Cancer Biomarkers最新文献

Background: Cervical cancer (CC) is the fourth most common malignancy. The significant prognostic factors are tumor size and lympho-vascular space invasion. Considering that these are nonspecific factors, research has been aimed at finding a specific molecular marker related to a higher incidence of relapse and mortality in patients with CC.

Objective: Our study investigated the prognostic value of L1 cell adhesion molecule (L1CAM) expression in rare histological subtypes of cervical cancer (adenocarcinomas and adenosquamous cell carcinomas).

Methods: This is a single-institution retrospective study with 35 patients who underwent radical hysterectomy for early-stage cervical adenocarcinoma or adenosquamous cell carcinoma in 2007 through 2017. Paraffin sections of the tumor were used for L1CAM analysis by immunohistochemistry.

Results: L1CAM expression was positive in 15 (42.8%) of the 35 tumors. L1CAM expression did not differ significantly in regard to the stage of disease, tumor size, grading, or lymphovascular space invasion (LVSI) (p = 0.619, p = 0.341, p = 0.445, p = 0.999). Progression-free interval and overall survival did not differ between L1CAM-positive and L1CAM-negative groups (p = 0.704, p = 0.386, respectively).

Conclusions: In our study, L1CAM expression is not a negative prognostic factor associated with aggressive tumor behavior, recurrence risk and overall survival.

Background: The tumor microenvironment (TME) is increasingly recognized as a key player in colorectal cancer biology, however, its potential for improving diagnosis, prognosis, and treatment remains unclear. The major aim of this study is to explore the prognostic value of TME related gene in colorectal cancer. Method: Expression matrices and clinical data of colorectal cancer obtained from public databases were divided into TME relevant clusters according to immune characterization. A 11-gene molecular classifier was constructed based on differentially expressed genes between TME clusters and machine learning regression processes. Results: The efficacy and effectiveness of TME based prognostic signature (TPS) were examined in both the training and validation groups. The result indicated that TPS was able to serve as a superior prognosis indicator for colorectal cancer, alone or jointly with other clinical factors. Also, the study demonstrated that high risk colorectal cancer defined by TPS was considered to link with elevated immune infiltration, increased tumor mutation, and worse overall prognosis. Finally, potential therapeutic agents specialized for different risk subgroups of TPS was also identified to improve personalized treatment for colorectal cancer in the future. Conclusions: TPS might be a novel tool to improve the prognosis judgement and personalized treatment of the colorectal cancer in the future.

Background: Endometrial cancer (EC) is the fourth most common gynecologic malignancy among women. Histopathologic examination is considered gold-standard for diagnosis of EC. However, these examinations sometimes not be useful in distinguishing early stage types of EC.

Objectives: The current study aimed to investigate the clinicopathological significance of Lipocalin-2 (LCN2), matrix metalloproteinase-9 (MMP9), and ferritin in tumor progression.

Methods: A total of 98 patients (55 women newly diagnosed with early-stage endometrial cancer [study group] and 43 women with benign endometrial pathologies [control group]) were enrolled.

Results: There was a significant difference between diagnosis (p < 0.001), surgical procedure (p < 0.001), pathology (p = 0.002), stage (p < 0.001), lymphovascular invasion (LVI) (p = 0.002), myometrial invasion (p < 0.001), and staining intensity (p < 0.001), MMP9 (p = 0.023), LCN2 (p < 0.001), glucocorticoid (GC) (p = 0.048), tumor necrosis factor-alpha (TNF-α) (p = 0.044), menopause duration (p = 0.001), body weight (p < 0.001), and body mass index (BMI) (p < 0.001) were found to be higher, and ferritin levels (p = 0.047) were lower in the endometrial adenocarcinoma group compared to the benign endometrial pathologies.

Conclusion: LCN2, MMP9, and ferritin are practical markers in early cases of endometrial cancer. Serum LCN2 and MMP9 levels may be good clinical tools for the auxiliary diagnosis of early-stage endometrial cancer. Ferritin was also significantly sensitive. Therefore, detecting these markers together may be more beneficial for cancer diagnosis.

Background: Pathological angiogenesis is crucial for tumor progression, thus targeting neovascularization is regarded as an effective strategy for cancer therapy. Vascular endothelial growth factor (VEGF), a specific pro-vascular endothelial regulator, contributes to aberrant tumor angiogenesis.

Objective: To identify sequence polymorphisms of VEGF gene and the effects on breast cancer.

Methods: Protein-DNA binding was validated by EMSA and ChIP assay. Gene expression levels were detected by qPCR and western blot. The CCK-8, wound healing and transwell assays were used to assess proliferation, migration, and invasion. Tube formation, CAM, ELISA and IHC assays were performed to evaluate tumor angiogenesis.

Results: A novel 18-bp indel mutation of the VEGF promoter was detected in breast cancer cases, and the deletion allele (DD) presented dominant distribution in patients comparing to the insert type (II). Further analysis revealed that the 18-bp deletion eliminated the recognition sites of GA binding protein alpha (GABPα), which was confirmed by binding experiments. Functionally, the GABPα expression is decreased in breast cancer tissues, and acts as a tumor suppressor to inhibit proliferation, migration, invasion and angiogenesis of breast cancer cells, accompanied by accelerated tumor cell apoptosis. In addition, consistent regulatory roles were investigated in mouse models in response to GABPα overexpression or knockdown as well. Mechanistically, we revealed that GABPα inhibited breast cancer progression and angiogenesis by downregulating VEGF transcription via the 18-bp promoter sequences.

Conclusions: Our findings provide insights into angiogenic targeted strategy aiming at GABPα-VEGF axis in clinical diagnosis and therapy of breast cancer.

RNA-binding protein (RBP) plays pivotal roles in the malignant progression of cancer by regulating gene expression. In this paper, we aimed to develop RBP-based prognostic signature and identify critical hub RBPs in bladder cancer (BLCA). Firstly, a risk model based on differentially expressed RBP gens (DERBPs) between normal and tumor tissues was successfully established, which can predict the tumor stromal score and drug sensitivity. Then two another RBP risk models based on miRNA-correlated RBPs or lncRNA-correlated RBPs were also established, and RBMS3 was identified as the overlapping gene in the three models. Data from multiple bioinformatics databases revealed that RBMS3 was an independent prognostic factor for overall survival (OS), and was associated with an immunosuppressive tumor microenvironment (TME) in BLCA. Further, Single-cell RNA-Seq (scRNA-Seq) data and the human protein altas (HPA) database showed that RBMS3 expression (both mRNA and protein) were up-regulated in BLCA tumor and tumor stromal cells. Finally, RBMS3 was shown to be associated with worse response to BLCA immunotherapy. Overall, RBMS3 is a key prognostic RBP with TME remodeling function and may serve as a target for BLCA immunotherapy.

Background: Histologic grading of lung adenocarcinoma (LUAD) is predictive of outcome but is only possible after surgical resection. A radiomic biomarker predictive of grade has the potential to improve preoperative management of early-stage LUAD.

Objective: Validate a prognostic radiomic score indicative of lung cancer aggression (SILA) in surgically resected stage I LUAD (n= 161) histologically graded as indolent low malignant potential (LMP), intermediate, or aggressive vascular invasive (VI) subtypes.

Methods: The SILA scores were generated from preoperative CT-scans using the previously validated Computer-Aided Nodule Assessment and Risk Yield (CANARY) software.

Results: Cox proportional regression showed significant association between the SILA and 7-year recurrence-free survival (RFS) in a univariate (p< 0.05) and multivariate (p< 0.05) model incorporating age, gender, smoking status, pack years, and extent of resection. The SILA was positively correlated with invasive size (spearman r= 0.54, p= 8.0 × 10 - 14) and negatively correlated with percentage of lepidic histology (spearman r=-0.46, p= 7.1 × 10 - 10). The SILA predicted indolent LMP with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.74 and aggressive VI with an AUC of 0.71, the latter remaining significant when invasive size was included as a covariate in a logistic regression model (p< 0.01).

Conclusions: The SILA scoring of preoperative CT scans was prognostic and predictive of resected pathologic grade.

Background: Continued improvement in deep learning methodologies has increased the rate at which deep neural networks are being evaluated for medical applications, including diagnosis of lung cancer. However, there has been limited exploration of the underlying radiological characteristics that the network relies on to identify lung cancer in computed tomography (CT) images.

Objective: In this study, we used a combination of image masking and saliency activation maps to systematically explore the contributions of both parenchymal and tumor regions in a CT image to the classification of indeterminate lung nodules.

Methods: We selected individuals from the National Lung Screening Trial (NLST) with solid pulmonary nodules 4-20 mm in diameter. Segmentation masks were used to generate three distinct datasets; 1) an Original Dataset containing the complete low-dose CT scans from the NLST, 2) a Parenchyma-Only Dataset in which the tumor regions were covered by a mask, and 3) a Tumor-Only Dataset in which only the tumor regions were included.

Results: The Original Dataset significantly outperformed the Parenchyma-Only Dataset and the Tumor-Only Dataset with an AUC of 80.80 ± 3.77% compared to 76.39 ± 3.16% and 78.11 ± 4.32%, respectively. Gradient-weighted class activation mapping (Grad-CAM) of the Original Dataset showed increased attention was being given to the nodule and the tumor-parenchyma boundary when nodules were classified as malignant. This pattern of attention remained unchanged in the case of the Parenchyma-Only Dataset. Nodule size and first-order statistical features of the nodules were significantly different with the average malignant and benign nodule maximum 3d diameter being 23 mm and 12 mm, respectively.

Conclusion: We conclude that network performance is linked to textural features of nodules such as kurtosis, entropy and intensity, as well as morphological features such as sphericity and diameter. Furthermore, textural features are more positively associated with malignancy than morphological features.