This study attempted to build a prognostic riskscore model for pancreatic cancer (PC) patients based on vesicle-mediated transport protein-related genes (VMTGs). We initially conducted differential expression analysis and Cox regression analysis, followed by the construction of a riskscore model to classify PC patients into high-risk (HR) and low-risk (LR) groups. The GEO GSE62452 dataset further validated the model. Kaplan-Meier survival analysis was employed to analyze the survival rate of the HR group and LR group. Cox analysis confirmed the independent prognostic ability of the riskscore model. Additionally, we evaluated immune status in both HR and LR groups, utilizing data from the GDSC database to predict drug response among PC patients. We identified six PC-specific genes from 724 VMTGs. Survival analysis revealed that the survival rate of the HR group was lower than that of the LR group (P<0.05). Cox analysis confirmed that the prognostic riskscore model could independently predict the survival status of PC patients (P<0.001). Immunological analysis revealed that the ESTIMATE score, immune score, and stroma score of the HR group were considerably lower than those of the LR group, and the tumor purity score of the HR group was higher. The IC50 values of Gemcitabine, Irinotecan, Oxaliplatin, and Paclitaxel in the LR group were considerably lower than those in the HR group (P<0.001). In summary, the VMTG-based prognostic riskscore model could stratify PC risk and effectively predict the survival of PC patients.

Glioma, a type of brain tumor, poses significant challenges due to its heterogeneous nature and limited treatment options. Interferon-related genes (IRGs) have emerged as potential players in glioma pathogenesis, yet their expression patterns and clinical implications remain to be fully elucidated. We conducted a comprehensive analysis to investigate the expression patterns and functional enrichment of IRGs in glioma. This involved constructing protein-protein interaction networks, heatmap analysis, survival curve plotting, diagnostic and prognostic assessments, differential expression analysis across glioma subgroups, GSVA, immune infiltration analysis, and drug sensitivity analysis. Our analysis revealed distinct expression patterns and functional enrichment of IRGs in glioma. Notably, IFNW1 and IFNA21 were markedly downregulated in glioma tissues compared to normal tissues, and higher expression levels were associated with improved overall survival and disease-specific survival. Furthermore, these genes showed diagnostic capabilities in distinguishing glioma tissues from normal tissues and were significantly downregulated in higher-grade and more aggressive gliomas. Differential expression analysis across glioma subgroups highlighted the association of IFNW1 and IFNA21 expression with key pathways and biological processes, including metabolic reprogramming and immune regulation. Immune infiltration analysis revealed their influence on immune cell composition in the tumor microenvironment. Additionally, elevated expression levels were associated with increased resistance to chemotherapeutic agents. Our findings underscore the potential of IFNW1 and IFNA21 as diagnostic biomarkers and prognostic indicators in glioma. Their roles in modulating glioma progression, immune response, and drug sensitivity highlight their significance as potential therapeutic targets. These results contribute to a deeper understanding of glioma biology and may inform the development of personalized treatment strategies for glioma patients.

Paget\'s \"Seed and Soil\" theory, proposed in 1889, emphasizes the importance of the microenvironment where cancer cells grow in metastatic sites. Over a century later, this concept remains a cornerstone in comprehending cancer biology and devising treatment strategies. The \"Seed and Soil\" theory, which initially explained how cancer spreads to distant organs, now also applies to the tumor microenvironment (TME) within primary tumors. This theory emphasizes the critical interaction between cancer cells (\"seeds\") and their surrounding environment (\"soil\") and how this interaction affects both tumor progression within the primary site and at metastatic sites. An important point to note is that the characteristics of the TME are not static but dynamic, undergoing substantial changes during tumor progression and after treatment with therapeutic drugs. Cancer-associated fibroblasts (CAFs), recognized as the principal noncancerous cellular component within the TME, play multifaceted roles in tumor progression including promoting angiogenesis, remodeling the extracellular matrix, and regulating immune responses. In this comprehensive review, we focus on the findings regarding how the dynamics of CAFs contribute to cancer progression and drug sensitivity. Understanding the dynamics of CAFs could provide new insights into cancer pathology and lead to important advancements in cancer research and treatment.

UNASSIGNED: Multiple myeloma (MM) exhibits considerable heterogeneity in treatment responses and survival rates, even when standardized care is administered. Ongoing efforts are focused on developing prognostic models to predict these outcomes more accurately. Recently, neutrophil extracellular traps (NETs) have emerged as a potential factor in MM progression, sparking investigation into their role in prognostication.
UNASSIGNED: In this study, a multi-gene risk scoring model was constructed using the intersection of NTEs and differentially expressed genes (DEGs), applying the least absolute shrinkage and selection operator (LASSO) Cox regression model. A nomogram was established, and the prognostic model\'s effectiveness was determined via Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). The ESTIMATE algorithm and immune-related single-sample gene set enrichment analysis (ssGSEA) were employed to evaluate the level of immune infiltration. The sensitivity of chemotherapy drugs was assessed using the Genomics of Drug Sensitivity in Cancer (GDSC) database. Ultimately, the presence of the detected genes was confirmed through quantitative real-time polymerase chain reaction (qRT-PCR) analysis in MM cell specimens.
UNASSIGNED: 64 NETs-DEGs were yielded, and through univariate Cox regression and LASSO regression analysis, we constructed a risk score composed of six genes: CTSG, HSPE1, LDHA, MPO, PINK1, and VCAM1. MM patients in three independent datasets were classified into high- and low-risk groups according to the risk score. The overall survival (OS) of patients in the high-risk group was significantly reduced compared to the low-risk group. Furthermore, the risk score was an independent predictive factor for OS. In addition, interactions between the risk score, immune score, and immune cell infiltration were investigated. Further analysis indicated that patients in the high-risk group were more sensitive to a variety of chemotherapy and targeted drugs, including bortezomib. Moreover, the six genes provided insights into the progression of plasma cell disorders.
UNASSIGNED: This study offers novel insights into the roles of NETs in prognostic prediction, immune status, and drug sensitivity in MM, serving as a valuable supplement and enhancement to existing grading systems.

Background: Liver hepatocellular carcinoma (LIHC) is one of the leading causes of cancer-related death. The prognostic outcomes of advanced LIHC patients are poor. Hence, reliable prognostic biomarkers for LIHC are urgently needed. Methods: Data for vesicle-mediated transport-related genes (VMTRGs) were profiled from 338 LIHC and 50 normal tissue samples downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were performed to construct and optimize the prognostic risk model. Five GEO datasets were used to validate the risk model. The roles of the differentially expressed genes (DEGs) were investigated via Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses. Differences in immune cell infiltration between the high- and low-risk groups were evaluated using five algorithms. The \"pRRophetic\" was used to calculate the anticancer drug sensitivity of the two groups. Transwell and wound healing assays were performed to assess the role of GDP dissociation inhibitor 2 (GDI2) on LIHC cells. Results: A total of 166 prognosis-associated VMTRGs were identified, and VMTRGs-based risk model was constructed for the prognosis of LIHC patients. Four VMTRGs (GDI2, DYNC1LI1, KIF2C, and RAB32) constitute the principal components of the risk model associated with the clinical outcomes of LIHC. Tumor stage and risk score were extracted as the main prognostic indicators for LIHC patients. The VMTRGs-based risk model was significantly associated with immune responses and high expression of immune checkpoint molecules. High-risk patients were less sensitive to most chemotherapeutic drugs but benefited from immunotherapies. In vitro cellular assays revealed that GDI2 significantly promoted the growth and migration of LIHC cells. Conclusions: A VMTRGs-based risk model was constructed to predict the prognosis of LIHC patients effectively. This risk model was closely associated with the immune infiltration microenvironment. The four key VMTRGs are powerful prognostic biomarkers and therapeutic targets for LIHC.

Background: Cervical cancer (CC) is the fourth most common cancer among women worldwide. As part of the brisk cross-talk between the host and the tumor, prognosis can be affected through inflammatory responses or the tumor microenvironment. However, further exploration of the inflammatory response-related genes that have prognostic value, microenvironment infiltration, and chemotherapeutic therapies in CC is needed. Methods: The clinical data and mRNA expression profiles of CC patients were downloaded from a public database for this study. In the TCGA cohort, a multigene prognostic signature was constructed by least absolute shrinkage and selection operator (LASSO) and Cox analyses. CC patients from the GEO cohort were used for validation. K‒M analysis was used to compare overall survival (OS) between the high- and low-risk groups. Univariate and multivariate Cox analyses were applied to determine the independent predictors of OS. The immune cell infiltration and immune-related functional score were calculated by single-sample gene set enrichment analysis (GSEA). Immunohistochemistry was utilized to validate the protein expression of prognostic genes in CC tissues. Results: A genetic signature model associated with the inflammatory response was built by LASSO Cox regression analysis. Patients in the high-risk group had a significantly lower OS rate. The predictive ability of the prognostic genes was evaluated by means of receiver operating characteristic (ROC) curve analysis. The risk score was confirmed to be an independent predictor of OS by univariate and multivariate Cox analyses. The immune status differed between the high-risk and low-risk groups, and the cancer-related pathways were enriched in the high-risk group according to functional analysis. The risk score was significantly related to tumor stage and immune infiltration type. The expression levels of five prognostic genes (LCK, GCH1, TNFRSF9, ITGA5, and SLC7A1) were positively related to sensitivity to antitumor drugs. Additionally, the expression of prognostic genes was significantly different between CC tissues and myoma patient cervix (non-tumorous) tissues in the separate sample cohort. Conclusion: A model consisting of 5 inflammation-related genes can be used to predict prognosis and influence immune status in CC patients. Furthermore, the inhibition or enhancement of these genes may become a novel alternative therapy.

BACKGROUND: Vesicular transport (VT) has a complex relationship with tumor progression and immunity. But prognostic significance of VT in clear cell renal cell carcinoma (ccRCC) is unclear. Thus, we aimed to establish a prognostic model according to VT to predict overall survival of ccRCC patients.
METHODS: We used patient data from TCGA database and built a prognostic model with 13 VT-related genes (VTRGs) by differential expression analysis, LASSO regression, and univariate/multivariate Cox analysis. The model was validated internally and externally, and survival analysis and ROC curves depicted excellent predictive ability. Furthermore, higher modeled riskscores corresponded to more advanced tumor progression. To further understand the potential reasons for different prognoses in patients, we did enrichment analysis on differentially expressed genes identified by the model in risk groups. The expression levels and roles of SAA1 and KIF18B in ccRCC were verified by qRT-PCR and cell function experiments.
RESULTS: Humoral immune response and cAMP signaling pathway may be the biological processes and pathways leading to poor prognosis. Further analysis of immune microenvironment presented that ccRCC patients with poor prognoses had highly immune-infiltrated characteristics. We compared the drug response data of samples from different prognostic patients in the GDSC database and identified drug sensitivity differences associated with prognosis. Finally, we demonstrated that SAA1 and KIF18B could increase the proliferation, migration, and invasion ability of ccRCC cells using cellular experiments.
CONCLUSIONS: In summary, we further revealed the importance of VTRGs in ccRCC prognosis.

Aim: This study aimed to investigate the role of brain-derived neurotrophic factor (BDNF) in colon adenocarcinoma, specifically its impact on sensitivity to carboplatin. Methods: mRNA and clinical information of colon adenocarcinoma samples were obtained from TCGA database. Differential expression analysis, transcription factor prediction, gene set enrichment analysis were performed in silico. qRT-PCR, western blot, CCK-8 and CHIP assay were employed. Results: BDNF demonstrated high expression in colon adenocarcinoma. Silencing of BDNF enhanced carboplatin sensitivity, while exerting opposite effects on epithelial-mesenchymal transition (EMT). BDNF was enriched in Hedgehog (HH) signaling pathway. SALL4 was identified as an upstream regulator of BDNF. Upregulation of BDNF by SALL4 promoted EMT and inhibited carboplatin sensitivity. Conclusion: SALL4 promoted BDNF expression to facilitate the aggressive phenotypes of colon adenocarcinoma.
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OBJECTIVE: To investigate the prevalence, antimicrobial susceptibility, and the effects on pregnancy and neonatal outcomes of Group A Streptococcal (GAS) infections in the vagina of perinatal women.
METHODS: From June 2020 to October 2022, 270 perinatal pregnant women underwent vaginal swabs for GAS culture. The antibiotic sensitivity of the positive strains was assessed. Based on GAS detection results, the patients were divided into an observation group (GAS positive) and a control group (GAS negative). Clinical data from both groups were collected to compare the vaginal microecological changes. The adverse outcomes for pregnancy and infants in both groups were retrospectively analyzed. Univariate and multivariate analyses were used to identify the risk factors for adverse outcomes.
RESULTS: Among the 270 pregnant women, 30 tested positive for GAS and 240 tested negative, with a colonization rate of 11.1%. No resistance to penicillin, ampicillin, linezolid, vancomycin, or tigecycline was found among the GAS strains. The resistance rates to tetracycline and clindamycin were 73.3% and 70.0%, respectively. Higher vaginal pH (≥4.5), and increased incidences of bacterial vaginitis, aerobic vaginitis, and microecological imbalances were observed in the observation group compared to the control group (all P<0.05). The observation group also experienced more adverse pregnancy and infant outcomes, such as chorioamnionitis, postpartum infections, fetal distress, and neonatal pneumonia (all P<0.05). Univariate and multivariate analyses indicated that a vaginal pH≥4.5 and microecological imbalance were positively associated with poor maternal and infant outcomes in women with GAS infections (all P<0.05).
CONCLUSIONS: The study found no β-lactam resistant GAS strains. Additionally, a higher vaginal pH (≥4.5) and microecological imbalance were linked to an increased risk of adverse pregnancy and infant outcomes in women with GAS infections.

OBJECTIVE: Chronic suppurative otitis media (CSOM) is a chronic inflammation of the mucoperiosteal lining of the middle ear cleft, presenting with recurrent ear discharge through a tympanic membrane perforation. The present study aims to assess the spectrum of bacterial infection among CSOM cases and detect the isolated organism\'s antibiotic sensitivity pattern.
METHODS: The prospective hospital-based observational study was conducted from June 2021 to June 2022 and included 94 CSOM cases. An aural swab of the ear discharge was collected from each patient under aseptic precautions. The swab was utilized for Gram\'s staining and the aerobic bacterial pathogen culture. The organisms isolated were tested for antibiotic sensitivity using the Kirby-Bauer disc diffusion method.
RESULTS: The most affected age group was the second decade of life (27.7%, n=26), with a male-to-female ratio of 1.35:1. The mean duration of ear discharge was 24.0±14.7 months, mostly mucoid ear discharge (39.4%, n=37). Among gram-positive bacteria, methicillin-resistant Staphylococcus aureus was isolated in 16 (17.0%) cases. Pseudomonas aeruginosa was the most isolated gram-negative bacteria strain in 26 (27.7%) cases. Cotrimoxazole (67.7%, n=21) had the highest sensitivity towards gram-positive bacteria isolates. Amongst gram-negative bacteria, amikacin and ciprofloxacin were the most sensitive, with 78.0% (n=39) susceptibility.
CONCLUSIONS: Evaluating the spectrum of infecting organisms of CSOM and their antibiotic sensitivity may help initiate prompt treatment with the appropriate antibiotic regimen, thereby preventing future complications.