Cancer Genome Research Articles

NR3C2 affects the proliferation and invasiveness of colon cancer cells through the Wnt/β-Catenin signaling pathway

PurposeThe aim of this study was to explore the potential correlation between the nuclear receptor subfamily 3 group C member 2 (NR3C2) and outcomes of colon cancer, along with the mechanisms underlying this association.MethodmRNA (messenger RNA) data and clinical records pertaining to colon cancer were retrieved from The Cancer Genome Atlas (TCGA) database. The analysis of NR3C2 expression discrepancies between normal colon and tumor tissues was conducted using R software. In addition, we also studied the relationship between NR3C2 expression and prognosis, pathological parameters. The relative role of NR3C2 were further predicted through bioinformatics methods and receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value of NR3C2 in colon cancer. Single-cell data from colon cancer samples in the GEO (Gene Expression Omnibus) database further investigated the mechanism of the lower survival associated with NR3C2 dysregulation. NR3C2 expression in three fresh colon cancer samples and their respective paracancer samples was determined. Furthermore, colon cancer cell models overexpressing NR3C2 and with knockdown NR3C2 were constructed by lentiviral vector transfection. Cell Counting Kit-8 assay, transplantation of tumors in nude mice and transwell assays were used to examine the proliferation, migration and invasion of colon cancer cells. The effect on the Wnt/β-catenin pathway, activities of cellular autophagy and cell apoptosis were examined by assessing the expression levels of several key proteins, including Bcl-2, Bax, and LC3.ResultsWe found that NR3C2 was found a significantly lower level in colon cancer tissues than in adjacent tissues, which was associated with distant and lymphatic metastases, clinical stage, and poor clinical outcome, and it was an independent prognostic factor and potential marker of colon cancer. Single-cell transcriptome data identified the subset of circulating T and B cells with high expression of NR3C2, which is involved in TNF signaling pathway. Functional experiments show that downregulation of NR3C2 resultsed in the activation of the Wnt/β-catenin signaling pathway, and promotesd the proliferation and invasion of colon cancer cells while suppressing cell autophagy and apoptosis.ConclusionNR3C2 may regulate Wnt/β-catenin to affect the proliferation, invasion apoptosis and autophagy of colon cancer, and this axis is a potential target for the treatment of colon cancer.

The lncRNA and miRNA regulatory axis in HPV16-positive oropharyngeal cancers

The global rise of oropharyngeal cancers (OPC) associated with the human papillomavirus (HPV) type 16 necessitates a deeper understanding of their underlying molecular mechanisms. Our study utilised RNA-sequencing data from The Cancer Genome Atlas (TCGA) to identify and analyse differentially expressed (DE) long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) in HPV16-positive OPC, and to elucidate the interplay within the lncRNA/miRNA/mRNA regulatory network. We revealed 1929 DE lncRNAs and identified a significant expression shift in 37 of these, suggesting a regulatory 'sponge' function for miRNAs that modulate cellular processes. Notably, the lncRNA Linc00911 exhibited decreased expression in HPV16-positive OPC, a change directly attributable to HPV oncogenes E6 and E7 as confirmed by RT-qPCR in cell lines and patient samples. Our comprehensive analysis presents an expansive landscape of ncRNA-mRNA interactions, offering a resource for the ongoing pursuit of elucidating the molecular underpinnings of HPV-driven OPC.

Large-scale analysis of whole genome sequencing data from formalin-fixed paraffin-embedded cancer specimens demonstrates preservation of clinical utility

Whole genome sequencing (WGS) provides comprehensive, individualised cancer genomic information. However, routine tumour biopsies are formalin-fixed and paraffin-embedded (FFPE), damaging DNA, historically limiting their use in WGS. Here we analyse FFPE cancer WGS datasets from England’s 100,000 Genomes Project, comparing 578 FFPE samples with 11,014 fresh frozen (FF) samples across multiple tumour types. We use an approach that characterises rather than discards artefacts. We identify three artefactual signatures, including one known (SBS57) and two previously uncharacterised (SBS FFPE, ID FFPE), and develop an “FFPEImpact” score that quantifies sample artefacts. Despite inferior sequencing quality, FFPE-derived data identifies clinically-actionable variants, mutational signatures and permits algorithmic stratification. Matched FF/FFPE validation cohorts shows good concordance while acknowledging SBS, ID and copy-number artefacts. While FF-derived WGS data remains the gold standard, FFPE-samples can be used for WGS if required, using analytical advancements developed here, potentially democratising whole cancer genomics to many.

MtPCDI: a machine learning-based prognostic model for prostate cancer recurrence.

This research seeks to formulate a prognostic model for forecasting prostate cancer recurrence by examining the interaction between mitochondrial function and programmed cell death (PCD). The research involved analyzing four gene expression datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) using univariate Cox regression. These analyses identified genes linked with mitochondrial function and PCD that correlate with recurrence prognosis. Various machine learning algorithms were then employed to construct an optimal predictive model. A key outcome was the creation of a mitochondrial-related programmed cell death index (mtPCDI), which effectively predicts the prognosis of prostate cancer patients. It was observed that individuals with lower mtPCDI exhibited higher immune activity, correlating with better recurrence outcomes. The study demonstrates that mtPCDI can be used for personalized risk assessment and therapeutic decision-making, highlighting its clinical significance and providing insights into the biological processes affecting prostate cancer recurrence.

Anti-Müllerian hormone type II receptor protein expression in non-small cell lung cancer and the effect of AMH/AMHR2 signaling on cancer cell proliferation.

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related deaths worldwide despite advances in cancer therapeutics. In several gynecological cancers, anti-Müllerian hormone receptor type 2 (AMHR2) mediates AMH-induced growth inhibition and is expressed at high levels. Furthermore, 5%-8% of NSCLCs exhibit high AMHR2 expression, suggesting that AMH may inhibit the progression of some lung cancers. However, the clinical relevance of AMHR2 expression and its role in lung cancer is not fully clarified. Immunostaining was performed on 79 surgical specimens of NSCLC. The Cancer Genome Atlas RNA-seq data for lung adenocarcinoma were analyzed, and gene ontology and gene set enrichment analyses were performed. In cellular experiments, AMHR2-overexpressing NSCLC cell lines were established, and the role of the AMH-AMHR2 pathway in cell proliferation with recombinant human AMH protein treatment was examined. A total of 13 cases (16.5%) were positive for immunostaining in lung adenocarcinoma tissues; no positive signals were detected in lung squamous carcinoma tissues. Gene expression variation analysis using The Cancer Genome Atlas data showed that the expression of genes related to the cell cycle was downregulated in the AMHR2-high group. Cellular experiments showed that activation of the AMH-AMHR2 pathway suppressed cell proliferation. In lung adenocarcinoma tissues with high expression of AMHR2, activation of the AMH-AMHR2 pathway may suppress cell proliferation.

Hypothesis paper: GDF15 demonstrated promising potential in Cancer diagnosis and correlated with cardiac biomarkers

BackgroundCardiovascular toxicity represents a significant adverse consequence of cancer therapies, yet there remains a paucity of effective biomarkers for its timely monitoring and diagnosis. To give a first evidence able to elucidate the role of Growth Differentiation Factor 15 (GDF15) in the context of cancer diagnosis and its specific association with cardiac indicators in cancer patients, thereby testing its potential in predicting the risk of CTRCD (cancer therapy related cardiac dysfunction).MethodsAnalysis of differentially expressed genes (DEGs), including GDF15, was performed by utilizing data from the public repositories of the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Cardiomyopathy is the most common heart disease and its main clinical manifestations, such as heart failure and arrhythmia, are similar to those of CTRCD. Examination of GDF15 expression was conducted in various normal and cancerous tissues or sera, using available database and serum samples. The study further explored the correlation between GDF15 expression and the combined detection of cardiac troponin-T (c-TnT) and N-terminal prohormone of brain natriuretic peptide (NT-proBNP), assessing the combined diagnostic utility of these markers in predicting risk of CTRCD through longitudinal electrocardiograms (ECG).ResultsGDF15 emerged as a significant DEG in both cancer and cardiomyopathy disease models, demonstrating good diagnostic efficacy across multiple cancer types compared to healthy controls. GDF15 levels in cancer patients correlated with the established cardiac biomarkers c-TnT and NT-proBNP. Moreover, higher GDF15 levels correlated with an increased risk of ECG changes in the cancer cohort.ConclusionGDF15 demonstrated promising diagnostic potential in cancer identification; higher GDF15, combined with elevated cardiac markers, may play a role in the monitoring and prediction of CTRCD risk.

Molecular subtypes and nomogram for predicting the prognosis of cervical cancer based on a matrix-immune signature

Cervical cancer is a kind of tumor related to chronic HPV infection. Currently, the treatment of cervical cancer is guided mainly by clinicopathological factors. The role of tumor microenvironment in the prognosis and treatment of cervical cancer has been ignored. We aimed to use bioinformatics to identify the molecular subtypes in cervical cancer and construct a predictive nomogram combining a matrix-immune signature (MIS) and clinicopathological factors to support treatment decisions. Two cervical cancer subtypes with different prognoses were identified based on matrix- and immune-genes in TCGA-CESC. The MIS was developed using Cox regression and Lasso algorithm and verified in the Cancer Genome Characterization Initiative (CGCI) using time-dependent receiver operating characteristic (ROC) curve analysis. Multivariable analysis identified lymph node metastases, lymphovascular space invasion, and the MIS as independent prognostic factors, which were used to construct the predictive nomogram. The areas under the ROC curve of the model were 0.872, 0.879, and 0.803 for the 1-, 3-, and 5-year periods, respectively. The C-index was 0.845. Calibration curves confirmed the excellent prognosis prediction of the nomogram. The nomogram indicted a 3-year survival rate of > 90% in patients with a total score > 110.1. The constructed predictive nomogram has significant implications for prognostic assessment and treatment selection in cervical cancer.

Prognostic prediction signature and molecular subtype for liver cancer: A CTC/CTM‑related gene prediction model and independent external validation.

Liver cancer is the second leading cause of tumor-related death worldwide, and a serious threat to lives and health. Circulating tumor cells (CTCs) facilitate the progression of various cancers, including liver cancer. The relationship between CTC/circulating tumor microemboli-related genes (CRGs) and the prognosis of liver cancer is unclear. The aim of the present study was to identify CTC/circulating tumour microemboli-related genes (CRGs) in hepatocellular carcinoma and to investigate their clinical significance. Transcriptomic data from The Cancer Genome Atlas (International Cancer Genome Consortium (ICGC) and GSE117623 databases were combined, and differentially expressed CRGs were identified. These were subsequently analyzed via least absolute shrinkage and selection operator and multivariate Cox analyses, and a five-gene risk signature was constructed. The signature was validated in the ICGC and GSE14520 dataset with survival analysis and receiver operating characteristic curve analysis. Immunocyte infiltration, tumor mutation burden (TMB), tumor immune dysfunction and exclusion (TIDE), and the somatic mutation rate were also compared between high- and low-risk groups, based on the median predictive index, to further evaluate the immunotherapeutic value of the model. Molecular subtypes of liver cancer were characterized by the non-negative matrix factorization method and potential therapeutic compounds were evaluated for different subtypes. Nomograms were utilized to predict the prognosis of patients, and the signature was compared with previous literature models. Additionally, the biological function of one of the CRGs, tumor protein p53 inducible protein 3 (TP53I3), in liver cancer was further explored through in vitro experiments. Analysis of the prognostic characteristics of the five CRGs led to the identification of two liver cancer subtypes. Patients in the low-risk group had a longer survival compared with those in the high-risk group, and patients in the latter group were associated with a higher TMB, immunocyte infiltration and somatic mutation rate, and lower TIDE scores. The prognostic profile was validated in the ICGC and GSE14520 datasets and exhibited a good predictive performance. In vitro analysis showed that the knockdown of TP53I3 suppressed liver cancer cell proliferation. In summary, CRGs were used to develop a new prognostic signature to predict the prognosis of patients with liver cancer. This signature may be used to assess the prognosis of patients and may provide new insights for clinical management strategies. In addition, TP53I3 is potentially a therapeutic target for liver cancer.

Identification of lysine lactylation (kla)-related lncRNA signatures using XGBoost to predict prognosis and immune microenvironment in breast cancer patients

Breast cancer (BC) stands as a predominant global malignancy, significantly contributing to female mortality. Recently uncovered, histone lysine lactylation (kla) has assumed a crucial role in cancer progression. However, the correlation with lncRNAs remains ambiguous. Scrutinizing lncRNAs associated with Kla not only improves clinical breast cancer management but also establishes a groundwork for antitumor drug development. We procured breast tissue samples, encompassing both normal and cancerous specimens, from The Cancer Genome Atlas (TCGA) database. Utilizing Cox regression and XGBoost methods, we developed a prognostic model using identified kla-related lncRNAs. The model's predictive efficacy underwent validation across training, testing, and the overall cohort. Functional analysis concerning kla-related lncRNAs ensued. We identified and screened 8 kla-related lncRNAs to formulate the risk model. Pathway analysis disclosed the connection between immune-related pathways and the risk model of kla-related lncRNAs. Significantly, the risk scores exhibited a correlation with both immune cell infiltration and immune function, indicating a clear association. Noteworthy is the observation that patients with elevated risk scores demonstrated an increased tumor mutation burden (TMB) and decreased tumor immune dysfunction and exclusion (TIDE) scores, suggesting heightened responses to immune checkpoint blockade. Our study uncovers a potential link between Kla-related lncRNAs and BC, providing innovative therapeutic guidelines for BC management.

The disruption of NEAT1-miR-125b-5p-SLC1A5 cascade defines the oncogenicity and differential immune profile in head and neck squamous cell carcinoma

Metabolic reprogramming sustains malignant head and neck squamous cell carcinoma (HNSCC) to overcome stressful microenvironments, and increased glutamine uptake is a common metabolic hallmark in cancers. Since metabolic reprogramming has been recognized as a new therapeutic target for tumor cells, understanding the regulatory axis of glutamine uptake in HNSCC and its potential downstream effects in its pathogenesis of HNSCC would be incredibly beneficial. Bioinformatic analysis of the Cancer Genome Atlas (TCGA)-HNSCC dataset and RNAseq analysis performed on HNSCC indicated that SLC1A5 was the most dysregulated transporter among the seven homologous glutamate or neutral amino acid transporters in the SLC1A family. To further clarify the role of SLC1A5 in HNSCC, we knocked down SLC1A5 expression. This knockdown decelerated cell growth, induced G0/G1 arrest, diminished tumorigenicity, and increased cleavage caspase3, LC3B, and intracellular Fe2+. Inhibitors against apoptosis, autophagy, or ferroptosis rescued the cell viability repressed by SLC1A5 knockdown. SLC1A5 knockdown also suppressed glutamine uptake, enhanced oxidative stress, and increased sensitivity to cisplatin. CRISPR/dCas9-mediated SLC1A5 induction conferred cisplatin resistance and reduced apoptosis, autophagy, and ferroptosis. Reporter assays and western blot data demonstrated that miR-125b-5p targets and attenuates SLC1A5, while the si-NEAT1 increases miR-125b-5p expression. Analysis of the TCGA-HNSCC databases showed concordant upregulation of NEAT1 and downregulation of miR-125b-5p, along with SLC1A5 upregulation in tumors. Analysis of transcriptomic data revealed that tumors harboring higher SLC1A5 expression had significantly lower immune scores in CD8+, monocytes, and dendritic cells, and higher scores in M0 and M1 macrophages. Disruptions in immune modulation, metabolism, and oxidative stress components were associated with SLC1A5 aberrations in HNSCC. This study concludes that the NEAT1/miR-125b-5p/SLC1A5 cascade modulates diverse activities in oncogenicity, treatment efficacy, and immune cell profiles in head and neck/oral carcinoma.

LLM-AIx: An open source pipeline for Information Extraction from unstructured medical text based on privacy preserving Large Language Models.

In clinical science and practice, text data, such as clinical letters or procedure reports, is stored in an unstructured way. This type of data is not a quantifiable resource for any kind of quantitative investigations and any manual review or structured information retrieval is time-consuming and costly. The capabilities of Large Language Models (LLMs) mark a paradigm shift in natural language processing and offer new possibilities for structured Information Extraction (IE) from medical free text. This protocol describes a workflow for LLM based information extraction (LLM-AIx), enabling extraction of predefined entities from unstructured text using privacy preserving LLMs. By converting unstructured clinical text into structured data, LLM-AIx addresses a critical barrier in clinical research and practice, where the efficient extraction of information is essential for improving clinical decision-making, enhancing patient outcomes, and facilitating large-scale data analysis. The protocol consists of four main processing steps: 1) Problem definition and data preparation, 2) data preprocessing, 3) LLM-based IE and 4) output evaluation. LLM-AIx allows integration on local hospital hardware without the need of transferring any patient data to external servers. As example tasks, we applied LLM-AIx for the anonymization of fictitious clinical letters from patients with pulmonary embolism. Additionally, we extracted symptoms and laterality of the pulmonary embolism of these fictitious letters. We demonstrate troubleshooting for potential problems within the pipeline with an IE on a real-world dataset, 100 pathology reports from the Cancer Genome Atlas Program (TCGA), for TNM stage extraction. LLM-AIx can be executed without any programming knowledge via an easy-to-use interface and in no more than a few minutes or hours, depending on the LLM model selected.

Construction of a cuproptosis‑related lncRNA signature to predict biochemical recurrence of prostate cancer.

Biochemical recurrence (BCR) is common in prostate cancer (PCa), and patients with BCR usually have a poor prognosis. Cuproptosis is a unique type of cell death, and copper homeostasis is crucial to the occurrence and development of malignancies. The present study aimed to explore the prognostic value of cuproptosis-related long non-coding RNAs (lncRNAs; CRLs) in PCa and to develop a predictive signature for forecasting BCR in patients with PCa. Using The Cancer Genome Atlas database, transcriptomic, mutation and clinical data were collected from patients with PCa. A total of 121 CRLs were identified using Pearson's correlation coefficient. Subsequently, a 6-CRL signature consisting of AC087276.2, CNNM3-DT, AC090198.1, AC138207.5, METTL14-DT and LINC01515 was created to predict the BCR of patients with PCa through Cox and least absolute shrinkage and selection operator regression analyses. Kaplan-Meier curve analysis demonstrated that high-risk patients had a low BCR-free survival rate. In addition, there was a substantial difference between the high- and low-risk groups in the immune microenvironment, immune therapy, drug sensitivity and tumor mutational burden. A nomogram integrating the Gleason score, 6-CRL signature and clinical T-stage was established and evaluated. Finally, the expression of signature lncRNAs in PCa cells was verified through reverse transcription-quantitative PCR. In conclusion, the 6-CRL signature may be a potential tool for making predictions regarding BCR in patients with PCa, and the prognostic nomogram may be considered a practical tool for clinical decision-making.

Exploring serine-arginine rich splicing factors: potential predictive markers for dysregulation in oral cancer

BackgroundDysregulated splicing events are a common phenomenon in cancer with the Serine-arginine-rich splicing factor (SRSF) family emerging as pivotal regulators of gene expression, exerting influence over constitutive and alternative splicing processes. Although aberrations in a few SRSF family members have been implicated in various cancers, the comprehensive roles of other family constituents remain underexplored.MethodsThis study delves into the expression profile of the entire SRSF family (SRSF1-SRSF12) in 23 cancerous cell lines originating from diverse tissues using quantitative Real-Time PCR. Further, the transcript levels of the SRSF family were examined in oral cancer patient samples stratified into Pre-cancer (n = 15), Early cancer (n = 11), Late cancer (n = 14), and adjacent non-tumor tissues (n = 26) as controls. The results were corroborated by a parallel investigation utilizing the transcriptomics data of oral squamous cell carcinoma (OSCC) patients (n = 319) and controls (n = 35) available in The Cancer Genome Atlas (TCGA) database.ResultsOur investigation reveals a notable upregulation in the expression levels of key splicing factors, namely SRSF3, SRSF9, and SRSF10 in all oral cancer cell lines (SCC-4, UM-SCC-84, CAL33, SAS-H1). Conversely, no significant associations between SRSF family members and other cancer cell lines were discerned. Further, the expression profile of the SRSF family in oral cancer patient samples revealed significant upregulation of SRSF1, SRSF3, SRSF7, SRSF9, SRSF10, and SRSF11 in patients with late-stage oral cancer compared to controls. Transcriptomics data from TCGA database demonstrated remarkable upregulation of SRSF1, SRSF4, SRSF9, SRSF10, and SRSF11 in OSCC patients.ConclusionCollectively our results underscore the critical involvement of SRSF family members in the context of oral cancer, highlighting their potential as key players in the altered splicing dynamics associated with cancer progression.Graphical abstract

Comprehensive analysis of an mRNA co-expression network and a ceRNA network reveals potential prognostic biomarkers in oral squamous cell carcinoma

BackgroundOral squamous cell carcinoma (OSCC) is a prevalent and aggressive oral cancer with a poor prognosis. Its polygenic risk is likely influenced by complex transcriptional disorders involving networks of co-expressed and functionally related genes, though such investigations are limited in OSCC.MethodsWe analyzed the GSE37991 dataset, comprising 40 OSCC and 40 normal oral tissue samples from the Gene Expression Omnibus. Tumor-specific modules were identified using weighted correlation network analysis (WGCNA), leading to the selection of hub mRNAs and lncRNAs. These lncRNAs were used to construct lncRNA–mRNA and competing endogenous RNA networks. We further examined the expression profiles and survival data of these genes from the Cancer Genome Atlas. Prognostic markers were identified and validated through 5-year survival analysis and Cox proportional hazards modeling. RT-qPCR was used to validate the expression levels in clinical OSCC tissues.ResultsWe identified 1847 differentially expressed genes in OSCC tissues. WGCNA revealed four OSCC-specific modules, screening 120 hub mRNAs and five hub lncRNAs. Two prognostic markers (AQP5, IL-26) from hub mRNAs and three (FRMD5, INHBB, GUCY1A3) from the lncRNA–mRNA network were associated with survival. Validation showed lower expression of AQP5 and GUCY1A3, and higher expression of FRMD5 and INHBB in OSCC compared to normal tissues.ConclusionThis study enhances our understanding of transcriptional dysregulation in OSCC and may highlights AQP5, IL-26, FRMD5, INHBB, and GUCY1A3 as promising prognostic biomarkers.

EIF1AX mutation in thyroid nodules: a histopathologic analysis of 56 cases in the context of institutional practices.

EIF1AX mutation has been identified as a driver mutation for papillary thyroid carcinoma (PTC) by The Cancer Genome Atlas (TCGA) study. Subsequent studies confirmed this mutation in PTC and Anaplastic Thyroid Carcinoma (ATC) but also reported EIF1AX mutation in Follicular nodular disease (FND) and benign thyroid nodules. In this study, we review thyroid nodules with EIF1AX mutation from two institutions: a tertiary care hospital (YNHH, n = 22) and a major cancer referral center (MSKCC, n = 34) and report the varying histomorphology in the context of additional genetic abnormalities and institutional practices. Pathology diagnoses were reviewed according to the WHO 5th edition and correlated with the type of EIF1AX mutation and additional concurrent molecular alterations, if any. Most cases were splice site type mutations. Cases consisted of 9 FND, 7 follicular (FA) or oncocytic adenomas (OA), 2 non-invasive follicular thyroid neoplasms with papillary-like nuclear features (NIFTP) and 38 follicular-cell derived thyroid carcinomas. Of 8 cases with isolated EIF1AX mutation, 7 were FND, FA or OA (88%) and one was an oncocytic carcinoma (12%). Of 12 cases with EIF1AX and one additional molecular alteration, 9 (75%) were FND, FA or OA, 2 (17%) were NIFTPs and one (8%) was a poorly differentiated thyroid carcinoma. All 36 cases with EIF1AX mutation and molecular alterations were malignant (100%) and included TP53 and TERT promoter mutations associated with ATC (n = 8) and high-grade follicular cell-derived non-anaplastic carcinoma (HGC, n = 2). Isolated EIF1AX mutation was noted only in thyroid nodules seen at YNHH and were predominantly encountered in benign thyroid nodules including FND. Accumulation of additional genetic abnormalities appears to be progressively associated with malignant tumors.

A protein expression atlas on tissue samples and cell lines from cancer patients provides insights into tumor heterogeneity and dependencies.

The Cancer Genome Atlas (TCGA) and the Cancer Cell Line Encyclopedia (CCLE) are foundational resources in cancer research, providing extensive molecular and phenotypic data. However, large-scale proteomic data across various cancer types for these cohorts remain limited. Here, we expand upon our previous work to generate high-quality protein expression data for approximately 8,000 TCGA patient samples and around 900 CCLE cell line samples, covering 447 clinically relevant proteins, using reverse-phase protein arrays. These protein expression profiles offer profound insights into intertumor heterogeneity and cancer dependency and serve as sensitive functional readouts for somatic alterations. We develop a systematic protein-centered strategy for identifying synthetic lethality pairs and experimentally validate an interaction between protein kinase A subunit α and epidermal growth factor receptor. We also identify metastasis-related protein markers with clinical relevance. This dataset represents a valuable resource for advancing our understanding of cancer mechanisms, discovering protein biomarkers and developing innovative therapeutic strategies.

A novel super-enhancer-related risk model for predicting prognosis and guiding personalized treatment in hepatocellular carcinoma

BackgroundOur research endeavored to develop a robust predictive signature grounded in super-enhancer-related genes (SERGs), with the dual objectives of forecasting survival outcomes and evaluating the tumor immune microenvironment (TiME) in hepatocellular carcinoma (HCC).MethodsHCC RNA-sequencing data were retrieved from The Cancer Genome Atlas (TCGA), and 365 patients were randomly assigned to training or testing sets in 1:1 ratio. SERGs of HCC were downloaded from Super-Enhancer Database (SEdb). On the basis of training set, a SERGs signature was identified, and its prognostic value was confirmed by internal and external validation (GSE14520) sets. We subsequently examined the model for potential functional enrichment and the degree of tumor immune infiltration. Additionally, we carried out in vitro experiments to delve into the biological functions of CBX2 gene.ResultsAn SE-related prognostic model including CBX2, TPX2, EFNA3, DNASE1L3 and SOCS2 was established and validated. According to this risk model, patients in the high-risk group had a significantly worse prognosis, and their immune cell infiltration was significantly different from that of low-risk group. Moreover, the high-risk group exhibited a significant enrichment of tumor-associated pathological pathways. The SERGs signature can generally be utilized to screen HCC patients who are likely to respond to immunotherapy, as there is a positive correlation between the risk score and the Tumor Immune Dysfunction and Exclusion (TIDE) score. Furthermore, the downregulation of the CBX2 gene expression was found to inhibit HCC cell viability, migration, and cell cycle progression, while simultaneously promoting apoptosis.ConclusionsWe developed a novel HCC prognostic model utilizing SERGs, indicating that patients with high-risk score not only face a poorer prognosis but also may exhibit a diminished therapeutic response to immune checkpoint inhibitors (ICIs). This model is designed to tailor personalized treatment strategies to the individual needs of each patient, thereby improving the overall clinical outcomes for HCC patients. Furthermore, CBX2 is a promising candidate for therapeutic intervention in HCC.

Abnormally High Expression of DNAJB6 Accelerates Malignant Progression of Lung Adenocarcinoma

DNAJB6, a major member of the DNAJ/HSP40 family, plays an important role in tumor development. We explored the effect of DNAJB6 expression on the prognosis of patients and its biological role in lung adenocarcinoma (LUAD). mRNA and clinical data were obtained from The Cancer Genome Atlas (TCGA). Enriched pathways were determined by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A nomogram incorporating DNAJB6 and three clinical features was constructed to predict the survival rate. DNAJB6 expression and function in LUAD were explored using immunohistochemistry, Western blotting, proliferation, cell cycle analysis, RNA sequencing, and xenograft tumor assays. DNAJB6 mRNA levels were elevated in the LUAD-TCGA dataset. DNAJB6 protein levels were higher in LUAD tumor tissues than in normal tissues. A high DNAJB6 level was an independent risk factor for poor prognosis in patients with LUAD. The proportion of tumor-infiltrating immune cells significantly differed between high and low DNAJB6 expression. DNAJB6 was associated with cell cycle pathways; therefore, its knockdown induced G2/M cell cycle arrest and inhibited LUAD cell proliferation. This is the first report of the DNAJB6 requirement for LUAD cell proliferation and its potentially crucial role in LUAD prognosis.

Targeting SRSF10 might inhibit M2 macrophage polarization and potentiate anti-PD-1 therapy in hepatocellular carcinoma.

The efficacy of immune checkpoint blockade therapy in patients with hepatocellular carcinoma (HCC) remains poor. Although serine- and arginine-rich splicing factor (SRSF) family members play crucial roles in tumors, their impact on tumor immunology remains unclear. This study aimed to elucidate the role of SRSF10 in HCC immunotherapy. To identify the key genes associated with immunotherapy resistance, we conducted single-nuclear RNA sequencing, multiplex immunofluorescence, and The Cancer Genome Atlas and Gene Expression Omnibus database analyses. We investigated the biological functions of SRSF10 in immune evasion using in vitro co-culture systems, flow cytometry, various tumor-bearing mouse models, and patient-derived organotypic tumor spheroids. SRSF10 was upregulated in various tumors and associated with poor prognosis. Moreover, SRSF10 positively regulated lactate production, and SRSF10/glycolysis/ histone H3 lysine 18 lactylation (H3K18la) formed a positive feedback loop in tumor cells. Increased lactate levels promoted M2 macrophage polarization, thereby inhibiting CD8+ T cell activity. Mechanistically, SRSF10 interacted with the 3'-untranslated region of MYB, enhancing MYB RNA stability, and subsequently upregulating key glycolysis-related enzymes including glucose transporter 1 (GLUT1), hexokinase 1 (HK1), lactate dehydrogenase A (LDHA), resulting in elevated intracellular and extracellular lactate levels. Lactate accumulation induced histone lactylation, which further upregulated SRSF10 expression. Additionally, lactate produced by tumors induced lactylation of the histone H3K18la site upon transport into macrophages, thereby activating transcription and enhancing pro-tumor macrophage activity. M2 macrophages, in turn, inhibited the enrichment of CD8+ T cells and the proportion of interferon-γ+CD8+ T cells in the tumor microenvironment (TME), thus creating an immunosuppressive TME. Clinically, SRSF10 could serve as a biomarker for assessing immunotherapy resistance in various solid tumors. Pharmacological targeting of SRSF10 with a selective inhibitor 1C8 enhanced the efficacy of programmed cell death 1 (PD-1) monoclonal antibodies (mAbs) in both murine and human preclinical models. The SRSF10/MYB/glycolysis/lactate axis is critical for triggering immune evasion and anti-PD-1 resistance. Inhibiting SRSF10 by 1C8 may overcome anti-PD-1 tolerance in HCC.

Comprehensive scRNA-seq analysis to identify new markers of M2 macrophages for predicting the prognosis of prostate cancer

Background Prostate cancer (PCa) has become the highest incidence of malignant tumor among men in the world. Tumor microenvironment (TME) is necessary for tumor growth. M2 macrophages play an important role in many solid tumors. This research aimed at the role of M2 macrophages’ prognosis value in PCa. Methods Single-cell RNA-seq (scRNA-seq) data and mRNA expression data were obtained from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA). Quality control, normalization, reduction, clustering, and cell annotation of scRNA-seq data were preformed using the Seruat package. The sub-populations of the tumor-associated macrophages (TAMs) were analysis and the marker genes of M2 macrophage were selected. Differentially expressed genes (DEGs) in PCa were identified using limma and the immune infiltration was detected using CIBERSORTx. Then, a weighted correlation network analysis (WGCNA) was constructed to identify the M2 macrophage-related modules and genes. Integration of the marker genes of M2 macrophage from scRNA-seq data analysis and hub genes from WGCNA to select the prognostic gene signature based on Univariate and LASSO regression analysis. The risk score was calculated, and the DEGs, biological function, immune characteristics related to risk score were explored. And a predictive nomogram was constructed. CCK8, Transwell, and wound healing were used to verify cell phenotype changes after co-cultured. Results A total of 2431 marker genes of M2 macrophage and 650 hub M2 macrophage-related genes were selected based on scRNA-seq data and WGCNA. Then, 113 M2 macrophage-related genes were obtained by overlapping the scRNA-seq data and WGCNA results. Nine M2 macrophage-related genes (SMOC2, PLPP1, HES1, STMN1, GPR160, ABCG1, MAZ, MYC, and EPCAM) were screened as prognostic gene signatures. M2 risk score was calculated, the DEGs, Immune score, stromal score, ESTIMATE score, tumor purity, and immune cell infiltration, immune checkpoint expression, and responses of immunotherapy and chemotherapy were identified. And a predictive nomogram was constructed. CCK8, Transwell invasion, and wound healing further verified that M2 macrophages promoted the proliferation, invasion, and migration of PCa (p < 0.05). Conclusions We uncovered that M2 macrophages and relevant genes played key roles in promoting the occurrence, development, and metastases of PCa and played as convincing predictors in PCa.