Integrated Fourier Transform Infrared Imaging and Proteomics for Identification of a Candidate Histochemical Biomarker in Bladder Cancer

Proteomic analysis of the urothelial cancer landscape

Urothelial bladder cancer (UC) has a wide tumor biological spectrum with challenging prognostic stratification and relevant therapy-associated morbidity. Most molecular classifications relate only indirectly to the therapeutically relevant protein level. We improve the pre-analytics of clinical samples for proteome analyses and characterize a cohort of 434 samples with 242 tumors and 192 paired normal mucosae covering the full range of UC. We evaluate sample-wise tumor specificity and rank biomarkers by target relevance. We identify robust proteomic subtypes with prognostic information independent from histopathological groups. In silico drug prediction suggests efficacy of several compounds hitherto not in clinical use. Both in silico and in vitro data indicate predictive value of the proteomic clusters for these drugs. We underline that proteomics is relevant for personalized oncology and provide abundance and tumor specificity data for a large part of the UC proteome ( www.cancerproteins.org ).

Diagnostic Challenges during Inflammation and Cancer: Current Biomarkers and Future Perspectives in Navigating through the Minefield of Reactive versus Dysplastic and Cancerous Lesions in the Digestive System

In the setting of pronounced inflammation, changes in the epithelium may overlap with neoplasia, often rendering it impossible to establish a diagnosis with certainty in daily clinical practice. Here, we discuss the underlying molecular mechanisms driving tissue response during persistent inflammatory signaling along with the potential association with cancer in the gastrointestinal tract, pancreas, extrahepatic bile ducts, and liver. We highlight the histopathological challenges encountered in the diagnosis of chronic inflammation in routine practice and pinpoint tissue-based biomarkers that could complement morphology to differentiate reactive from dysplastic or cancerous lesions. We refer to the advantages and limitations of existing biomarkers employing immunohistochemistry and point to promising new markers, including the generation of novel antibodies targeting mutant proteins, miRNAs, and array assays. Advancements in experimental models, including mouse and 3D models, have improved our understanding of tissue response. The integration of digital pathology along with artificial intelligence may also complement routine visual inspections. Navigating through tissue responses in various chronic inflammatory contexts will help us develop novel and reliable biomarkers that will improve diagnostic decisions and ultimately patient treatment.

AHNAKs roles in physiology and malignant tumors

The AHNAK family currently consists of two members, namely AHNAK and AHNAK2, both of which have a molecular weight exceeding 600 kDa. Homologous sequences account for approximately 90% of their composition, indicating a certain degree of similarity in terms of molecular structure and biological functions. AHNAK family members are involved in the regulation of various biological functions, such as calcium channel modulation and membrane repair. Furthermore, with advancements in biological and bioinformatics technologies, research on the relationship between the AHNAK family and tumors has rapidly increased in recent years, and its regulatory role in tumor progression has gradually been discovered. This article briefly describes the physiological functions of the AHNAK family, and reviews and analyzes the expression and molecular regulatory mechanisms of the AHNAK family in malignant tumors using Pubmed and TCGA databases. In summary, AHNAK participates in various physiological and pathological processes in the human body. In multiple types of cancers, abnormal expression of AHNAK and AHNAK2 is associated with prognosis, and they play a key regulatory role in tumor progression by activating signaling pathways such as ERK, MAPK, Wnt, and MEK, as well as promoting epithelial-mesenchymal transition.

Multiparameter interferometric polarization-enhanced imaging differentiates carcinoma in situ from inflammation of the bladder: an ex vivo study

Significance

Successful differentiation of carcinoma in situ (CIS) from inflammation in the bladder is key to preventing unnecessary biopsies and enabling accurate therapeutic decisions. Current standard-of-care diagnostic imaging techniques lack the specificity needed to differentiate these states, leading to false positives.

Aim

We introduce multiparameter interferometric polarization-enhanced (MultiPIPE) imaging as a promising technology to improve the specificity of detection for better biopsy guidance and clinical outcomes.

Approach

In this ex vivo study, we extract tissue attenuation-coefficient-based and birefringence-based parameters from MultiPIPE imaging data, collected with a bench-top system, to develop a classifier for the differentiation of benign and CIS tissues. We also analyze morphological features from second harmonic generation imaging and histology slides and perform imaging-to-morphology correlation analysis.

Results

MultiPIPE enhances specificity to differentiate CIS from benign tissues by nearly 20% and reduces the false-positive rate by more than four-fold over clinical standards. We also show that the MultiPIPE measurements correlate well with changes in morphological features in histological assessments.

Conclusions

The results of our study show the promise of MultiPIPE imaging to be used for better differentiation of bladder inflammation from flat tumors, leading to a fewer number of unnecessary procedures and shorter operating room (OR) time.

Fast and label-free automated detection of microsatellite status in early colon cancer using artificial intelligence integrated infrared imaging

PURPOSE

Microsatellite instability (MSI) due to mismatch repair (MMR) defects accounts for 15-20% of colon cancers (CC). MSI testing is currently standard of care in CC with immunohistochemistry of the four MMR proteins representing the gold standard. Instead, label-free quantum cascade laser (QCL) based infrared (IR) imaging combined with artificial intelligence (AI) may classify MSI/microsatellite stability (MSS) in unstained tissue sections user-independently and tissue preserving.

METHODS

Paraffin-embedded unstained tissue sections of early CC from patients participating in the multicentre AIO ColoPredict Plus (CPP) 2.0 registry were analysed after dividing into three groups (training, test, and validation). IR images of tissue sections using QCL-IR microscopes were classified by AI (convolutional neural networks [CNN]) using a two-step approach. The first CNN (modified U-Net) detected areas of cancer while the second CNN (VGG-Net) classified MSI/MSS. End-points were area under receiver operating characteristic (AUROC) and area under precision recall curve (AUPRC).

RESULTS

The cancer detection in the first step was based on 629 patients (train n = 273, test n = 138, and validation n = 218). Resulting classification AUROC was 1.0 for the validation dataset. The second step classifying MSI/MSS was performed on 547 patients (train n = 331, test n = 69, and validation n = 147) reaching AUROC and AUPRC of 0.9 and 0.74, respectively, for the validation cohort.

CONCLUSION

Our novel label-free digital pathology approach accurately and rapidly classifies MSI vs. MSS. The tissue sections analysed were not processed leaving the sample unmodified for subsequent analyses. Our approach demonstrates an AI-based decision support tool potentially driving improved patient stratification and precision oncology in the future.

Evaluation of grade and invasiveness of bladder urothelial carcinoma using infrared imaging and machine learning

Urothelial bladder carcinoma (BC) is primarily diagnosed with a subjective examination of biopsies by histopathologists, but accurate diagnosis remains time-consuming and of low diagnostic accuracy, especially for low grade non-invasive BC. We propose a novel approach for high-throughput BC evaluation by combining infrared (IR) microscopy of bladder sections with machine learning (partial least squares-discriminant analysis) to provide an automated prediction of the presence of cancer, invasiveness and grade. Cystoscopic biopsies from 50 patients with clinical suspicion of BC were histologically examined to assign grades and stages. Adjacent tissue cross-sections were IR imaged to provide hyperspectral datasets and cluster analysis segregated IR images to extract the average spectra of epithelial and subepithelial tissues. Discriminant models, which were validated using repeated random sampling double cross-validation, showed sensitivities (AUROC) ca. 85% (0.85) for the identification of cancer in epithelium and subepithelium. The diagnosis of non-invasive and invasive cases showed sensitivity values around 80% (0.84-0.85) and 76% (0.73-0.80), respectively, while the identification of low and high grade BC showed higher sensitivity values 87-88% (0.91-0.92). Finally, models for the discrimination between cancers with different invasiveness and grades showed more modest AUROC values (0.67-0.72). This proves the high potential of IR imaging in the development of ancillary platforms to screen bladder biopsies.

Distinguishing Asphyxia from Sudden Cardiac Death as the Cause of Death from the Lung Tissues of Rats and Humans Using Fourier Transform Infrared Spectroscopy

The ability to determine asphyxia as a cause of death is important in forensic practice and helps us to judge whether a case is criminal. However, in some cases where the deceased has underlying heart disease, death by asphyxia cannot be determined by traditional autopsy and morphological observation under a microscope because there are no specific morphological features for either asphyxia or sudden cardiac death (SCD). Here, Fourier transform infrared (FTIR) spectroscopy was employed to distinguish asphyxia from SCD. A total of 40 lung tissues (collected at 0 h and 24 h postmortem) from 20 rats (10 died from asphyxia and 10 died from SCD) and 16 human lung tissues from 16 real cases were used for spectral data acquisition. After data preprocessing, 2675 spectra from rat lung tissues and 1526 spectra from human lung tissues were obtained for subsequent analysis. First, we found that there were biochemical differences in the rat lung tissues between the two causes of death by principal component analysis and partial least-squares discriminant analysis (PLS-DA), which were related to alterations in lipids, proteins, and nucleic acids. In addition, a PLS-DA classification model can be built to distinguish asphyxia from SCD. Second, based on the spectral data obtained from lung tissues allowed to decompose for 24 h, we could still distinguish asphyxia from SCD even when decomposition occurred in animal models. Nine important spectral features that contributed to the discrimination in the animal experiment were selected and further analyzed. Third, 7 of the 9 differential spectral features were also found to be significantly different in human lung tissues from 16 real cases. A support vector machine model was finally built by using the seven variables to distinguish asphyxia from SCD in the human samples. Compared with the linear PLS-DA model, its accuracy was significantly improved to 0.798, and the correct rate of determining the cause of death was 100%. This study shows the application potential of FTIR spectroscopy for exploring the subtle biochemical differences resulting from different death processes and determining the cause of death even after decomposition.

Plasma Proteomics Enable Differentiation of Lung Adenocarcinoma from Chronic Obstructive Pulmonary Disease (COPD)

Chronic obstructive pulmonary disease (COPD) is a major risk factor for the development of lung adenocarcinoma (AC). AC often develops on underlying COPD; thus, the differentiation of both entities by biomarker is challenging. Although survival of AC patients strongly depends on early diagnosis, a biomarker panel for AC detection and differentiation from COPD is still missing. Plasma samples from 176 patients with AC with or without underlying COPD, COPD patients, and hospital controls were analyzed using mass-spectrometry-based proteomics. We performed univariate statistics and additionally evaluated machine learning algorithms regarding the differentiation of AC vs. COPD and AC with COPD vs. COPD. Univariate statistics revealed significantly regulated proteins that were significantly regulated between the patient groups. Furthermore, random forest classification yielded the best performance for differentiation of AC vs. COPD (area under the curve (AUC) 0.935) and AC with COPD vs. COPD (AUC 0.916). The most influential proteins were identified by permutation feature importance and compared to those identified by univariate testing. We demonstrate the great potential of machine learning for differentiation of highly similar disease entities and present a panel of biomarker candidates that should be considered for the development of a future biomarker panel.

Integrative Proteo-Genomic Analysis for Recurrent Survival Prognosis in Colon Adenocarcinoma

Background

The survival prognosis is the hallmark of cancer progression. Here, we aimed to develop a recurrence-related gene signature to predict the prognosis of colon adenocarcinoma (COAD).

Methods

The proteomic data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and genomic data from the cancer genomic maps [The Cancer Genome Atlas (TCGA)] dataset were analyzed to identify co-differentially expressed genes (cDEGs) between recurrence samples and non-recurrence samples in COAD using limma package. Functional enrichment analysis, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was conducted. Univariate and multivariate Cox regressions were applied to identify the independent prognostic feature cDEGs and establish the signature whose performance was evaluated by Kaplan–Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index (C-index), and calibration curve. The area under the receiver operating characteristic (ROC) curve (AUROC) and a nomogram were calculated to assess the predictive accuracy. GSE17538 and GSE39582 were used for external validation. Quantitative real-time PCR and Western blot analysis were carried out to validate our findings.

Results

We identified 86 cDEGs in recurrence samples compared with non-recurrence samples. These genes were primarily enriched in the regulation of carbon metabolic process, fructose and mannose metabolism, and extracellular exosome. Then, an eight-gene-based signature (CA12, HBB, NCF1, KBTBD11, MMAA, DMBT1, AHNAK2, and FBLN2) was developed to separate patients into high- and low-risk groups. Patients in the low-risk group had significantly better prognosis than those in the high-risk group. Four prognostic clinical features, including pathological M, N, T, and RS model status, were screened for building the nomogram survival model. The PCR and Western blot analysis results suggested that CA12 and AHNAK2 were significantly upregulated, while MMAA and DMBT1 were downregulated in the tumor sample compared with adjacent tissues, and in non-recurrent samples compared with non-recurrent samples in COAD.

Conclusion

These identified recurrence-related gene signatures might provide an effective prognostic predictor and promising therapeutic targets for COAD patients.

Toward a SERS Diagnostic Tool for Discrimination between Cancerous and Normal Bladder Tissues via Analysis of the Extracellular Fluid

Vibrational spectroscopy provides the possibility for sensitive and precise detection of chemical changes in biomolecules due to development of cancers. In this work, label-free near-infrared surface enhanced Raman spectroscopy (SERS) was applied for the differentiation between cancerous and normal human bladder tissues via analysis of the extracellular fluid of the tissue. Specific cancer-related SERS marker bands were identified by using a 1064 nm excitation wavelength. The prominent spectral marker band was found to be located near 1052 cm^-1 and was assigned to the C-C, C-O, and C-N stretching vibrations of lactic acid and/or cysteine molecules. The correct identification of 80% of samples is achieved with even limited data set and could be further improved. The further development of such a detection method could be implemented in clinical practice for the aid of surgeons in determining of boundaries of malignant tumors during the surgery.

Application of Mass Spectrometry Imaging in Uro-Oncology: Discovering Potential Biomarkers

A growing need is emerging worldwide for new molecular markers which could enhance the accuracy of diagnostic and therapeutic methods for detecting urogenital cancers. Mass spectrometry imaging (MSI) is a very promising tool in this regard. In this review, we attempt to provide a subjective summary of the latest publications on potential biomarkers of renal, bladder, prostate, and testicular malignancies detected with MSI through the eyes of a clinical urologist.

The Obscure Potential of AHNAK2

Simple Summary

AHNAK2 is a relatively newly discovered protein. It can interact with many other proteins. This protein is increased in cells of variety of different cancers. AHNAK2 may play a vital role in cancer formation. AHNAK2 may have a role in early detection of cancer. This obscure potential of AHNAK2 is being studied.

Abstract

AHNAK2 is a protein discovered in 2004, with a strong association with oncogenesis in various epithelial cancers. It has a large 616 kDa tripartite structure and is thought to take part in the formation of large multi-protein complexes. High expression is found in clear cell renal carcinoma, pancreatic ductal adenocarcinoma, uveal melanoma, and lung adenocarcinoma, with a relation to poor prognosis. Little work has been done in exploring the function and relation AHNAK2 has with cancer, with early studies showing promising potential as a future biomarker and therapeutic target.

Advances in Digital Pathology: From Artificial Intelligence to Label-Free Imaging

BACKGROUND

Digital pathology, in its primary meaning, describes the utilization of computer screens to view scanned histology slides. Digitized tissue sections can be easily shared for a second opinion. In addition, it allows tissue image analysis using specialized software to identify and measure events previously observed by a human observer. These tissue-based readouts were highly reproducible and precise. Digital pathology has developed over the years through new technologies. Currently, the most discussed development is the application of artificial intelligence to automatically analyze tissue images. However, even new label-free imaging technologies are being developed to allow imaging of tissues by means of their molecular composition.

SUMMARY

This review provides a summary of the current state-of-the-art and future digital pathologies. Developments in the last few years have been presented and discussed. In particular, the review provides an outlook on interesting new technologies (e.g., infrared imaging), which would allow for deeper understanding and analysis of tissue thin sections beyond conventional histopathology.

KEY MESSAGES

In digital pathology, mathematical methods are used to analyze images and draw conclusions about diseases and their progression. New innovative methods and techniques (e.g., label-free infrared imaging) will bring significant changes in the field in the coming years.

Proteomics as a Complementary Technique to Characterize Bladder Cancer

Simple Summary

Although immunohistochemistry is a routine technique in clinics, and genomics has been rapidly incorporated, proteomics is a step behind. This general situation is also the norm in bladder cancer research. This review shows the contributions of proteomics to the molecular classification of bladder cancer, and to the study of histopathology due to tissue insults caused by tumors. Furthermore, the importance of proteomics for understanding the cellular and molecular changes as a consequence of the therapy of bladder cancer cannot be neglected.

Abstract

Bladder cancer (BC) is the most common tumor of the urinary tract and is conventionally classified as either non-muscle invasive or muscle invasive. In addition, histological variants exist, as organized by the WHO-2016 classification. However, innovations in next-generation sequencing have led to molecular classifications of BC. These innovations have also allowed for the tracing of major tumorigenic pathways and, therefore, are positioned as strong supporters of precision medicine. In parallel, immunohistochemistry is still the clinical reference to discriminate histological layers and to stage BC. Key contributions have been made to enlarge the panel of protein immunomarkers. Moreover, the analysis of proteins in liquid biopsy has also provided potential markers. Notwithstanding, their clinical adoption is still low, with very few approved tests. In this context, mass spectrometry-based proteomics has remained a step behind; hence, we aimed to develop them in the community. Herein, the authors introduce the epidemiology and the conventional classifications to review the molecular classification of BC, highlighting the contributions of proteomics. Then, the advances in mass spectrometry techniques focusing on maintaining the integrity of the biological structures are presented, a milestone for the emergence of histoproteomics. Within this field, the review then discusses selected proteins for the comprehension of the pathophysiological mechanisms of BC. Finally, because there is still insufficient knowledge, this review considers proteomics as an important source for the development of BC therapies.

Proteomic and bioinformatic profiling of neutrophils in CLL reveals functional defects that predispose to bacterial infections

Patients with chronic lymphocytic leukemia (CLL) typically suffer from frequent and severe bacterial infections. Although it is well known that neutrophils are critical innate immune cells facilitating the early defense, the underlying phenotypical and functional changes in neutrophils during CLL remain largely elusive. Using a murine adoptive transfer model of CLL, we demonstrate aggravated bacterial burden in CLL-bearing mice upon a urinary tract infection with uropathogenic Escherichia coli. Bioinformatic analyses of the neutrophil proteome revealed increased expression of proteins associated with interferon signaling and decreased protein expression associated with granule composition and neutrophil migration. Functional experiments validated these findings by showing reduced levels of myeloperoxidase and acidification of neutrophil granules after ex vivo phagocytosis of bacteria. Pathway enrichment analysis indicated decreased expression of molecules critical for neutrophil recruitment, and migration of neutrophils into the infected urinary bladder was significantly reduced. These altered migratory properties of neutrophils were also associated with reduced expression of CD62L and CXCR4 and correlated with an increased incidence of infections in patients with CLL. In conclusion, this study describes a molecular signature of neutrophils through proteomic, bioinformatic, and functional analyses that are linked to a reduced migratory ability, potentially leading to increased bacterial infections in patients with CLL.

Quantum Cascade Laser-Based Infrared Imaging as a Label-Free and Automated Approach to Determine Mutations in Lung Adenocarcinoma

Therapeutic decisions in lung cancer critically depend on the determination of histologic types and oncogene mutations. Therefore, tumor samples are subjected to standard histologic and immunohistochemical analyses and examined for relevant mutations using comprehensive molecular diagnostics. In this study, an alternative diagnostic approach for automatic and label-free detection of mutations in lung adenocarcinoma tissue using quantum cascade laser-based infrared imaging is presented. For this purpose, a five-step supervised classification algorithm was developed, which was not only able to detect tissue types and tumor lesions, but also the tumor type and mutation status of adenocarcinomas. Tumor detection was verified on a data set of 214 patient samples with a specificity of 97% and a sensitivity of 95%. Furthermore, histology typing was verified on samples from 203 of the 214 patients with a specificity of 97% and a sensitivity of 94% for adenocarcinoma. The most frequently occurring mutations in adenocarcinoma (KRAS, EGFR, and TP53) were differentiated by this technique. Detection of mutations was verified in 60 patient samples from the data set with a sensitivity and specificity of 95% for each mutation. This demonstrates that quantum cascade laser infrared imaging can be used to analyze morphologic differences as well as molecular changes. Therefore, this single, one-step measurement provides comprehensive diagnostics of lung cancer histology types and most frequent mutations.

Prognostic Impact of AHNAK2 Expression in Patients Treated with Radical Cystectomy

Data regarding expression levels of AHNAK2 in bladder cancer (BCa) have been very scarce. We retrospectively reviewed clinical data including clinicopathological features in 120 patients who underwent radical cystectomy (RC) for BCa. The expression levels of AHNAK2 in the specimens obtained by RC were classified as low expression (LE) or high expression (HE) by immunohistochemical staining. Statistical analyses were performed to compare associations between the two AHNAK2 expression patterns and the prognoses in terms of recurrence-free survival (RFS) and cancer-specific survival (CSS). A Kaplan-Meier analysis showed that patients with HE had a significantly worse RFS and CSS than those with LE (hazard ratio [HR]: 1.78, 95% confidence interval [CI]: 1.02-2.98, p = 0.027 and HR: 1.91, 95% CI: 1.08-3.38, p = 0.023, respectively). In a multivariate analysis, independent risk factors for worse RFS and CSS were shown as HE (HR: 1.96, 95% CI: 1.08-3.53, p = 0.026 and HR: 2.22, 95% CI: 1.14-4.31, p = 0.019, respectively) and lymph node metastasis (HR: 2.04, 95% CI: 1.09-3.84, p = 0.026 and HR: 1.19, 95% CI: 1.25-4.97, p = 0.009, respectively). The present study showed that AHNAK2 acts as a novel prognostic biomarker in patients with RC for BCa.

Proteomic comparison between different tissue preservation methods for identification of promising biomarkers of urothelial bladder cancer

Samples in biobanks are generally preserved by formalin-fixation and paraffin-embedding (FFPE) and/or optimal cutting temperature compound (OCT)-embedding and subsequently frozen. Mass spectrometry (MS)-based analysis of these samples is now available via developed protocols, however, the differences in results with respect to preservation methods needs further investigation. Here we use bladder urothelial carcinoma tissue of two different tumor stages (Ta/T1-non-muscle invasive bladder cancer (NMIBC), and T2/T3-muscle invasive bladder cancer (MIBC)) which, upon sampling, were divided and preserved by FFPE and OCT. Samples were parallel processed from the two methods and proteins were analyzed with label-free quantitative MS. Over 700 and 1200 proteins were quantified in FFPE and OCT samples, respectively. Multivariate analysis indicates that the preservation method is the main source of variation, but also tumors of different stages could be differentiated. Proteins involved in mitochondrial function were overrepresented in OCT data but missing in the FFPE data, indicating that these proteins are not well preserved by FFPE. Concordant results for proteins such as HMGCS2 (uniquely quantified in Ta/T1 tumors), and LGALS1, ANXA5 and plastin (upregulated in T2/T3 tumors) were observed in both FFPE and OCT data, which supports the use of MS technology for biobank samples and encourages the further evaluation of these proteins as biomarkers.

A representation learning approach for recovering scatter-corrected spectra from Fourier-transform infrared spectra of tissue samples

Infrared spectra obtained from cell or tissue specimen have commonly been observed to involve a significant degree of scattering effects, often Mie scattering, which probably overshadows biochemically relevant spectral information by a nonlinear, nonadditive spectral component in Fourier transform infrared (FTIR) spectroscopic measurements. Correspondingly, many successful machine learning approaches for FTIR spectra have relied on preprocessing procedures that computationally remove the scattering components from an infrared spectrum. We propose an approach to approximate this complex preprocessing function using deep neural networks. As we demonstrate, the resulting model is not just several orders of magnitudes faster, which is important for real-time clinical applications, but also generalizes strongly across different tissue types. Using Bayesian machine learning approaches, our approach unveils model uncertainty that coincides with a band shift in the amide I region that occurs when scattering is removed computationally based on an established physical model. Furthermore, our proposed method overcomes the trade-off between computation time and the corrected spectrum being biased towards an artificial reference spectrum.

Investigation of urinary components in rat model of ketamine-induced bladder fibrosis based on metabolomics

Background

Ketamine abuse has been linked to the system's damage, presenting with lower urinary tract symptoms (LUTS). While the pathogenesis of ketamine-induced urinary damage is not fully understood, fibrosis is believed to be a potential mechanism. A metabolomic investigation of the urinary metabolites in ketamine abuse was conducted to gain insights into its pathogenesis.

Methods

A rat model of ketamine induced bladder fibrosis was established through tail vein injection of ketamine hydrochloride and control group was established through tail vein injection of the equivalent normal saline. Hematoxylin and eosin (H&E) staining and Masson trichrome staining were performed to evaluated bladder pathology. Urinary components were detected based on a metabolomic approach using ultra-high performance liquid tandem chromatography quadrupole time of flight mass spectrometry (UHPLC-QTOFMS platform). Orthogonal projections analyzed the data to latent structures discriminant analysis (OPLS-DA) and bioinformatics analysis.

Results

The rat model of ketamine induced bladder fibrosis was confirmed through H&E and Masson trichrome staining. There were marked differences in the urinary metabolites between the experimental group and the control group. Compared to the control group, 16 kinds of differential metabolites were up-regulated and 102 differential metabolites were down-regulated in the urine samples of the ketamine group. Bioinformatics analysis revealed the related metabolic pathways.

Conclusions

Using a ketamine-induced bladder fibrosis rat model, this study identified the differential urinary metabolites expressed following ketamine treatment. These results provide vital clues for exploring the pathogenesis of ketamine-induced LUTS and may further contribute to the disease's diagnosis and treatment.

AHNAK2 is a novel prognostic marker and correlates with immune infiltration in papillary thyroid cancer: Evidence from integrated analysis

Papillary thyroid cancer (PTC) is the most prevalent endocrine tumor, and its incidence is still increasing. The mechanisms of PTC dedifferentiation and malignant progression remain unclear. In this study, we identified AHNAK2 as a key gene in PTC by differential expression analysis among four GEO datasets and validated its overexpression profile by data from the Oncomine, TCGA, and HPA databases and IHC staining analysis. AHNAK2 upregulation significantly correlated with advanced grades, stages, and lymph node events. Survival analysis suggested that AHNAK2 overexpression was coupled with poor overall survival. The immune infiltration analysis by TIMER and CIBERSORT indicated that AHNAK2 expression tightly correlated with the infiltration of diverse immune cell types, especially T cell subtypes. In addition, AHNAK2 is correlated with the expression of other conventional key genes of TC, such as PIK3CA, MAPK1, CTNNB1, and SLC5A5. AHNAK2 may be a novel prognostic marker for PTC.

Bioinformatics analysis of the clinical value and potential mechanisms of AHNAK2 in papillary thyroid carcinoma

BACKGROUND

AHNAK2 has been recently reported as a biomarker in many cancers. However, a systematic investigation of AHNAK2 in papillary thyroid carcinoma (PTC) has not been conducted.

RESULTS

AHNAK2 is overexpressed in PTC tissues and could be an independent prognostic factor. AHNAK2 expression was significantly high in patients with advanced stage, advanced T classification, lymph node metastasis, increased BRAF mutations and decreased RAS mutations. Cell adhesion-, cell junction-, and immune-related pathways were the most frequently noted in gene set enrichment analysis. AHNAK2 expression in PTC was positively correlated with immune infiltration and negatively correlated with AHNAK2 methylation. AHNAK2 expression was significantly positively correlated with tumor progression and poor overall survival (OS) in pan-cancer patients.

CONCLUSIONS

AHNAK2 is a good biomarker for the diagnosis and prognosis of PTC. AHNAK2 may promote thyroid cancer progression through cell adhesion-, cell junction-, and immune-related pathways. Methylation may act as an upstream regulator to inhibit the expression and biological function of AHNAK2. Additionally, AHNAK2 has broad prognostic value in pan-cancer.

METHODS

Based on The Cancer Genome Atlas (TCGA) data, we screened AHNAK2-related genes through weighted gene coexpression network analysis and explored the clinical value and the potential mechanism of AHNAK2 in PTC by multiomics analysis.

AHNAK2 Is Associated with Poor Prognosis and Cell Migration in Lung Adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD), as the main subtype of lung cancer, is one of the common causes of cancer-related deaths worldwide. The AHNAK family is correlated with cell structure and migration, cardiac calcium channel signaling, and tumor metastasis. Previous studies showed AHNAK2 could promote tumor progression and cell migration in melanoma and renal clear cell carcinoma. However, the role of AHNAK2 in LUAD remains unknown.

METHODS

We examined the levels of AHNAK2 in pathological specimens and the database of Clinical Proteomic Tumor Analysis Consortium-Lung adenocarcinoma (CPTAC-LUAD), The Cancer Genome Atlas-Lung Adenocarcinoma (TCGA-LUAD), Gene Expression Omnibus dataset (GSE72094, GSE26939), and The Genotype-Tissue Expression (GTEx) of lung tissue samples. Univariate Cox regression, multivariate Cox regression, and Kaplan-Meier survival analysis were performed to reveal the relationship between AHNAK2 and prognosis. A nomogram was constructed to predict 2- or 3-year overall survival and validated via calibration curves, receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA). Furthermore, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to explore the functional role of AHNAK2 in lung adenocarcinoma. Finally, by transfecting siRNA, we examined the regulatory effect of AHNAK2 on cell migration.

RESULTS

The expression of AHNAK2 was upregulated in tumor samples and correlated with poor prognosis in LUAD patients. Nomogram with AHNAK2 and clinical parameters showed a good prediction in overall survival (OS), especially the 2-year OS. In addition, functional analyses and wound healing assay suggested that AHNAK2 might be involved in the regulation of migration in LUAD.

CONCLUSION

In summary, our study showed that AHNAK2 might be a novel biomarker in LUAD and revealed the potential mechanism of AHNAK2 in LUAD progression which could provide new insights for target therapy.

Grayscale representation of infrared microscopy images by extended multiplicative signal correction for registration with histological images

Fourier-transform infrared (FTIR) microspectroscopy is rounding the corner to become a label-free routine method for cancer diagnosis. In order to build infrared-spectral based classifiers, infrared images need to be registered with Hematoxylin and Eosin (H&E) stained histological images. While FTIR images have a deep spectral domain with thousands of channels carrying chemical and scatter information, the H&E images have only three color channels for each pixel and carry mainly morphological information. Therefore, image representations of infrared images are needed that match the morphological information in H&E images. In this paper, we propose a novel approach for representation of FTIR images based on extended multiplicative signal correction highlighting morphological features that showed to correlate well with morphological information in H&E images. Based on the obtained representations, we developed a strategy for global-to-local image registration for FTIR images and H&E stained histological images of parallel tissue sections.

Label-free, automated classification of microsatellite status in colorectal cancer by infrared imaging

Challenging histopathological diagnostics in cancer include microsatellite instability-high (MSI-H) colorectal cancer (CRC), which occurs in 15% of early-stage CRC and is caused by a deficiency in the mismatch repair system. The diagnosis of MSI-H cannot be reliably achieved by visual inspection of a hematoxylin and eosin stained thin section alone, but additionally requires subsequent molecular analysis. Time- and sample-intensive immunohistochemistry with subsequent fragment length analysis is used. The aim of the presented feasibility study is to test the ability of quantum cascade laser (QCL)-based infrared (IR) imaging as an alternative diagnostic tool for MSI-H in CRC. We analyzed samples from 100 patients with sporadic CRC UICC stage II and III. Forty samples were used to develop the random forest classifier and 60 samples to verify the results on an independent blinded dataset. Specifically, 100% sensitivity and 93% specificity were achieved based on the independent 30 MSI-H- and 30 microsatellite stable (MSS)-patient validation cohort. This showed that QCL-based IR imaging is able to distinguish between MSI-H and MSS for sporadic CRC - a question that goes beyond morphological features - based on the use of spatially resolved infrared spectra used as biomolecular fingerprints.

Deimination Protein Profiles in Alligator mississippiensis Reveal Plasma and Extracellular Vesicle-Specific Signatures Relating to Immunity, Metabolic Function, and Gene Regulation

Alligators are crocodilians and among few species that endured the Cretaceous-Paleogene extinction event. With long life spans, low metabolic rates, unusual immunological characteristics, including strong antibacterial and antiviral ability, and cancer resistance, crocodilians may hold information for molecular pathways underlying such physiological traits. Peptidylarginine deiminases (PADs) are a group of calcium-activated enzymes that cause posttranslational protein deimination/citrullination in a range of target proteins contributing to protein moonlighting functions in health and disease. PADs are phylogenetically conserved and are also a key regulator of extracellular vesicle (EV) release, a critical part of cellular communication. As little is known about PAD-mediated mechanisms in reptile immunology, this study was aimed at profiling EVs and protein deimination in Alligator mississippiensis. Alligator plasma EVs were found to be polydispersed in a 50-400-nm size range. Key immune, metabolic, and gene regulatory proteins were identified to be posttranslationally deiminated in plasma and plasma EVs, with some overlapping hits, while some were unique to either plasma or plasma EVs. In whole plasma, 112 target proteins were identified to be deiminated, while 77 proteins were found as deiminated protein hits in plasma EVs, whereof 31 were specific for EVs only, including proteins specific for gene regulatory functions (e.g., histones). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed KEGG pathways specific to deiminated proteins in whole plasma related to adipocytokine signaling, while KEGG pathways of deiminated proteins specific to EVs included ribosome, biosynthesis of amino acids, and glycolysis/gluconeogenesis pathways as well as core histones. This highlights roles for EV-mediated export of deiminated protein cargo with roles in metabolism and gene regulation, also related to cancer. The identification of posttranslational deimination and EV-mediated communication in alligator plasma revealed here contributes to current understanding of protein moonlighting functions and EV-mediated communication in these ancient reptiles, providing novel insight into their unusual immune systems and physiological traits. In addition, our findings may shed light on pathways underlying cancer resistance, antibacterial and antiviral resistance, with translatable value to human pathologies.

Spatially multiplexed RNA in situ hybridization to reveal tumor heterogeneity

Multiplexed RNA in situ hybridization for the analysis of gene expression patterns plays an important role in investigating development and disease. Here, we present a method for multiplexed RNA-ISH to detect spatial tumor heterogeneity in tissue sections. We made use of a microfluidic chip to deliver ISH-probes locally to regions of a few hundred micrometers over time periods of tens of minutes. This spatial multiplexing method can be combined with ISH-approaches based on signal amplification, with bright field detection and with the commonly used format of formalin-fixed paraffin-embedded tissue sections. By using this method, we analyzed the expression of HER2 with internal positive and negative controls (ActB, dapB) as well as predictive biomarker panels (ER, PgR, HER2) in a spatially multiplexed manner on single mammary carcinoma sections. We further demonstrated the applicability of the technique for subtype differentiation in breast cancer. Local analysis of HER2 revealed medium to high spatial heterogeneity of gene expression (Cohen effect size r = 0.4) in equivocally tested tumor tissues. Thereby, we exemplify the importance of using such a complementary approach for the analysis of spatial heterogeneity, in particular for equivocally tested tumor samples. As the method is compatible with a range of ISH approaches and tissue samples, it has the potential to find broad applicability in the context of molecular analysis of human diseases.

Traditional Classification and Novel Subtyping Systems for Bladder Cancer

Bladder cancer is the most common tumor in the urinary system, with approximately 420,000 new cases and 160,000 deaths per year. The European Organization for Research and Treatment of Cancer (EOTRC) classifies non-muscular invasive bladder cancer (NMIBC) into low-risk, medium-risk and high-risk groups based on a comprehensive analysis of NMIBC pathological parameters and the risk of recurrence and progression to muscular invasive bladder cancer (MIBC). Traditional classification systems are based on pathologic grading, staging systems, and clinical prognosis. However, the pathological parameters of the tumor cannot fully reflect the “intrinsic characteristics” of bladder cancer, and tumors with a similar pathology exhibit different biological behaviors. Furthermore, although the traditional classification system cannot accurately predict the risk of recurrence or the progression of bladder cancer patients (BCs) individually, this method is widely used in clinical practice because of its convenient operation. With the development of sequencing and other technologies, the genetics-based molecular subtyping of bladder cancer has become increasingly studied. Compared with the traditional classification system, it provides more abundant tumor biological information and is expected to assist or even replace the traditional typing system in the future.

PDAC-ANN: an artificial neural network to predict pancreatic ductal adenocarcinoma based on gene expression

BACKGROUND

Although the pancreatic ductal adenocarcinoma (PDAC) presents high mortality and metastatic potential, there is a lack of effective therapies and a low survival rate for this disease. This PDAC scenario urges new strategies for diagnosis, drug targets, and treatment.

METHODS

We performed a gene expression microarray meta-analysis of the tumor against normal tissues in order to identify differentially expressed genes (DEG) shared among all datasets, named core-genes (CG). We confirmed the CG protein expression in pancreatic tissue through The Human Protein Atlas. It was selected five genes with the highest area under the curve (AUC) among these proteins with expression confirmed in the tumor group to train an artificial neural network (ANN) to classify samples.

RESULTS

This microarray included 461 tumor and 187 normal samples. We identified a CG composed of 40 genes, 39 upregulated, and one downregulated. The upregulated CG included proteins and extracellular matrix receptors linked to actin cytoskeleton reorganization. With the Human Protein Atlas, we verified that fourteen genes of the CG are translated, with high or medium expression in most of the pancreatic tumor samples. To train our ANN, we selected the best genes (AHNAK2, KRT19, LAMB3, LAMC2, and S100P) to classify the samples based on AUC using mRNA expression. The network classified tumor samples with an f1-score of 0.83 for the normal samples and 0.88 for the PDAC samples, with an average of 0.86. The PDAC-ANN could classify the test samples with a sensitivity of 87.6 and specificity of 83.1.

CONCLUSION

The gene expression meta-analysis and confirmation of the protein expression allow us to select five genes highly expressed PDAC samples. We could build a python script to classify the samples based on RNA expression. This software can be useful in the PDAC diagnosis.

A Panel of Urinary Long Non-coding RNAs Differentiate Bladder Cancer from Urocystitis

Liquid biopsy is becoming a promising method for non-invasive cancer detection. In several proof-of-concept studies, long non-coding RNAs (lncRNAs) were found to be potential biomarkers for bladder cancer detection. The objective of this study was to discover a panel of cell-free, urinary lncRNAs as liquid biopsy biomarkers to non-invasively differentiate bladder cancer from chronic urocystitis. To this end, we collected urine samples from both bladder cancer patients and urocystitis patients. These samples were divided into discovery group and validation group. In the discovery group, the expression levels of 16 cell-free urinary lncRNAs were measured by qPCR to discover candidate biomarkers. The diagnostic performance of the candidate lncRNA biomarkers was then evaluated, which led to a panel of lncRNA biomarkers for bladder cancer detection. The performance of this panel of biomarkers was further evaluated in the validation group to see if these lncRNA biomarkers could discriminate the bladder cancer patients from urocystitis patients. We found that all of the 16 lncRNAs evaluated in this study demonstrated significant difference (p<0.05) of expression between bladder cancer patients and urocystitis patients. Nine lncRNAs provided decent diagnostic performance with area under the receiver operating characteristic (ROC) curve (AUC) reaching 0.70 or higher. We then selected the top four lncRNAs, namely UCA1-201, HOTAIR, HYMA1 and MALAT1, to form a panel of urinary biomarkers. Using this panel, bladder cancer patients could be discriminated from urocystitis patients, with sensitivity and specificity reaching 95.7% and 94.3%, respectively. Finally, we confirmed the applicability of the four-lncRNA panel in an independent validation study that included 60 bladder cancer patients and 60 urocystitis patients. Our study paves the way for further studies aimed at large-scale clinical tests of developing lncRNA biomarkers in urine for bladder cancer diagnostics.

TGFBI Protein Is Increased in the Urine of Patients with High-Grade Urothelial Carcinomas, and Promotes Cell Proliferation and Migration

Here, we discovered TGFBI as a new urinary biomarker for muscle invasive and high-grade urothelial carcinoma (UC). After biomarker identification using antibody arrays, results were verified in urine samples from a study population consisting of 303 patients with UC, and 128 urological and 58 population controls. The analyses of possible modifying factors (age, sex, smoking status, urinary leukocytes and erythrocytes, and history of UC) were calculated by multiple logistic regression. Additionally, we performed knockdown experiments with TGFBI siRNA in bladder cancer cells and investigated the effects on proliferation and migration by wound closure assays and BrdU cell cycle analysis. TGFBI concentrations in urine are generally increased in patients with UC when compared to urological and population controls (1321.0 versus 701.3 and 475.6 pg/mg creatinine, respectively). However, significantly increased TGFBI was predominantly found in muscle invasive (14,411.7 pg/mg creatinine), high-grade (8190.7 pg/mg) and de novo UC (1856.7 pg/mg; all p < 0.0001). Knockdown experiments in vitro led to a significant decline of cell proliferation and migration. In summary, our results suggest a critical role of TGFBI in UC tumorigenesis and particularly in high-risk UC patients with poor prognosis and an elevated risk of progression on the molecular level.