applying least absolute shrinkage and selection operator regression to the TCGA cohort. The International Cancer Genome Consortium cohort and the cohort from Peking Union Medical College Hospital were utilized for validation. Carbonyl cyanide m-chlorophenylhydrazone was used to induce mitophagy in HCC cell lines to obtain our own mitophagy signature. Real-time polymerase chain reaction was used for the experimental validation of the expression of model genes. Two mitophagy-related subtypes with distinct prognoses, clinical characteristics, immune states, and biological function patterns were identified based on the mitophagy-related DEGs. The subtype that showed higher mitophagy-related DEG expression had worse survival outcomes, suppressed immune function, higher immune-checkpoint gene expression, and a better response to immunotherapy, indicating that this subpopulation in HCC may benefit from immune-checkpoint blockade therapy and other immunotherapies. A risk model consisting of nine mitophagy-related genes was constructed and its performance was confirmed in two validation cohorts. The risk score was an independent risk factor even when age, sex, and tumor stage were considered. Our study identified two distinct mitophagy subtypes and built a mitophagy signature, uncovering mitophagy heterogeneity in HCC and its association with immune status and prognosis. These findings shed light on the treatment of HCC, especially with immunotherapy.

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.

Controlling nutritional status score as a prognostic marker to predict overall survival in resected biliary tract cancers.

BACKGROUND: The aim of our study was to explore the prognostic significance of the preoperative controlling nutritional status (CONUT) score and establish a nomogram to predict overall survival (OS) and to achieve a more accurate prognostic risk stratification. METHODS: Clinicopathological records of 371 patients who underwent surgical resection for biliary tract cancers (BTC) from December 2002 to December 2017 were reviewed retrospectively. The associations of the CONUT score with clinicopathological factors and OS were evaluated. Univariate and multivariable Cox regression analysis were used to screen out independent predictors. A nomogram was developed and validated to estimate OS. RESULTS: The CONUT score was an independent predictor of OS [hazard ratio 1.478, 95% confidence interval (CI), 1.078-2.025, P=0.015]. And patients with a high CONUT score tended to have a poor prognosis with poor differentiation (P=0.011) of tumor cells and longer hospital stays (P=0.046). Besides the CONUT score, carbohydrate antigen 19-9, surgical method, and the American Joint Committee on Cancer (AJCC; 7th edition) TNM stage were contained in the final prognostic model. An OS nomogram was generated to visually predict 1-, 3-, and 5-year OS. The C-index was 0.714 (95% CI, 0.673-0.755) and 0.679 (95% CI, 0.616-0.742) in the development and validation cohort respectively. The nomogram provided superior discriminative power than the AJCC TNM staging system. The nomogram also demonstrated good risk stratification power in the entire cohort of BTC patients as well as for both BTC and surgical method subgroups. CONCLUSIONS: The nomogram based on the CONUT score can predict OS in patients with BTCs, and it performed better than the AJCC TNM staging system.

Three-dimensional bio-printing of primary human hepatocellular carcinoma for personalized medicine.

Hepatocellular carcinoma (HCC) is one of the most lethal tumors worldwide. This study aims to address the lack of faithful and available in vitro models for patient-specific drug screening for HCC. We recently established a novel modeling system using three-dimensional (3D) bioprinting technology and constructed hepatorganoids with HepaRG cells, which retain the liver function and prolong the survival of mice with liver failure after abdominal transplantation. Here we extend this modeling system to establish individualized model for hepatocellular carcinoma. HCC specimens were obtained from six patients after surgery. Primary HCC cells were isolated and mixed with gelatin and sodium alginate to form the bioink for printing. Patient-derived three-dimensional bio-printed HCC (3DP-HCC) models were successfully established afterward and grew well during long-term culture. These models retained the features of parental HCCs, including stable expression of the biomarker, stable maintenances of the genetic alterations and expression profiles. 3DP-HCC models are capable of displaying the results of drug screening intuitively and quantitatively. In conclusion, 3DP-HCC models are faithful in vitro models that are reliable in long-term culture and able to predict patient-specific drugs for personalized treatment.

Three-dimensional bioprinted hepatorganoids prolong survival of mice with liver failure.

OBJECTIVE: Shortage of organ donors, a critical challenge for treatment of end-stage organ failure, has motivated the development of alternative strategies to generate organs in vitro. Here, we aim to describe the hepatorganoids, which is a liver tissue model generated by three-dimensional (3D) bioprinting of HepaRG cells and investigate its liver functions in vitro and in vivo. DESIGN: 3D bioprinted hepatorganoids (3DP-HOs) were constructed using HepaRG cells and bioink, according to specific 3D printing procedures. Liver functions of 3DP-HOs were detected after 7 days of differentiation in vitro, which were later transplanted into Fah-deficient mice. The in vivo liver functions of 3DP-HOs were evaluated by survival time and liver damage of mice, human liver function markers and human-specific debrisoquine metabolite production. RESULTS: 3DP-HOs broadly acquired liver functions, such as ALBUMIN secretion, drug metabolism and glycogen storage after 7 days of differentiation. After transplantation into abdominal cavity of Fah-/-Rag2-/- mouse model of liver injury, 3DP-HOs further matured and displayed increased synthesis of liver-specific proteins. Particularly, the mice acquired human-specific drug metabolism activities. Functional vascular systems were also formed in transplanted 3DP-HOs, further enhancing the material transport and liver functions of 3DP-HOs. Most importantly, transplantation of 3DP-HOs significantly improved the survival of mice. CONCLUSIONS: Our results demonstrated a comprehensive proof of principle, which indicated that 3DP-HO model of liver tissues possessed in vivo hepatic functions and alleviated liver failure after transplantation, suggesting that 3D bioprinting could be used to generate human liver tissues as the alternative transplantation donors for treatment of liver diseases.