Multimodality Imaging Evaluation of Nasal Rhabdomyosarcoma in Adults: A Case Report and Literature Review.

BACKGROUND: Alveolar rhabdomyosarcoma (ARMS) predominantly affects adolescents aged 10-15 years and is distinguished by its high aggressiveness and adverse prognosis compared with other sarcomas. It exhibits a pronounced tendency for lymphatic and hematogenous metastases at early stages. ARMS commonly manifests in the limbs and genitourinary system, with occurrences in the head and neck region being relatively uncommon. The role of CT, MRI, and 18F-FDG positron emission tomography combined with computed tomography (PET/CT) in the diagnostic process of ARMS is yet to be fully established. CASE REPORT: We report the case of a 49-year-old woman who presented with hematological nasal discharge for one month. CT imaging revealed a soft tissue mass in the left nasal cavity. MRI demonstrated a marginally hypo- to isointense signal on T1-weighted images, a hyperintense signal on T2-weighted images, and heterogeneous enhancement post-contrast. 18F-FDG PET/CT identified a hypermetabolic lesion located within the left nasal cavity. Surgical intervention entailed the excision of the left intranasal mass and the skull base lesion. Postoperative pathological analysis indicated ARMS. CONCLUSION: Sinus ARMS is notably malignant and associated with a dismal prognosis. Accurate diagnosis depends on histopathological and immunohistochemical evaluation, complemented by genetic analysis for specific chromosomal translocations and fusion genes. Imaging techniques, including CT, MRI, and PET/CT, are crucial for assessing lesion extent and metastasis, supporting disease diagnosis, informing treatment choices, facilitating surgical planning, and monitoring response to therapy.

miR-99b/let-7e/miR-125a cluster suppresses pancreatic cancer through regulation of NR6A1.

This experiment investigates how the miR-99b/let-7e/miR-125a cluster regulates the mechanism of NR6A1 involved in the invasive and metastatic effects of pancreatic cancer (PCa). Bioinformatics prediction and dual luciferase reporter gene assay were applied to verify the targeted relationship between miR-99b/let-7e/miR-125a and NR6A1. ASPC1 cells underwent transfection with lentiviruses to overexpress miR-99b/let-7e/miR-125a (individual or together) to explore functions of miR-99b/let-7e/miR-125a cluster governing NR6A1 in PCa. The detection of tumorigenesis was verified by tumor formation assay in nude mice in vivo, and mouse models of liver metastasis of PCa observed cell metastasis of PCa. MiR-99b/let-7e/miR-125a cluster was screened for differential expression in PCa. NR6A1 was confirmed as a target gene of the miR-99b/let-7e/miR-125a cluster. Findings demonstrated that overexpression of the miR-99b/let-7e/miR-125a cluster inhibited cell invasion, metastasis, proliferation, and tumorigenesis in PCa. Conversely, overexpressed NR6A1, a crucial gene in the miR-99b/let-7e/miR-125a cluster, promoted cell invasion, migration, and proliferation in PCa. Moreover, the overexpression of the miR-99b/let-7e/miR-125a cluster inhibited liver metastases and tumor formation. Thus, the study concludes that the miR-99b/let-7e/miR-125a cluster impedes the invasion and metastasis of PCa cells via targeting the NR6A1 gene.

Key Roles of p53 Signaling Pathway-Related Factors GADD45B and SERPINE1 in the Occurrence and Development of Gastric Cancer.

p53 can function as an independent and unfavorable prognosis biomarker in cancer patients. We tried to identify the key factors of the p53 signaling pathway involved in gastric cancer (GC) occurrence and development based on the genotype-tissue expression (GTEx) and the Cancer Genome Atlas (TCGA) screening. We downloaded gene expression data and clinical data of GC included in the GTEx and TCGA databases, followed by differential analysis. Then, the key factors in the p53 signaling pathway were identified, followed by an analysis of the correlation between key factors and the prognosis of GC patients. Human GC cell lines were selected for in vitro cell experiments to verify the effects of key prognostic factors on the proliferation, migration, invasion, and apoptosis of GC cells. We found 4,944 significantly differentially expressed genes (DEGs), of which 2,465 were upregulated and 2,479 downregulated in GC. Then, 27 DEGs were found to be involved in the p53 signaling pathway. GADD45B and SERPINE1 genes were prognostic high-risk genes. The regression coefficients of GADD45B and SERPINE1 were positive. GADD45B was poorly expressed, while SERPINE1 was highly expressed in GC tissues, highlighting their prognostic role in GC. The in vitro cell experiments confirmed that overexpression of GADD45B or silencing of SERPINE1 could inhibit the proliferation, migration, and invasion and augment the apoptosis of GC cells. Collectively, the p53 signaling pathway-related factors GADD45B and SERPINE1 may be key genes that participate in the development of GC.

Association between radiomics features of DCE-MRI and CD8+ and CD4+ TILs in advanced gastric cancer.

Objective: The aim of this investigation was to explore the correlation between the levels of tumor-infiltrating CD8+ and CD4+ T cells and the quantitative pharmacokinetic parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in patients with advanced gastric cancer. Methods: We retrospectively analyzed the data of 103 patients with histopathologically confirmed advanced gastric cancer (AGC). Three pharmacokinetic parameters, Kep, Ktrans, and Ve, and their radiomics characteristics were obtained by Omni Kinetics software. Immunohistochemical staining was used to determine CD4+ and CD8+ TILs. Statistical analysis was subsequently performed to assess the correlation between radiomics characteristics and CD4+ and CD8+ TIL density. Results: All patients included in this study were finally divided into either a CD8+ TILs low-density group (n = 51) (CD8+ TILs < 138) or a high-density group (n = 52) (CD8+ TILs ≥ 138), and a CD4+ TILs low-density group (n = 51) (CD4+ TILs < 87) or a high-density group (n = 52) (CD4+ TILs ≥ 87). ClusterShade and Skewness based on Kep and Skewness based on Ktrans both showed moderate negative correlation with CD8+ TIL levels (r = 0.630-0.349, p < 0.001), with ClusterShade based on Kep having the highest negative correlation (r = -0.630, p < 0.001). Inertia-based Kep showed a moderate positive correlation with the CD4+ TIL level (r = 0.549, p < 0.001), and the Correlation based on Kep showed a moderate negative correlation with the CD4+ TIL level, which also had the highest correlation coefficient (r = -0.616, p < 0.001). The diagnostic efficacy of the above features was assessed by ROC curves. For CD8+ TILs, ClusterShade of Kep had the highest mean area under the curve (AUC) (0.863). For CD4+ TILs, the Correlation of Kep had the highest mean AUC (0.856). Conclusion: The radiomics features of DCE-MRI are associated with the expression of tumor-infiltrating CD8+ and CD4+ T cells in AGC, which have the potential to noninvasively evaluate the expression of CD8+ and CD4+ TILs in AGC patients.

Prediction of CD3 T cells and CD8 T cells expression levels in non-small cell lung cancer based on radiomic features of CT images.

Background: In this work, radiomics characteristics based on CT scans were used to build a model for preoperative evaluation of CD3 and CD8 T cells expression levels in patients with non-small cell lung cancer (NSCLC). Methods: Two radiomics models for evaluating tumor-infiltrating CD3 and CD8 T cells were created and validated using computed tomography (CT) images and pathology information from NSCLC patients. From January 2020 to December 2021, 105 NSCLC patients with surgical and histological confirmation underwent this retrospective analysis. Immunohistochemistry (IHC) was used to determine CD3 and CD8 T cells expression, and all patients were classified into groups with high and low CD3 T cells expression and high and low CD8 T cells expression. The CT area of interest had 1316 radiomic characteristics that were retrieved. The minimal absolute shrinkage and selection operator (Lasso) technique was used to choose components from the IHC data, and two radiomics models based on CD3 and CD8 T cells abundance were created. Receiver operating characteristic (ROC), calibration curve, and decision curve analyses were used to examine the models' ability to discriminate and their clinical relevance (DCA). Results: A CD3 T cells radiomics model with 10 radiological characteristics and a CD8 T cells radiomics model with 6 radiological features that we created both demonstrated strong discrimination in the training and validation cohorts. The CD3 radiomics model has an area under the curve (AUC) of 0.943 (95% CI 0.886-1), sensitivities, specificities, and accuracy of 96%, 89%, and 93%, respectively, in the validation cohort. The AUC of the CD8 radiomics model was 0.837 (95% CI 0.745-0.930) in the validation cohort, with sensitivity, specificity, and accuracy values of 70%, 93%, and 80%, respectively. Patients with high levels of CD3 and CD8 expression had better radiographic results than patients with low levels of expression in both cohorts (p<0.05). Both radiomic models were therapeutically useful, as demonstrated by DCA. Conclusions: When making judgments on therapeutic immunotherapy, CT-based radiomic models can be utilized as a non-invasive way to evaluate the expression of tumor-infiltrating CD3 and CD8 T cells in NSCLC patients.

TIMP-2 as a predictive biomarker in 5-Fu-resistant colorectal cancer.

PURPOSE: This study aims to evaluate the value of tissue inhibitors of MMPs-2 (TIMP-2) to indicate 5-Fluorouracil (5-Fu) resistance status in colorectal cancer. METHODS: The 5-Fu resistance of colorectal cancer cell lines was detected using Cell-Counting Kit-8 (CCK-8) and calculated using IC50. Enzyme-linked immunosorbent assay (ELISA) and real time-quantitative polymerase chain reaction (RT-qPCR) were used to detect TIMP-2 expression level in the culture supernatant and serum. Twenty-two colorectal cancer patients' TIMP-2 levels and clinical characteristics were analyzed before and after chemotherapy. Additionally, the patient-derived xenograft (PDX) model of 5-Fu resistance was used to evaluate the feasibility of TIMP-2 as a predictive biomarker of 5-Fu resistance. RESULTS: Our experimental results display that TIMP-2 expression is elevated in colorectal cancer drug-resistant cell lines, and its expression level is closely related to 5-Fu resistance. Moreover, TIMP-2 in colorectal cancer patient serum undergoing 5-Fu-based chemotherapy could indicate their drug resistance status, and its efficacy is higher than CEA and CA19-9. Finally, PDX model animal experiments reveal that TIMP-2 can detect 5-Fu resistance in colorectal cancer earlier than tumor volume. CONCLUSION: TIMP-2 is a good indicator of 5-Fu resistance in colorectal cancer. Monitoring the serum TIMP-2 level can help the clinician identify 5-Fu resistance in colorectal cancer patients earlier during chemotherapy.

Integrated characterisation of cancer genes identifies key molecular biomarkers in stomach adenocarcinoma.

AIMS: Gastric cancer is one of the leading causes for cancer mortality. Recent studies have defined the landscape of genomic alterations of gastric cancer and their association with clinical outcomes. However, the pathogenesis of gastric cancer has not been completely characterised. METHODS: Driver genes were detected by five computational tools, MutSigCV, OncodriveCLUST, OncodriveFM, dendrix and edriver, using mutation data of stomach adenocarcinoma (STAD) from the cancer genome altas database, followed by an integrative investigation. RESULTS: TTN, TP53, LRP1B, CSMD3, OBSCN, ARID1A, FAT4, FLG, PCLO and CSMD1 were the 10 most frequently mutated genes. PIK3CD, NLRC3, FMNL1, TRAF3IP3 and CR1 were the top five hub genes of the blue coexpression module positively correlated with pathological tumour stage and lymph node stage (p values <0.05 for all cases). Hierarchical clustering analysis of copy number variations of driver genes revealed three subgroups of STAD patients, and cluster 2 tumours were significantly associated with lower lymph node stage, less number of positive lymph nodes and higher microsatellite instability and better overall survival than cluster 1 and cluster 3 tumours (p values <0.05 for all cases, Wilcoxon rank-sum test or log rank test). High expression in one or more of DNER, LHCGR, NLRP14, OR4N2, PSG6, TTC29 and ZNF568 genes was associated with increased mortality (p values <0.05 for all cases, log rank test). CONCLUSIONS: The driver genes shed insights into the tumourigenesis of gastric cancer and the genes DNER, LHCGR, NLRP14, OR4N2, PSG6, TTC29 and ZNF568 pave the way for developing prognostic biomarkers for the disease.