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: 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.

Background: The national lung screening trial (NLST) demonstrated a reduction in lung cancer mortality with lowdose CT (LDCT) compared to chest x-ray (CXR) screening. Overdiagnosis was high (79%) among bronchoalveolar carcinoma (BAC) currently replaced by adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and adenocarcinoma of low malignant potential (LMP) exhibiting 100% disease specific survival (DSS).

Objective: Compare the outcomes and proportions of BAC, AIS, MIA, and LMP among NLST screendetected stage IA NSCLC with overdiagnosis rate.

Methods: Whole slide images were reviewed by a thoracic pathologist from 174 of 409 NLST screen-detected stage IA LUAD. Overdiagnosis rates were calculated from follow-up cancer incidence rates.

Results: Most BAC were reclassified as AIS/MIA/LMP (20/35 = 57%). The 7-year DSS was 100% for AIS/MIA/LMP and 94% for BAC. Excluding AIS/MIA/LMP, BAC behaved similarly to NSCLC (7-year DSS: 86% vs. 83%, p= 0.85) The overdiagnosis rate of LDCT stage IA NSCLC was 16.6% at 11.3-years, matching the proportion of AIS/MIA/LMP (16.2%) but not AIS/MIA (3.5%) or BAC (22.8%).

Conclusions: AIS/MIA/LMP proportionally matches the overdiagnosis rate among stage IA NSCLC in the NLST, exhibiting 100% 7-year DSS. Biomarkers designed to recognize AIS/MIA/LMP preoperatively, would be useful to prevent overtreatment of indolent screen-detected cancers.

Background: Large community cohorts are useful for lung cancer research, allowing for the analysis of risk factors and development of predictive models.

Objective: A robust methodology for (1) identifying lung cancer and pulmonary nodules diagnoses as well as (2) associating multimodal longitudinal data with these events from electronic health record (EHRs) is needed to optimally curate cohorts at scale.

Methods: In this study, we leveraged (1) SNOMED concepts to develop ICD-based decision rules for building a cohort that captured lung cancer and pulmonary nodules and (2) clinical knowledge to define time windows for collecting longitudinal imaging and clinical concepts. We curated three cohorts with clinical data and repeated imaging for subjects with pulmonary nodules from our Vanderbilt University Medical Center.

Results: Our approach achieved an estimated sensitivity 0.930 (95% CI: [0.879, 0.969]), specificity of 0.996 (95% CI: [0.989, 1.00]), positive predictive value of 0.979 (95% CI: [0.959, 1.000]), and negative predictive value of 0.987 (95% CI: [0.976, 0.994]) for distinguishing lung cancer from subjects with SPNs.

Conclusion: This work represents a general strategy for high-throughput curation of multi-modal longitudinal cohorts at risk for lung cancer from routinely collected EHRs.

Background: Lung adenocarcinoma (LUAD) is a prevalent form of malignancy globally. Disulfidptosis is novel programmed cell death pathway based on disulfide proteins, may have a positive impact on the development of LUAD treatment strategies.

Objective: To investigate the impact of disulfidptosis-related genes (DRGs) on the prognosis of LUAD, developed a risk model to facilitate the diagnosis and prognostication of patients. We also explored ACTN4 (DRGs) as a new therapeutic biomarker for LUAD.

Methods: We investigated the expression patterns of DRGs in both LUAD and noncancerous tissues. To assess the prognostic value of the DRGs, we developed risk models through univariate Cox analysis and lasso regression. The expression and function of ACTN4 was evaluated by qRT-PCR, immunohistochemistry and in vitro experiments. The TIMER examined the association between ACTN4 expression and immune infiltration in LUAD.

Results: Ten differentially expressed DRGs were identified. And ACTN4 was identified as potential risk factors through univariate Cox regression analysis (P< 0.05). ACTN4 expression and riskscore were used to construct a risk model to predict overall survival in LUAD, and high-risk demonstrated a significantly higher mortality rate compared to the low-risk cohort. qRT-PCR and immunohistochemistry assays indicated ACTN4 was upregulated in LUAD, and the upregulation was associated with clinicopathologic features. In vitro experiments showed the knockdown of ACTN4 expression inhibited the proliferation in LUAD cells. The TIMER analysis demonstrated a correlation between the expression of ACTN4 and the infiltration of diverse immune cells. Elevated ACTN4 expression was associated with a reduction in memory B cell count. Additionally, the ACTN4 expression was associated with m6A modification genes.

Conclusions: Our study introduced a prognostic model based on DRGs, which could forecast the prognosis of patients with LUAD. The biomarker ACTN4 exhibits promise for the diagnosis and management of LUAD, given its correlation with tumor immune infiltration and m6A modification.