Translational cancer research最新文献

Background: Mucinous adenocarcinoma (MAC) is a peculiar histological subtype of colorectal cancer (CRC) with distinct medical, disease-related, and genetic characteristics. The prognosis of MAC is generally poorer less favorable compared to non-specific adenocarcinoma (AC), but the prognostic indicator of MAC is rare. Therefore, this study aims to identify potential biomarkers and construct a prognostic model to better predict patient outcomes in MAC.

Methods: We conducted differential genes expression investigation, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO)-Cox regression model using RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) to pinpoint hub genes. Then, the hub genes were used to construct a prognostic model for MAC. Kaplan-Meier survival, receiver operating characteristic (ROC), and Cox regression analysis were used to assess the prognostic utility of the model. The potential biological function of the hub gene was examined using gene set enrichment analysis (GSEA).

Results: Four hub genes, FAM174B, CREB3L1, SPDEF, and RAP1GAP, were identified between MAC and AC by differential genes expression analysis, WGCNA, and LASSO regression analysis. The prognostic signature model was constructed based on these four hub genes, which could divide MAC into low- and high-risk groups. The overall survival (OS) was notably lower in the high-risk group compared to the low-risk group (P=0.007). The area under the curves (AUCs) for 1-, 3-, and 5-year OS were 0.61 [95% confidence interval (CI): 0.73-0.49], 0.69 (95% CI: 0.76-0.63), and 0.77 (95% CI: 0.83-0.71), respectively. We also found that FAM174B expression was closely related to the OS of MAC (P=0.02). Further, the expression of FAM174B was positively correlated with MAC's Mucin type O-glycan biosynthesis. Finally, it was indicated that FAM174B was positively correlated with the critical molecules of mucus formation, MUC5AC (P=0.004, r=0.33), MUC5B (P<0.001, r=0.43), and MUC2 (P<0.001, r=0.39).

Conclusions: We have developed and validated a four-gene prognostic model to predict the survival of MAC. Additionally, we found that FAM174B might correlate with mucin production in MAC.

Background: The primary cause of mortality in patients with ovarian cancer (OC) is tumor metastasis. A comprehensive understanding of the mechanisms underlying metastasis in OC is essential for accurate prognosis prediction and the development of targeted therapeutic agents. Our findings indicate that alpha-2 Heremans Schmid glycoprotein (AHSG) is downregulated in OC exosomes. Consequently, the objective of this study was to identify novel prognostic markers and potential therapeutic targets for OC.

Methods: Exosomes derived from OC cells and patient ascites were purified and applied to OC cells to assess their migratory ability using wound-healing and transwell assays. AHSG expression was enhanced by overexpressing lentivirus, and the resulting exosomes were isolated and co-cultured with OC cells to verify their effect on the migration ability of OC.

Results: Exosomes in ovarian malignant ascites have been demonstrated to promote OC metastasis. However, our findings indicate that AHSG is down-regulated in OC tissues and ascites exosomes. Furthermore, overexpression of AHSG in OC cells has been shown to markedly decrease their migratory ability, as well as reduce the migratory ability of cancer cells after co-culture of its exosomes with cancer cells.

Conclusions: The low expression of AHSG in exosomes derived from OC tissues and ascites is associated with metastatic progression in OC patients. Additionally, cancer-derived AHSG can be transported to OC cells via exosomes, where it inhibits OC migration in vitro and in vivo by regulating the p53/FAK/Src signaling pathway. The present study demonstrated that AHSG, derived from cancer cells, exerts a negative regulatory effect on OC cell motility, migration, and metastasis. These findings suggest that AHSG is a potential candidate for OC treatment.

Background: Gastric cancer (GC) is a prevalent malignant tumor of the digestive system, characterized by a poor prognosis and high recurrence rate. Perineural invasion (PNI), the neoplastic infiltration of nerves, is a significant predictor of survival outcome in GC. Accurate preoperative identification of PNI could facilitate patient stratification and optimal preoperative treatment. We therefore established and validated a preoperative risk assessment model for GC patients with PNI.

Methods: We collected data from 1,195 patients who underwent surgical resection at our hospital between October 2020 and December 2023, with PNI confirmed by pathological examination. We gathered laboratory data, including blood cell count, blood type, coagulation index, biochemical indexes, and tumor markers. Eligible patients were randomly divided into a training set and a testing set at a ratio of 7:3. The important risk factors of PNI were evaluated by random forest package in RStudio. Receiver operating characteristic-area under the curve (ROC-AUC) analysis was used to evaluate the discriminatory ability of the factors for PNI. Univariate and multivariate logistic regression analyses were utilized to verity independent risk factors for patients with PNI, and the logistic regression model and nomogram were constructed based on the results. Calibration curve and decision curve analysis (DCA) were conducted to assess the predictive model. Finally, we verified the prediction equation model using the testing set.

Results: In the training set, 416 GC patients were pathologically diagnosed with PNI. The top 5 important risk factors for PNI were identified as carcinoembryonic antigen (CEA), fibrinogen-to-lymphocyte ratio (FLR), D-dimer, platelet-to-lymphocyte ratio (PLR), and carbohydrate antigen 19-9 (CA19-9), with optimal cut-off values of 3.89 ng/mL, 2.08, 0.24 mg/L, 122.37, and 14.85 U/mL, respectively. Multivariate logistic regression analysis confirmed that CEA, FLR, D-dimer, PLR, CA19-9, and CA72-4 as independent risk factors for PNI (P<0.05). We formulated the following predictive equation: Logit(P) = -1.211 + 0.695 × CEA + 0.546 × FLR + 0.686 × D-dimer + 0.653 × PLR + 0.515 × CA19-9 + 0.518 × CA72-4 (χ²=105.675, P<0.001). The model demonstrated an ROC-AUC value of 0.719 [95% confidence interval (CI): 0.681-0.757] in the training set, with a sensitivity of 68.51% and a specificity of 67.60%. The ROC-AUC value was 0.791 (95% CI: 0.750-0.831) in the testing set (sensitivity: 69.57%, specificity: 56.41%). Calibration curve and DCA confirmed that the model has good discrimination and accuracy.

Conclusions: We successfully established and validated a prediction model for GC patients with PNI based on hematological indicators, hoping that this model can provide an adjunctive tool for predicting PNI in clinical work.

Background: Leukocyte transendothelial migration-related genes (LTEMGs) play a crucial role in the immune response and have been extensively studied in various pathological conditions, including inflammation, infection, and cancer. In recent years, increasing attention has been given to understanding the biological mechanisms of LTEMGs in the context of tumor progression and metastasis. The potential function of LTEMGs in cancer progression remains unclear. The aim of this study is to systematically delineate the relationship between LTEMGs and tumor prognosis and immune microenvironment at the pan-cancer level, providing new biomarkers for personalized immunotherapy.

Methods: The gene alteration, messenger RNA (mRNA) expression, and prognostic value of LTEMGs in pan-cancer were evaluated using Bulk and single-cell RNA (scRNA) sequence data. The LTEMGs score was calculated by R package "GSVA". The association of LTEMGs score with tumor microenvironment and immunotherapy response were deeply explored.

Results: We assessed the mRNA expression of 114 LTEMGs across various cancers, finding significant upregulation in acute myeloid leukemia (LAML) and pancreatic adenocarcinoma (PAAD). Prognostic analysis indicated most LTEMGs were risk factors in low-grade glioma (LGG), PAAD, uveal melanoma (UVM), and LAML. The LTEMGs score, highest in kidney renal clear cell carcinoma (KIRC) and lowest in UVM, was higher in tumor tissues compared to normal tissues in several cancers. The score was a risk factor for overall survival (OS) in LGG, UVM, and others, but protective in KIRC and some others. LTEMGs score correlated positively with Kirsten rat sarcoma viral oncogene homolog (KRAS) signaling, apoptosis, and immune responses. It also correlated with immune and stromal scores, and immune-related pathways. Higher LTEMGs score was linked to greater immune cell infiltration and poorer immunotherapy outcomes. Single-cell analysis revealed higher LTEMGs score in endothelial and monocyte cells, consistent with reduced immunotherapy responsiveness.

Conclusions: Our results reveal that LTEMGs are closely associated with tumor microenvironment. Patients with high LTEMGs score might be resistant to immunotherapy.

Tumors and the ovarian corpus luteum have complex mechanisms in the growth microenvironment. Angiogenesis is especially important for demonstrating the molecular mechanism of dynamic cellular function in tumors and corpus luteum. Angiogenesis in tumors and corpus luteum seems to have a similar function, and Ral-interacting protein 76 (RLIP76) and vascular endothelial growth factor (VEGF) are expressed in the tissues of tumors and ovarian corpus luteum. RLIP76 is a potential factor with VEGF in the tumor and corpus luteum angiogenesis. RLIP76 regulates a small GTPase (R-Ras) in cell survival, spreading, and migration. VEGF activates angiogenic functions in tumor and endothelial cells. Hypoxia-inducible factor-1 (HIF-1) is important in tumor growth, tumor angiogenesis, and corpus luteum. VEGF and HIF-1 regulate the angiogenic function of RLIP76, and RLIP76 controls vascular growth in endothelial and tumor cells. RLIP76, R-Ras, VEGF, and HIF-1 may be useful in the research of corpus luteum and cancer therapy and the study of mechanisms of tumor and corpus luteum angiogenesis. This review will help to elucidate the roles of RLIP76 and VEGF in tumor and corpus luteum angiogenesis, tumorigenesis, and the specific regulation of RLIP76 and VEGF. Thus, we reviewed the potential role of RLIP76 and VEGF in the angiogenesis of the tumor and corpus luteum in the tumor and ovarian microenvironment.

Background: Gastric cancer (GC) has a high incidence and mortality rate with a poor prognosis, so it is crucial to search for new biomarkers. The role of NETosis, a newly identified type of programmed cell death, in GC and its underlying mechanisms have yet to be explored and still require thorough investigation. Our research seeks to enhance our comprehension of NETosis and may offer novel approaches for treating GC.

Methods: Utilizing The Cancer Genome Atlas-stomach adenocarcinoma (TCGA-STAD) dataset for training and the GSE84433 dataset for validation, our study delved into the associations between NETosis-related genes and the clinical risk of GC. Through comprehensive clustering, enrichment, and immune infiltration analyses, we evaluated the prognostic relevance of these NETosis genes in vivo. Furthermore, we devised a NETosis-related risk signature (NRRS) to assess its implications in risk stratification, survival prognosis, immune infiltration, and drug sensitivity. The NRRS's accuracy was validated by immunohistochemical staining.

Results: By applying consensus clustering to data from 62 NETosis-related genes, we categorized patients into two distinct subgroups, C1 and C2. These subgroups demonstrated significant differences. Following this, we developed the NRRS using the least absolute shrinkage and selection operator (LASSO) regression analysis. This process involved the selection of the following genes: CXCR4, NRP1, PDCD1, CTLA4, AKR1B1, SERPINE1, RGS2, SLCO2A1, TNFAIP2, RNASE1, DOC2B, APOD, ENTPD2, and CCL24. High-risk and low-risk groups can be accurately distinguished. We further verify in the verification set. These results suggest that NETosis is related to the microenvironment of GC. Our designed NRRS can predict the survival of patients with GC.

Conclusions: We emphasized the relationship between NETosis and GC. We built and validated the value of NRRS. This contributes to deepening our view of NETosis and potentially provides new strategies for GC treatment.

Background: Capicua transcriptional repressor (CIC)-rearranged sarcoma (CRS) is a rare and highly aggressive undifferentiated small round cell sarcoma (USRCS), which genetically displays a characteristic gene fusion between CIC gene with other genes such as DUX4.

Case description: We report a rare case with CIC-LEUTX fusion. The 45-year-old male patient presented to our department with frequent dry cough and lumbar pain. Computed tomography (CT) scan indicated multiple pulmonary nodules on both sides, hilar lymph node enlargement, left lower lobe infection and presence of pleural effusion in left quadrant. Enlargement and uneven density were seen in the left kidney, together with perinephric exudate, renal calculi, thickening and filling deficiency in renal veins, and small retroperitoneal lymph nodes. Immunohistochemistry (IHC) showed negativity in CK7, CK20, CD10, vimentin, PAX-8, P63, MelanA and synapatophysin, but GATA3 and CK were positive. RNA-based next generation sequencing (NGS) detection revealed CRS of gene fusion in CIC (exon 20) and LEUTX (exon 3). After treatment with a variety of targeted and chemotherapy drugs, the patient showed a poor response with a survival time of merely 7 months.

Conclusions: This case of USRCS harboring CIC-LEUTX fusion with renal involvement could help to expand our understanding on the diagnosis and treatment of CRS harboring CIC-LEUTX fusion.

Background: Patients diagnosed with hepatocellular carcinoma (HCC) generally have an unfavorable outlook, with lung metastasis being a prevalent factor contributing to mortality. The metastatic microenvironment is critical to the tumor metastatic process. The exact impact of Triggering Receptor Expressed on Myeloid Cells 1 (TREM1) on tumor metastasis and the microenvironment of metastasis is still not known. By analyzing online databases and a clinical cohort, we evaluated the predictive significance of TREM1 and its correlation with the tumor microenvironment (TME).

Methods: Using the Gene Expression Omnibus (GEO) dataset (GSE141016), genes differentially expressed in liver cancer and lung metastases were analyzed. Data from liver hepatocellular carcinoma (LIHC) of The Cancer Genome Atlas (TCGA) were acquired through RNA sequencing. The abundance of tumor-infiltrating immune cells was estimated using Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE). The single sample gene set enrichment analysis (ssGSEA) algorithm was utilized to determine the association between TREM1 and immune cells. The level of TREM1 and immune cells were determined in formalin-fixed paraffin-embedding (FFPE) specimens.

Results: Increased expression of TREM1 in HCC was linked to a poorer clinical prognosis and elevated incidence of lung metastasis. Furthermore, TREM1 was found to be associated with multiple immune cells in the TME. We noticed that lung metastases in the same patient had higher levels of TREM1 protein compared to primary liver cancer. Additionally, lung metastases exhibited increased neutrophil numbers and neutrophil extracellular traps (NETs) formation compared to primary liver cancer. Moreover, there was a positive correlation between TREM1 and both neutrophils and NETs.

Conclusions: Increased expression of TREM1 in HCC is linked to a poorer clinical outlook and elevated incidence of lung metastasis, suggesting its potential as a prognostic biomarker for patients with liver cancer lung metastasis.

Background: Members of the S100 gene family are frequently dysregulated in various cancers, including ovarian cancer (OC). Despite this, the prognostic implications of individual S100 genes in OC remain poorly understood. This study aimed to explore the prognostic significance of S100A1 expression in OC and assess its potential as a therapeutic target.

Methods: To investigate the role of S100A1 in OC, we utilized the Gene Expression Profiling Interactive Analysis (GEPIA) database and the University of ALabama at Birmingham Cancer Data Analysis Portal (UALCAN) database. Protein levels of S100A1 in OC tissues were assessed using western blotting and immunohistochemistry. Bioinformatics analyses were performed to correlate S100A1 expression with clinical outcomes. Functional assays were conducted to evaluate the impact of S100A1 knockout on OC cell proliferation and migration. Additionally, we investigated the effect of S100A1 on ferroptosis and lipid reactive oxygen species (ROS) levels in tumor cells.

Results: Our analyses revealed that S100A1 protein levels were significantly elevated in OC tissues compared to normal tissues. Elevated S100A1 expression was associated with poor clinical outcomes in OC patients. Functional assays demonstrated that the knockout of S100A1 led to a decrease in both proliferation and migration of OC cells in vitro. Furthermore, S100A1 was found to inhibit ferroptosis in OC cells, resulting in lower levels of lipid ROS within tumor cells.

Conclusions: High levels of S100A1 are indicative of adverse clinical outcomes in OC. Our findings suggest that S100A1 could serve as a valuable prognostic marker and a potential therapeutic target for OC treatment.