P16 expression and clinicopathological features of oral and oropharyngeal squamous cell carcinoma

Background: There is an epidemiological shift in head and neck squamous cell carcinoma (HNSCC) attributable to HPV infection. HPV positive HNSCC has unique biology, risk factors, clinicopathological characteristics and outcome. There is a large variation in the published prevalence of HPV-related HNSCCs in India ranging from 7 to 78.7%. This study aims to find the P16 expression in the oral cavity and oropharyngeal SCC, thereby prevalence of HPV in our setting and to define the clinicopathological characteristics of HPV positive tumours in our setting.Methods: 210 specimens of primary Oral squamous cell carcinoma (OSCC) and Oropharyngeal Squamous cell carcinoma (OPSCC) were included. Immunohistochemistry was done using monoclonal mouse p16 antibody. Clinical details of each case were collected. Analysis was done using SPSS software and the association of P16 and clinicopathological variables were calculated using Fishers exact test.Results: P16 positive expression is observed only in 1/122 (0.82%) of OSCC and 8/88 (9%) of OPSCC. P16 positivity showed significant association with Grade of tumor (p= 0.008) and histological variant of SCC (p=0.00). 77.7% of P16 positive tumours are Grade 2 and 66.6% of Basaloid SCC was P16 positive. There is no significant association between p16 expression and other variables (subsite, age, gender, alcoholism, smoking, betel chewing and stage).Conclusions: P16 positivity was higher in oropharyngeal than in oral cancer. However, the HPV positivity rates are lower than other parts of India.

Investigating the Relationship Between the Expression Level of Mucin Gene Cluster (MUC2, MUC5A, and MUC5B) and Clinicopathological Characterization of Colorectal Cancer

Background: Colorectal cancer (CRC) is one of the most common cancers in the world and has a high mortality rate. It is accepted that dysfunction in the expression of mucins are associated with the occurrence and development of CRC. Therefore, the present study aimed to investigate the expression of MUC2, MUC5A, and MUC5B genes in CRC and their relationship with clinicopathological variables. Materials and Methods: The population included 28 patients after a colonoscopy and confirmation of the results. Tumors and parallel adjacent normal tissues from CRC patients were collected. RNA extraction and cDNA synthesis were performed using the corresponding kits. The gene primer was designed and RT-PCR was used to evaluate gene expression. The t-test and ANOVA were used to examine the differences between the different groups. Data analysis was performed using Prism8 software. Tumors from CRC patients were retrospectively collected from Taleghani Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Results: The results showed that the expression of MUC2, MUC5A, and MUC5B genes was lower in patients with CRC aged 50 years or younger than was in older patients (P<0.05). Only the MUC5B gene expression was associated with tumor grades, which was higher in poorly differentiated tumors. The expression of MUC5A and MUC2 genes was higher in stage IV of the tumor than in other stages (P<0.05). Conclusion: Among the changes in the expression of MUC secretory genes, including MUC2, MUC5A, and MUC5B and clinicopathological variables, there was a relationship that could have prognostic and diagnostic value in CRC. [GMJ.2021;10:e2030]

Identification of a Five Autophagy Subtype-Related Gene Expression Pattern for Improving the Prognosis of Lung Adenocarcinoma

Background: Autophagy plays an important role in lung adenocarcinoma (LUAD). In this study, we aimed to explore the autophagy-related gene (ARG) expression pattern and to identify promising autophagy-related biomarkers to improve the prognosis of LUAD.Methods: The gene expression profiles and clinical information of LUAD patients were downloaded from the Cancer Genome Atlas (TCGA), and validation cohort information was extracted from the Gene Expression Omnibus database. The Human Autophagy Database (HADb) was used to extract ARGs. Gene expression data were analyzed using the limma package and visualized using the ggplot2 package as well as the pheatmap package in R software. Functional enrichment analysis was also performed for the differentially expressed ARGs (DEARGs). Then, consensus clustering revealed autophagy-related tumor subtypes, and differentially expressed genes (DEGs) were screened according to the subtypes. Next, the univariate Cox and multivariate Cox regression analyses were used to identify independent prognostic ARGs. After overlapping DEGs and the independent prognostic ARGs, the predictive risk model was established and validated. Correlation analyses between ARGs and clinicopathological variables were also explored. Finally, the TIMER and TISIDB databases were used to further explore the correlation analysis between immune cell infiltration levels and the risk score as well as clinicopathological variables in the predictive risk model.Results: A total of 222 genes from the HADb were identified as ARGs, and 28 of the 222 genes were pooled as DEARGs. The most significant GO term was autophagy (p = 3.05E-07), and KEGG analysis results indicated that 28 DEARGs were significantly enriched in the ErbB signaling pathway (p &lt; 0.001). Then, consensus clustering analysis divided the LUAD into two clusters, and a total of 168 DEGs were identified according to cluster subtypes. Then univariate and multivariate Cox regression analyses were used to identify 12 genes that could serve as independent prognostic indicators. After overlapping 168 DEGs and 12 genes, 10 genes (ATG4A, BAK1, CAPNS1, CCR2, CTSD, EIF2AK3, ITGB1, MBTPS2, SPHK1, ST13) were selected for the further exploration of the prognostic pattern. Survival analysis results indicated that this risk model identified the prognosis (p = 4.379E-10). Combined with the correlation analysis results between ARGs and clinicopathological variables, five ARGs were screened as prognostic genes. Among them, SPHK1 expression levels were positively correlated with CD4+ T cells and dendritic cell infiltration levels.Conclusions: In this study, we constructed a predictive risk model and identified a five autophagy subtype-related gene expression pattern to improve the prognosis of LUAD. Understanding the subtypes of LUAD is helpful to accurately characterize the LUAD and develop personalized treatment.

A New Ferroptosis-Related lncRNA Signature Predicts the Prognosis of Bladder Cancer Patients

Background: Ferroptosis is closely related to the occurrence and development of cancer. An increasing number of studies have induced ferroptosis as a treatment strategy for cancer. However, the predictive value of ferroptosis-related lncRNAs in bladder cancer (BC) still need to be further elucidated. The purpose of this study was to construct a predictive signature based on ferroptosis-related long noncoding RNAs (lncRNAs) to predict the prognosis of BC patients.Methods: We downloaded RNA-seq data and the corresponding clinical and prognostic data from The Cancer Genome Atlas (TCGA) database and performed univariate and multivariate Cox regression analyses to obtain ferroptosis-related lncRNAs to construct a predictive signature. The Kaplan-Meier method was used to analyze the overall survival (OS) rate of the high-risk and low-risk groups. Gene set enrichment analysis (GSEA) was performed to explore the functional differences between the high- and low-risk groups. Single-sample gene set enrichment analysis (ssGSEA) was used to explore the relationship between the predictive signature and immune status. Finally, the correlation between the predictive signature and the treatment response of BC patients was analyzed.Results: We constructed a signature composed of nine ferroptosis-related lncRNAs (AL031775.1, AL162586.1, AC034236.2, LINC01004, OCIAD1-AS1, AL136084.3, AP003352.1, Z84484.1, AC022150.2). Compared with the low-risk group, the high-risk group had a worse prognosis. The ferroptosis-related lncRNA signature could independently predict the prognosis of patients with BC. Compared with clinicopathological variables, the ferroptosis-related lncRNA signature has a higher diagnostic efficiency, and the area under the receiver operating characteristic curve was 0.707. When patients were stratified according to different clinicopathological variables, the OS of patients in the high-risk group was shorter than that of those in the low-risk group. GSEA showed that tumor- and immune-related pathways were mainly enriched in the high-risk group. ssGSEA showed that the predictive signature was significantly related to the immune status of BC patients. High-risk patients were more sensitive to anti-PD-1/L1 immunotherapy and the conventional chemotherapy drugs sunitinib, paclitaxel, cisplatin, and docetaxel.Conclusion: The predictive signature can independently predict the prognosis of BC patients, provides a basis for the mechanism of ferroptosis-related lncRNAs in BC and provides clinical treatment guidance for patients with BC.

A Combined Nomogram Model to Predict Disease-free Survival in Triple-Negative Breast Cancer Patients With Neoadjuvant Chemotherapy

Background: To investigate whether the radiomics signature (Rad-score) of DCE-MRI images obtained in triple-negative breast cancer (TNBC) patients before neoadjuvant chemotherapy (NAC) is associated with disease-free survival (DFS). Develop and validate an intuitive nomogram based on radiomics signatures, MRI findings, and clinicopathological variables to predict DFS.Methods: Patients (n = 150) from two hospitals who received NAC from August 2011 to May 2017 were diagnosed with TNBC by pathological biopsy, and follow-up through May 2020 was retrospectively analysed. Patients from one hospital (n = 109) were used as the training group, and patients from the other hospital (n = 41) were used as the validation group. ROIs were drawn on 1.5 T MRI T1W enhancement images of the whole volume of the tumour obtained with a 3D slicer. Radiomics signatures predicting DFS were identified, optimal cut-off value for Rad-score was determined, and the associations between DFS and radiomics signatures, MRI findings, and clinicopathological variables were analysed. A nomogram was developed and validated for individualized DFS estimation.Results: The median follow-up time was 53.5 months, and 45 of 150 (30.0%) patients experienced recurrence and metastasis. The optimum cut-off value of the Rad-score was 0.2528, which stratified patients into high- and low-risk groups for DFS in the training group (p&lt;0.001) and was validated in the external validation group. Multivariate analysis identified three independent indicators: multifocal/centric disease status, pCR status, and Rad-score. A nomogram based on these factors showed discriminatory ability, the C-index of the model was 0.834 (95% CI, 0.761–0.907) and 0.868 (95% CI, 0.787–949) in the training and the validation groups, respectively, which is better than clinicoradiological nomogram(training group: C-index = 0.726, 95% CI = 0.709–0.743; validation group: C-index = 0.774,95% CI = 0.743–0.805).Conclusion: The Rad-score derived from preoperative MRI features is an independent biomarker for DFS prediction in patients with TNBC to NAC, and the combined radiomics nomogram improved individualized DFS estimation.

Classification of Septic Shock Phenotypes Based on the Presence of Hypotension and Hyperlactatemia in Cats

Background: Three different phenotypes of septic shock based on changes in blood pressure and lactate are recognized in people. Dysoxic shock, representing the combination of fluid-refractory hypotension and hyperlactatemia, is characterized by greater disease severity and mortality compared to cryptic shock (hyperlactatemia alone) and vasoplegic shock (hypotension with normal blood lactate). Little is known about septic shock and specifically its phenotypes in cats.Objective: To analyze the characteristics and prognostic implications of three septic shock phenotypes in cats with sepsis.Methods: Cats with septic shock were prospectively included. Septic shock was defined by the presence of hypotension (mean blood pressure &lt;60 mmHg) requiring vasopressor support and/or persistent hyperlactatemia (&gt;4 mmol/L) and classified in three subgroups: dysoxic shock, vasoplegic shock and cryptic shock. Clinical and clinicopathological variables including APPLEfast and APPLEfull scores, occurrence of multi-organ dysfunction syndrome (MODS; presence of at least two dysfunctional organs simultaneously) and outcome were compared among subgroups. Cats with sepsis showing normal blood pressure and lactate concentrations hospitalized during the study period were included as uncomplicated sepsis, and compared to cats with septic shock for selected variables. Length of hospital stay and mortality were evaluated in the whole study population. Odds ratios for mortality were calculated using logistic regression analysis. Significance was set at P &lt; 0.05.Results: The study enrolled 48 cats with uncomplicated sepsis and 37 cats with septic shock (dysoxic shock n = 17; vasoplegic shock n = 11; cryptic shock n = 7). Cats with dysoxic shock had significantly higher APPLEfast and APPLEfull scores compared to vasoplegic and cryptic shock. Mortality rates were not significantly different among cryptic (57%), dysoxic (65%) and vasoplegic shock (91%), while MODS occurrence was significantly lower in cats with cryptic shock (57%) compared to patients affected by dysoxic (94%) and vasoplegic (100%) shock. Cats with septic shock had higher frequency of MODS and greater mortality rate than cats with uncomplicated sepsis.Conclusion: Despite similar in-hospital mortality, cats with dysoxic and vasoplegic shock are characterized by having higher occurrence of multi- organ dysfunction compared to cats affected by cryptic shock. Results from this study suggest novel means of identifying high-risk subgroups of septic cats.

Various Expression and Clinical Significance of Checkpoint Molecules in Advanced Osteosarcoma: Possibilities for Novel Immunotherapy

Background: The fact that studies on anti-programmed cell death 1 (PD-1) or its relevant ligand 1 (PD-L1) have yielded such few responses greatly decreases the confidence in immunotherapy with checkpoint inhibitors for advanced osteosarcoma. We examined 35 advanced osteosarcoma specimens, characterized the expression of various checkpoint molecules with immunohistochemistry and analyzed the relationship of the expression of these checkpoint molecules with patients’ clinical courses. Methods: Immunohistochemistry for B7-H3, CD47, PD-L1, TIM3, TGF-β, CXCR 4, CD27, IDO1, KIRs and SDF-1 was performed on 35 resected advanced osteosarcoma specimens. Associations between the marker levels and clinicopathological variables and survival were evaluated. Results: The positive rates of B7-H3, CD47, PD-L1, TIM3, and TGF-β expression in this sample of 35 heavily treated osteosarcomas were 29% (10/35), 15% (5/35), 9% (3/35), 6% (2/35) and 6% (2/35), respectively, and diverse staining intensities were observed. Among these advanced patients, 15/35 (43%) had positive checkpoint expression, of whom 33% (5/15) showed evidence of the coexpression of more than 1 checkpoint molecule. We did not find any obvious correlation with clinicopathological characteristics and the positive expression of these molecules; however, for this small sample, a tendency for benefiting from anti-PD-1 therapy was observed in patients with positive B7-H3 expression (P = 0.057). Conclusions: Our study first reported that only a small subset of progressive osteosarcomas expressed tumor immune-associated checkpoint molecules. Those osteosarcomas that had ever been responsive to anti-PD-1 therapy usually had evidence of the coexpression of multiple checkpoint molecules.

Clinical and Histological Predictors of Renal Survival in Patients with Biopsy-Proven Diabetic Nephropathy

<b><i>Introduction:</i></b> Clinical indicators or pathological features alone cannot reliably predict renal survival in patients with biopsy-confirmed diabetic nephropathy (DN). Therefore, this analysis sought to develop and validate a predictive model incorporating both clinical and pathological markers to predict renal outcomes in patients with biopsy-confirmed DN. <b><i>Methods:</i></b> A predictive nomogram was developed based upon data pertaining to 194 patients with biopsy-confirmed DN. The prognostic relevance of individual clinicopathological variables was assessed through univariate and multivariate Cox regression analyses. A prognostic nomogram was then developed and validated based upon concordance (C)-index values and calibration curves. Internal validation was conducted through bootstrap resampling, while the clinical utility of this model was assessed via a decision curve analysis (DCA) approach. <b><i>Results:</i></b> Nephrotic-range 24-h proteinuria, late-stage CKD, glomerular classification III–IV, and IFTA score 2–3 were all identified as independent predictors of poor renal outcomes in DN patients and were incorporated into our final nomogram. Calibration curves revealed good agreement between predicted and actual 3- and 5-year renal survival in DN patients with the C-index value for this nomogram at 0.845 (95% CI: 0.826–0.864). DCA revealed that our nomogram was superior to models based solely upon clinical indicators. <b><i>Conclusion:</i></b> A predictive nomogram incorporating clinical and pathological indicators was developed and validated for the prediction of renal survival outcomes in patients with biopsy-confirmed DN. This model will be of value to clinicians, as it can serve as an easy-to-use and reliable tool for physicians to guide patient management based on individualized risk.