Fast Screening of Whole Blood and Tumor Tissue for Bladder Cancer Biomarkers Using Stochastic Needle Sensors

Exploiting volatile fingerprints for bladder cancer diagnosis: A scoping review of metabolomics and sensor-based approaches

Bladder cancer (BC) represents a significant global health concern, for which early detection is essential to improve patient outcomes. This review evaluates the potential of the urinary volatile organic compounds (VOCs) as biomarkers for detecting and staging BC. The methods used include gas chromatography-mass spectrometry (GC-MS)-based metabolomics and electronic-nose (e-nose) sensors. The GC-MS studies that have been published reveal diverse results in terms of diagnostic performance. The sensitivities range from 27 % to an impressive 97 %, while specificities vary between 43 % and 94 %. Furthermore, the accuracies reported in these studies range from 80 to 89 %. In the urine of BC patients, a total of 80 VOCs were discovered to be significantly altered when compared to controls. These VOCs encompassed a variety of chemical classes such as alcohols, aldehydes, alkanes, aromatic compounds, fatty acids, ketones, and terpenoids, among others. Conversely, e-nose-based studies displayed sensitivities from 60 to 100 %, specificities from 53 to 96 %, and accuracies from 65 to 97 %. Interestingly, conductive polymer-based sensors performed better, followed by metal oxide semiconductor and optical sensors. GC-MS studies have shown improved performance in detecting early stages and low-grade tumors, providing valuable insights into staging. Based on these findings, VOC-based diagnostic tools hold great promise for early BC detection and staging. Further studies are needed to validate biomarkers and their classification performance. In the future, advancements in VOC profiling technologies may significantly contribute to improving the overall survival and quality of life for BC patients.

Discovery of Notch Pathway-Related Genes for Predicting Prognosis and Tumor Microenvironment Status in Bladder Cancer

Background: Notch signaling is a key regulator of immune cell differentiation and linked to autoimmune diseases, tumorigenesis and tumor-induced immunomodulation. An abnormally activated Notch signaling pathway contributes to almost all of the key features of cancer, including tumor angiogenesis, stemness, and epithelial-mesenchymal transition. Consequently, we investigated Notch pathway-related genes for developing prognostic marker and assessing immune status in bladder cancer.

Methods: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were utilized to analyze RNA-seq data for bladder cancer. Cluster subtypes were identified using the NMF algorithm. In order to establish a prognostic risk signature, the least absolute shrinkage and selection operator (Lasso) and Cox regression analysis was utilized. GSEA was carried out to investigate the molecular mechanisms. Immune cell infiltration levels in bladder cancer were calculated using the CIBERSORT algorithm. External clinical tissue samples were used to validate the expression levels of signature genes.

Results: Based on the NMF algorithm, bladder cancer samples were divided into two cluster subtypes and displayed different survival outcome and immune microenvironment. A six-gene risk signature (DTX3L, CNTN1, ENO1, GATA3, MAGEA1, and SORBS2) was independent for prognosis and showed good stability. The infiltration of immune cells and clinical variables were significantly different among the risk groups of patients. Response to immunotherapy also differed between different risk groups. Furthermore, the mRNA expression levels of the signature genes were verified in tissue samples by qRT-PCR.

Conclusion: We established a 6-gene signature associated with Notch pathway in bladder cancer to effectively predict prognosis and reflect immune microenvironment status.

Stochastic Microsensors Based on Carbon Nanotubes for Molecular Recognition of the Isocitrate Dehydrogenases 1 and 2

Two three-dimensional (3D) stochastic microsensors based on immobilization of protoporphyrin IX (PIX) in single-walled carbon nanotubes (SWCNT) and multi-walled carbon nanotubes (MWCNT) decorated with copper (Cu) and gold (Au) nanoparticles were designed and used for the molecular recognition of isocitrate dehydrogenase 1 (IDH1) and isocitrate dehydrogenase 2 (IDH2) in biological samples (brain tumor tissues, whole blood). The linear concentration ranges obtained for the molecular recognition and quantification of IDH1 and IDH2 were: IDH1 (1 × 10⁻⁵–1 × 10² ng mL⁻¹) and IDH2 (5 × 10⁻⁸ − 5 × 10² ng mL⁻¹). The limits of quantification obtained using the proposed microsensors were: 10 fg mL^–1 for IDH1 and 5 × 10⁻³ fg mL⁻¹ for IDH2. The highest sensitivities were obtained for the microsensor based on MWCNT. High recoveries versus enzyme-linked immunosorbent assay (ELISA) standard method were recorded for the assays of IDH1 and IDH2, all values being higher than 99.00%, with relative standard deviations (RSD) lower than 0.10%.

Sensitive Detection of Heregulin-α from Biological Samples Using a Disposable Stochastic Sensor Based on Plasma Deposition of GNPs-AgPs' Nanofilms on Silk

A composite material comprised of graphene nanoplatelet and silver particles (GNPs–AgPs) was used for the deposition of GNPs–AgPs’ nanofilms with cold plasma on silk. α-Cyclodextrin was used as a modifier of the active surface of the disposable sensor. The disposable stochastic sensor was used in screening tests for the assay of heregulin-α in whole blood and tissue samples. The disposable stochastic sensor showed a low limit of determination (4.10 fg mL⁻¹) and can be used with high sensitivity on a wide concentration range (4.10 fg mL⁻¹–0.04 µg mL⁻¹). The screening method was validated against ELISA when good correlations (confirmed also by the t-test) were obtained.

Stochastic biosensors based on N- and S-doped graphene for the enantioanalysis of aspartic acid in biological samples

Metabolomics plays a very important role in cancer mechanism and also in the early diagnosis of cancer. The enantioanalysis of aminoacids found in biological samples may favor the development of new screening tests for the early diagnosis of cancer. Two stochastic biosensors, based on the immobilization of α-hemolysin and hemin in exfoliated reduced graphene modified with nitrogen and sulphur, were proposed for the enantioanalysis of aspartic acid in biological samples (whole blood, urine, saliva, and tissue). The proposed biosensors showed low limits of quantification, high sensitivity, and large linear concentration ranges. The recoveries of d- and l-aspartic acid in biological samples were higher than 95.00%, with a relative standard deviation lower than 1.00%.

Stochastic biosensors based on N- and S-doped graphene modified with hemin or α-hemolysin contributed to establishing the metabolomics of gastric cancer by performing the enantioanalysis of aspartic acid in different biological samples.

3D stochastic microsensors for molecular recognition and determination of heregulin-α in biological samples

Two 3D stochastic microsensors based on single and multi-walled carbon nanotubes modified with 2,3,7,8,12,13,17,18-octaethyl-21H,23H-porphine manganese (III) chloride were proposed for the molecular recognition and determination of heregulin-α, in whole blood samples and tumour brain tissues. The proposed 3D stochastic sensors had limits of determinations of 102 fg mL^-1 and high sensitivities. The linear concentration ranges of the two 3D stochastic microsensors covered the healthy people as well as the patients confirmed with brain cancer. Determination of heregulin-α was done in whole blood and tissue samples using the screening method based on the proposed 3D stochastic microsensors as well as using the ELISA method; very good correlations were obtained between the two methods proving that the proposed method can be used in screening tests of whole blood and tumoural tissue samples for molecular recognition and determination of heregulin-α.