Screening Protein Prognostic Biomarkers for Stomach Adenocarcinoma Based on The Cancer Proteome Atlas

LZTS2 methylation as a potential diagnostic and prognostic marker in LIHC and STAD: Evidence from bioinformatics and in vitro analyses

The rising mortality rate from cancer, driven by the absence of reliable biomarkers, highlights the pressing need for advanced diagnostic and prognostic strategies. This study investigates LZTS2's role as a pan-cancer biomarker, emphasizing its predictive value for immunotherapy and therapeutic targeting. Unlike existing biomarkers such as AFP in hepatocellular carcinoma or HER2 in gastric cancer, which exhibit tissue-specific utility, LZTS2 demonstrates unique cross-cancer applicability, as evidenced by its consistent dysregulation in both liver hepatocellular carcinoma (LIHC) and stomach adenocarcinoma (STAD) alongside emerging associations with other malignancies. Leveraging advanced bioinformatics tools and databases including UALCAN, KM-plotter, and The Cancer Genome Atlas (TCGA), alongside experimental validation in LIHC and STAD cell lines, we analyze LZTS2 expression patterns and their clinical relevance. Notably, LZTS2's dual role-acting as a tumor suppressor in some cancers while promoting oncogenesis in others-distinguishes it from conventional single-function markers, offering novel insights into its regulatory versatility. Our findings reveal that LZTS2 mutations and expression levels are closely associated with cancer progression and patient survival, solidifying its potential as a prognostic biomarker. Notably, LZTS2 expression correlates with various clinicopathological parameters, underscoring its significance in cancer biology. Pathway analysis highlights LZTS2's involvement in critical biological processes, providing actionable insights for therapeutic interventions. Quantitative real-time polymerase chain reaction (qRT-PCR) and quantitative methylation-specific PCR (qMSP) experimental validations confirm these results, further establishing LZTS2's utility as a multi-dimensional biomarker that integrates genetic, epigenetic, and immunological features-a capability rarely observed in existing markers. This comprehensive analysis positions LZTS2 as a pivotal player in cancer progression, opening promising avenues for enhanced clinical management.

Deciphering the Controversial Role of TP53 Inducible Glycolysis and Apoptosis Regulator (TIGAR) in Cancer Metabolism as a Potential Therapeutic Strategy

Tumor metabolism has emerged as a critical target in cancer therapy, revolutionizing our understanding of how cancer cells grow, survive, and respond to treatment. Historically, cancer research focused on genetic mutations driving tumorigenesis, but in recent decades, metabolic reprogramming has been recognized as a hallmark of cancer. The TP53 inducible glycolysis and apoptosis regulator, or TIGAR, affects a wide range of cellular and molecular processes and plays a key role in cancer cell metabolism by regulating the balance between glycolysis and antioxidant defense mechanisms. Cancer cells often exhibit a shift towards aerobic glycolysis (the Warburg effect), which allows rapid energy production and gives rise to biosynthetic intermediates for proliferation. By inhibiting glycolysis, TIGAR can reduce the proliferation rate of cancer cells, particularly in early-stage tumors or specific tissue types. This metabolic shift may limit the resources available for rapid cell division, thereby exerting a tumor-suppressive effect. However, this metabolic shift also leads to increased levels of reactive oxygen species (ROS), which can damage the cell if not properly managed. TIGAR helps protect cancer cells from excessive ROS by promoting the pentose phosphate pathway (PPP), which generates NADPH-a key molecule involved in antioxidant defense. Through its actions, TIGAR decreases the glycolytic flux while increasing the diversion of glucose-6-phosphate into the PPP. This reduces ROS levels and supports biosynthesis and cell survival by maintaining the balance of nucleotides and lipids. The role of TIGAR has been emerging as a prognostic and potential therapeutic target in different types of cancers. This review highlights the role of TIGAR in different types of cancer, evaluating its potential role as a diagnostic marker and a therapeutic target.

RNF4 mediated degradation of PDHA1 promotes colorectal cancer metabolism and metastasis

This study investigates the role of RNF4-mediated ubiquitination and degradation of PDHA1 in colorectal cancer (CRC) metabolism and metastasis. Integrating (The Cancer Genome Atlas) TCGA and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases, proteomic, clinical, and metabolomic analyses were performed, revealing PDHA1 as a prognostic marker in CRC. Immunohistochemical staining confirmed lower PDHA1 expression in metastatic CRC tissues. In vitro experiments demonstrated that PDHA1 overexpression inhibited CRC cell proliferation, migration, and invasion. RNF4 was identified as a key mediator in the ubiquitination degradation of PDHA1, influencing glycolytic pathways in CRC cells. Metabolomic analysis of serum samples from metastatic CRC patients further supported these findings. In vivo experiments, including xenograft and metastasis models, validated that RNF4 knockdown stabilized PDHA1, inhibiting tumor formation and metastasis. This study highlights the critical role of RNF4-mediated PDHA1 ubiquitination in promoting glycolytic metabolism, proliferation, and metastasis in CRC.

AC099850.3 promotes HBV-HCC cell proliferation and invasion through regulating CD276: a novel strategy for sorafenib and immune checkpoint combination therapy

BACKGROUND

This study investigates the molecular mechanisms of CC@AC&SF@PP NPs loaded with AC099850.3 siRNA and sorafenib (SF) for improving hepatitis B virus-related hepatocellular carcinoma (HBV-HCC).

METHODS

A dataset of 44 HBV-HCC patients and their survival information was selected from the TCGA database. Immune genes related to survival status were identified using the ImmPort database and WGCNA analysis. A prognostic risk model was constructed and analyzed using Lasso regression. Differential analysis was performed to screen key genes, and their significance and predictive accuracy for HBV-HCC were validated using Kaplan-Meier survival curves, ROC analysis, CIBERSORT analysis, and correlation analysis. The correlation between AC099850.3 and the gene expression matrix was calculated, followed by GO and KEGG enrichment analysis using AC099850.3 and its co-expressed genes. HepG2.2.15 cells were selected for in vitro validation, and lentivirus interference, cell cycle determination, CCK-8 experiments, colony formation assays, Transwell experiments, scratch experiments, and flow cytometry were performed to investigate the effects of key genes on HepG2.2.15 cells. A subcutaneous transplanted tumor model in mice was constructed to verify the inhibitory effect of key genes on HBV-HCC tumors. Subsequently, pH-triggered drug release NPs (CC@AC&SF@PP) were prepared, and their therapeutic effects on HBV-HCC in situ tumor mice were studied.

RESULTS

A prognostic risk model (AC012313.9, MIR210HG, AC099850.3, AL645933.2, C6orf223, GDF10) was constructed through bioinformatics analysis, showing good sensitivity and specificity in diagnostic prediction. AC099850.3 was identified as a key gene, and enrichment analysis revealed its impact on cell cycle pathways. In vitro cell experiments demonstrated that AC099850.3 promotes HepG2.2.15 cell proliferation and invasion by regulating immune checkpoint CD276 expression and cell cycle progression. In vivo, subcutaneously transplanted tumor experiments showed that AC099850.3 promotes the growth of HBV-HCC tumors in nude mice. Furthermore, pH-triggered drug release NPs (CC@AC&SF@PP) loaded with AC099850.3 siRNA and SF were successfully prepared and delivered to the in situ HBV-HCC, enhancing the effectiveness of combined therapy for HBV-HCC.

CONCLUSIONS

AC099850.3 accelerates the cell cycle progression and promotes the occurrence and development of HBV-HCC by upregulating immune checkpoint CD276 expression. CC@AC&SF@PP NPs loaded with AC099850.3 siRNA and SF improve the effectiveness of combined therapy for HBV-HCC.

Bioinformatics Analysis and Validation of Potential Markers Associated with Prediction and Prognosis of Gastric Cancer

Gastric cancer (GC) is one of the most common cancers worldwide. Most patients are diagnosed at the progressive stage of the disease, and current anticancer drug advancements are still lacking. Therefore, it is crucial to find relevant biomarkers with the accurate prediction of prognoses and good predictive accuracy to select appropriate patients with GC. Recent advances in molecular profiling technologies, including genomics, epigenomics, transcriptomics, proteomics, and metabolomics, have enabled the approach of GC biology at multiple levels of omics interaction networks. Systemic biological analyses, such as computational inference of "big data" and advanced bioinformatic approaches, are emerging to identify the key molecular biomarkers of GC, which would benefit targeted therapies. This review summarizes the current status of how bioinformatics analysis contributes to biomarker discovery for prognosis and prediction of therapeutic efficacy in GC based on a search of the medical literature. We highlight emerging individual multi-omics datasets, such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics, for validating putative markers. Finally, we discuss the current challenges and future perspectives to integrate multi-omics analysis for improving biomarker implementation. The practical integration of bioinformatics analysis and multi-omics datasets under complementary computational analysis is having a great impact on the search for predictive and prognostic biomarkers and may lead to an important revolution in treatment.

Downregulation of lncRNA EPB41L4A-AS1 promotes gastric cancer cell proliferation, migration and invasion

BACKGROUND

The incidence of gastric cancer ranks the first among digestive tract tumors in China. However, there are no specific symptoms in the early stage of the tumor and the diagnosis process is complex, so more effective detection methods are very needed. In this study, a novel long noncoding RNA (lncRNA) was introduced as a diagnostic biomarker for gastric cancer, which brought new thinking to the exploration of its pathological mechanism and clinical prediction.

METHODS

The level of lncRNA EPB41L4A-AS1 (EPB41L4A-AS1) in gastric cancer serum and cells was verified via real-time quantitative polymerase chain reaction (RT-qPCR). Receiver operating characteristic (ROC) curve was performed based on the EPB41L4A-AS1 level, and the diagnostic possibility of EPB41L4A-AS was analyzed. The chi-square test evaluated the correlation between EPB41L4A-AS expression and clinical information. The cells were cultured and transfected in vitro, and the mediations of abnormal EPB41L4A-AS level on the viability and motility of gastric cancer cells were verified through cell counting kit-8 (CCK-8) and Transwell assay. Furthermore, luciferase activity assay was performed to confirm the sponge molecule microRNA-17-5p (miR-17-5p) of EPB41L4A-AS1.

RESULTS

EPB41L4A-AS1 was decreased in gastric cancer, and low EPB41L4A-AS1 level indicated resultful diagnostic value. Overexpression of EPB41L4A-AS1 inhibited the activity of gastric cancer cells, while knockdown of EPB41L4A-AS1 promoted tumor deterioration. EPB41L4A-AS1 directly targeted and regulated the expression ofmiR-17-5p.

CONCLUSION

This study elaborated that EPB41L4A-AS1 is lowly expressed in gastric cancer. Silencing EPB41L4A-AS1 was beneficial to cell proliferation, migration, and invasion. EPB41L4A-AS1 provides a new possibility for the diagnosis of gastric cancer patients by targeting miR-17-5p.

Identification of protein signatures for lung cancer subtypes based on BPSO method

The objective of this study was to identify protein biomarkers that can distinguish between LUAD and LUSC, critical for personalized treatment plans. The proteomic profiling data of LUAD and LUSC samples from TCPA database, along with phenotype and survival information from TCGA database were downloaded and preprocessed for analysis. We used BPSO feature selection method and identified 10 candidate protein biomarkers that have better classifying performance, as analyzed by t-SNE and PCA algorithms. To explore the causalities among these proteins and their associations with tumor subtypes, we conducted the PCStable algorithm to construct a regulatory network. Results indicated that 4 proteins, MIG6, CD26, NF2, and INPP4B, were directly linked to the lung cancer subtypes and may be useful in guiding therapeutic decision-making. Besides, spearman correlation, Cox proportional hazard model and Kaplan-Meier curve was employed to validate the biological significance of the candidate proteins. In summary, our study highlights the importance of protein biomarkers in the classification of lung cancer subtypes and the potential of computational methods for identifying key biomarkers and understanding their underlying biological mechanisms.

Screening and Identification of a Prognostic Model of Ovarian Cancer by Combination of Transcriptomic and Proteomic Data

The integration of transcriptome and proteome analysis can lead to the discovery of a myriad of biological insights into ovarian cancer. Proteome, clinical, and transcriptome data about ovarian cancer were downloaded from TCGA's database. A LASSO-Cox regression was used to uncover prognostic-related proteins and develop a new protein prognostic signature for patients with ovarian cancer to predict their prognosis. Patients were brought together in subgroups using a consensus clustering analysis of prognostic-related proteins. To further investigate the role of proteins and protein-coding genes in ovarian cancer, additional analyses were performed using multiple online databases (HPA, Sangerbox, TIMER, cBioPortal, TISCH, and CancerSEA). The final resulting prognosis factors consisted of seven protective factors (P38MAPK, RAB11, FOXO3A, AR, BETACATENIN, Sox2, and IGFRb) and two risk factors (AKT_pS473 and ERCC5), which can be used to construct a prognosis-related protein model. A significant difference in overall survival (OS), disease-free interval (DFI), disease-specific survival (DSS), and progression-free interval (PFI) curves were found in the training, testing, and whole sets when analyzing the protein-based risk score (p < 0.05). We also illustrated a wide range of functions, immune checkpoints, and tumor-infiltrating immune cells in prognosis-related protein signatures. Additionally, the protein-coding genes were significantly correlated with each other. EMTAB8107 and GSE154600 single-cell data revealed that the genes were highly expressed. Furthermore, the genes were related to tumor functional states (angiogenesis, invasion, and quiescence). We reported and validated a survivability prediction model for ovarian cancer based on prognostic-related protein signatures. A strong correlation was found between the signatures, tumor-infiltrating immune cells, and immune checkpoints. The protein-coding genes were highly expressed in single-cell RNA and bulk RNA sequencing, correlating with both each other and tumor functional states.

Identification of a Novel Protein-Based Prognostic Model in Gastric Cancers

Gastric cancer (GC) is the third leading cause of cancer-related deaths worldwide. However, there are still no reliable biomarkers for the prognosis of this disease. This study aims to construct a robust protein-based prognostic prediction model for GC patients. The protein expression data and clinical information of GC patients were downloaded from the TCPA and TCGA databases, and the expressions of 218 proteins in 352 GC patients were analyzed using bioinformatics methods. Additionally, Kaplan-Meier (KM) survival analysis and univariate and multivariate Cox regression analysis were applied to screen the prognosis-related proteins for establishing the prognostic prediction risk model. Finally, five proteins, including NDRG1_pT346, SYK, P90RSK, TIGAR, and XBP1, were related to the risk prognosis of gastric cancer and were selected for model construction. Furthermore, a significant trend toward worse survival was found in the high-risk group (p = 1.495 × 10-7). The time-dependent ROC analysis indicated that the model had better specificity and sensitivity compared to the clinical features at 1, 2, and 3 years (AUC = 0.685, 0.673, and 0.665, respectively). Notably, the independent prognostic analysis results revealed that the model was an independent prognostic factor for GC patients. In conclusion, the robust protein-based model based on five proteins was established, and its potential benefits in the prognostic prediction of GC patients were demonstrated.

Construction and validation of a novel and superior protein risk model for prognosis prediction in esophageal cancer

Esophageal cancer (EC) is recognized as one of the most common malignant tumors in the word. Based on the biological process of EC occurrence and development, exploring molecular biomarkers can provide a good guidance for predicting the risk, prognosis and treatment response of EC. Proteomics has been widely used as a technology that identifies, analyzes and quantitatively acquires the composition of all proteins in the target tissues. Proteomics characterization applied to construct a prognostic signature will help to explore effective biomarkers and discover new therapeutic targets for EC. This study showed that we established a 8 proteins risk model composed of ASNS, b-Catenin_pT41_S45, ARAF_pS299, SFRP1, Vinculin, MERIT40, BAK and Atg4B via multivariate Cox regression analysis of the proteome data in the Cancer Genome Atlas (TCGA) to predict the prognosis power of EC patients. The risk model had the best discrimination ability and could distinguish patients in the high- and low-risk groups by principal component analysis (PCA) analysis, and the high-risk patients had a poor survival status compared with the low-risk patients. It was confirmed as one independent and superior prognostic predictor by the receiver operating characteristic (ROC) curve and nomogram. K-M survival analysis was performed to investigate the relationship between the 8 proteins expressions and the overall survival. GSEA analysis showed KEGG and GO pathways enriched in the risk model, such as metabolic and cancer-related pathways. The high-risk group presented upregulation of dendritic cells resting, macrophages M2 and NK cells activated, downregulation of plasma cells, and multiple activated immune checkpoints. Most of the potential therapeutic drugs were more appropriate treatment for the low-risk patients. Through adequate analysis and verification, this 8 proteins risk model could act as a great prognostic evaluation for EC patients and provide new insight into the diagnosis and treatment of EC.

Protein expression profiling identifies a prognostic model for ovarian cancer

BACKGROUND

Owing to the high morbidity and mortality, ovarian cancer has seriously endangered female health. Development of reliable models can facilitate prognosis monitoring and help relieve the distress.

METHODS

Using the data archived in the TCPA and TCGA databases, proteins having significant survival effects on ovarian cancer patients were screened by univariate Cox regression analysis. Patients with complete information concerning protein expression, survival, and clinical variables were included. A risk model was then constructed by performing multiple Cox regression analysis. After validation, the predictive power of the risk model was assessed. The prognostic effect and the biological function of the model were evaluated using co-expression analysis and enrichment analysis.

RESULTS

394 patients were included in model construction and validation. Using univariate Cox regression analysis, we identified a total of 20 proteins associated with overall survival of ovarian cancer patients (p < 0.01). Based on multiple Cox regression analysis, six proteins (GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1) were used for model construction. Patients in the high-risk group had unfavorable overall survival (p < 0.001) and poor disease-specific survival (p = 0.001). All these six proteins also had survival prognostic effects. Multiple Cox regression analysis demonstrated the risk model as an independent prognostic factor (p < 0.001). In receiver operating characteristic curve analysis, the risk model displayed higher predictive power than age, tumor grade, and tumor stage, with an area under the curve value of 0.789. Analysis of co-expressed proteins and differentially expressed genes based on the risk model further revealed its prognostic implication.

CONCLUSIONS

The risk model composed of GSK3α/β, HSP70, MEK1, MTOR, BAD, and NDRG1 could predict survival prognosis of ovarian cancer patients efficiently and help disease management.