Aquaporins as Prognostic Biomarker in Prostate Cancer

Kushwaha, Prem Prakash; Verma, Shiv; Gupta, Sanjay

doi:10.3390/cancers15020331

Cited by 9 publications

(4 citation statements)

References 97 publications

(122 reference statements)

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“…This topic thus requires much more investigation. Secondly, some aquaporins are prospective biomarkers of prognostic significance in prostate cancer ( Kushwaha et al, 2023 ), the role of aquaporin five in lung cancer beingalso questioned ( Jaskiewicz et al, 2023 ). Water exchange through aquaporin-4 being measured by MRI, transmembrane water-efflux rates are a biomarker of proliferative glioma ( Ruan and Keshari, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

The biophysics of water in cell biology: perspectives on a keystone for both marine sciences and cancer research

Pouliquen

2024

Front. Cell Dev. Biol.

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The biophysics of water, has been debated over more than a century. Although its importance is still underestimated, significant breakthroughs occurred in recent years. The influence of protein condensation on water availability control was documented, new findings on water-transport proteins emerged, and the way water molecules rearrange to minimize free energy at interfaces was deciphered, influencing membrane thermodynamics. The state of knowledge continued to progress in the field of deep-sea marine biology, highlighting unknown effects of high hydrostatic pressure and/or temperature on interactions between proteins and ligands in extreme environments, and membrane structure adaptations. The role of osmolytes in protein stability control under stress is also discussed here in relation to fish egg hydration/buoyancy. The complexity of water movements within the cell is updated, all these findings leading to a better view of their impact on many cellular processes. The way water flow and osmotic gradients generated by ion transport work together to produce the driving force behind cell migration is also relevant to both marine biology and cancer research. Additional common points concern water dynamic changes during the neoplastic transformation of cells and tissues, or embryo development. This could improve imaging techniques, early cancer diagnosis, and understanding of the molecular and physiological basis of buoyancy for many marine species.

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Section: Discussionmentioning

confidence: 99%

The biophysics of water in cell biology: perspectives on a keystone for both marine sciences and cancer research

Pouliquen

2024

Front. Cell Dev. Biol.

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“…A clinically relevant biomarker should be safe and obtained through a non-invasive protocol, have high sensitivity and specificity and high positive and negative predictive values, and make possible clinical decisions [11]. In recent years, a number of clinical and biological prognostic biomarkers of prostate cancer have been reported, such as androgen receptor variant V7 [12], PTEN [13], or c-MYC gene inactivation [14], although, none of them have been approved as a prognostic biomarker for use in the clinical settings [15]. Other biomarkers such as TMPRSS2-ERG gene fusion [16], non-coding RNA (PCA3) [17,18], and kallikrein included in basic PHI (prostate health index) or 4K tests have shown to increase sensitivity and specificity of serum PSA in prostate cancer patients.…”

Section: Introductionmentioning

confidence: 99%

GNL3 and PA2G4 as Prognostic Biomarkers in Prostate Cancer

Kumar

Shuaib

Alasmari

et al. 2023

Cancers

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Prostate cancer is a multifocal and heterogeneous disease common in males and remains the fifth leading cause of cancer-related deaths worldwide. The prognosis of prostate cancer is variable and based on the degree of cancer and its stage at the time of diagnosis. Existing biomarkers for the prognosis of prostate cancer are unreliable and lacks specificity and sensitivity in guiding clinical decision. There is need to search for novel biomarkers having prognostic and predictive capabilities in guiding clinical outcomes. Using a bioinformatics approach, we predicted GNL3 and PA2G4 as biomarkers of prognostic significance in prostate cancer. A progressive increase in the expression of GNL3 and PA2G4 was observed during cancer progression having significant association with poor survival in prostate cancer patients. The Receiver Operating Characteristics of both genes showed improved area under the curve against sensitivity versus specificity in the pooled samples from three different GSE datasets. Overall, our analysis predicted GNL3 and PA2G4 as prognostic biomarkers of clinical significance in prostate cancer.

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“…Recently, Kumar et al used PCa expression data on The cancer genome Atlas (TCGA) dataset and performed bioinformatics analysis to identify prognostic biomarkers. They suggested GNL3 and PA2G4 as novel prognostic biomarkers (7). Also, some researchers have used bioinformatics analysis and proposed a panel of miRNAs as a diagnostic tool to increase accuracy and speci city of prostate cancer diagnosis (8,9).…”

Section: Introductionmentioning

confidence: 99%

The potential prognostic value of Cartilage acidic protein 1 and secretoglobin family 1D member 1 in prostate cancer

Sokhangouy,

Asadnia,

Negahbanzaferanloo

et al. 2023

Preprint

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Background Prostate cancer is a prevalent malignancy with a broad range of clinical outcomes, necessitating improved prognostic biomarkers for precise patient stratification and personalized treatment. This study leverages machine learning techniques to identify and validate novel prognostic biomarkers using DNAseq and RNAseq data for prostate cancer. Methods Whole genome sequencing and gene expression profiling in patients were used from TCGA to identify DEGs and genetic alterations. Next, deep learning was utilized to determine key genes. Gene Ontology, Reactom, GSEA, and Human Disease Ontology were employed to study the involved biological process and pathways. Survival analysis of patients with prostate cancer with reference on dysregulated genes was conducted using Cox regression and Kaplan-Meier analysis. The STRING database was used to build a protein-protein interaction (PPI) network. Moreover, candidate genes were subjected to machine learning -based analysis and the Receiver operating characteristic (ROC) curve. Results We identified a total of 609 DEGs in patients, of which 358 were downregulated and 251 were upregulated. Deep learning results identified 20 genes, and these were combined with the analysis of DNA-seq. Survival analysis of patients with prostatic cancer showed that dysregulated expression of ASB12, BLOC1S1, CRTAC1, KCNQ1, KISS1, M2T2A, RNF207, SCGB1D1, SLC13A2, SORBS1, TGFBR3, WSCD2, ANFKFY1, CRYBA4, MIR204, QRFP, SNX15, and YWHAH genes were related with a poor clinical prognosis. The combio-ROC curve analysis reveals ed that TGFRB3, SCGB1D1 and CRTAC1 were potential diagnostic biomarkers with a great higher sensitivity and AUC than currently available biomarkers. Also, the combination of SCGB1D1 and CRTAC1 demonstrated the greatest accuracy, sensitivity, and specificity towards diagnostic applications. The potential value of these markers was validated in six other datasets. Conclusion Our findings demonstrated the potential value of SCGB1D1 and CRTAC1 as novel biomarkers and therapeutic targets in prostate cancer which had a higher AUC, sensitivity, and specificity compared to PSA, indicating further functional investigations on the potential value of emerging markers in prostate cancer.

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Aquaporins as Prognostic Biomarker in Prostate Cancer

Cited by 9 publications

References 97 publications

The biophysics of water in cell biology: perspectives on a keystone for both marine sciences and cancer research

The biophysics of water in cell biology: perspectives on a keystone for both marine sciences and cancer research

GNL3 and PA2G4 as Prognostic Biomarkers in Prostate Cancer

The potential prognostic value of Cartilage acidic protein 1 and secretoglobin family 1D member 1 in prostate cancer

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