FAM170B-AS1 is usually expressed low in all organs except for testicular tissues. No study was performed to explore its role in breast cancer (BC). Contradictory results were reported about hsa-miR-1202 and hsa-miR-146a-5p in BC. The present study aimed to explore the involvement of FAM170B-AS1 in BC using bioinformatics predictive tools, followed by a practical validation besides exploring the impact of hsa-miR-1202 and hsa-miR-146a-5p in BC. This study enrolled 96 female patients with BC, 30 patients with benign breast diseases (BBD), and 25 control subjects. The expressions of circulating FAM170B-AS1, hsa-miR-1202, and hsa-miR-146a-5p were quantified using qRT-PCR. These ncRNAs' associations, predictive, and diagnostic roles in BC were statistically tested. The underlying miRNA/mRNA targets of FAM170B-AS1 in BC were bioinformatically predicted followed by confirmation based on the GEPIA and TCGA databases. The expression of FAM170B-AS1 was upregulated in sera of BC patients and hsa-miR-1202 was upregulated in sera of BBD and BC patients while that of hsa-miR-146a-5p was downregulated in BC. These FAM170B-AS1 was significantly associated with BC when compared to BBD. FAM170B-AS1 and hsa-miR-1202 were statistically associated with the BC's stage, grade, and LN metastasis. FAM170B-AS1 and hsa-miR-146a-5p gave the highest specificity and sensitivity for BC. KRAS and EGFR were predicted to be targeted by FAM170B-AS1 through interaction with hsa-miR-143-3p and hsa-miR-7-5p, respectively. Based on the TCGA database, cancer patients having mutations in FAM170B show good overall survival. The present study reported that for the first time, FAM170B-AS1 may be a potential risk factor, predictive, and diagnostic marker for BC. In addition, FAM170B-AS1 might be involved in BC by interacting with hsa-miR-143-3p/KRAS and hsa-miR-7-5p/EGFR through enhancement or repression that may present a new therapeutic option for BC.

GPX4 has attracted much attention as a key molecule of cell ferroptosis, but its role in cell apoptosis is rarely reported, and its role in apoptosis of thyroid cancer (TC) cell has not been reported. The analysis of TCGA database showed that both GPX4 and FKBP8 were highly expressed in TC tumor tissues; The expression of GPX4 and FKBP8 were positively correlated. The immunohistochemical analysis further confirmed that GPX4 and FKBP8 were highly expressed in TC tumor tissues. In addition, the high expression of GPX4 and FKBP8 were both significantly correlated with the poor prognosis of TC. Silencing GPX4 significantly inhibited the proliferation, induced apoptosis of TC cells, and reduced tumor growth in mice. The co-immunoprecipitation assay revealed a physical interaction between GPX4 and FKBP8 observed in the TC cells. Knockdown of FKBP8 significantly inhibited the proliferation and induced apoptosis of TC cells. Rescue experiments suggested that knockdown of FKBP8 could reverse the strengthens of cell proliferation and apoptosis and the higher expression of FKBP8 and Bcl-2 caused by overexpression of GPX4. Our results suggest that the GPX4/FKBP8/Bcl-2 axis promotes TC development by inhibiting TC cell apoptosis, which provides potential molecular targets for TC therapeutic strategies.

High grade epithelial ovarian carcinoma is an aggressive tumor. Treatment includes platinum therapy, however it recurs in most patients due to therapy resistance. In this project, we study the immunohistochemical (IHC) expression of five potential biomarkers/prognostic markers in high grade epithelial ovarian carcinoma: EGFR, HLA-G, CD70, c-MET, and NY-ESO1. A cohort of 274 patients is used. We compare the IHC expression with age, stage, ascites status, family history of cancer, disease free survival (DFS) and overall survival (OS). EGFR expression is significantly correlated with family history and worse OS. HLA-G is associated with worse OS. To confirm the results of EGFR and HLA-G, a second separated cohort of 248 patients is used. Positive EGFR expression again shows worse OS, while HLA-G expression has worse prognostic trend. CD70 has a worse OS trend. C-MET and NY-ESO1 do not have any clinical correlations. EGFR can potentially serve as target in future clinical immune therapy trials.

We aimed to analyze lncRNAs, miRNAs, and mRNA expression profiles of bladder cancer (BC) patients, thereby establishing a gene signature-based risk model for predicting prognosis of patients with BC. We downloaded the expression data of lncRNAs, miRNAs and mRNA from The Cancer Genome Atlas (TCGA) as training cohort including 19 healthy control samples and 401 BC samples. The differentially expressed RNAs (DERs) were screened using limma package, and the competing endogenous RNAs (ceRNA) regulatory network was constructed and visualized by the cytoscape. Candidate DERs were screened to construct the risk score model and nomogram for predicting the overall survival (OS) time and prognosis of BC patients. The prognostic value was verified using a validation cohort in GSE13507. Based on 13 selected. lncRNAs, miRNAs and mRNA screened using L1-penalized algorithm, BC patients were classified into two groups: high-risk group (including 201 patients ) and low risk group (including 200 patients). The high-risk group's OS time ( hazard ratio [HR], 2.160; 95% CI, 1.586 to 2.942; P= 5.678e-07) was poorer than that of low-risk groups' (HR, 1.675; 95% CI, 1.037 to 2.713; P= 3.393 e-02) in the training cohort. The area under curve (AUC) for training and validation datasets were 0.852. Younger patients (age ⩽ 60 years) had an improved OS than the patients with advanced age (age > 60 years) (HR 1.033, 95% CI 1.017 to 1.049; p= 2.544E-05). We built a predictive model based on the TCGA cohort by using nomograms, including clinicopathological factors such as age, recurrence rate, and prognostic score. The risk model based on 13 DERs patterns could well predict the prognosis for patients with BC.

Osteosarcoma (OS) is a relatively rare malignant bone tumor in teenagers; however, its molecular mechanisms are not yet understood comprehensively. The study aimed to use necroptosis-related genes (NRGs) and their relationships with immune-related genes to construct a prognostic signature for OS. TARGET-OS was used as the training dataset, and GSE 16091 and GSE 21257 were used as the validation datasets. Univariate regression, survival analysis, and Kaplan-Meier curves were used to screen for hub genes. The immune-related targets were screened using immune infiltration assays and immune checkpoints. The results were validated using nomogram and decision curve analyses (DCA). Using univariate Cox regression analysis, TNFRSF1A was screened from 14 NRGs as an OS prognostic signature. Functional enrichment was analyzed based on the median expression of TNFRSF1A. The prognosis of the TNFRSF1A low-expression group in the Kaplan-Meier curve was notably worse. Immunohistochemistry analysis showed that the number of activated T cells and tumor purity increased considerably. Furthermore, the immune checkpoint lymphocyte activation gene 3 (LAG-3) is a possible target for intervention. The nomogram accurately predicted 1-, 3-, and 5-year survival rates. DCA validated the model (C = 0.669). TNFRSF1A can be used to elucidate the potential relationship between the immune microenvironment and NRGs in OS pathogenesis.

This study explores a deep learning (DL) approach to predicting bone metastases in breast cancer (BC) patients using clinical information, such as the fat index, and features like Computed Tomography (CT) images. CT imaging data and clinical information were collected from 431 BC patients who underwent radical surgical resection at Harbin Medical University Cancer Hospital. The area of muscle and adipose tissue was obtained from CT images at the level of the eleventh thoracic vertebra. The corresponding histograms of oriented gradients (HOG) and local binary pattern (LBP) features were extracted from the CT images, and the network features were derived from the LBP and HOG features as well as the CT images through deep learning (DL). The combination of network features with clinical information was utilized to predict bone metastases in BC patients using the Gradient Boosting Decision Tree (GBDT) algorithm. Regularized Cox regression models were employed to identify independent prognostic factors for bone metastasis. The combination of clinical information and network features extracted from LBP features, HOG features, and CT images using a convolutional neural network (CNN) yielded the best performance, achieving an AUC of 0.922 (95% confidence interval [CI]: 0.843-0.964, P< 0.01). Regularized Cox regression results indicated that the subcutaneous fat index was an independent prognostic factor for bone metastasis in breast cancer (BC). Subcutaneous fat index could predict bone metastasis in BC patients. Deep learning multimodal algorithm demonstrates superior performance in assessing bone metastases in BC patients.

This article aims to investigate the clinical value of hemoglobin/red cell distribution width ratio (Hb/RDW), C-reactive protein/albumin ratio (CAR) and plateletcrit (PCT) combined with carcinoembryonic antigen (CEA) in colorectal cancer (CRC) auxiliary diagnosis. We retrospectively analyzed in 718 subjects (212 with CRC, 209 with benign colorectal lesions (BCL), 111 with other cancers, and 186 healthy controls). The CAR, PCT, and CEA in the CRC group were higher than those in the BCL, other cancers, and the healthy control group. However, Hb/RDW in the CRC group was lower than the other three groups. Moreover, there were significant differences in Hb/RDW and CEA among different T-N-M stages (all P< 0.05). Multivariate logistic regression showed that low level of Hb/RDW and high level of CAR, CEA, PCT were risk factors for CRC, and are correlated with CRC stage. Additionally, the area under the receiver operating characteristic curve (AUC) of Hb/RDW+CEA (AUC: 0.735), CAR+CEA (AUC: 0.748), PCT+CEA (AUC: 0.807) was larger than that of Hb/RDW (AUC: 0.503), CAR (AUC: 0.614), or PCT (AUC: 0.713) alone (all P< 0.001) in distinguishing CRC from BCL. Hb/RDW, CAR, PCT, and CEA are independent risk factors for CRC. Hb/RDW, CAR, and PCT combined with CEA have significant value for auxiliary differential diagnosis of CRC and BCL.

Esophageal adenocarcinoma (EAC) occurs following a series of histological changes through epithelial-mesenchymal transition (EMT). A variable expression of normal and aberrant genes in the tissue can contribute to the development of EAC through the activation or inhibition of critical molecular signaling pathways. Gene expression is regulated by various regulatory factors, including transcription factors and microRNAs (miRs). The exact profile of miRs associated with the pathogenesis of EAC is largely unknown, though some candidate miRNAs have been reported in the literature. To identify the unique miR profile associated with EAC, we compared normal esophageal tissue to EAC tissue using bulk RNA sequencing. RNA sequence data was verified using qPCR of 18 selected genes. Fourteen were confirmed as being upregulated, which include CDH11, PCOLCE, SULF1, GJA4, LUM, CDH6, GNA12, F2RL2, CTSZ, TYROBP, and KDELR3 as well as the downregulation of UGT1A1. We then conducted Ingenuity Pathway Analysis (IPA) to analyze for novel miR-gene relationships through Causal Network Analysis and Upstream Regulator Analysis. We identified 46 miRs that were aberrantly expressed in EAC compared to control tissues. In EAC tissues, seven miRs were associated with activated networks, while 39 miRs were associated with inhibited networks. The miR-gene relationships identified provide novel insights into potentially oncogenic molecular pathways and genes associated with carcinogenesis in esophageal tissue. Our results revealed a distinct miR profile associated with dysregulated genes. The miRs and genes identified in this study may be used in the future as biomarkers and serve as potential therapeutic targets in EAC.

Large community cohorts are useful for lung cancer research, allowing for the analysis of risk factors and development of predictive models. 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. 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. 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. This work represents a general strategy for high-throughput curation of multi-modal longitudinal cohorts at risk for lung cancer from routinely collected EHRs.

P-Element-induced wimpy testis (PIWI) proteins, when in combination with PIWI-interacting RNA (piRNA), are engaged in the epigenetic regulation of gene expression in germline cells. Different types of tumour cells have been found to exhibit abnormal expression of piRNA, PIWIL-mRNAs, and proteins. We aimed to determine the mRNA expression profiles of PIWIL1, PIWIL2, PIWIL3, & PIWIL4, in hepatocellular carcinoma patients, and to associate their expression patterns with clinicopathological features. The expression patterns of PIWIL1, PIWIL2, PIWIL3, PIWIL4 mRNA, was assessed via real-time quantitative polymerase chain reaction (RT-QPCR), on tissue and serum samples from HCC patients, their impact for diagnosis was evaluated by ROC curves, prognostic utility was determined, and In Silico analysis was conducted for predicted variant detection, association with HCC microRNAs and Network Analysis. Expression levels were significantly higher in both HCC tissue and serum samples than in their respective controls (p< 0.001). Additionally, the diagnostic performance was assessed, Risk determination was found to be statistically significant. PIWIL mRNAs are overexpressed in HCC tissue and serum samples, the expression patterns could be valuable molecular markers for HCC, due to their association with age, tumour grade and pattern. To the best of our knowledge, our study is the first to report the expression levels of all PIWIL mRNA and to suggest their remarkable values as diagnostic and prognostic biomarkers, in addition to their correlation to HCC development. Additionally, a therapeutic opportunity might be also suggested through in silico miRNA prediction for HCC and PIWIL genes through DDX4 and miR-124-3p.