Cancer Biomarker Discovery for Precision Medicine: New Progress

Multiomic characterization, immunological and prognostic potential of SMAD3 in pan-cancer and validation in LIHC

SMAD3, a protein-coding gene, assumes a pivotal role within the transforming growth factor-beta (TGF-β) signaling pathway. Notably, aberrant SMAD3 expression has been linked to various malignancies. Nevertheless, an extensive examination of the comprehensive pan-cancer impact on SMAD3's diagnostic, prognostic, and immunological predictive utility has yet to be undertaken. Bioinformatics methods were employed to systematically investigate the potential carcinogenic impact of SMAD3. We extensively harnessed data from authoritative sources, including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), cBioPortal, Human Protein Atlas (HPA), UALCAN, and various other databases. Our study encompassed a comprehensive analysis of the following aspects: differential SMAD3 expression and its association with prognosis across diverse cancer types, gene mutations, immune cell infiltration, single-cell sequencing analysis, DNA methylation patterns, and drug sensitivity profiles. In vitro experiments were conducted with the primary objective of appraising both the expression profile and the precise functional attributes of SMAD3 within the milieu of Liver Hepatocellular Carcinoma (LIHC). Our findings revealed significant variations in SMAD3 expression between cancerous and adjacent normal tissues. High levels of SMAD3 expression were consistently associated with unfavorable prognoses across multiple cancer types,. Additionally, our analysis of SMAD3 methylation patterns in human cancers unveiled a favorable prognosis linked to elevated DNA methylation levels in pan-cancer. Furthermore, we identified positive associations between SMAD3 expression and RNAm6A methylation-related genes in the majority of cancers. Moreover, SMAD3 expression displayed substantial correlations with immune cell infiltration. Notably, immune checkpoint genes exhibited significant associations with SMAD3 expression across diverse cancers. Single-cell sequencing results elucidated the pan-cancer single-cell expression landscape of SMAD3. Within specific cancer subtypes, SMAD3 expression exhibited a noteworthy positive association with distinctive facets of malignancy. Finally, in our comprehensive analysis of drug sensitivity, we discerned a catalog of prospective therapeutic agents. In our comprehensive analysis across multiple cancer types, we observed a significant disparity in SMAD3 expression compared to normal tissues, and this findings suggest that SMAD3 holds promise as both a prognostic biomarker and a therapeutic target against various cancers. Difference displayed a noteworthy association with patient prognosis.

Application of acid-activated near-infrared viscosity fluorescent probe targeting lysosomes in cancer visualization

The higher viscosity and lower pH in lysosomes of cancer cells highlight their potential as biomarkers for cancer. Therefore, the development of acid-activated viscosity fluorescent probes is significant for the early diagnosis and treatment of cancer. Based on this, we have designed and synthesized a near-infrared fluorescent probe based on the 2-(2-hydroxyphenyl)benzothiazole (HBT) group, namely HBTH, to monitor the viscosity changes within lysosomes. It has been demonstrated that HBTH was extremely sensitive to viscosity, with a strong linear relationship between fluorescence intensity and log(viscosity) within the range of (logη) = 0-3.06 (a correlation coefficient of 0.98), proving its capability for quantitative viscosity measurement. In particular, the most obvious fluorescence enhancement of HBTH was only efficiently triggered by the combined effect of low pH and high viscosity. Furthermore, HBTH can rapidly localize to lysosomes by wash-free procedure at a low concentration (100 nM) and achieve high-fidelity imaging within 20 s. It can also monitor the dynamic processes of lysosomes in cells, viscosity changes under drug stimuli, and lysosomal behavior during mitophagy. Importantly, HBTH is capable of identifying tumors in tumor-bearing nude mice through in vivo imaging. These features make HBTH a powerful tool for the early diagnosis and treatment of cancer.

A combined immune and exosome-related risk signature as prognostic biomakers in acute myeloid leukemia

OBJECTIVES

Acute myeloid leukemia (AML) is one of the common hematological diseases with low survival rates. Studies have highlighted the dysregulated expression of immune-related and exosome-related genes (ERGs) in cancers. Nevertheless, it remains to be determined whether combining these genes have a prognostic significance in AML.

METHODS

Immune-ERG profiles for 151 AML patients from TCGA were analyzed. A risk model was constructed and optimized through the combination of univariate Cox regression and LASSO regression analysis. GEO datasets were utilized as the external validation for the robustness of the risk model. In addition, we performed KEGG and GO enrichment analyses to investigate the role played by these genes in AML. The variations in immune cell infiltrations among risk groups were assessed through four algorithms. Expression of hub gene in specific cell was analyzed by single-cell RNA seq.

RESULTS

A total of 85 immune-ERGs associated with prognosis were identified, enabling the construction of a risk model for AML. The risk model based on five immune-ERGs (CD37, NUCB2, LSP1, MGST1, and PLXNB1) demonstrated a correlation with the clinical outcomes. Additionally, age, FAB classification, cytogenetics risk, and risk score were identified as independent prognostic factors. The five immune-ERGs exhibited correlations with cytokine-cytokine receptor interaction, and antigen processing and presentation. Notably, the risk model demonstrated significant associations with immune responses and the expression of immune checkpoints.

CONCLUSIONS

An immune-ERG-based risk model was developed to effectively predict prognostic outcomes for AML patients. There is potential for immune therapy in AML targeting the five hub genes.

Biological functions and molecular mechanisms of LINC01116 in cancer

LINC01116, a long non-coding RNA (lncRNA), serves as an important regulator in the progression of cancer cells and has attracted increased attention in biological fields. It is overexpressed in various cancer cells and is significantly correlated with cancer development and poor prognosis in cancer patients. Moreover, LINC01116 regulates the gene expression of various cancers through intricate pathways, such as sponging the microRNAs or other non-genic manners. These signaling pathways greatly affect the cancer's biological functions, including cell growth, migration, invasion, and chemoresistance. Hence, LINC01116 may serve as a prognostic biomarker and therapeutic target for human cancer. This paper summarizes the current evidence regarding the biological functions and molecular mechanisms of LINC01116 in the progression of cancer, providing theoretical references for LINC01116-related cancer treatment in the future.

18F-Fluorodeoxyglucose Positron Emission Tomography-Based Risk Score Model for Prediction of Five-Year Survival Outcome after Curative Resection of Non-Small-Cell Lung Cancer

The aim of our retrospective study is to develop and assess an imaging-based model utilizing 18F-FDG PET parameters for predicting the five-year survival in non-small-cell lung cancer (NSCLC) patients after curative surgery. A total of 361 NSCLC patients who underwent curative surgery were assigned to the training set (n = 253) and the test set (n = 108). The LASSO regression model was used to construct a PET-based risk score for predicting five-year survival. A hybrid model that combined the PET-based risk score and clinical variables was developed using multivariate logistic regression analysis. The predictive performance was determined by the area under the curve (AUC). The individual features with the best predictive performances were co-occurrence_contrast (AUC = 0.675) and SUL peak (AUC = 0.671). The PET-based risk score was identified as an independent predictor after adjusting for clinical variables (OR 5.231, 95% CI 1.987-6.932; p = 0.009). The hybrid model, which integrated clinical variables, significantly outperformed the PET-based risk score alone in predictive accuracy (AUC = 0.771 vs. 0.696, p = 0.022), a finding that was consistent in the test set. The PET-based risk score, especially when integrated with clinical variables, demonstrates good predictive ability for five-year survival in NSCLC patients following curative surgery.

Tumor biomarkers for diagnosis, prognosis and targeted therapy

Tumor biomarkers, the substances which are produced by tumors or the body's responses to tumors during tumorigenesis and progression, have been demonstrated to possess critical and encouraging value in screening and early diagnosis, prognosis prediction, recurrence detection, and therapeutic efficacy monitoring of cancers. Over the past decades, continuous progress has been made in exploring and discovering novel, sensitive, specific, and accurate tumor biomarkers, which has significantly promoted personalized medicine and improved the outcomes of cancer patients, especially advances in molecular biology technologies developed for the detection of tumor biomarkers. Herein, we summarize the discovery and development of tumor biomarkers, including the history of tumor biomarkers, the conventional and innovative technologies used for biomarker discovery and detection, the classification of tumor biomarkers based on tissue origins, and the application of tumor biomarkers in clinical cancer management. In particular, we highlight the recent advancements in biomarker-based anticancer-targeted therapies which are emerging as breakthroughs and promising cancer therapeutic strategies. We also discuss limitations and challenges that need to be addressed and provide insights and perspectives to turn challenges into opportunities in this field. Collectively, the discovery and application of multiple tumor biomarkers emphasized in this review may provide guidance on improved precision medicine, broaden horizons in future research directions, and expedite the clinical classification of cancer patients according to their molecular biomarkers rather than organs of origin.

Nuclear protein NOP2 serves as a poor-prognosis predictor of LUAD and aggravates the malignancy of lung adenocarcinoma cells

Recent studies have shown that NOP2, a nucleolar protein, is up-regulated in various cancers, suggesting a potential link to tumor aggressiveness and unfavorable outcomes. This study examines NOP2's role in lung adenocarcinoma (LUAD), a context where its implications remain unclear. Utilizing bioinformatics, we assessed 513 LUAD and 59 normal tissue samples from The Cancer Genome Atlas (TCGA) to explore NOP2's diagnostic and prognostic significance in LUAD. Additionally, in vitro experiments compared NOP2 expression between Beas-2b and A549 cells. Advanced databases and analytical tools, including LINKEDOMICS, STRING, and TISIDB, were employed to further elucidate NOP2's association with LUAD. Our findings indicate a significantly higher expression of NOP2 mRNA and protein in A549 cells compared to Beas-2b cells (P < 0.001). In LUAD, elevated NOP2 levels were linked to decreased Overall Survival (OS) and advanced clinical stages. Univariate Cox analysis revealed that high NOP2 expression correlated with poorer OS in LUAD (P < 0.01), a finding independently supported by multivariate Cox analysis (P < 0.05). The relationship between NOP2 expression and LUAD risk was presented via a Nomogram. Additionally, Gene Set Enrichment Analysis (GSEA) identified seven NOP2-related signaling pathways. A focal point of our research was the interplay between NOP2 and tumor-immune interactions. Notably, a negative correlation was observed between NOP2 expression and the immune infiltration levels of macrophages, neutrophils, mast cells, Natural Killer (NK) cells, and CD8 + T cells in LUAD. Moreover, the expression of NOP2 was related to the sensitivity of various chemotherapeutic drugs. In vitro, we found that downregulating NOP2 can decrease the proliferation, migration and invasion of A549 cells. Furthermore, NOP2 can regulate Caspase3-mediated apoptosis. Collectively, particularly regarding prognosis, immune infiltration and vitro experiments, these findings suggest NOP2's potential of serving as a poor-prognostic biomarker for LUAD and aggravating the malignancy of lung adenocarcinoma cells.

Genetic and molecular characterization of metabolic pathway-based clusters in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive types of squamous cell carcinoma and represents a significant proportion of esophageal cancer. Metabolic reprogramming plays a key role in the occurrence and development of ESCC. Unsupervised clustering analysis was employed to stratify ESCC samples into three clusters: MPC1-lipid type, MPC2-amino acid type, and MPC3-energy type, based on the enrichment scores of metabolic pathways extracted from the Reactome database. The MPC3 cluster exhibited characteristics of energy metabolism, with heightened glycolysis, cofactors, and nucleotide metabolism, showing a trend toward increased aggressiveness and poorer survival rates. On the other hand, MPC1 and MPC2 primarily involved lipid and amino acid metabolism, respectively. In addition, liquid chromatography‒mass spectrometry-based metabolite profiles and potential therapeutic agents were explored and compared among ESCC cell lines with different MPCs. MPC3 amplified energy metabolism markers, especially carnitines. In contrast, MPC1 and MPC2 predominantly had elevated levels of lipids (primarily triacylglycerol) and amino acids, respectively. Furthermore, MPC3 demonstrated a suboptimal clinical response to PD-L1 immunotherapy but showed increased sensitivity to the doramapimod chemotherapy regimen, as evident from drug sensitivity evaluations. These insights pave the way for a more personalized therapeutic approach, potentially enhancing treatment precision for ESCC patients.

Selection and identification of a prohibitin 2-binding DNA aptamer for tumor tissue imaging and targeted chemotherapy

Tumor cell-targeting molecules play a vital role in cancer diagnosis, targeted therapy, and biomarker discovery. Aptamers are emerging as novel targeting molecules with unique advantages in cancer research. In this work, we have developed several DNA aptamers through cell-based systematic evolution of ligands by exponential enrichment (Cell-SELEX). The selected SYL-6 aptamer can bind to a variety of cancer cells with high signal. Tumor tissue imaging demonstrated that SYL-6-Cy5 fluorescent probe was able to recognize multiple clinical tumor tissues but not the normal tissues, which indicates great potential of SYL-6 for clinical tumor diagnosis. Meanwhile, we identified prohibitin 2 (PHB2) as the molecular target of SYL-6 using mass spectrometry, pull-down and RNA interference assays. Moreover, SYL-6 can be used as a delivery vehicle to carry with doxorubicin (Dox) chemotherapeutic agents for antitumor targeted chemotherapy. The constructed SYL-6-Dox can not only selectively kill tumor cells in vitro, but also inhibit tumor growth with reduced side effects in vivo. This work may provide a general tumor cell-targeting molecule and a potential biomarker for cancer diagnosis and targeted therapy.

GPX3-Mediated Oxidative Stress Affects Pyrimidine Metabolism Levels in Stomach Adenocarcinoma via the AMPK/mTOR Pathway

Background

Amino acid metabolism, including ATP production, nucleotide synthesis, and redox homeostatic processes, are associated with proliferation and differentiation of tumor cells. This study aimed to identify novel prognostic biomarkers and potential therapeutic targets of amino acid metabolism-related genes for stomach adenocarcinoma (STAD).

Methods

RNA sequencing transcriptome data in the TCGA-STAD (training set) and GTEx datasets (validation set) were used. The LIMMA R program enabled the differentially expressed amino acid metabolism-related genes (AAMRGs) to be found. A prognostic risk score model based on clinical phenotypic features was built using LASSO regression and step multi-Cox analyses. Gene set enrichment analysis (GSEA) was used to find potential molecular pathways associated with STAD. Hierarchical cluster analysis was used to evaluate pyrimidine metabolism. Cultured STAD cells assessed the proliferation of STAD and upregulation of GPX3 expression by CCK8 and flow cytometry. Transwell and wound healing assays assessed the impact of GPX3 on invasion and migration of STAD cells. Western blot and qRT-PCR were used to measure changes in pyrimidine metabolism-related markers and active molecules involved in the AMPK/mTOR signaling pathway.

Results

Three AAMRGs, DNMT1, F2R, and GPX3, could independently predict the course of STAD. Pyrimidine metabolism appeared to be significantly associated with these by GSEA and clustering analyses. Pyrimidine metabolism was negatively correlated with GPX3. Functional studies using an overexpressed GPX3 plasmid showed an enhanced migration and invasion of STAD cells as well as the expression of genes associated with pyrimidine metabolism and the AMPK/mTOR signaling pathway. By using a CAD siRNA, it was found that that GPX3 affected 5-fluorouracil resistance during de novo synthesis of pyrimidine through the CAD-UMPS signaling axis.

Conclusions

GPX3 which regulates the level of pyrimidine metabolism through the AMPK/mTOR pathway was found to be closely associated with STAD. Our findings demonstrate GPX3 is a reliable biomarker for the prognosis of amino acid metabolism and a probable target for STAD therapy.

Tumor abnormal protein as a promising biomarker for screening solid malignancies and monitoring recurrence and metastasis

Background

Tumor abnormal protein (TAP), the sugar chain protein released by tumor cells during metabolism, allows the development of a technique that exploits aggregated tumor-associated abnormal sugar chain signals in diagnosing malignancies. Clinically, we have found that TAP detection can well predict some malignancies, but several physicians have not paid attention, and related studies have been minimal.

Methods

We evaluated TAP's ability to distinguish between malignancies and benign diseases by receiver operating characteristic (ROC) curve analysis and studied the possibility of monitoring malignancy progression by evaluating TAP levels in follow-up. We used Kaplan-Meier survival curves and Cox proportional hazard regression models to investigate the relationship between TAP and prognosis.

Results

TAP levels were higher in whole solid malignancies and every type of solid malignancy than in benign patients. ROC curve analysis showed that TAP levels aid in distinguishing between malignancies and benign diseases. TAP levels decreased in patients with complete remission (CR) after treatment and increased in patients with relapse from CR. Patients with metastases had higher TAP levels than non-CR patients without metastases. There was no difference in overall survival among patients with different TAP levels, and multivariate analysis suggested that TAP was not an independent risk factor for solid malignancies.

Conclusion

TAP is an effective screening biomarker for many solid malignancies that can be used to monitor the progression of malignancies but not to prognosticate.

A prognostic signature for lung adenocarcinoma by five genes associated with chemotherapy in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is one of the most common subtypes of lung cancer. Finding prognostic biomarkers is helpful in stratifying LUAD patients with different prognosis.

METHODS

We explored the correlation of LUAD prognosis and genes associated with chemotherapy in LUAD and obtained data of LUAD patients from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Drug sensitivity data were acquired from the Genomics of Drug Sensitivity in Cancer (GDSC) database. Differential and enrichment analyses were used to screen the target genes utilizing limma and "clusterProfiler" packages. Then univariate and LASSO Cox analyses were used to select the prognosis-related genes. Survival analysis was used to estimate the overall survival (OS) of different groups.

RESULTS

Twenty-three differentially expressed genes (DEGs) were screened between LUAD samples and healthy samples, and BTK, FGFR2, PIM2, CHEK1, and CDK1 were selected to construct a prognostic signature. The OS of patients in the high-risk group (risk score higher than 0.69) was worse than that in the low-risk group (risk score lower than 0.69).

CONCLUSION

The risk score model constructed by five genes is a potential prognostic biomarker for LUAD patients.

Establishment and validation of a novel risk model based on CD8T cell marker genes to predict prognosis in thyroid cancer by integrated analysis of single-cell and bulk RNA-sequencing

Papillary thyroid cancer (PTC) is a histological type of thyroid cancer, and CD8T is important for the immune response. The single-cell RNA data were acquired from Gene Expression Omnibus. SingleR package was used for cluster identification, and CellChat was exploited to evaluate the interaction among several cell types. Bulk RNA data obtained from the cancer genome atlas were used for determination of prognosis using Kaplan-Meier and Receiver Operating Characteristic curve. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis were applied for assessment of function enrichment. The drug sensitivity was calculated in Gene Set Cancer Analysis. The regulatory network was constructed by STRING and Cytoscape. We identified 23 cell clusters and 10 cell types. Cell communication results showed CD8T cell was vital among all immune cell types. Enrichment analysis found the marker genes of CD8T cell was enriched in some signal pathways related to tumor development. Overall, FAM107B and TUBA4A were considered as hub genes and used to construct a risk model. Most immune checkpoint expressions were upregulated in tumor group. Tumor mutation burden results indicated that prognosis of PTC was not related to the mutation of hub genes. Drug sensitivity analysis showed some drugs could be effectively used for the treatment of PTC, and regulatory network identified some targets for the immunotherapy. A 2-gene model of PTC was developed based on the single-cell RNA and bulk RNA data. Besides, we found CD8T was essential for the immune response in PTC.

Digital image analysis workflows for evaluation of cell behavior and tumor microenvironment to aid therapeutic assessment in high-risk neuroblastoma

Digital pathology and artificial intelligence are promising emerging tools in precision oncology as they provide more robust and reproducible analysis of histologic, morphologic and topologic characteristics of tumor cells and the surrounding microenvironment. This study aims to develop digital image analysis workflows for therapeutic assessment in preclinical in vivo models. For this purpose, we generated pipelines that enable automatic detection and quantification of vitronectin and αvβ3 in heterotopic high-risk neuroblastoma xenografts, demonstrating that digital analysis workflows can be used to provide robust detection of vitronectin secretion and αvβ3 expression by malignant neuroblasts and to evaluate the possibility of combining traditional chemotherapy (etoposide) with extracellular matrix-targeted therapies (cilengitide). Digital image analysis added evidence for the relevance of territorial vitronectin as a therapeutic target in neuroblastoma, since its expression is modified after treatment, with a mean percentage of 60.44% in combined therapy tumors vs 45.08% in control ones. In addition, the present study revealed the efficacy of cilengitide for reducing αvβ3 expression, with a mean αvβ3 positivity of 34.17% in cilengitide treated material vs 66.14% in control and with less tumor growth when combined with etoposide, with a final mean volume of 0.04 cm3 in combined therapy vs 1.45 cm3 in control. The results of this work highlight the importance of extracellular matrix-focused therapies in preclinical studies to improve therapeutic assessment for high-risk neuroblastoma patients.

Prediction of prognosis and immunotherapy response in lung adenocarcinoma based on CD79A, DKK1 and VEGFC

Background

Tumor immune microenvironment (TIME) is crucial for tumor initiation, progression, and metastasis; however, its relationship with lung adenocarcinoma (LUAD) is unknown. Traditional predictive models screen for biomarkers that are too general and infrequently associated with immune genes.

Methods

RNA sequencing data of LUAD patients and immune-related gene sets were retrieved from public databases. Using the common genes shared by The Cancer Genome Atlas (TCGA) and Immunology Database and Analysis Portal (ImmPort), differential gene expression analysis, survival analysis, Lasso regression analysis, and univariate and multivariate Cox regression analyses were performed to generate a novel risk score model. LUAD cohort in International Cancer Genome Consortium (ICGC), GSE68465 cohort in Gene Expression Omnibus (GEO) and an immunohistochemical assay were used to validate the key genes constructed risk score. The LUAD-related prognosis, clinical indicators, immune infiltrate characteristics, response to immunotherapy, and response to chemotherapeutic agents in different risk groups were evaluated by CIBERSORT, ImmuCellAI, pRRophetic and other tools.

Results

The risk score model was constructed using CD79a molecule (CD79A), Dickkopf WNT signaling pathway inhibitor 1 (DKK1), and vascular endothelial growth factor C (VEGFC). High risk score was identified as a negative predictor for overall survival (OS) in subgroup analyses with tumor stage, TNM classification, therapy outcome, and ESTIMATE scores (P < 0.05). Low risk score was positively associated with plasma cells, memory B cells, CD8 T cells, regulatory T cells and γδT cells (P < 0.05). In low-risk group, programmed cell death 1 receptor (PD1), cytotoxic T-lymphocyte associated protein 4 (CTLA4), and lymphocyte activating 3 (LAG3) and indoleamine 2,3-dioxygenase (IDO) were more robustly expressed (P < 0.05). The treatment responses of immune checkpoint blockade (ICB) therapy and chemotherapy were likewise superior in low-risk group (P < 0.05). In immunohistochemical analysis, the tumor group had significantly higher levels of CD79A, DKK1, and VEGFC than the adjacent normal group (P < 0.01).

Conclusions

CD79A, DKK1 and VEGFC are important differential genes related to LUAD, risk score could reliably predict prognosis, composition of TIME and immunotherapy responses in LUAD patients. The excellent performance of the risk model shows its strong and broad application potential.

The application of Aptamer in biomarker discovery

Biomarkers are detectable molecules that can reflect specific physiological states of cells, organs, and organisms and therefore be regarded as indicators for specific diseases. And the discovery of biomarkers plays an essential role in cancer management from the initial diagnosis to the final treatment regime. Practically, reliable clinical biomarkers are still limited, restricted by the suboptimal methods in biomarker discovery. Nucleic acid aptamers nowadays could be used as a powerful tool in the discovery of protein biomarkers. Nucleic acid aptamers are single-strand oligonucleotides that can specifically bind to various targets with high affinity. As artificial ssDNA or RNA, aptamers possess unique advantages compared to conventional antibodies. They can be flexible in design, low immunogenicity, relative chemical/thermos stability, as well as modifying convenience. Several SELEX (Systematic Evolution of Ligands by Exponential Enrichment) based methods have been generated recently to construct aptamers for discovering new biomarkers in different cell locations. Secretome SELEX-based aptamers selection can facilitate the identification of secreted protein biomarkers. The aptamers developed by cell-SELEX can be used to unveil those biomarkers presented on the cell surface. The aptamers from tissue-SELEX could target intracellular biomarkers. And as a multiplexed protein biomarker detection technology, aptamer-based SOMAScan can analyze thousands of proteins in a single run. In this review, we will introduce the principle and workflow of variations of SELEX-based methods, including secretome SELEX, ADAPT, Cell-SELEX and tissue SELEX. Another powerful proteome analyzing tool, SOMAScan, will also be covered. In the second half of this review, how these methods accelerate biomarker discovery in various diseases, including cardiovascular diseases, cancer and neurodegenerative diseases, will be discussed.

Identification of cancer protein biomarker based on cell specific peptide and its potential role in predicting tumor metastasis

The identification of tumor related membrane protein is important for both cancer diagnosis and targeted therapy. Currently, the number of ideal clinical biomarkers is still limited partially because of lacking efficient methods in biomarker discovery. Targeting peptides are generated by library screening and can recognize their cognate targets with high specificity and affinity. In addition, these functional peptides have been considered to be a valuable molecule for both imaging detection and targeting therapy in oncology. The selected peptides can be used to identify cell-surface protein biomarkers of cancer cells. In our study, the peptide (VECYLIRDNLCIY) derived from the bacteria displaying library worked as a bait to capture its binding partner and aldolase A was identified as the candidate. The results indicated that aldolase A' expression level on the cell membrane was regulated by PI3K and aldolase A located on the membrane could inhibit the aggression of tumors through mediating cell metabolic pathway. Aldolase A could work as the joint for the metabolic and signal pathways related to tumor progression. In our work, we demonstrated a promising technology for selecting and identifying binding partners for cell-specific peptides that enables discovery of new tumor biomarkers, showing great scientific study values and clinical translation potencies. SIGNIFICANCE: MS-based cancer biomarker discovery provides promising target candidates for cancer diagnosis and therapy. However, the inevitable limits make it inconvenient in the process of sample preparation and data analysis. In this way, the small molecular probes show some advantages due to their readily availability and specific binding affinity such as the aptamers screened with SELEX technology and peptides derived from displaying libraries. In the present study, aldolase A was proved to be the membrane binding partner of a specific peptidic ligand towards ZR-75-1 tumor cell. It was discovered that membrane aldolase A was more sensitive and observable than other subcellular fractions in response to cellular metabolic state alteration or glucose availability. In addition, the reduced membrane-localized aldolase A expression indicated a more aggressive tumor phenotype and was accompanied by the upregulation of MMP-2/MMP-9. The non-glycolysis activity endowed it with potential utility as a tumor diagnostic marker and therapeutic target. This work demonstrates the practicability of screened peptide in cancer biomarker discovery, facilitating the development of diagnostic tools and therapeutic approaches to cancer, and markedly improves our understanding of cancer biology.

Mucins as Potential Biomarkers for Early Detection of Cancer

Early detection significantly correlates with improved survival in cancer patients. So far, a limited number of biomarkers have been validated to diagnose cancers at an early stage. Considering the leading cancer types that contribute to more than 50% of deaths in the USA, we discuss the ongoing endeavors toward early detection of lung, breast, ovarian, colon, prostate, liver, and pancreatic cancers to highlight the significance of mucin glycoproteins in cancer diagnosis. As mucin deregulation is one of the earliest events in most epithelial malignancies following oncogenic transformation, these high-molecular-weight glycoproteins are considered potential candidates for biomarker development. The diagnostic potential of mucins is mainly attributed to their deregulated expression, altered glycosylation, splicing, and ability to induce autoantibodies. Secretory and shed mucins are commonly detected in patients' sera, body fluids, and tumor biopsies. For instance, CA125, also called MUC16, is one of the biomarkers implemented for the diagnosis of ovarian cancer and is currently being investigated for other malignancies. Similarly, MUC5AC, a secretory mucin, is a potential biomarker for pancreatic cancer. Moreover, anti-mucin autoantibodies and mucin-packaged exosomes have opened new avenues of biomarker development for early cancer diagnosis. In this review, we discuss the diagnostic potential of mucins in epithelial cancers and provide evidence and a rationale for developing a mucin-based biomarker panel for early cancer detection.

Aptamer-conjugated gold nanoparticles and their diagnostic and therapeutic roles in cancer

The burden of incidence rate and mortality of cancer is increasing rapidly, and the development of precise intervention measures for cancer detection and treatment will help reduce the burden and pain of cancer. At present, the sensitivity and specificity of tumor markers such as CEA and CA-125 used clinically are low, while PET, SPECT, and other imaging diagnoses with high sensitivity possess shortcomings, including long durations to obtain formal reports and the inability to identify the molecular pathological type of cancer. Cancer surgery is limited by stage and easy to recur. Radiotherapy and chemotherapy often cause damage to normal tissues, leading to evident side effects. Aptamers can selectively and exclusively bind to biomarkers and have, therefore, gained attention as ligands to be targeted for cancer detection and treatment. Gold nanoparticles (AuNPs) are considered as promising nano carriers for cancer diagnosis and treatment due to their strong light scattering characteristics, effective biocompatibility, and easy surface modification with targeted agents. The aptamer-gold nanoparticles targeting delivery system developed herein can combine the advantages of aptamers and gold nanoparticles, and shows excellent targeting, high specificity, low immunogenicity, minor side effects, etc., which builds a bridge for cancer markers to be used in early and efficient diagnosis and precise treatment. In this review, we summarize the latest progress in the application of aptamer-modified gold nanoparticles in cancer targeted diagnosis and delivery of therapeutic agents to cancer cells and emphasize the prospects and challenges of transforming these studies into clinical applications.

Pyrimidinergic receptor P2Y6 expression is elevated in lung adenocarcinoma and is associated with poor prognosis

BACKGROUD

Previous in vitro studies have indicated that pyrimidinergic receptor P2Y6 (P2RY6, P2Y6 receptor) may function as a cancer-promoting factor in lung adenocarcinoma (LUAD). However, the prognostic significance of P2RY6 expression in LUAD has not been investigated.

OBJECTIVE

This study aimed to assess the impact of P2RY6 expression on the survival of patients with LUAD.

METHODS

First, we assessed P2RY6 mRNA and protein expression in LUAD and non-cancerous lung tissues using the online bioinformatics analysis tool GEPIA, fresh LUAD tissues, and LUAD tissue microarrays (TMAs). Second, we investigated the correlation between P2RY6 expression and clinicopathological parameters of LUAD patients based on data from The Cancer Genome Atlas (TCGA) database and TMAs. Finally, we analyzed the prognostic significance of P2RY6 expression in LUAD using the online survival analysis tool Kaplan-Meier Plotter and data from TMAs.

RESULTS

We demonstrated that P2RY6 mRNA and protein expression levels in LUAD tissues were significantly higher than those in non-cancerous lung tissues. The expression of P2RY6 in LUAD was positively correlated with poor differentiation, more lymph node metastasis, and more advanced clinical stage. Higher P2RY6 expression level was correlated with shorter survival of the LUAD patients. Univariate and multivariate Cox regression analyses indicated that higher P2RY6 tumor expression was an independent unfavorable prognostic factor for LUAD patients.

CONCLUSIONS

P2RY6 expression was elevated in LUAD and correlated with poor prognosis.

Vesicle-mediated transport-related genes are prognostic predictors and are associated with tumor immunity in lung adenocarcinoma

Background

Globally, lung adenocarcinoma (LUAD) is the leading cause of cancer-related deaths. It is a progressive disorder that arises from multiple genetic and environmental factors. Dysregulated expression of vesicle-mediated transport-related genes (VMTRGs) have been reported in several cancers. However, the prognostic significance of VMTRGs in LUAD has yet to be established.

Methods

The VMTRG profiling data for 482 LUAD patients and 59 normal controls were downloaded from The Cancer Genome Altas (TCGA). Univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were performed to construct and optimize the risk model. Several GEO datasets were used to validate the risk model. The roles of these genes were investigated via the Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analyses. Differences in immune cell infiltrations between risk groups were evaluated using five algorithms. "pRRophetic" was used to investigate anti-cancer drug sensitivities in two groups. Expression of these five genes in LUAD samples and adjacent normal tissues were evaluated by qRT-PCR. Colony formation and wound healing assays were performed to assess the significance of CNIH1 and AP3S1 in LUAD cells.

Results

We identified 85 prognosis-associated VMTRGs that could be constructed a risk model for LUAD patients, indicating their potential importance in LUAD development. The risk model including the five VMTRGs (CNIH1, KIF20A, GALNT2, GRIA1, and AP3S1) was associated with clinical outcomes. Tumor stage and risk score were found to be independent prognostic factors for LUAD patients. The five VMTRGs were also correlated with activation of the Notch and p53 signaling pathways. The risk model was significantly associated with immune responses and with high-level expression of immune checkpoints. High-risk group patients were more sensitive to several chemotherapeutic drugs and Lapatinib. Furthermore, CNIH1 and AP3S1 promoted LUAD cell growth and migration in vitro.

Conclusion

We constructed a VMTRG-based risk model for effective prediction of prognostic outcomes for LUAD patients. The risk model was associated with immune infiltration levels. These five hub genes are potential targets for immune therapy combined with chemotherapy in LUAD.

High SURF4 expression is associated with poor prognosis of breast cancer

SURF4 has been suggested as an oncogene in cancer. However, the role of SURF4 in breast cancer has not been demonstrated yet. The data were obtained from TCGA database and 1104 patients were analyzed using bioinformatics analysis. SURF4 is significantly (P < 0.001) highly expressed in tumor. High expression of SURF4 was observed in T4, infiltrating ductal carcinoma, ER negative, PR negative, and HER2 positive, female, patients without lymph node metastasis, HER2 overexpression type, and deceased patients. As for characteristics correlated with high expression of SURF4, gender, histological type, molecular subtype, ER, PR, HER2, and vital status exhibited significant differences. The age (HR: 2.317, P < 0.001), stage (HR: 2.090, P < 0.001), and SURF4 expression (HR: 1.958, P = 0.005) exhibited independent prognostic value for overall survival (OS). Patients with high SURF4 expression, higher age, equivocal HER2, higher stages, or positive margin status had shorter OS. The stage (HR: 1.579, P < 0.001), and margin status (HR: 1.463, P = 0.006) exhibited independent prognostic value for relapse-free survival of breast cancer. High expression of SURF4 was first found in breast cancer. High SURF4 expression was observed in breast cancer tissue and cell. SURF4 promoted the proliferation and migration of 4T1 cells. SURF4 may be a biomarker in diagnosis and prognosis of breast cancer.

PADI1 and Its Co-Expressed Gene Signature Unveil Colorectal Cancer Prognosis and Immunotherapy Efficacy

Peptidyl arginine deiminase 1 (PADI1) catalyzes protein citrullination and has a role in regulating immune responses. The tumor immune microenvironment has been reported to be important in colorectal cancer (CRC), which was correlated with the ability of CRC patients to benefit from immunotherapy. However, there is a lack of molecular markers for matching CRC immunotherapy. Previously, single-gene risk models have only considered the effect of individual genes on intrinsic tumor properties, ignoring the role of genes and their co-expressed genes as a whole. In this study, we analyzed the differential expression of PADI1 in colorectal cancer (CRC). We found that PADI1 was highly expressed in CRC. Subgroup survival analysis revealed a prognostic survival difference for PADI1 in CRC patients aged less than 65 years, male, T stage, N0, M0, and stage I-II (p < 0.05). In addition, we analyzed the functions and signaling pathways associated with PADI1 in CRC and found that it was highly enriched in several immune-related functions and pathways. Then, a set of PADI1 co-expressed genes (PCGs) risk-prognosis scores was developed with PADI1 as the core, which could accurately predict the prognosis of CRC (p < 0.05). PCGs risk score can be an independent prognostic factor for CRC. A new set of Norman plot models were developed for clinical characteristics with age, sex, and TNM stage, which can accurately predict CRC 1, 3, and 5 years survival, and calibration curves and decision curve analysis (DCA) validated the accuracy of the models. The risk score assessed the immune microenvironment of CRC and found that the immune score was higher in the low-risk group, and CD4+ T cells, helper T cells, and eosinophils were more infiltrated in the low-risk group (p < 0.05). Immunotherapy efficacy was better in the low-risk group (p < 0.05). The underlying mechanism may be that the high-risk group of PCGs was enriched in some pathways that promote immune escape and immune dysfunction. In conclusion, PCGs may better predict CRC prognosis and immunotherapeutic response.

High-confidence cancer patient stratification through multiomics investigation of DNA repair disorders

Multiple cancer types have limited targeted therapeutic options, in part due to incomplete understanding of the molecular processes underlying tumorigenesis and significant intra- and inter-tumor heterogeneity. Identification of novel molecular biomarkers stratifying cancer patients with different survival outcomes may provide new opportunities for target discovery and subsequent development of tailored therapies. Here, we applied the artificial intelligence-driven PandaOmics platform ( https://pandaomics.com/ ) to explore gene expression changes in rare DNA repair-deficient disorders and identify novel cancer targets. Our analysis revealed that CEP135, a scaffolding protein associated with early centriole biogenesis, is commonly downregulated in DNA repair diseases with high cancer predisposition. Further screening of survival data in 33 cancers available at TCGA database identified sarcoma as a cancer type where lower survival was significantly associated with high CEP135 expression. Stratification of cancer patients based on CEP135 expression enabled us to examine therapeutic targets that could be used for the improvement of existing therapies against sarcoma. The latter was based on application of the PandaOmics target-ID algorithm coupled with in vitro studies that revealed polo-like kinase 1 (PLK1) as a potential therapeutic candidate in sarcoma patients with high CEP135 levels and poor survival. While further target validation is required, this study demonstrated the potential of in silico-based studies for a rapid biomarker discovery and target characterization.

Coexpression Network Analysis-Based Identification of Critical Genes Differentiating between Latent and Active Tuberculosis

Methods

Three Gene Expression Omnibus (GEO) microarray datasets (GSE19491, GSE98461, and GSE152532) were downloaded, with GSE19491 and GSE98461 then being merged to form a training dataset. Hub genes capable of differentiating between ATB and LTBI were then identified through differential expression analyses and a WGCNA analysis of this training dataset. Receiver operating characteristic (ROC) curves were then used to gauge to the diagnostic accuracy of these hub genes in the test dataset (GSE152532). Gene expression-based immune cell infiltration and the relationship between such infiltration and hub gene expression were further assessed via a single-sample gene set enrichment analysis (ssGSEA).

Results

In total, 485 differentially expressed genes were analyzed, with the WGCNA approach yielding 8 coexpression models. Of these, the black module was the most closely correlated with ATB. In total, five hub genes (FBXO6, ATF3, GBP1, GBP4, and GBP5) were identified as potential biomarkers associated with LTBI progression to ATB based on a combination of differential expression and LASSO analyses. The area under the ROC curve values for these five genes ranged from 0.8 to 0.9 in the test dataset, and ssGSEA revealed the expression of these genes to be negatively correlated with lymphocyte activity but positively correlated with myeloid and inflammatory cell activity.

Conclusion

The five hub genes identified in this study may play a novel role in tuberculosis-related immunopathology and offer value as novel biomarkers differentiating LTBI from ATB.

High incidence combination of multiple primary malignant tumors of the digestive system

BACKGROUND

Clinical reports of multiple primary malignant tumors (MPMTs) in the digestive system are increasing. In China, although the survival rate of patients with MPMTs is increasing, the quality of life is very low. Many patients have reached the advanced stage when the second primary tumor is found, resulting in no early intervention and treatment. This is due to the misunderstanding of MPMTs by clinicians, who treat such tumors as metastases. Therefore, before a patient has a second primary tumor, doctors should understand some common combinations of digestive system MPMTs to provide clinical guidance to the patient.

AIM

To explore the high incidence combination of digestive system MPMTs under heterochronism and synchronization.

METHODS

A total of 1902 patients with MPMTs at Peking Union Medical College Hospital were analyzed retrospectively. They were divided into metachronous MPMT and synchronous MPMT groups, and then the high incidence combinations of the first primary cancer and the second primary cancer in metachronous cancer and synchronous cancer were sorted. Sex and age differences between metachronous and synchronous tumors were tested by the chi square test and t test, respectively. A P value < 0.05 was considered as statistically significant, and SPSS version 26.0 (SPSS Inc., Chicago, Illinois, United States) was used for statistical analysis.

RESULTS

Among the 1902 patients with MPMTs confirmed by pathology, 1811 (95.2%) cases were secondary primary cancers, 89 (4.7%) cases were tertiary primary cancers, and 2 (0.1%) cases were quaternary primary cancers. Most (88.2%) of the secondary primary cancers were identified as metachronous multiple primary cancers six months after diagnosis of the first primary cancer. The top ten most common MPMTs in the first primary cancer group ranged from high to low as follows: Breast cancer, thyroid cancer, nonuterine cancer, lung cancer, colon cancer, kidney cancer, uterine cancer, bladder cancer, rectal cancer, and gastric cancer. The highest incidence rate of the first primary cancer in male metachronous cancer was lung cancer (11.6%), the highest incidence rate of the second primary cancer was still lung cancer (24.9%), the highest incidence rate of the first primary cancer in female metachronous cancer was breast cancer (32.7%), and the highest incidence rate of the second primary cancer was lung cancer (20.8%). Among them, breast cancer, nonuterine cancer and uterine cancer were female-specific malignant tumor types, and thyroid cancer also accounted for 79.6% of female patients. The top five metachronous cancer combinations, independent of female-specific malignant tumor types and thyroid cancer, were colon cancer and lung cancer (26 cases), kidney cancer and lung cancer (25 cases), rectal cancer and lung cancer (20 cases), gastric cancer and lung cancer (17 cases), and bladder cancer and lung cancer (17 cases). The most common synchronous cancer combination was colon cancer and rectal cancer (15 cases).

CONCLUSION

Screening for lung cancer should be performed six months after the detection of colon cancer while rectal cancer screening should be performed within six months.

Co-expression network analysis identifies potential candidate hub genes in severe influenza patients needing invasive mechanical ventilation

BACKGROUND

Influenza is a contagious disease that affects people of all ages and is linked to considerable mortality during epidemics and occasional outbreaks. Moreover, effective immunological biomarkers are needed for elucidating aetiology and preventing and treating severe influenza. Herein, we aimed to evaluate the key genes linked with the disease severity in influenza patients needing invasive mechanical ventilation (IMV). Three gene microarray data sets (GSE101702, GSE21802, and GSE111368) from blood samples of influenza patients were made available by the Gene Expression Omnibus (GEO) database. The GSE101702 and GSE21802 data sets were combined to create the training set. Hub indicators for IMV patients with severe influenza were determined using differential expression analysis and Weighted correlation network analysis (WGCNA) from the training set. The receiver operating characteristic curve (ROC) was also used to evaluate the hub genes from the test set's diagnostic accuracy. Different immune cells' infiltration levels in the expression profile and their correlation with hub gene markers were examined using single-sample gene set enrichment analysis (ssGSEA).

RESULTS

In the present study, we evaluated a total of 447 differential genes. WGCNA identified eight co-expression modules, with the red module having the strongest correlation with IMV patients. Differential genes were combined to obtain 3 hub genes (HLA-DPA1, HLA-DRB3, and CECR1). The identified genes were investigated as potential indicators for patients with severe influenza who required IMV using the least absolute shrinkage and selection operator (LASSO) approach. The ROC showed the diagnostic value of the three hub genes in determining the severity of influenza. Using ssGSEA, it has been revealed that the expression of key genes was negatively correlated with neutrophil activation and positively associated with adaptive cellular immune response.

CONCLUSION

We evaluated three novel hub genes that could be linked to the immunopathological mechanism of severe influenza patients who require IMV treatment and could be used as potential biomarkers for severe influenza prevention and treatment.

Advances in the functions of CTRP6 in the development and progression of the malignancy

CTRP6, a member of the C1q/TNF-related protein (CTRP) family, has gained increasing scientific interest because of its regulatory role in tumor progression. Previous studies have shown that CTRP6 is closely involved in regulating various pathophysiological processes, including glucose and lipid metabolism, cell proliferation, apoptosis, and inflammation. To date, CTRP6 has been identified as related to eight different malignancies, including lung cancer, oral cancer, gastric cancer, colon cancer, liver cancer, bladder cancer, renal cancer, and ovarian cancer. CTRP6 is reported to be associated with tumor progression by activating a series of related signal networks. This review article mainly discusses the biochemistry and pleiotropic pathophysiological functions of CTRP6 as a new molecular mediator in carcinogenesis, hoping that the information summarized herein could make a modest contribution to the development of novel cancer treatments in the future.

Analysis of the prognostic, diagnostic and immunological role of HSP90α in malignant tumors

Heat shock protein 90α (HSP90α) encoded by the HSP90AA1 gene, is the stress inducible isoform of the molecular chaperone HSP90, and was demonstrated as a promising hallmark to diagnose, prognosis in malignant tumors. This study is to evaluate the value of HSP90α in diagnosis, prognosis and immunotherapy of malignant tumors by investigating the expression of HSP90α in plasma of various tumors and analyzing the expression of HSP90α at gene and protein levels via pan-cancer database. We founded that levels of HSP90α in malignant tumors groups were significantly higher than healthy controls in serum. Pan-cancer analysis showed that HSP90AA1 was highly expressed in 27 of 33 tumors, but low in individual cancers (such as renal malignancies). The plasma HSP90α level was positively correlated with the stage of malignant tumor, but there was no significant difference between HSP90AA1 and the stage of most tumors. Cox regression analysis showed that HSP90AA1 expression was significantly correlated with OS in only 6 of the 32 cancers, including LIHC, KIRC, HNSC, LUAD, BRCA and MESO. Up-regulation of HSP90AA1 in most tumors was positively correlated with PDCD1LG2 and CD274 immune checkpoint genes. T cell CD8+ was positively correlated with HSP90AA1 in COAD, DLBC and UVM, and negatively correlated with HSP90AA1 in ESCA, GBM, HNSC, KIRC, KIRP, UCEC and STAD. The AUC of HSP90α are generally high in different tumor groups, which indicated its diagnostic value in malignant tumors. In conclusion, serum HSP90α in patients with malignant tumor is generally elevated, which is of positive significance as an independent diagnosis and combined diagnosis. However, we found that the expression level of HSP90AA1 gene in most tumors was not completely consistent with the serum level, and even down-regulated in some tumors. Plasma levels can be used as biomarkers of poor prognosis in some tumors, but it cannot be used as a biomarker for poor prognosis of all tumors, and more in-depth studies are needed.

Pan-Cancer Analysis of PDIA3: Identifying It as a Potential Biomarker for Tumor Prognosis and Immunotherapy

Protein disulfide isomerase A3 (PDIA3) is a kind of thiol oxidoreductase with a wide range of functions, and its expression is elevated in a variety of tumors, which is closely related to the invasion and metastasis of tumor cells, and has a significant impact on the immunogenicity of tumor cells. Although more and more studies have shown that PDIA3 plays an important role in the occurrence and development of many tumors, there is no systematic pan-cancer study on PDIA3. Therefore, in this study, the differential expression of PDIA3 in 33 kinds of tumors was analyzed to explore its ability to regulate tumor immunity as a biomarker and evaluate its role in different cancer onset stages or clinical prognosis. In this paper, by analyzing the multilevel data including 33 kinds of cancers in the databases of Cancer Genome Atlas (TCGA), UCSC Xena, Cancer Cell Encyclopedia (CCLE), Genotypic Tissue Expression (GTEx), Human Protein Atlas (HPA), cBioPortal, and GDC; the differential expression level of PDIA3 in different types of malignant tumors and its relationship with prognosis and the potential correlation between PDIA3 expression and microsatellite instability (MSI), tumor mutation load (TMB), mismatch repair gene (MMR), DNA methylation level, and immune infiltration level were analyzed with bioinformatics. The results showed that PDIA3 was highly expressed in 19 types of cancers, but downregulated only in THCA. Next, PDIA3 in different tumors was positively or negatively correlated with patient outcome, Kaplan-Meier survival analysis showed that PDIA3 plays an important role in the prognosis of patients with KIRP, KICH, and CESC and may be used as a prognostic biomarker, and the methylation level of PDIA3 promoter region was closely related to patient outcome in eight tumors. The expression level of PDIA3 was correlated with TMB in 13 tumors and MSI in 9 tumors. Among them, the expression level of PDIA3 in THYM has the strongest correlation with TMB, and the expression level of PDIA3 in READ has the strongest correlation with MSI. In addition, the expression of PDIA3 in eight kinds of tumors, including BRCA, HNSC, THYM, LGG, LUAD, LUSC, PRAD, and THCA, had the highest correlation with the infiltration degree of immune cells, and the expression of PDIA3 had the highest correlation with the infiltration degree of 11 kinds of immune cells, including regulatory T cell and macrophages. And LGG is the tumor most likely to be affected by the tumor microenvironment to affect its development and prognosis. To sum up, this study suggests that PDIA3 plays an important role in the occurrence and development of KIRP, KICH, and CESC and in the immunotherapeutic response of THYM, READ, and LGG and can be used as a prognostic biomarker for these tumors.

A Novel M6A-Related Genes Signature Can Impact the Immune Status and Predict the Prognosis and Drug Sensitivity of Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is a primary cause of cancer-related death around the world and has a poor outcome and high incidence. Treatment options are, however, restricted. One of the most critical factors in cancer and metastasis is the N6-methyladenine (m6A) alteration on RNA. This modification could alter gene expression and even function at numerous levels, such as the stability, translocation and translation of RNA splicing. This study aimed to construct an m6A-related genes signature to accurately predict the prognosis of LUAD patients. From TCGA datasets, the LUAD patient data and m6A-related genes were retrieved. LUAD patients’ mutational features and differentially expressed genes (DEGs) were investigated. An univariate and LASSO model with m6A-related genes were constructed for the prediction of outcomes in LUAD. It was possible to develop a prognostic nomogram that could quantitatively predict LUAD patients’ overall survival chances at 1, 3, and 5 years. Research into biological processes and cell pathways was carried out using GSEA. This study found six m6A-related DEGs in LUAD patients, and three of these DEGs(HNRNPC, IGFBP3 and IGF2BP1) were linked to the clinical outcomes of LUAD patients. We found that the overall survival rate for all LUAD patients with high-risk subgroup was considerably lower. According to ROC curves, the prognostic signature demonstrated a high degree of accuracy in predicting future outcomes. In addition, we created a novel nomogram achieved great accuracy with this one as well. The researchers also found that the novel signature might favorably modulate the immune response, and high-risk scores samples were more susceptible to numerous chemotherapeutic medicines. Overall, we developed a m6A-related gene prognostic signature that effectively predicted outcomes of LUAD patients and gave an immunological perspective for creating customized therapeutics.

The FOXO family of transcription factors: key molecular players in gastric cancer

Gastric cancer (GC) is the fifth most frequently diagnosed cancer worldwide and the third leading cause of cancer-related death with an oncological origin. Despite its decline in incidence and mortality in recent years, GC remains a global public problem that seriously threatens patients' health and lives. The forkhead box O proteins (FOXOs) are a family of evolutionarily conserved transcription factors (TFs) with crucial roles in cell fate decisions. In mammals, the FOXO family consists of four members FOXO1, 3a, 4, and 6. FOXOs play crucial roles in a variety of biological processes, such as development, metabolism, and stem cell maintenance, by regulating the expression of their target genes in space and time. An accumulating amount of evidence has shown that the dysregulation of FOXOs is involved in GC progression by affecting multiple cellular processes, including proliferation, apoptosis, invasion, metastasis, cell cycle progression, carcinogenesis, and resistance to chemotherapeutic drugs. In this review, we systematically summarize the recent findings on the regulatory mechanisms of FOXO family expression and activity and elucidate its roles in GC progression. Moreover, we also highlight the clinical implications of FOXOs in GC treatment.

Identification of Important Modules and Biomarkers That Are Related to Immune Infiltration Cells in Severe Burns Based on Weighted Gene Co-Expression Network Analysis

Background: Immunosuppression is an important trigger for infection and a significant cause of death in patients with severe burns. Nevertheless, the prognostic value of immune-related genes remains unclear. This study aimed to identify the biomarkers related to immunosuppression in severe burns. Methods: The gene expression profile and clinical data of 185 burn and 75 healthy samples were obtained from the GEO database. Immune infiltration analysis and gene set variation analysis were utilized to identify the disorder of circulating immune cells. A weighted gene co-expression network analysis (WGCNA) was carried out to select immune-related gene modules. Enrichment analysis and protein-protein interaction (PPI) network were performed to select hub genes. Next, LASSO and logistic regression were utilized to construct the hazard regression model with a survival state. Finally, we investigated the correlation between high- and low-risk patients in total burn surface area (TBSA), age, and inhalation injury. Results: Gene set variation analysis (GSVA) and immune infiltration analysis showed that neutrophils increased and T cells decreased in severe burns. In WGCNA, four modular differently expressed in burns and controls were related to immune cells. Based on PPI and enrichment analysis, 210 immune-related genes were identified, mainly involved in T-cell inhibition and neutrophil activation. In LASSO and logistic regression, we screened out key genes, including LCK, SKAP1 and GZMB, and LY9. In the ROC analysis, the area under the curve (AUC) of key genes was 0.945, indicating that the key genes had excellent diagnostic value. Finally, we discovered that the key genes were related to T cells, and the regression model performed well when accompanied by TBSA and age. Conclusion: We identified LCK, SKAP1, GZMB, and LY9 as good prognostic biomarkers that may play a role in post-burn immunosuppression against T-cell dysfunction and as potential immunotherapeutic targets for transformed T-cell dysfunction.

Integrated Bioinformatics Analysis for Identifying the Significant Genes as Poor Prognostic Markers in Gastric Adenocarcinoma

Gastric adenocarcinoma (GAC) is the most common histological type of gastric cancer and imposes a considerable health burden globally. The purpose of this study was to identify significant genes and key pathways participated in the initiation and progression of GAC. Four datasets (GSE13911, GSE19826, GSE54129, and GSE79973) including 171 GAC and 77 normal tissues from Gene Expression Omnibus (GEO) database were collected and analyzed. Through integrated bioinformatics analysis, we obtained 69 commonly differentially expressed genes (DEGs) among the four datasets, including 20 upregulated and 49 downregulated genes. The prime module in protein-protein interaction network of DEGs, including ADAMTS2, COL10A1, COL1A1, COL1A2, COL8A1, BGN, and SPP1, was enriched in protein digestion and absorption, ECM-receptor interaction, focal adhesion, PI3K-Akt signaling pathway, and amoebiasis. Furthermore, expression and survival analysis found that all seven hub genes were highly expressed in GAC tissues and 6 of them (except for SPP1) were able to predict poor prognosis of GAC. Finally, we verified the 6 high-expressed hub genes in GAC tissues via immunohistochemistry, Western blot, and RNA quantification analysis. Altogether, we identified six significantly upregulated DEGs as poor prognostic markers in GAC based on integrated bioinformatical methods, which could be potential molecular markers and therapeutic targets for GAC patients.

A Novel Mitochondrial-Related Gene Signature for the Tumor Immune Microenvironment Evaluation and Prognosis Prediction in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) remains the most common deadly disease and has a poor prognosis. More and more studies have reported that mitochondrial-related genes (MTRGs) were associated with the clinical outcomes of multiple tumors solely. In this study, we aimed to develop a novel prognostic model based on MTRGs. Differentially expressed MTRGs were identified from TCGA-LUAD and GSE31210 cohorts. Univariate Cox regression analysis was utilized to screen differentially expressed MTRGs that were related to prognosis of LUAD. Then, LASSO Cox regression analysis was used to develop a prognostic signature. ESTIMATE was used for estimating the fractions of immune cell types. In this study, we identified 44 overlapping differentially expressed MTRGs in TCGA-LUAD and GSE31210 cohorts. Among 44 overlapping differentially expressed MTRGs, nine genes were associated with prognosis of LUAD. When the penalty parameter lambda was the minimum, there were six genes meeting the conditions of constructing the signature, including SERPINB5, CCNB1, FGR MAOB, SH3BP5, and CYP24A1. The survival analysis suggested that prognosis of patients in the high-risk group was significantly worse than that in the low-risk group. Cox regression analyses showed that the risk score was an independent predictor of LUAD prognosis. As with the results of ESTIMATE score, the degree of immune cell infiltration in the low-risk group was higher than that in the high-risk group, such as TIL, Treg, and B cells. In addition, TMB and cancer stem cell infiltration were higher in the low-risk group than the high-risk group. In conclusion, we developed a novel MTRG signature acting as a negative independent prognostic factor. In the future, individualized treatments and medical decision-making may benefit from using the predicted model.

Activatable Dual Cancer-Related RNA Imaging and Combined Gene-Chemotherapy through the Target-Induced Intracellular Disassembly of Functionalized DNA Tetrahedron

The desire for a cancer theranostic system with simultaneously accurate diagnosis and efficient therapy is undeniably interminable. Heretofore, theranostic systems with simple components were designed for cancer theranostics but with confined accuracy of diagnosis and side effects of administered drugs. Here, we report an activatable theranostic system for simultaneously imaging dual cancer-related RNAs, mRNA Bcl-2 and piRNA-36026, and combined gene-chemotherapy through the target-induced intracellular disassembly of DNA tetrahedron. Briefly, five customized oligonucleotides are used to assemble the functionalized DNA tetrahedron. The relevant functional nucleic acids, including the antisequence of mRNA Bcl-2, the antisequence of piRNA-36026, and aptamer AS1411, are designed in the customized oligonucleotides with the signal reporters Cy3 and Cy5. Doxorubicin (DOX) is loaded in the functionalized DNA tetrahedron by inlaying between cytosine and guanine to form the activatable cancer theranostic system. The activatable cancer theranostic system is able to recognize MCF-7 cells by aptamer AS1411 and then enter the cells. In the presence of targets, the antisequences in the activatable cancer theranostic system hybridize with intracellular mRNA Bcl-2 and piRNA-36026, leading to the fluorescence signal recovery of Cy3 and Cy5 and the downregulation of two targets in the cytoplasm as well as the consequent apoptosis of MCF-7 cells in the form of gene therapy. Interestingly, as the antisequences are designed in the assembly strands, the hybridization between targets and the antisequences results in the disassembly of the activatable cancer theranostic system and the release of DOX as well as sequential chemotherapy. Advantageously, the activatable cancer theranostic system can achieve imaging of dual cancer-related RNAs with an imaging time window as long as 15 h and exhibit an obvious therapeutic effect in vivo. Therefore, this work is in furtherance of exploration for activatable cancer theranostic systems with high accuracy and efficiency and sheds new light on the development of precision medicine.

Pan-cancer analysis revealed the significance of the GTPBP family in cancer

Background: At present, cancer is still one of the principal diseases to represent a serious danger to human health. Although research on the pathogenesis and treatment of cancer is progressing rapidly, the current knowledge on this topic is far from sufficient. Some tumors with poor prognoses lack effective prognostic biomarkers.

Methods: Firstly, the Wilcoxon test was used to analyse the expression of GTPBP1-GTPBP10 in cancerous and normal tissues. Subsequently, we explored the expression of GTPBP1-10 in cancer by way of a paired t-test and plotted the survival curve using KM and univariate Cox regression analysis to explore the relationship between GTPBP1-10 and the prognosis of cancer. We then explored the significance of the GTPBP family in the tumor microenvironment.

Results: The results showed that many members of the GTPBP family are differentially expressed in a variety of cancers and alter the prognosis of a number of cancers. Members of the GTPBP family may serve as novel prognostic markers for these tumors. Moreover, members of the GTPBP family are correlated with the immune microenvironment of tumors, which is valuable in terms of adding to our understanding of the mechanisms of tumor genesis. Finally, we identified drugs showing a high correlation with GTPBP family members, which are therefore conducive to the development of GTPBP family member-based treatment regimens.

Conclusions: The 10 members of the GTPBP family have prognostic value in multiple tumor types and are associated with the immune microenvironment. Our study may provide a reference for the diagnosis and treatment of tumors.

The regulation of PBXs and their emerging role in cancer

Pre‐B‐cell leukaemia transcription factor (PBX) proteins are a subfamily of evolutionarily conserved, atypical homeodomain transcription factors that belong to the superfamily of three amino acid loop extension (TALE) homeodomain proteins. Members of the PBX family play crucial roles in regulating multiple pathophysiological processes, such as the development of organs, congenital cardiac defects and carcinogenesis. The dysregulation of PBXs has been shown to be closely associated with many diseases, particularly cancer. However, the detailed mechanisms of PBX dysregulation in cancer progression are still inconclusive. In this review, we summarize the recent advances in the structures, functions and regulatory mechanisms of PBXs, and discuss their underlying mechanisms in cancer progression. We also highlight the great potential of PBXs as biomarkers for the early diagnosis and prognostic evaluation of cancer as well as their therapeutic applications. The information reviewed here may expand researchers’ understanding of PBXs and could strengthen the clinical implication of PBXs in cancer treatment.

HMGA1 Has Predictive Value in Response to Chemotherapy in Gastric Cancer

Gastric cancer is a serious health problem worldwide. Although its incidence is decreasing, the five-year survival rate remains low. Thus, it is essential to identify new biomarkers that could promote better diagnosis and treatment of patients with gastric cancer. High-mobility group AT-hook 1 (HMGA1) is a non-histone, chromatin-binding protein that has been found overexpressed in several tumor types. It has been correlated with invasion, metastasis, and drug resistance, leading to worse patient survival. The aim of this work was to evaluate the clinical value of HMGA1 in gastric cancer. HMGA1 expression was analyzed by immunohistochemistry in a single hospital series (n = 323) of gastric adenocarcinoma cases (stages I to IV) with clinicopathological and treatment data. In this series, HMGA1 expression showed no significant relevance as a prognostic biomarker. Nevertheless, a significantly better overall survival was observed in cases with high levels of HMGA1 when they were treated with chemotherapy, compared to the nontreated ones, implying that they can benefit more from treatment than patients with low expression of HMGA1. We thereby show for the first time that HMGA1 expression has a substantial value as a biomarker of response to chemotherapy in gastric cancer.

miR-301-3p directly regulates Cx43 to mediate the development of gastric cancer

OBJECTIVE

Identifying novel biomarkers involved in the development of gastric cancer (GC) can provide potential therapeutic strategies and improve clinical prognosis. miR-301-3p and Cx43 are reportedly dysregulated in GC. miR-301-3p and Cx43 interaction, and their functions in GC progression, are still poorly understood.

METHODS

The expression levels of miR-301-3p and Cx43 in GC tissues and cell lines with various differentiation degrees were evaluated by RT-qPCR. The interaction between miR-301-3p and Cx43 was assessed by dual-luciferase reporter assays. CCK8 and Transwell assays were employed to assess the effects of the miR-301-3p-Cx43 axis on GC cell proliferation, migration, and invasion.

RESULTS

Cx43 was significantly downregulated in GC tissues and cell lines, while miR-301-3p expression was negatively correlated with Cx43 mRNA levels. The expression levels of Cx43 and miR-301-3p were closely associated with the differentiation, TNM stage, vascular invasion, and lymph node metastasis status of GC patients. Cx43 overexpression could suppress the proliferation, migration, and invasion of GC cells. Cx43 mRNA is a direct target of miR-301-3p, and transfection of an miR-301-3p mimic could reverse the inhibitory effects of Cx43.

CONCLUSION

The miR-301-3p-Cx43 axis is involved in the development and progression of GC by affecting the proliferation, migration, and invasion of GC cells.

LINC00261: a burgeoning long noncoding RNA related to cancer

Long noncoding RNAs (lncRNAs), are transcripts longer than 200 nucleotides that are considered to be vital regulators of many cellular processes, particularly in tumorigenesis and cancer progression. long intergenic non-protein coding RNA 261 (LINC00261), a recently discovered lncRNA, is abnormally expressed in a variety of human malignancies, including pancreatic cancer, gastric cancer, colorectal cancer, lung cancer, hepatocellular carcinoma, breast cancer, laryngeal carcinoma, endometrial carcinoma, esophageal cancer, prostate cancer, choriocarcinoma, and cholangiocarcinoma. LINC00261 mainly functions as a tumor suppressor that regulates a variety of biological processes in the above-mentioned cancers, such as cell proliferation, apoptosis, motility, chemoresistance, and tumorigenesis. In addition, the up-regulation of LINC00261 is closely correlated with both favorable prognoses and many clinical characteristics. In the present review, we summarize recent research documenting the expression and biological mechanisms of LINC00261 in tumor development. These findings suggest that LINC00261, as a tumor suppressor, has bright prospects both as a biomarker and a therapeutic target.

Improving Model Performance on the Stratification of Breast Cancer Patients by Integrating Multiscale Genomic Features

In clinical cancer research, it is a hot topic on how to accurately stratify patients based on genomic data. With the development of next-generation sequencing technology, more and more types of genomic features, such as mRNA expression level, can be used to distinguish cancer patients. Previous studies commonly stratified patients by using a single type of genomic features, which can only reflect one aspect of the cancer. In fact, multiscale genomic features will provide more information and may be helpful for clinical prediction. In addition, most of the conventional machine learning algorithms use a handcrafted gene set as features to construct models, which is generally selected by a statistical method with an arbitrary cut-off, e.g., p value < 0.05. The genes in the gene set are not necessarily related to the cancer and will make the model unreliable. Therefore, in our study, we thoroughly investigated the performance of different machine learning methods on stratifying breast cancer patients with a single type of genomic features. Then, we proposed a strategy, which can take into account the degree of correlation between genes and cancer patients, to identify the features from mRNAs and microRNAs, and evaluated the performance of the models with the new combined features of the multiscale genomic features. The results showed that, compared with the models constructed with a single type of features, the models with the multiscale genomic features generated by our proposed method achieved better performance on stratifying the ER status of breast cancer patients. Moreover, we found that the identified multiscale genomic features were closely related to the cancer by gene set enrichment analysis, indicating that our proposed strategy can well reflect the biological relevance of the genes to breast cancer. In conclusion, modelling with multiscale genomic features closely related to the cancer not only can guarantee the prediction performance of the models but also can effectively provide candidate genes for interpreting the mechanisms of cancer.

PDXGEM: patient-derived tumor xenograft-based gene expression model for predicting clinical response to anticancer therapy in cancer patients

Background

Cancer is a highly heterogeneous disease with varying responses to anti-cancer drugs. Although several attempts have been made to predict the anti-cancer therapeutic responses, there remains a great need to develop highly accurate prediction models of response to the anti-cancer drugs for clinical applications toward a personalized medicine. Patient derived xenografts (PDXs) are preclinical cancer models in which the tissue or cells from a patient’s tumor are implanted into an immunodeficient or humanized mouse. In the present study, we develop a bioinformatics analysis pipeline to build a predictive gene expression model (GEM) for cancer patients’ drug responses based on gene expression and drug activity data from PDX models.

Results

Drug sensitivity biomarkers were identified by performing an association analysis between gene expression levels and post-treatment tumor volume changes in PDX models. We built a drug response prediction model (called PDXGEM) in a random-forest algorithm by using a subset of the drug sensitvity biomarkers with concordant co-expression patterns between the PDXs and pretreatment cancer patient tumors. We applied the PDXGEM to several cytotoxic chemotherapies as well as targeted therapy agents that are used to treat breast cancer, pancreatic cancer, colorectal cancer, or non-small cell lung cancer. Significantly accurate predictions of PDXGEM for pathological response or survival outcomes were observed in extensive independent validations on multiple cancer patient datasets obtained from retrospective observational studies and prospective clinical trials.

Conclusion

Our results demonstrated the strong potential of using molecular profiles and drug activity data of PDX tumors in developing a clinically translatable predictive cancer biomarkers for cancer patients. The PDXGEM web application is publicly available at http://pdxgem.moffitt.org.