Cancer Biomarkers

Despite the major advances in the treatment, the overall survival of osteosarcoma remains poor. MicroRNAs (miRNAs) are involved in tumorigenesis and progression though modulating their target genes. In the present study, the roles of miR-1285-3p in osteosarcoma was investigated.

Microarray profiling was applied to distinguish the up and down regulated microRNAs in osteosarcoma. Quantitative real-time PCR (qRT-PCR) assay was performed to detect the expression of miR-1285-3p and YAP1 expression. MTT and transwell assays were carried out to determine the cells proliferation and invasion respectively. Moreover, dual luciferase reporter assay was performed to evaluate the binding efficiency between miR-1285-3p and the 3’UTR of YAP1.

MiR-1285-3p was down regulated in osteosarcoma tissues and cell lines and the reduction of miR-1285-3p expression predicted a poor overall survival of osteosarcoma patients. Ectopic expression of miR-1285-3p inhibited osteosarcoma cell proliferation, colony formation and invasion. In addition, YAP1 was further demonstrated as a direct target of miR-1285-3p. Moreover, overexpression of YAP1 reversed the inhibitory effects of miR-1285-3p on osteosarcoma cells proliferation and invasion.

MiR-1285-3p which was low expressed in osteosarcoma inhibited the proliferation and invasion of osteosarcoma cells via direct targeting YAP1. These results suggested that miR-1285-3p might be a potential therapeutic targets and biomarker in osteosarcoma.

Breast cancer is one of the leading causes of death in women worldwide. Histopathology analysis of breast tissue is an essential tool for diagnosing and staging breast cancer. In recent years, there has been a significant increase in research exploring the use of deep-learning approaches for breast cancer detection from histopathology images.

To provide an overview of the current state-of-the-art technologies in automated breast cancer detection in histopathology images using deep learning techniques.

This review focuses on the use of deep learning algorithms for the detection and classification of breast cancer from histopathology images. We provide an overview of publicly available histopathology image datasets for breast cancer detection. We also highlight the strengths and weaknesses of these architectures and their performance on different histopathology image datasets. Finally, we discuss the challenges associated with using deep learning techniques for breast cancer detection, including the need for large and diverse datasets and the interpretability of deep learning models.

Deep learning techniques have shown great promise in accurately detecting and classifying breast cancer from histopathology images. Although the accuracy levels vary depending on the specific data set, image pre-processing techniques, and deep learning architecture used, these results highlight the potential of deep learning algorithms in improving the accuracy and efficiency of breast cancer detection from histopathology images.

This review has presented a thorough account of the current state-of-the-art techniques for detecting breast cancer using histopathology images. The integration of machine learning and deep learning algorithms has demonstrated promising results in accurately identifying breast cancer from histopathology images. The insights gathered from this review can act as a valuable reference for researchers in this field who are developing diagnostic strategies using histopathology images. Overall, the objective of this review is to spark interest among scholars in this complex field and acquaint them with cutting-edge technologies in breast cancer detection using histopathology images.

A lack of disease-specific symptoms and good tumour markers makes early detection and diagnosis of pancreatic ductal adenocarcinoma (PDAC) challenging.

To analyse the tissue expression and circulating levels of four stroma-derived substances (type IV collagen, endostatin/type XVIII collagen, osteopontin and tenascin C) and four conventional tumour markers (CA 19-9, TPS, CEA and Ca 125) in a PDAC cohort.

Tissue expression of markers in normal pancreas and PDAC tissue was analysed with immunofluorescence. Plasma concentrations of markers were measured before and after surgery. Patients with non-malignant disorders served as controls.

The conventional and stromal substances were expressed in the cancer cell compartment and the stroma, respectively. Although most patients had increased levels of many markers before surgery, 2/12 (17%) of patients had normal levels of Ca 19-9 at this stage. High preoperative endostatin/type XVIII collagen, and postoperative type IV collagen was associated with short survival. Neither the pre- nor postoperative levels of TPS, Ca 125 or CA 19-9 were associated to survival.

PDAC is characterized by an abundant stroma. These initial observations indicate that the stroma can be a source of PDAC tumour markers that are found in different compartments of the cancer, thus reflecting different aspects of tumour biology.

To investigate FOXO1 expression in epithelial ovarian cancer (EOC), and to explore its correlation with clinicopathological parameters and prognosis of EOC.

Two hundred and sixteen cases of paraffin-embedded EOC and 41 paratumor tissues from 2009 to 2017 that had been pathologically confirmed at the memorial hospital of Sun Yat-sen University were included in this study, and the expression of FOXO1 was performed by immunohistochemistry using a polyclonal antibody specific for FOXO1.

FOXO1 protein expression is associated with Recurrence free and overall survival in EOC patients; In addition, FOXO1 expression is associated with age, FIGO stage, intraperitoneal metastasis, intestinal metastasis, vital status, intraperitoneal recurrence and differentiation grade; Moreover, in a multivariate model FOXO1 overexpression was an independent predictor of poor survival in EOC.

FOXO1 may play a candidate oncogenic role in EOC, and FOXO1 is a useful independent prognostic marker in EOC, and it may provide a candidate target therapy in future.

The prognosis of patients with recurrent and/or metastatic oral squamous cell carcinoma (OSCC) remains poor, and its incidence is especially high in developing countries. Multiple long non-coding RNAs (lncRNAs) are recently identified as crucial oncogenic factors or tumor suppressors. This study aimed to probe into the role of lncRNA small nucleolar RNA host gene 17 (SNHG17) on the progression of OSCC.

The expression level of SNHG17 in OSCC samples was tested using quantitative real-time polymerase chain reaction (qRT-PCR). Human OSCC cell lines CAL-27 and Tca8113 were used in in vitro studies. Cell counting kit-8 (CCK-8) and BrdU assays were used to assess the effect of SNHG17 on OSCC cell proliferation. Flow cytometry was used to study the effect of SNHG17 on OSCC cell apoptosis. Transwell assay was conducted to detect the effect of SNHG17 on migration and invasion. Moreover, luciferase reporter assay was employed to confirm targeting relationship between miR-375 and SNHG17. Additionally, Western blot was used to observe the regulatory function of SNHG17 on PAX6.

SNHG17 expression in OSCC clinical samples was significantly increased and was correlated with unfavorable pathological indexes. Its overexpression remarkably accelerated proliferation and metastasis of OSCC cells, while reduced apoptosis. Accordingly, knockdown of SNHG17 suppressed the malignant phenotypes of OSCC cells. Overexpression of SNHG17 significantly reduced the expression of miR-375 by sponging it, but enhanced the expression of PAX6.

SNHG17 is a sponge of tumor suppressor miR-375 in OSCC, enhances the expression of PAX6 indirectly, and functions as an oncogenic lncRNA.

New prognostic factors such as IgVh mutational status, ZAP-70 protein expression and cytogenetic abnormalities have shown to offer important prognostic information for patients with chronic lymphocytic leukemia (CLL). Our aim was to evaluate the optimal cut-off for IgVh mutational status, ZAP-70 expression and cytogenetic abnormalities in association with disease progression defined as the need for treatment within 3 years from diagnosis in 170 patients with B-CLL.

Receiver operating characteristics (ROC) analysis and multivariate general linear models (GLMs) were used to investigate the most significant cut-off values of these biomarkers and their prognostic impact.

Our findings estimated that the optimal cut-off for IgVh mutation status and for ZAP-70 protein expression was 97% and 16.5% respectively and a high concordance between the two was demonstrated. We identified 30% as being the best-cut-off for 17p-, 11q- and 6q-. In univariate analysis 17p- was found to be a significant predictor of the event only for the whole population. Multivariate analysis including all biological parameters, identified 11q deletion as the only significant regressor.

We assessed that IgVh mutational status, ZAP-70 protein and 6q- are powerful prognostic markers. Analyses of all these factors revealed that 11q deletion was the strongest predictor of disease progression in B-CLL.

Gastric cancer (GC) is one of the leading causes of cancer-related death in East Asia and some South American countries, but its mechanism has not been clarified clearly. Proline-, glutamic acid-, and leucine-rich protein-1 (PELP1), a co-regulatory molecule of estrogen receptor α (ER α ), is up-regulated in series of cancers such as endometrial carcinoma, ovarian cancer, colorectal cancer, breast cancer, and non-small cell lung cancer. However, PELP1’s role in GC is still obscure, and its aberrant expression in cancers also remains to be explained.

Immunohistochemical staining and Real-time PCR were used to compare the expression level of PELP1 in GC tissues and adjacent tissues. Western blot was used to detect the expression of PELP1 in cell lines. Kaplan-meier analysis and chi-square test were applied to evaluate the potential of PELP1 to function as a cancer biomarker. RNA interference was used to inhibit PELP1 expression in GC cells, followed by detecting cell proliferation, apoptosis, migration and invasion. Luciferase assay was conducted to validate whether miR-15 family members can directly target PELP1.

In this study, we validated that PELP1 was significantly up-regulated in GC samples and cell lines. It was also demonstrated that the up-regulation of PELP1 was associated with several clinicopathologic features such as tumor diameter ( P < 0.001), serum CEA level ( P = 0.034), and lymphatic metastasis ( P = 0.0009) of GC patients, and its high expression was correlated with shorter disease-free survival and overall survival of the patients. Knockdown of PELP1 remarkably arrested the proliferation， migration and invasion, while promoted apoptosis. We also confirmed that miR-15 family microRNAs, most of which were down-regulated and tumor suppressor in cancers, were posttranscriptional regulators of PELP1.

In conclusion, we demonstrated that PELP1 was an oncogene of GC associated with patients’ prognosis and miR-15 family members contributed to its aberrant expression in cancers.

Increasing evidence have demonstrated that serum extracellular vesicle microRNAs (EV-miRNAs) are promising noninvasive biomarkers for various cancer types.

In this study, we aimed to investigate and evaluate the potential clinical significance of serum EV-miR-10b for acute myeloid leukemia (AML).

Blood samples were collected from a cohort of 95 de novo AML patients and 80 healthy individuals. Of all AML patients, 51 patients were considered as cytogenetic normal AML (CN-AML). Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) was performed to measure the expression levels of serum EV-miR-10b.

The extracellular vesicles we extracted from the serum samples were positive for EV/exosome markers including TSG101, CD63, Flotillin-1 and CD9. The expression levels of serum EV-miR-10b were significantly higher in AML/CN-AML patients than healthy controls. In addition, serum EV-miR-10b expression was strongly correlated with aggressive clinical characteristics. Moreover, receiver operating characteristic analysis showed serum EV-miR-10b yielded an area under the curve of 0.875, with 77.89% specificity and 82.50% sensitivity in identifying AML patients from normal controls. Furthermore, AML patients with higher serum EV-miR-10b expression had significantly shorter survival and serum EV-miR-10b was demonstrated to be an independent prognostic marker for overall survival in AML.

Taken together, serum EV-miR-10b might serve as a promising biomarker for predicting the prognosis of AML.

Pancreatic ductal adenocarcinoma (PDAC) prognosis has not improved over the last decades because of the lack of effective diagnostic and therapeutic methods in the early stage of the disease.

Several gene expression profiles were downloaded from the Expression Omnibus (GEO) database. We calculated the differentially expressed mRNAs (DEGs) and miRNAs (DEmiRs). Then, we constructed a miRNA-mRNA regulatory network by using the miRWalk database. For the DEGs regulated by DEmiRs, we introduced GEPIA to confirm these DEGs’ expression and effect on overall survival. We used other GEO datasets and mRNA-miRNA target databases to validate these DEGs and their relationship with DEmiRs. All these potential core DEGs regulated by DEmiRs were also analyzed at the single-cell level to confirm their cell type source.

CCNB2 and KCNN4, which were regulated by several micro RNAs, showed relatively high expression levels in PDAC patients and significant association with worse overall survival. Furthermore, we identified many DEGs at single-cell level and found that 10 oncogenes were significantly upregulated in type 2 ductal cell type, thereby further demonstrating that type 2 ductal cells might be major sources of malignant cells and are valuable therapeutic targets in PDAC.

Our data added some new insights into the molecular mechanism of PDAC and may be helpful for finding potential biomarkers for diagnosis. These discovery at single-cell level may also be useful for developing new therapeutic targets for PDAC patients.

The optimal timing of combined chemotherapy with radiotherapy for locally advanced nasopharyngeal carcinoma (LA-NPC) is undetermined.

This study aimed to compare the therapeutic efficacy of neoadjuvant chemotherapy (NACT) followed by radiotherapy (RT) and concurrent chemoradiotherapy (CCRT).

Five hundred and thirty-eight patients diagnosed with LA-NPC and treated with NACT + RT or CCRT alone were enrolled in the study. Restricted cubic spline regression (RCS) was used to determine the relationship between age and the hazard Ratio of death. A Kaplan-Meier analysis was performed to evaluate overall survival (OS) related to NACT + RT or CCRT alone. Cox proportional hazards models were used to adjust for potential confounding factors.

Compared with the CCRT alone regimen, the NACT + RT regimen showed a significantly better OS rate with a 62% decreased risk of death in a subgroup of patients aged ⩾ 45 years (hazard ratio, HR: 0.38; 95% confidence interval, CI: 0.24–0.61). In patients aged < 45 years, the risk of death was significantly increased when NACT + RT was chosen compared with CCRT (HR: 4.10; 95% CI: 2.09–8.07).

Age is a significant biomarker when selecting NACT + RT or CCRT alone in patients with locally advanced NPC.

This study was to explore the effects of RNA interference mediated vascular endothelial growth factor (VEGF) gene silencing on biological behavior of renal cell carcinoma (RCC), transplanted renal tumor and angiogenesis in nude mice.

The specific siRNA sequence targeting VEGF were designed and synthesized to construct hVEGF-siRNA plasmid which was transfected into RCC 786-O cells. Reverse transcriptase-polymerase chain reaction (RT-PCR) was used for the detection of VEGF gene expression and western blot was adopted for the examination of VEGF protein expression. The 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay was used to detect cell growth as well as cell migration and invasion. The transplanted renal tumor models in nude mice were established, and the growth condition of nude mice, and VEGF protein expression in transplanted tumor slices and the microvessel density (MVD) were detected.

The expression level of VEGF mRNA in VEGF-siRNA group was significant lower than that in the control group and negative group, suggesting that establishment of plasmid specifically inhibited the expression of VEGF gene The expression level of VEGF protein in VEGF-siRNA group was significant lower than that in the control group and negative group. VEGF gene silencing has the significant inhibition effects on proliferation, migration and invasion of RCC 786-O cells. The tumor weight, VEGF protein positive rate and MVD in VEGF-siRNA group were significant lower than those in negative group and blank group.

The VEGF gene silencing could inhibit the cell proliferation, migration and invasion of RCC 786-O cells; inhibition of VEGF protein expression could prevent transplanted RCC growth and tumor angiogenesis.