OBJECTIVE: To assess the prognostic value of three novel biomarkers, DNA ploidy, stroma-tumor fraction, and nucleotyping, seeking for more accurate stratification in stage II colon cancer.
METHODS: A total of 417 patients with complete follow up information were enrolled in this study and divided into three clinical risk groups. IHC was performed to examine MSI status. DNA ploidy, stroma and nucleotyping were estimated using automated digital imaging system. Kaplan-Meier survival curves, Cox proportional hazards regression models, and correlation analyses were carried out to process our data.
RESULTS: In the whole cohort of stage II colon cancer, nucleotyping and DNA ploidy were significant prognostic factors on OS in univariate analyses. The combination of nucleotyping and DNA ploidy signified superior OS and DFS. Difference was not significant between low-stroma and high-stroma patients. In multivariable analyses, nucleotyping and the combination of nucleotyping and DNA ploidy were proven the dominant contributory factors for OS. In the low-risk group, we found the combination of nucleotyping and DNA ploidy as the independent prognostic factor statistically significant in both univariate and multivariable, while in the high-risk group, the nucleotyping.
CONCLUSIONS: Our study has proven nucleotyping and the combination of DNA ploidy and nucleotyping as independent prognostic indicators, thus expanding the application of nucleotyping as a predictor from high risk stage II colon cancer to whole risks.

Since the advent of nanomedicine, physicians have harnessed these approaches for the prophylaxis, detection, and therapy of life-threatening diseases, particularly cancer. Nanoparticles have demonstrated notable efficacy in cancer therapy, showcasing the primary application of nanotechnology in targeted drug delivery. Pancreatic cancer stands out as the most lethal solid tumour in humans. The low survival rate is attributed to its highly aggressive nature, intrinsic resistance to chemotherapeutics, and the lack of successful therapies, compounded by delayed diagnosis due to nonspecific symptoms and the absence of rapid diagnostic strategies. Despite these challenges, nanotechnology-based carrier methods have been successfully employed in imaging and therapy approaches. Overcoming drug resistance in pancreatic cancer necessitates a comprehensive understanding of the microenvironment associated with the disease, paving the way for innovative nanocarriers. Hindered chemotherapy infiltration, attributed to inadequate vascularization and a dense tumour stroma, is a major hurdle that nanotechnology addresses. Intelligent delivery techniques, based on the Enhanced Permeability and Retention effect, form the basis of recently developed anticancer nanocarriers. These advancements aim to enhance drug accumulation in tumour locations, offering a potential solution to the treatment-resistant nature of cancer. Addressing the challenges in pancreatic cancer treatment demands innovative therapies, and the emergence of active nanocarriers presents a promising avenue for enhancing outcomes. This review specifically delves into the latest advancements in nanotechnology for the treatment of pancreatic cancer.

UNASSIGNED: E2F transcription factors are associated with tumor development, but their underlying mechanisms in gastric cancer (GC) remain unclear. This study explored whether E2Fs determine the prognosis or immune and therapy responses of GC patients.
UNASSIGNED: E2F regulation patterns from The Cancer Genome Atlas (TCGA) were systematically investigated and E2F patterns were correlated with the characteristics of cellular infiltration in the tumor microenvironment (TME). A principal component analysis was used to construct an E2F scoring model based on prognosis-related differential genes to quantify the E2F regulation of a single tumor. This scoring model was then tested in patient cohorts to predict effects of immunotherapy.
UNASSIGNED: Based on the expression profiles of E2F transcription factors in GC, two different regulatory patterns of E2F were identified. TME and survival differences emerged between the two clusters. Lower survival rates in the Cluster2 group were attributed to limited immune function due to stromal activation. The E2F scoring model was then constructed based on the E2F-related prognostic genes. Evidence supported the E2F score as an independent and effective prognostic factor and predictor of immunotherapy response. A gene-set analysis correlated E2F score with the characteristics of immune cell infiltration within the TME. The immunotherapy cohort database showed that patients with a higher E2F score demonstrated better survival and immune responses.
UNASSIGNED: This study found that differences in GC prognosis might be related to the E2F patterns in the TME. The E2F scoring system developed in this study has practical value as a predictor of survival and treatment response in GC patients.

Primary cilia are distributed extensively within the corneal epithelium and endothelium. However, the presence of cilia in the corneal stroma and the dynamic changes and roles of endothelial and stromal cilia in corneal homeostasis remain largely unknown. Here, we present compelling evidence for the presence of primary cilia in the corneal stroma, both in vivo and in vitro. We also demonstrate dynamic changes of both endothelial and stromal cilia during corneal development. In addition, our data show that cryoinjury triggers dramatic cilium formation in the corneal endothelium and stroma. Furthermore, depletion of cilia in mutant mice lacking intraflagellar transport protein 88 compromises the corneal endothelial capacity to establish the effective tissue barrier, leading to an upregulation of α-smooth muscle actin within the corneal stroma in response to cryoinjury. These observations underscore the essential involvement of corneal endothelial and stromal cilia in maintaining corneal homeostasis and provide an innovative strategy for the treatment of corneal injuries and diseases.

N6-methyladenosine (m6A) RNA methylation plays a pivotal role in immune responses and the onset and advancement of cancer. Nonetheless, the precise impact of m6A modification in lung adenocarcinoma (LUAD) and its associated tumor microenvironment (TME) remains to be fully elucidated. Here, we distinguished distinct m6A modification patterns within two separate LUAD cohorts using a set of 21 m6A regulators. The TME characteristics associated with these two patterns align with the immune-inflamed and immune-excluded phenotypes, respectively. We identified 2064 m6A-related genes, which were used as a basis to divide all LUAD samples into three distinct m6A gene clusters. We applied a scoring system to evaluate the m6A gene signature of the m6A modification pattern in individual patients. To authenticate the categorization significance of m6A modification patterns, we established a correlation between m6A score and TME infiltration profiling, tumor somatic mutations, and responses to immunotherapy. A high level of m6A modification may be associated with the aggressiveness and poor prognosis of LUAD. Further studies should investigate the mechanism of action of m6A regulators and m6A-related genes to improve the diagnosis and treatment of patients with LUAD.

For decades, most studies of ovarian aging have focused on its functional units, known as follicles, which include oocytes and granulosa cells. However, in the ovarian stroma, there are a variety of somatic components that bridge the gap between general aging and ovarian senescence. Physiologically, general cell types, microvascular structures, extracellular matrix, and intercellular molecules affect folliculogenesis and corpus luteum physiology alongside the ovarian cycle. As a result of damage caused by age-related metabolite accumulation and external insults, the microenvironment of stromal cells is progressively remodeled, thus inevitably perturbing ovarian physiology. With the established platforms for follicle cryopreservation and in vitro maturation and the development of organoid research, it is desirable to develop strategies to improve the microenvironment of the follicle by targeting the perifollicular environment. In this review, we summarize the role of stromal components in ovarian aging, describing their age-related alterations and associated effects. Moreover, we list some potential techniques that may mitigate ovarian aging based on their effect on the stromal microenvironment.

As the most lethal gynecologic cancer, ovarian cancer (OV) holds the potential of being immunotherapy-responsive. However, only modest therapeutic effects have been achieved by immunotherapies such as immune checkpoint blockade. This study aims to propose a generalized stroma-immune prognostic signature (SIPS) to identify OV patients who may benefit from immunotherapy.
The 2097 OV patients included in the study were significant with high-grade serous ovarian cancer in the III/IV stage. The 470 immune-related signatures were collected and analyzed by the Cox regression and Lasso algorithm to generalize a credible SIPS. Correlations between the SIPS signature and tumor microenvironment were further analyzed. The critical immunosuppressive role of stroma indicated by the SIPS was further validated by targeting the major suppressive stroma component (CAFs, Cancer-associated fibroblasts) in vitro and in vivo. With four machine-learning methods predicting tumor immune subtypes, the stroma-immune signature was upgraded to a 23-gene signature.
The SIPS effectively discriminated the high-risk individuals in the training and validating cohorts, where the high SIPS succeeded in predicting worse survival in several immunotherapy cohorts. The SIPS signature was positively correlated with stroma components, especially CAFs and immunosuppressive cells in the tumor microenvironment, indicating the critical suppressive stroma-immune network. The combination of CAFs\' marker PDGFRB inhibitors and frontline PARP inhibitors substantially inhibited tumor growth and promoted the survival of OV-bearing mice. The stroma-immune signature was upgraded to a 23-gene signature to improve clinical utility. Several drug types that suppress stroma-immune signatures, such as EGFR inhibitors, could be candidates for potential immunotherapeutic combinations in ovarian cancer.
The stroma-immune signature could efficiently predict the immunotherapeutic sensitivity of OV patients. Immunotherapy and auxiliary drugs targeting stroma could enhance immunotherapeutic efficacy in ovarian cancer.

UNASSIGNED: To automatically quantify colorectal tumor microenvironment (TME) in hematoxylin and eosin stained whole slide images (WSIs), and to develop a TME signature for prognostic prediction in colorectal cancer (CRC).
UNASSIGNED: A deep learning model based on VGG19 architecture and transfer learning strategy was trained to recognize nine different tissue types in whole slide images of patients with CRC. Seven of the nine tissue types were defined as TME components besides background and debris. Then 13 TME features were calculated based on the areas of TME components. A total of 562 patients with gene expression data, survival information and WSIs were collected from The Cancer Genome Atlas project for further analysis. A TME signature for prognostic prediction was developed and validated using Cox regression method. A prognostic prediction model combined the TME signature and clinical variables was also established. At last, gene-set enrichment analysis was performed to identify the significant TME signature associated pathways by querying Gene Ontology database and Kyoto Encyclopedia of Genes and Genomes database.
UNASSIGNED: The deep learning model achieved an accuracy of 94.2% for tissue type recognition. The developed TME signature was found significantly associated to progression-free survival. The clinical combined model achieved a concordance index of 0.714. Gene-set enrichment analysis revealed the TME signature associated genes were enriched in neuroactive ligand-receptor interaction pathway.
UNASSIGNED: The TME signature was proved to be a prognostic factor and the associated biologic pathways would be beneficial to a better understanding of TME in CRC patients.

UNASSIGNED: The aim of the present study was to observe the effect of the stroma proportion in hyperplasia nodules on the clinical symptoms of benign prostatic hyperplasia (BPH) patients and to identify the different genes and pathways in prostatic hyperplasia nodules between patients with epithelial-dominated hyperplasia (EDH) and stromal-dominated hyperplasia (SDH) nodules.
UNASSIGNED: Sixty-seven BPH patient samples underwent transurethral resection of the prostate (TURP) were collected and retrospectively analyzed. The differences in clinical parameters between the EDH and SDH groups were investigated. Collagen fiber percentage was assessed, and the correlation with clinical parameters was evaluated. mRNA sequencing in hyperplasia nodules of 8 BPH patients was performed, and differentially expressed genes (DEGs) between the EDH and SDH groups were screened. These DEGs were analyzed using GO, KEGG and PPI analysis.
UNASSIGNED: The results showed the IPSS was significantly higher in the SDH group than in the EDH group (p < 0.01). The collagen fiber percentage of BPH nodules was higher in the SDH group than in the EDH group (p < 0.05), and the collagen fiber percentage was positively correlated with the IPSS (r = 0.4058, p = 0.0007). A total of 172 DEGs were obtained, including 63 up-regulated genes and 109 down-regulated genes. GO and KEGG pathway enrichment analyses showed DEGs were mainly enriched in extracellular matrix structural constituents. The top 10 hub genes were associated to the components of extracellular matrix and fibrosis.
UNASSIGNED: These results suggested that the symptoms of BPH patients with SDH nodules may be associated with prostate fibrosis and fibrosis may be a significant contributing factor in BPH/LUTS patients with SDH nodules.

UNASSIGNED: Doppler ultrasonography is used to study ovarian vascular characteristics. However, the outcomes are reported with a considerable variability in literature. Here we review the differences in Doppler ultrasound-measured ovarian blood flow indices between women with and without ovarian dysfunction and seeks correlations between Doppler measures and ovarian markers.
UNASSIGNED: A literature search was conducted in electronic databases (Google Scholar, Ovid, PubMed, Science Direct, and Springer) to identify studies that used Doppler for ovarian blood flow examination and reported Doppler measures in women with and without ovarian dysfunction and/or the correlations between wDoppler indices and markers of ovarian dysfunction. After quality assessment of included studies, a meta-analysis of weighted mean differences (WMDs) between women with and without ovarian dysfunction in vascularization index (VI), flow index (FI), vascularization flow index (VFI), pulsatility index (PI) and resistance index (RI) was performed. Correlation coefficients between Doppler indices and markers of ovarian dysfunction were pooled to achieve overall estimates.
UNASSIGNED: A total of 27 studies [2,377 women with ovarian dysfunction and 308 controls; age 27.7 years, 95% confidence interval (CI): 26.4 to 29.1] were included. These studies were of moderate quality. The VI (WMD 9.75; P<0.0001), FI (WMD 2.73; P<0.0001), and VFI (WMD 1.29; P<0.0001) were significantly higher whereas PI (WMD -1.08; P=0.001) and RI (WMD -0.26; P<0.0001) were significantly lower in women with polycystic ovarian syndrome (PCOS) than in normal women. In women undergoing in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI), antral follicle count was positively correlated with VI (r=0.24; P=0.001), FI (r=0.42; P<0.0001), and VFI (r=0.25; P=0.002). In women with PCOS, testosterone had statistically non-significant correlations with VI (r=0.40; P=0.081), and VFI (r=0.39; P=0.063) and was inversely correlated with PI (r=-0.30; P<0.0001) and RI (r=-0.48; P<0.0001). In women with PCOS, luteinizing hormone (LH) was inversely correlated with PI (r=-0.26; P=0.086) and RI (r=-0.25; P=0.007).
UNASSIGNED: Doppler indices are found significantly different in women with and without ovarian dysfunction and have significant correlations with markers of ovarian dysfunction. These results support the use of Doppler ultrasound to examine ovarian dysfunction. High statistical heterogeneity observed herein should be studies in future investigations.