Cancer Prognostic Biomarkers Research Articles

Abstract A020: LncTransformer: Identifying novel prognostic long-noncoding RNA biomarkers for pancreatic cancer using deep learning

Abstract Pancreatic cancer remains a formidable challenge due to high mortality rates and limited therapeutic options due to late detection of the cancer. Recent literature has focused on investigating the potential of long noncoding RNAs (lncRNAs) as biomarkers in several cancers. To identify significantly altered expression of lncRNA in pancreatic cancer, I collected information from the Cancer Genome Atlas (TCGA) and extracted RNA- sequencing (RNA-seq) transcriptomic profiles of pancreatic carcinomas. Out of 60,660 gene transcripts shared between 151 pancreatic cancer patients, 38 lncRNAs were identified to be significantly differentially expressed. To find the lncRNAs related to metastatic progression of pancreatic cancer, different machine learning algorithms, including logistic regression (LR), support vector machine (SVM), random forest classifier (RFC), and a custom autoencoder model were trained to identify potential prognostic biomarkers. The custom deep learning model predicted metastatic prognostic biomarkers with a 92% accuracy, with the second-best accuracies coming from the SVM and RFC models, having an accuracy of 76%. Using the deep learning model, the following novel lncRNA and gene biomarkers were identified: LINC01300, SERPINB13, AC010789.1, TMPRSS15, DUSP5- DT, AL513128.3, MIR205HG, LINC00486, RF00019, LINC01115, and AC133530.1. Further investigating these results, specifically the identified lncRNA, the LINC01300 was identified to be potentially circulating in the blood. If further analysis proves this hypothesis correct then LINC01300 along with other identified genes could serve as biomarkers in a liquid biopsy. The identified biomarkers would be especially helpful in rural areas with lower accessibility to healthcare as it would allow for an easier, more efficient, and cost-effective method of pancreatic cancer detection. Based on these findings, further investigations of this gene panel in vitro and in vivo will be conducted, as they could be targeted for improved outcomes in pancreatic cancer patients and assist in the diagnosis of metastatic progression based on RNA-seq data of primary pancreatic tumors or the development of a liquid biopsy. Citation Format: Shivali Singh. LncTransformer: Identifying novel prognostic long-noncoding RNA biomarkers for pancreatic cancer using deep learning [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr A020.

Abstract A067: lncTransformer: Identifying novel prognostic biomarkers for pancreatic cancer using deep learning models

Abstract Pancreatic cancer remains a formidable challenge due to high mortality rates and limited therapeutic options due to late detection of the cancer. Recent literature has focused on investigating the potential of long noncoding RNAs (lncRNAs) as biomarkers in several cancers. To identify significantly altered expression of lncRNA in pancreatic cancer, I collected information from the Cancer Genome Atlas (TCGA) and extracted RNA-sequencing (RNA-seq) transcriptomic profiles of pancreatic carcinomas. Out of 60,660 gene transcripts shared between 151 pancreatic cancer patients, 38 lncRNAs were identified to be significantly differentially expressed. To find the lncRNAs related to metastatic progression of pancreatic cancer, different machine learning algorithms, including logistic regression (LR), support vector machine (SVM), random forest classifier (RFC), and a custom autoencoder model were trained to identify potential prognostic biomarkers. The custom deep learning model predicted metastatic prognostic biomarkers with a 92% accuracy, with the second-best accuracies coming from the SVM and RFC models, having an accuracy of 76%. Using the deep learning model, the following novel lncRNA and gene biomarkers were identified: LINC01300, SERPINB13, AC010789.1, TMPRSS15, DUSP5-DT, AL513128.3, MIR205HG, LINC00486, RF00019, LINC01115, and AC133530.1. Further investigating these results, specifically the identified lncRNA, the LINC01300 was identified to be potentially circulating in the blood. If further analysis proves this hypothesis correct then LINC01300 along with other identified genes could serve as a biomarker panel for a liquid biopsy. The identified biomarkers would be especially helpful in rural areas with lower accessibility to healthcare as it would allow for an easier, more efficient, and cost-effective method of pancreatic cancer detection. Based on these findings, further investigations of this gene panel are being conducted, in vitro and in vivo, to create a novel liquid biopsy to assist in the prognostic diagnosis of metastatic progression of pancreatic cancer. Citation Format: Shivali Singh. lncTransformer: Identifying novel prognostic biomarkers for pancreatic cancer using deep learning models [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A067.

Abstract A003: CD45 positive circulating tumor cells as a prognostic marker in prostate cancer

Abstract Prostate cancer is one of the most common types of cancer among male patients worldwide, making early diagnosis and effective treatment crucial for improving patient survival rates. In this regard, circulating tumor cells (CTCs) have garnered attention as an important biomarker for cancer staging and monitoring treatment response. In prostate cancer, CTCs are typically identified as keratin+/CD45- cells, with additional markers such as PSA or PSMA also being used. The conventional CTC identification way have dismissed CD45+/keratin+ double-positive cells as artifacts by nonspecific binding during the staining process. However, recent studies have reported the presence of CD45+/keratin+ CTCs in various types of cancers, highlighting metastatic potential and their ability to evade the immune system. These findings require re- examination of the role for the double positive CTCs. Therefore, this study aimed to identify the presence of CD45+ CTCs in prostate cancer and to assess their clinical significance. Blood samples were collected from 80 prostate cancer patients, followed by unbiased isolation of CTCs, which were stained using PSA, keratin, and CD45. The detected CTCs were categorized based on CD45 expression, and the clinical relevance of each group was validated using patient information. Through this study, we aimed to confirm whether the CD45+ CTCs can be a clinical indicator in prostate cancer. The results revealed that CD45+ CTCs accounted for 90% of all detected CTCs, likely due to their high immune evasion capabilities. Additionally, in 17% of the patients, only CD45+ CTCs were detected, indicating the potential to expand CTC detection even in patients where CTCs were previously undetected using conventional criteria. The number of CD45+ CTCs was significantly higher in patients with progressive disease compared to those with complete or partial response, showing greater statistical significance than analyses using CD45- CTCs. Furthermore, the number of PSA+ CTCs was closely linked to changes in plasma PSA levels, suggesting they could be used as a supporting marker to enhance the accuracy of current plasma PSA tests. These findings indicate that CD45+ CTCs in prostate cancer are not merely artifacts of nonspecific binding but are indeed meaningful cells closely related to cancer progression. This study suggests that CD45+ CTCs have the potential to be utilized as a prognostic biomarker in prostate cancer. By including CD45+ CTCs, which were previously overlooked in conventional CTC analysis methods, more sensitive and accurate cancer monitoring could be achieved. In-depth future studies on the clinical applicability of CD45+ CTCs could significantly contribute to predicting prognosis and developing personalized treatment strategies for prostate cancer patients. Citation Format: Hyeongjung Woo, Woon-Hae Kim, Hyun Young Shin, Hyerin Jung, Hyung Ho Lee, Minseok S. Kim, Jae Young Joung. CD45 positive circulating tumor cells as a prognostic marker in prostate cancer [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A003.

AR (CAG)n Microsatellite and APEX1 c.444T>G (p.Asp148Glu) Polymorphisms as Independent Prognostic Biomarkers in Prostate Cancer: Insights from an Argentinian Cohort

Background/Objectives: Prostate cancer (PCa) is the leading malignancy and the third most common cause of cancer-related death in Argentinian men. Predicting outcomes in localized PCa remains difficult due to tumor heterogeneity. In this study, we assessed the impact of AR (CAG)n and APEX1 c.444T>G polymorphisms on biochemical relapse in Argentine patients with localized PCa. Methods: We genotyped blood samples from 123 PCa patients for AR (CAG)n and APEX1 p.Asp148Glu (c.444T>G) polymorphisms. Associations with clinicopathological parameters and biochemical relapse-free survival (BRFS) were assessed. Results: AR (CAG)20–23 was associated with a family history of breast/ovarian cancer (p = 0.0469). The combination of AR (CAG)20–23 and APEX1 c.444TT/GG correlated with a 2.89 times higher risk of biochemical relapse (log-rank p = 0.006). Multivariable analysis confirmed AR and APEX1 polymorphisms as independent predictors of biochemical relapse (HR = 3.95, p = 0.002). In patients with PSA levels <10 ng/mL, combined AR (CAG)20–23 and APEX1 c.444TT/GG genotypes were significantly associated with an increased risk of biochemical relapse (HR = 2.61, p = 0.044). Multivariable analysis confirmed the prognostic significance of these genotypes (HR = 3.44, p = 0.02). Conclusions: This study has identified AR (CAG)n and APEX1 c.444T>G polymorphisms as independent predictors of PCa relapse in Argentinian patients, suggesting their potential use in improving prognostic models.

Extracellular Vesicle lncRNAs as Key Biomolecules for Cell-to-Cell Communication and Circulating Cancer Biomarkers

Extracellular vesicles (EVs) are secreted by almost every cell type and are considered carriers of active biomolecules, such as nucleic acids, proteins, and lipids. Their content can be uptaken and released into the cytoplasm of recipient cells, thereby inducing gene reprogramming and phenotypic changes in the acceptor cells. Whether the effects of EVs on the physiology of recipient cells are mediated by individual biomolecules or the collective outcome of the total transferred EV content is still under debate. The EV RNA content consists of several types of RNA, such as messenger RNA (mRNA), microRNA (miRNA), and long non-coding RNA (lncRNA), the latter defined as transcripts longer than 200 nucleotides that do not code for proteins but have important established biological functions. This review aims to update our insights on the functional roles of EV and their cargo non-coding RNA during cancer progression, to highlight the utility of EV RNA as novel diagnostic or prognostic biomarkers in cancer, and to tackle the technological advances and limitations for EV RNA identification, integrity assessment, and preservation of its functionality.

Immune Cell-Derived Exosomes: A Cell-Free Cutting-Edge Tumor Immunotherapy.

Extracellular vesicles (EVs) are cellular communication molecules and are classified into three major subpopulations, such as microvesicles, apoptotic bodies, and exosomes. Among these, exosomes-based cancer research is a cutting-edge investigation approach to cancer understanding. During cancer progression , tumor-derived exosomes can reprogram the cellular system and promote cancer. Circulating exosomes in the body fluids such as blood, plasma, serum, saliva, CSF, sweat, and tears play a key role in identifying diagnostic and prognostic cancer biomarkers. Diverse therapeutic sources of exosomes including stem cells, plants, and immune cells, etc. exhibit significant cancer-healing properties. Although cancer-targeting immunotherapy is an effective strategy, it has limitations such as toxicity, and high costs. In comparison, immune cell-derived exosomes-based immunotherapy is a cell-free approach for cancer treatment and has advantages like less toxicity, biocompatibility, reduced immunogenicity, and efficient, target-specific cancer therapeutic development. This review highlights the therapeutic signature of immune cell-derived exosomes for cancer treatment.

Pan-cancer analysis of IRF1 focusing on prognostic and immunological roles in non-small cell lung cancer

Interferon regulatory factor 1 (IRF1) significantly affects tumour occurrence and development. This study aimed to analyse its function as a pan-cancer prognostic indicator. We compared IRF1 expression and prognostic significance in normal and tumour samples from different databases. Accordingly, we performed in vitro experiments and immunohistochemistry (IHC) to investigate the role of IRF1 in non-small cell lung cancer (NSCLC). Our findings indicate that IRF1 expression is significantly correlated with prognosis, the tumour microenvironment, and immune cell infiltration. Furthermore, receiver operating characteristic (ROC) analysis revealed that IRF1 had high accuracy in distinguishing cancerous tissues from normal ones. Notably, IRF1 expression was linked to immune-related and immune checkpoint genes. Cell proliferation, invasion, and migration were significantly related to IRF1 expression. IHC indicated that IRF1 was downregulated in NSCLC tissues. Our study provides comprehensive bioinformatic analysis and experimental verification of IRF1, suggesting its potential as a prognostic biomarker in cancer.

GUCA2A dysregulation as a promising biomarker for accurate diagnosis and prognosis of colorectal cancer

Colorectal cancer is a leading cause of global mortality and presents a significant barrier to improving life expectancy. The primary objective of this study was to discern a unique differentially expressed gene (DEG) that exhibits a strong association with colorectal cancer. By achieving this goal, the research aims to contribute valuable insights to the field of translational medicine. We performed analysis of colorectal cancer microarray and the TCGA colon adenoma carcinoma (COAD) datasets to identify DEGs associated with COAD and common DEGs were selected. Furthermore, a pan-cancer analysis encompassing 33 different cancer types was performed to identify differential genes significantly expressed only in COAD. Then, comprehensively in-silico analysis including gene set enrichment analysis, constructing Protein–Protein interaction, co-expression, and competing endogenous RNA (ceRNA) networks, investigating the correlation between tumor-immune signatures in distinct tumor microenvironment and also the potential interactions between the identified gene and various drugs was executed. Further, the candidate gene was experimentally validated in tumoral colorectal tissues and colorectal adenomatous polyps by qRael-Time PCR. GUCA2A emerged as a significant DEG specific to colorectal cancer (|log2FC|> 1 and adjusted q-value < 0.05). Importantly, GUCA2A exhibited excellent diagnostic performance for COAD, with a 99.6% and 78% area under the curve (AUC) based on TCGA-COAD and colon cancer patients. In addition, GUCA2A expression in adenomatous polyps equal to or larger than 5 mm was significantly lower compared to smaller than 5 mm. Moreover, low expression of GUCA2A significantly impacted overall patient survival. Significant correlations were observed between tumor-immune signatures and GUCA2A expression. The ceRNA constructed included GUCA2A, 8 shared miRNAs, and 61 circRNAs. This study identifies GUCA2A as a promising prognostic and diagnostic biomarker for colorectal cancer. Further investigations are warranted to explore the potential of GUCA2A as a therapeutic biomarker.

Long non-coding RNAs as prognostic biomarkers in non-muscle invasive bladder cancer: A systematic review

Traditional prognostic tools for non-muscle invasive bladder cancer (NMIBC) often overestimate progression and recurrence risks, underscoring the need for more precise biomarkers. While long non-coding ribonucleic acids (lncRNAs) have been reviewed in bladder cancer, no review has focused on NMIBC. The aim of this study was to address this gap by investigating the role of lncRNAs in predicting NMIBC survival and progression. A systematic review was conducted using PubMed, Scopus, and Cochrane databases as of July 31, 2024. Prognostic studies investigating the association between lncRNA expression and survival outcomes, such as cancer-specific survival, disease-free survival, recurrence-free survival, or overall survival, using Kaplan-Meier curves or hazard ratios, were included. A total of three studies were analyzed, involving 279 NMIBC patients and focusing on three lncRNAs: urothelial cancer associated 1 (UCA1), growth arrest-specific 5 (GAS5), and up-regulated in non-muscle invasive bladder cancer (UNMIBC). Increased UCA1 expression was strongly associated with poor disease-free survival (hazard ratio (HR): 1.974; 95%CI: 1.061–3.673; p=0.032) and progression-free survival (HR: 3.476; 95%CI: 1.187–10.18; p=0.023). Reduced GAS5 expression was significantly associated with poor disease-free survival (HR: 2.659; 95%CI: 1.348–5.576; p=0.005) and progression-free survival (HR: 6.628; 95%CI: 1.494–29.40; p=0.013). Higher level of UNMIBC was strongly associated with poor recurrence-free survival (HR: 2.362; 95%CI: 1.504–4.837; p=0.007). In conclusion, lncRNAs have potential as prognostic biomarkers in NMIBC, with UCA1 and UNMIBC overexpression and GAS5 underexpression being significant in predicting disease recurrence and progression, highlighting the clinical relevance of monitoring these lncRNAs to improve prognosis and guide treatment decisions.

LncRNA MALAT1 as diagnostic and prognostic biomarker in colorectal cancers: A systematic review and meta-analysis.

This study investigated the relationship between the long non-coding RNA Metastasis-Associated Lung Adenocarcinoma Transcript 1 (MALAT1) expression and colorectal cancer (CRC) using a thorough systematic review and meta-analysis. Under the PRISMA guidelines, a systematic review was conducted on studies published from the databases' inception to September 18, 2023. Prognostic value and diagnostic accuracy were explored. Additionally, the association between levels of MALAT1 expression and pathological features was investigated. The statistical analysis was performed using the "meta" package of R. Among the pathological parameters examined, based on three studies involving 51 cases of metastatic CRC and 135 cases of non-metastatic CRC, a statistically significant correlation was found between the expression level of MALAT1 and distant metastasis, with an OR of 16.0118 (95% CI: 4.5618-56.2015). Three studies involving 378 cases reported overall survival and had a pooled HR of 2.3854 (95% CI: 1.3272-4.2875). Three studies involving 436 cases reported disease-free survival and had a pooled HR of 2.4772 (95% CI: 1.3774-4.4549). All prognosis studies utilized tumor tissue samples as specimens to assess the expression level of MALAT1. Case-to-control diagnostic studies with 126 cases and 126 controls had a pooled AUC value of 0.6173 (95% CI: 0.5436-0.6909), a pooled sensitivity of 0.675 (95% CI: 0.324-0.900), and a pooled specificity of 0.771 (95% CI: 0.685-0.839). The expression of MALAT1 in CRC is highly correlated with distant metastasis and has an impact on survival and prognosis. MALAT1 could also be employed as a diagnostic biomarker. More prospective studies should be performed to assess the MALAT1 diagnostic potential in the early stages of CRC.

Exploring the effect of exercise training on breast cancer's pathologic response and tumor immune microenvironment after neoadjuvant chemotherapy.

Pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) and the percentage of tumor-infiltrating lymphocytes (TILs) are established prognostic biomarkers in early breast cancer (BC). While exercise training is effective as supportive care throughout the BC journey, its impact on the efficacy of NAC is unknown. This study aims to investigate the influence of a supervised exercise training program (SETP) on pCR and TILs in BC women undergoing NAC. Retrospective exploratory analysis of the subgroup of BC patients treated with NAC included in a clinical trial randomizing to STEP and control arm. Endpoints were pCR, biopsy, and stromal TILs. Sixty-four participants were included, with a mean age of 50.3 ± 10.1years, predominantly stage II and III disease (n = 58, 90.6%), HER2 + (n = 23, 35.9%), or triple-negative (n = 19, 29.7%) tumors. pCR was achieved in 56.7% and 55.9% in the STEP and control arm (p = 0.950). In the STEP arm, median TILs were 5.0 (0.0-80.0) and 5.0 (5.0-30.0), while in the control arm, 5.0 (0.0-90.0) and 0.0 (0.0-30.0) for biopsy and tumor site, respectively. The difference in TILs between arms was 0.04 (confidence interval (CI 95%) - 13.6, 13.7; p = 0.995) and - 4.3 (CI 95% - 11.5, 2.9; (p = 0.233) for biopsy and tumor site, respectively. No statistically significant difference was discerned between the groups concerning TILs of the biopsy. However, a marginally higher TIL level at the tumor site was associated with the SETP arm. No differences were discerned within and between groups on both pCR and TILs, in possible relation to the exploratory nature of the analysis. Future adequately powered research is warranted.

CircLPHN3 correlates with prognosis in colorectal cancer and regulates cellular processes by targeting miR-142-5p.

Colorectal cancer (CRC) is often diagnosed late and has a poor prognosis. Circular RNAs (circRNAs) have been identified as prognostic biomarkers in various cancers, including CRC. The objective was to elucidate the role of circLPHN3 (hsa_circ_0069865) in CRC progression and to provide a promising prognostic marker for CRC. CircLPHN3 was identified through bioinformatics analysis of the GSE121842 dataset. The levels of circLPHN3 in CRC samples were analyzed by real time-quantitative polymerase chain reaction. Its clinical significance was assessed using the Kaplan-Meier curve and multivariate Cox regression. Downstream microRNAs of circLPHN3 were predicted with the RNAhybrid, Circular RNA Interactome, and starBase online databases. The target of miR-142-5p was predicted using miRDB, TargetScanHuman, starBase, and miRWalk databases. The relationship between circLPHN3, miR-142-5p, and LDB2 was verified by dual luciferase reporter assay. The function of circLPHN3 on CRC cell growth and metastasis was measured using Transwell and the cell counting kit-8 assay. Significant downregulation of circLPHN3 was found in CRC. CircLPHN3 was closely related to higher tumor node metastasis stage, lymph node metastasis, and predicted unfavorable prognosis. miR-142-5p was highly expressed in CRC and its expression was negatively regulated by circLPHN3. Overexpression of circLPHN3 curbed CRC cell growth, migration, and invasion, mediated by miR-142-5p. Moreover, LDB2 was identified as a target of miR-142-5p. CircLPHN3 acted as a prognostic biomarker and tumor suppressor for CRC via modulating miR-142-5p.

Pan-Immune-Inflammation Value as a Novel Prognostic Biomarker for Advanced Pancreatic Cancer

Pan-Immune-Inflammation Value as a Novel Prognostic Biomarker for Advanced Pancreatic Cancer

Assessment of microbiome signature for predicting prognosis of gastrointestinal cancers

The gut microbiome has emerged as a crucial factor in the prognosis and progression of gastrointestinal cancers, though its mechanisms remain largely unknown. This review aims to provide a comprehensive summary of the current research on the use of gut microbiome signatures as prognostic biomarkers in gastrointestinal cancers. We present an overview of emerging studies that explore microbial signatures as biomarkers for different gastrointestinal cancers, and explore the potential benefits and limitations of using these signatures as prognostic tools. Finally, we discuss how these microbial signatures can be harnessed to improve the detection, prevention, and treatment of gastrointestinal cancers.

Mechanisms of tamoxifen resistance: insight from long non-coding RNAs.

Breast cancer(BC) is the second most prevalent tumor in the world and one of the most lethal tumors in women. Patients with estrogen receptor-positive breast cancer can obtain significant advantages from endocrine therapies including tamoxifen, aromatase inhibitors, and others. However, the development of primary or acquired drug resistance ultimately leads to discontinuation of treatment with adverse consequences for breast cancer patients, and the underlying mechanisms have not been fully elucidated. Long non-coding RNAs (lncRNAs) play pivotal roles in orchestrating fundamental biochemical and cellular processes. They exert regulatory control over various processes including epigenetics, gene transcription, post-transcriptional modifications, and translation. Additionally, they influence key biological events like cell cycle progression, cell differentiation, and development. For the past few years, the relationship between lncRNAs and endocrine resistance has gained increasing attention, leading to a surge in related studies. LncRNAs mediate tamoxifen resistance in cancer by utilizing a variety of molecular mechanisms, including enhanced estrogen receptor (ER) signaling, inhibition of apoptosis, autophagy, exosome-mediated transfer, epigenetic alterations, epithelial-to-mesenchymal transition, and acting as competitive endogenous RNAs(ceRNAs). In this comprehensive review, we systematically summarize the critical role and intricate molecular mechanisms by which lncRNAs influence the development of tamoxifen resistance in breast cancer. Furthermore, we propose the potential clinical significance of lncRNAs as innovative therapeutic targets and prognostic biomarkers for breast cancer.

Circulating Tumor Cell Count and Overall Survival in Patients With Metastatic Hormone-Sensitive Prostate Cancer

In metastatic hormone-sensitive prostate cancer (mHSPC), new first-line combination therapies have enhanced overall survival (OS), but clinical outcomes for individual patients vary greatly and are difficult to predict. Peripheral blood circulating tumor cell (CTC) count is the most extensively validated prognostic liquid biomarker in metastatic castration-resistant prostate cancer (mCRPC), and recent studies have suggested that it may also be informative in mHSPC. To examine the prognostic value of CTC count in men with mHSPC. In this prognostic study, peripheral blood was drawn at registration (baseline) and at progression to mCRPC in the S1216 study (March 1, 2013, to July 15, 2017), a phase 3, prospective, randomized clinical trial in men with mHSPC. The CTCs were enumerated using a US Food and Drug Administration-cleared isolation platform. Counts were categorized as 0, 1 to 4, or 5 or more CTCs per 7.5 mL based on the prognostic value of these cut points in prior studies. The data analysis was performed between October 28, 2022, and June 15, 2023. Metastatic hormone-sensitive prostate cancer. Circulating tumor cell count was evaluated for an association with 3 prespecified trial end points: OS, progression-free survival, and 7-month prostate-specific antigen, after adjusting for other baseline covariates using proportional hazards and logistic regression models. Of 1313 S1216 participants (median [IQR] age, 68 [44-92] years), evaluable samples from 503 (median [IQR] age, 69 [46-90] years) with newly diagnosed mHSPC were collected at baseline, and 93 samples were collected at progression. Baseline counts were 5 or more CTCs per 7.5 mL in 60 samples (11.9%), 1 to 4 CTCs per 7.5 mL in 107 samples (21.3%), and 0 CTCs per 7.5 mL in 336 samples (66.8%). Median OS for men with 5 or more CTCs per 7.5 mL was 27.9 months (95% CI, 24.1-31.2 months) compared with 56.2 months (95% CI, 45.7-69.8 months) for men with 1 to 4 CTCs per 7.5 mL and not reached at 78.0 months follow-up for men with 0 CTCs per 7.5 mL. After adjusting for baseline clinical covariates, men with 5 or more CTCs per 7.5 mL at baseline had a significantly higher hazard of death (hazard ratio, 3.22; 95% CI, 2.22-4.68) and disease progression (hazard ratio, 2.46; 95% CI, 1.76-3.43) and a lower likelihood of prostate-specific antigen complete response (odds ratio, 0.26; 95% CI, 0.12-0.54) compared with men with 0 CTCs per 7.5 mL at baseline. Adding baseline CTC count to other known prognostic factors (covariates only: area under the curve, 0.73; 95% CI, 0.67-0.79) resulted in an increased prognostic value for 3-year survival (area under the curve, 0.79; 95% CI, 0.73-0.84). In this prognostic study, the findings validate CTC count as a prognostic biomarker that improved upon existing prognostic factors and estimated vastly divergent survival outcomes regardless of subsequent lines of therapy. As such, baseline CTC count in mHSPC may serve as a valuable noninvasive biomarker to identify men likely to have poor survival who may benefit from clinical trials of intensified or novel regimens.

UBE2T is a diagnostic and prognostic biomarker for endometrial cancer.

Endometrial cancer (UCEC) is one of the most common malignant tumors in gynecology, and early diagnosis is crucial for its treatment. Currently, there is a lack of early screening tests specific to UCEC, and treatment advances are limited. It is crucial to identify more sensitive biomarkers for screening, diagnosis, and predicting UCEC. Previous studies have shown that UBE2T is involved in the development of various tumors such as breast cancer and liver cancer, but research on the role of UBE2T in UCEC is limited. Using data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and UALCAN databases, we analyzed the differential expression of UBE2T mRNA and protein in endometrial cancer (UCEC), along with its clinical relevance. A total of 113 clinical samples were collected, and immunohistochemistry and Western blot analysis were employed to validate bioinformatics analysis results. Volcano plots were generated using UBE2T and its differentially expressed genes, and a protein-protein interaction (PPI) network was constructed. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), and immune infiltration analysis were used to predict the functional role of UBE2T in UCEC progression. Correlation between UBE2T expression and patient survival was analyzed using TCGA data, and Kaplan-Meier survival curves were plotted. UBE2T is significantly overexpressed in UCEC and correlates with poor prognosis. Its overexpression is closely associated with mitosis, cell cycle regulation, and histological grade in UCEC patients. UBE2T is highly expressed in UCEC and suppresses anti-tumor immune responses in UCEC patients. It serves as a key participant in UCEC progression, associated with a range of adverse outcomes, and holds potential as a clinical diagnostic and prognostic biomarker.

Bioinformatic analysis of molecular characteristics and oncogenic features of CARD14 in human cancer

Aberrant caspase recruitment domain family member 14 (CARD14) signaling has been strongly associated with inflammatory skin conditions. CARD14 acts as a scaffold protein, ultimately activating the transcription factor NF-KB. Although primarily studied in the context of inflammation, recent research has suggested its potential implications in tumorigenesis. In this study, we gathered The Cancer Genome Atlas (TCGA) tumor data to gauge the involvement of CARD14 in cancer, including genetic alterations, expression patterns, survival correlations, immune cell infiltration and functional interactions across diverse cancer types. We found heightened CARD14 expression in most tumors and there was a significant correlation between CARD14 expression and the prognosis of patients for certain tumors. For instance, patients with higher CARD14 expression had a better prognosis in sarcoma, lung, cervix and head and neck cancers. Moreover, CARD14 expression positively correlated with neutrophil infiltration in most of the cancer types analyzed. Finally, enrichment analysis showed that epithelial development and differentiation pathways were involved in the functional mechanism of CARD14. Our results show that CARD14 may have the potential to become a prognostic biomarker in several cancers, hence, further prospective studies will be required for its validation.

GD2 and GD3 gangliosides as prognostic biomarkers in high grade serous ovarian cancer

ObjectiveGangliosides GD2 and GD3 have been proposed to be of significance in diagnosis of ovarian masses. We aim to study serum GD2 and GD3 gangliosides as predictors of oncological outcomes among high grade serous (HGS) ovarian cancer (OC). Materials and methodsA retrospective study including biobanked serum samples of HGS OC treated between 2005 and 2016. Serum GD2 and GD3 concentrations were quantified using indirect ELISA and analyzed with respect to survival. ResultsSixty patients were included. Patients with GD3>12.8 ng/mL had shorter PFS when compared to patients with lower level; median 31 vs. 67 months, p = 0.005. Patients with GD2> 7.1 ng/mL had shorter median PFS than those with lower level of (23 vs. 52 months, p = 0.024.) Patients with GD3>14.5 ng/mL had shorter OS vs. patients with lower level (median 31 vs. 70 months, p = 0.002). In a Cox regression, following adjustment for age, CA-125, disease stage and age, serum elevated GD3 was independently associated with short PFS (adjusted hazard ratio 2.0, 95 % CI 1.1–3.8, p=.024). In a separate Cox regression, elevated GD2 was independently associated with PFS (adjusted hazard ratio3.0 (1.2–7.7). p=.019. High serum GD3 and GD2 were independently associated with short OS as well. ConclusionsHigh levels of serum GD2 and GD3 in HGS OC were associated with shorter PFS and OS. GD3 is superior to GD2 as a biomarker for prognosis.

Comprehensive analysis reveals that P4HA3 is a prognostic and diagnostic gastric cancer biomarker that can predict immunotherapy efficacy

Gastric cancer (GC) is one of the most challenging malignant tumors worldwide, primarily because of its high incidence and mortality rates. Prolyl 4-hydroxylase subunit alpha 3 (P4HA3) has been established as a pivotal factor for facilitating cell proliferation, invasion, and metastasis across multiple human tumors. Nevertheless, the precise role of P4HA3in GC has not been fully elucidated. In this study, we used data from The Cancer Genome Atlas (TCGA) to examine the role of P4HA3 as a potential biomarker for predicting immunotherapy response in patients with GC. Our comprehensive analysis of data from the TCGA, TIMER, and other databases revealed a significant association between elevated P4HA3 expression in GC and adverse prognostic outcomes. Furthermore, we confirmed that P4HA3 expression was strongly correlated with immune infiltrating cells, immune infiltration markers, the tumor mutational burden (TMB), microsatellite instability (MSI), the immune score, the stromal score, and immune checkpoints, thus highlighting P4HA3 as a crucial and dependable therapeutic target within the context of immune-based antitumor strategies. Our findings suggest that P4HA3 may function as an immune-related biomarker in the pathogenesis and treatment of GC, indicating that P4HA3 is a promising prognostic and therapeutic target for this malignancy.