A novel and accurate microfluidic assay of CD62L in bladder cancer serum samples

Applications of Biosensors in Bladder Cancer

Bladder cancer (BC) is the tenth most common cancer globally, predominantly affecting men. Early detection and treatment are crucial due to high recurrence rates and poor prognosis for advanced stages. Traditional diagnostic methods like cystoscopy and imaging have limitations, leading to the exploration of noninvasive methods such as liquid biopsy. This review highlights the application of biosensors in BC, including electrochemical and optical sensors for detecting tumor markers like proteins, nucleic acids, and other biomolecules, noting their clinical relevance. Emerging therapeutic approaches, such as antibody-drug conjugates, targeted therapy, immunotherapy, and gene therapy, are also explored, the role of biosensors in detecting corresponding biomarkers to guide these treatments is examined. Finally, the review addresses the current challenges and future directions for biosensor applications in BC, highlighting the need for large-scale clinical trials and the integration of advanced technologies like deep learning to enhance diagnostic accuracy and treatment efficacy.

Identification of a tumor microenvironment-related gene signature to improve the prediction of cervical cancer prognosis

Background

Previous studies have found that the microenvironment of cervical cancer (CESC) affects the progression and treatment of this disease. Thus, we constructed a multigene model to assess the survival of patients with cervical cancer.

Methods

We scored 307 CESC samples from The Cancer Genome Atlas (TCGA) and divided them into high and low matrix and immune scores using the ESTIMATE algorithm for differential gene analysis. Cervical cancer patients were randomly divided into a training group, testing group and combined group. The multigene signature prognostic model was constructed by Cox analyses. Multivariate Cox analysis was applied to evaluate the significance of the multigene signature for cervical cancer prognosis. Prognosis was assessed by Kaplan–Meier curves comparing the different groups, and the accuracy of the prognostic model was analyzed by receiver operating characteristic-area under the curve (ROC-AUC) analysis and calibration curve. The Tumor Immune Estimation Resource (TIMER) database was used to analyze the relationship between the multigene signature and immune cell infiltration.

Results

We obtained 420 differentially expressed genes in the tumor microenvironment from 307 patients with cervical cancer. A three-gene signature (SLAMF1, CD27, SELL) model related to the tumor microenvironment was constructed to assess patient survival. Kaplan–Meier analysis showed that patients with high risk scores had a poor prognosis. The ROC-AUC value indicated that the model was an accurate predictor of cervical cancer prognosis. Multivariate cox analysis showed the three-gene signature to be an independent risk factor for the prognosis of cervical cancer. A nomogram combining the three-gene signature and clinical features was constructed, and calibration plots showed that the nomogram resulted in an accurate prognosis for patients. The three-gene signature was associated with T stage, M stage and degree of immune infiltration in patients with cervical cancer.

Conclusions

This research suggests that the developed three-gene signature may be applied as a biomarker to predict the prognosis of and personalized therapy for CESC.

A novel seven-gene panel predicts the sensitivity and prognosis of head and neck squamous cell carcinoma treated with platinum-based radio(chemo)therapy

PURPOSE

The aim of the study is to identify a reliable gene panel to predict the prognosis of HNSCC patients by integrated genomic analysis.

METHODS

Co-expression gene networks were constructed by WGCNA using GSE113282 gene expression profile. The biological functional investigation was performed by GO and KEGG function enrichment analysis. The hub gene module was screened by PPI. The prognostic gene panel was established by Lasso regression analysis, and further progression-free survival (PFS) analysis was validated by Kaplan-Meier survival analysis using GSE102995 data.

RESULTS

We identified 195 genes associated with the overall survival (OS) status (correlation coefficients: - 0.42, and p value: 2e-05) by WGCNA. These genes were enriched in immune-related cytokines and pathways analyzed by GO and KEGG. Among the 195 genes, the module (42 genes) with the highest score was screened by PPI. A novel seven-gene predictive panel (CD19, CD40LG, CD5, CXCR6, FPR2, NCAM1, and SELL) was established by Lasso regression analysis, and the area under ROC curve (AUC) for 3-year OS status was 0.8298 and 0.7571, respectively, in the training set and the test set. The PFS time of the low-risk patients was significantly longer than the high-risk patients (p < 0.0001; log-rank test) by further validation using GSE102995 data.

CONCLUSION

The seven-gene panel may serve as a reliable predictive tool for HNSCC patients treated with platinum-based radio (chemo) therapy, and may be potential therapeutic targets for HNSCC patients.

Printed Electrodes in Microfluidic Arrays for Cancer Biomarker Protein Detection

Medical diagnostics is trending towards a more personalized future approach in which multiple tests can be digitized into patient records. In cancer diagnostics, patients can be tested for individual protein and genomic biomarkers that detect cancers at very early stages and also be used to monitor cancer progression or remission during therapy. These data can then be incorporated into patient records that could be easily accessed on a cell phone by a health care professional or the patients themselves on demand. Data on protein biomarkers have a large potential to be measured in point-of-care devices, particularly diagnostic panels that could provide a continually updated, personalized record of a disease like cancer. Electrochemical immunoassays have been popular among protein detection methods due to their inherent high sensitivity and ease of coupling with screen-printed and inkjet-printed electrodes. Integrated chips featuring these kinds of electrodes can be built at low cost and designed for ease of automation. Enzyme-linked immunosorbent assay (ELISA) features are adopted in most of these ultrasensitive detection systems, with microfluidics allowing easy manipulation and good fluid dynamics to deliver reagents and detect the desired proteins. Several of these ultrasensitive systems have detected biomarker panels ranging from four to eight proteins, which in many cases when a specific cancer is suspected may be sufficient. However, a grand challenge lies in engineering microfluidic-printed electrode devices for the simultaneous detection of larger protein panels (e.g., 50-100) that could be used to test for many types of cancers, as well as other diseases for truly personalized care.