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IdentifiHR predicts homologous recombination deficiency in high-grade serous ovarian carcinoma using gene expression.
BACKGROUND: Approximately half of all high-grade serous ovarian carcinomas (HGSCs) have a therapeutically targetable defect in homologous recombination (HR) DNA repair. While there are genomic and transcriptomic methods, developed for other cancers, to identify HR deficient (HRD) samples, there are no gene expression-based tools to predict HR status in HGSC specifically. We have built a HGSC-specific model to predict HR status using gene expression. METHODS: We separated The Cancer Genome Atlas (TCGA) cohort of HGSCs into training (n = 288) and testing (n = 73) sets and labelled each case as HRD or HR proficient (HRP) based on the clinical standard for classification. Using the training set, we performed differential gene expression analysis between HRD and HRP cases. The 2604 significantly differentially expressed genes were used to train a penalised logistic regression model. RESULTS: IdentifiHR uses the expression of 209 genes to predict HR status in HGSC. These genes preserve the genomic damage signal, capturing known regions of HR-specific copy number alteration which impact gene expression. IdentifiHR is 85% accurate in the TCGA test set and 86% accurate in an independent cohort of 99 samples, taken from primary tumours, ascites and normal fallopian tubes. Further, IdentifiHR is 84% accurate in pseudobulked single-cell HGSC sequencing from 37 patients and outperforms existing expression-based methods to predict HR status, being BRCAness, MutliscaleHRD and expHRD. CONCLUSIONS: IdentifiHR is an accurate model to predict HR status in HGSC. It is available as an open source R package, empowering researchers to robustly classify HR status when only transcriptomic sequencing data is available.
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Deciphering lactate metabolism in colorectal cancer: Prognostic modeling, immune infiltration, and gene mutation insights.
BACKGROUND: Colorectal cancer (CRC) remains a major global health burden due to its high incidence and mortality, with treatment efficacy often hindered by tumor heterogeneity, drug resistance, and a complex tumor microenvironment (TME). Lactate metabolism plays a pivotal role in reshaping the TME, promoting immune evasion and epithelial-mesenchymal transition, making it a promising target for novel therapeutic strategies and prognostic modeling in CRC. AIM: To offer an in-depth analysis of the role of lactate metabolism in CRC, highlighting its significance in the TME and therapeutic response. METHODS: Utilizing single-cell and transcriptomic data from the Gene Expression Omnibus and The Cancer Genome Atlas, we identified key lactate metabolic activities, particularly in the monocyte/macrophage subpopulation. RESULTS: Seven lactate metabolism-associated genes were significantly linked to CRC prognosis and used to construct a predictive model. This model accurately forecasts patient outcomes and reveals notable distinct patterns of immune infiltration and transcriptomic profiles mutation profiles between high- and low-risk groups. High-risk patients demonstrated elevated immune cell infiltration, increased mutation frequencies, and heightened sensitivity to specific drugs (AZD6482, tozasertib, and SB216763), providing a foundation for personalized treatment approaches. Additionally, a nomogram integrating clinical and metabolic data effectively predicted 1-, 3-, and 5-year survival rates. CONCLUSION: This report underscored the pivotal mechanism of lactate metabolism in CRC prognosis and suggest novel avenues for therapeutic intervention.
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Prognostic utility of TME-associated genes in pancreatic cancer.
Background: Pancreatic cancer (PC) is a deadly disease. The tumor microenvironment (TME) participates in PC oncogenesis. This study focuses on the assessment of the prognostic and treatment utility of TME-associated genes in PC. Methods: After obtaining the differentially expressed TME-related genes, univariate and multivariate Cox analyses and least absolute shrinkage and selection operator (LASSO) were performed to identify genes related to prognosis, and a risk model was established to evaluate risk scores, based on The Cancer Genome Atlas (TCGA) data set, and it was validated by external data sets from the Gene Expression Omnibus (GEO) and Clinical Proteomic Tumor Analysis Consortium (CPTAC). Multiomics analyses were adopted to explore the potential mechanisms, discover novel treatment targets, and assess the sensitivities of immunotherapy and chemotherapy. Results: Five TME-associated genes, namely, FERMT1, CARD9, IL20RB, MET, and MMP3, were identified and a risk score formula constructed. Next, their mRNA expressions were verified in cancer and normal pancreatic cells. Multiple algorithms confirmed that the risk model displayed a reliable ability of prognosis prediction and was an independent prognostic factor, indicating that high-risk patients had poor outcomes. Immunocyte infiltration, gene set enrichment analysis (GSEA), and single-cell analysis all showed a strong relationship between immune mechanism and low-risk samples. The risk score could predict the sensitivity of immunotherapy and some chemotherapy regimens, which included oxaliplatin and irinotecan. Various latent treatment targets (LAG3, TIGIT, and ARID1A) were addressed by mutation landscape based on the risk model. Conclusion: The risk model based on TME-related genes can reflect the prognosis of PC patients and functions as a novel set of biomarkers for PC therapy.
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Epigenetic regulation during cancer transitions across 11 tumour types.
Chromatin accessibility is essential in regulating gene expression and cellular identity, and alterations in accessibility have been implicated in driving cancer initiation, progression and metastasis1-4. Although the genetic contributions to oncogenic transitions have been investigated, epigenetic drivers remain less understood. Here we constructed a pan-cancer epigenetic and transcriptomic atlas using single-nucleus chromatin accessibility data (using single-nucleus assay for transposase-accessible chromatin) from 225 samples and matched single-cell or single-nucleus RNA-sequencing expression data from 206 samples. With over 1 million cells from each platform analysed through the enrichment of accessible chromatin regions, transcription factor motifs and regulons, we identified epigenetic drivers associated with cancer transitions. Some epigenetic drivers appeared in multiple cancers (for example, regulatory regions of ABCC1 and VEGFA; GATA6 and FOX-family motifs), whereas others were cancer specific (for example, regulatory regions of FGF19, ASAP2 and EN1, and the PBX3 motif). Among epigenetically altered pathways, TP53, hypoxia and TNF signalling were linked to cancer initiation, whereas oestrogen response, epithelial-mesenchymal transition and apical junction were tied to metastatic transition. Furthermore, we revealed a marked correlation between enhancer accessibility and gene expression and uncovered cooperation between epigenetic and genetic drivers. This atlas provides a foundation for further investigation of epigenetic dynamics in cancer transitions.
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Integrating multi-omics data revealing LIMK1 as a metastatic biomarker in LUAD patients with brain metastasis.
BACKGROUND: Lung adenocarcinoma (LUAD) is a major subtype of lung cancer that frequently develops brain metastases. Tumor heterogeneity substantially complicates disease management, and reliable biomarkers for metastatic progression remain lacking. LIM domain kinase 1 (LIMK1), a key regulator of cytoskeletal dynamics, has been implicated in tumor progression; however, its role in regulating brain metastasis in LUAD remains unclear. METHODS: Single-cell RNA sequencing data from 11 primary LUAD tumors and 10 brain metastases were analyzed to identify malignant epithelial subclusters associated with metastatic dissemination. Tumor evolutionary trajectories and gene regulatory networks were explored using pseudotime trajectory analysis and weighted gene co-expression network analysis. LIMK1 expression was validated using The Cancer Genome Atlas dataset, as well as RT–qPCR and Western blotting in LUAD tissues and cell lines. Functional assays, including cell proliferation, colony formation, migration, and invasion, were performed following LIMK1 knockdown in LUAD cell lines. RESULTS: Six malignant epithelial subclusters were identified, among which clusters 0 and 4 were significantly enriched in brain metastases and associated with poor survival outcomes. LIMK1 was identified as a hub gene within these metastasis-associated clusters. LIMK1 expression was markedly upregulated in LUAD tissues and cell lines, and elevated LIMK1 levels predicted an unfavorable prognosis. Functional experiments demonstrated that LIMK1 depletion significantly inhibited tumor cell proliferation, migration, and invasion, while partially reversing epithelial–mesenchymal transition (EMT). Mechanistically, LIMK1 was found to promote LUAD progression through activation of the PI3K/AKT/mTOR signaling pathway. CONCLUSIONS: LIMK1 represents a novel prognostic biomarker and metastasis-associated regulator in LUAD. By driving EMT and activating PI3K/AKT/mTOR signaling, LIMK1 facilitates malignant progression, highlighting its potential as a therapeutic target for LUAD patients with brain metastases.
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Effect of immune infiltration intensity on the efficacy of neoadjuvant immunotherapy for esophageal cancer.
BACKGROUND: Esophageal squamous cell carcinoma (ESCC) treatment often involves neoadjuvant therapy combining chemotherapy and immune checkpoint inhibitors. However, the effectiveness of these treatments is limited by immune infiltration in the tumor microenvironment. METHODS: We analyzed single-cell transcriptomic data from 22 patients with resectable ESCC, collected before and after neoadjuvant therapy. Differences in gene expression between patients achieving a complete pathological response (pCR) and those who did not were assessed. We further validated our findings using RNAseq data from The Cancer Genome Atlas (TCGA), and conducted quantitative qRT-PCR and Western blot analyses on tumor tissues from a clinical cohort. RESULTS: Significant differences in gene expression related to T cell activation, natural killer cell activity, and cytokine signaling were observed between pCR and non-pCR patients. Notable genes included CXCL10, CXCL11, ME1, MT1X, FAT1, OAS2, and MT2A. TCGA data confirmed a correlation between high gene expression and increased tumor mutational burden as well as improved survival rates, particularly for CXCL10. qRT-PCR revealed significant upregulation of CXCL10, CXCL11, ME1, MT1X, FAT1, OAS2, and MT2A in tumor tissues compared to normal tissues. Western blot analysis showed increased protein levels of CXCL10, CXCL11, OAS2, MT1E, and MT1X, while FAT1 was downregulated. CONCLUSION: Our study highlights the critical role of immune infiltration and associated molecular pathways in the efficacy of neoadjuvant immunotherapy for ESCC. Specific genes, such as CXCL10, are promising as predictive markers for treatment response and survival.
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SPP1, LYZ, and MCM5: potential diagnostic biomarkers for rheumatoid arthritis and cervical cancer comorbidity.
BACKGROUND: The comorbidity of rheumatoid arthritis (RA), a chronic autoimmune disease, with cervical cancer has garnered a lot of attention. Cervical cancer is much more common in RA patients than in the general population, which may be caused by immunosuppressive therapy, chronic inflammation, and poor clearance of the Human Papillomavirus (HPV). The purpose of this study is to explore the molecular mechanism of comorbidity between RA and cervical cancer and identify potential biomarkers through transcriptomics and single cell transcriptomics analysis. METHODS: In this study, transcriptome expression profile data of RA and cervical cancer were downloaded from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, and differential gene analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis were performed. Using multivariate Cox proportional hazard modeling and Lasso regression, independent differential genes linked to the prognosis of cervical cancer were screened. Molecular docking technology was used to predict the interaction between candidate gene encoded proteins and HPV 16 E6/E7. Intercellular communication and the expression patterns of potential genes in various cell groups were examined using single cell transcriptome data. Finally, the expression of candidate genes in cervical tissues of patients with RA combined with cervical cancer was verified by immunohistochemistry. RESULTS: The study found that those with RA had 493 up-regulated genes and 216 down-regulated genes, while individuals with cervical cancer had 2,600 up-regulated genes and 2,172 down-regulated genes. Cox regression analysis identified 35 genes independently associated with the prognosis of cervical cancer, of which SPP1, LYZ, and MCM5 were significantly regulated in both RA and cervical cancer. The HPV 16 E6/E7 specific binding sites of the proteins produced by these three genes were shown using molecular docking simulation. Especially, single cell transcriptomic analysis revealed that SPP1 was highly expressed in NK/T cells, Myeloid cells, and epithelial cells, and served as an important ligand receptor pair for communication between these cells. Immunohistochemistry results further verified the high expression of SPP1, LYZ, and MCM5 in patients with RA combined with cervical cancer. CONCLUSION: This study successfully identified SPP1, LYZ, and MCM5 as key hub genes for the comorbidity of RA and cervical cancer. By regulating processes like inflammation, immune evasion, and cell proliferation, these genes not only have a high diagnostic potential but may also contribute to the occurrence and development of cervical cancer.
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Construction of a neutrophil extracellular trap formation-related gene model for predicting the survival of lung adenocarcinoma patients and their response to immunotherapy.
BACKGROUND: Lung adenocarcinoma (LUAD) is associated with high morbidity and mortality rates. Increasing evidence indicates that neutrophil extracellular traps (NETs) play a critical role in tumor progression, metastasis and immunosuppression in the LUAD tumor microenvironment (TME). Nevertheless, the use of NET formation-related genes (NFRGs) to predict LUAD patient survival and response to immunotherapy has not been explored. Therefore, this study aimed to construct a NFRGs-based prognostic signature for stratifying LUAD patients and informing individualized management strategies. METHODS: The cell composition of the LUAD TME was investigated using the single-cell sequencing data in Single-Cell Lung Cancer Atlas (LuCA). NFRGs were identified to construct a prognostic signature based on The Cancer Genome Atlas (TCGA) cohort which was validated in the Gene Expression Omnibus (GEO) dataset. The univariate Cox and least absolute shrinkage and selection operator (LASSO) Cox regression models, receiver operating characteristic (ROC) and Brier Score were applied to assess the prognostic model. A nomogram was established to facilitate the clinical application of the risk score. The Estimation of STromal and Immune cells in MAlignant Tumor tissues (ESTIMATE) and Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were utilized to assess the TME and predict immunotherapy response. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was applied to quantify the expression levels of four NFRGs in LUAD paired tissue samples. RESULTS: Single‑cell RNA sequence analysis showed the importance of neutrophils in LUAD TME. We developed and validated a 4-NFRG (CAT, CTSG, ENO1, TLR2) prognostic signature based on TCGA and GEO cohorts, which stratified patients into high-risk and low-risk groups. Univariate and multivariate analyses showed that our risk model could independently predict the survival of LUAD patients. Patients in the low-risk group exhibited a more active immune microenvironment, lower TIDE scores, lower half-maximal inhibitory concentration (IC50) values and higher immune checkpoint molecule expression. Our risk signature could serve as a biomarker for predicting immunotherapeutic benefits. CONCLUSIONS: We developed a novel prognostic signature for LUAD patients based on NFRGs and emphasized the critical role of this signature in predicting LUAD patient survival and immunotherapy response.
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Comprehensive analysis of IGFL1 in colorectal cancer and its promotion of tumour progression via inhibition of lipophagy.
OBJECTIVE: Colorectal cancer (CRC) ranks among the most prevalent malignancies, with increasing incidence and mortality rates presenting a substantial public health challenge. While insulin growth factor like family member 1 (IGFL1) has been implicated in the regulation of various diseases, its functional role in colorectal cancer remains poorly characterised. This study therefore aims to elucidate the involvement of IGFL1 in CRC through an integrated approach combining bioinformatics analysis and experimental validation. METHODS: The expression of IGFL1 in CRC and its association with clinicopathological features, diagnostic relevance, and patient prognosis were evaluated using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Immunohistochemistry was performed to validate IGFL1 protein expression in CRC tissue samples. Immune cell infiltration levels and immune microenvironment scores related to IGFL1 expression were analysed using multiple computational algorithms, including CIBERSORT, ssGSEA, ESTIMATE, EPIC, MCP-counter, quanTIseq, TIMER, xCell, and CIBERSOR. Furthermore, IGFL1 expression patterns across distinct cellular subpopulations were examined using single-cell RNA sequencing datasets from the Tumor Immune Single-cell Hub (TISCH) database. The TIDE algorithm was applied to assess the potential clinical efficacy of immunotherapy in groups with high versus low IGFL1 expression, in addition to investigating correlations between IGFL1 expression and immune checkpoint markers. Genetic alterations of IGFL1 were analysed via cBioPortal, while the TIMER2.0 database was used to explore relationships between IGFL1 expression and key gene mutations in CRC. The CTRP and GDSC databases were employed to investigate associations between IGFL1 expression and sensitivity to conventional chemotherapy drugs. Finally, phenotypic validation and mechanistic studies were conducted using the CRC cell lines SW620 and HCT116. RESULTS: Our study demonstrates that IGFL1 expression is significantly up-regulated in CRC and possesses considerable diagnostic value. Elevated IGFL1 levels were consistently observed in clinical specimens, where high expression correlated with adverse clinicopathological features, poorer prognosis, and mutations in key oncogenes. Within the tumour microenvironment, IGFL1 appears to play a critical role in modulating the infiltration of diverse immune cell populations. Furthermore, IGFL1 expression influences both immunotherapy responsiveness and chemotherapy sensitivity in CRC patients. Genetic knockdown of IGFL1 markedly attenuated the malignant phenotype of CRC cells. RNA-sequencing analysis revealed that IGFL1 is closely linked to cholesterol metabolism, autophagy pathways, and ATP hydrolysis activity. Functionally, inhibition of IGFL1 enhanced lipophagy in CRC cells. Collectively, these findings indicate that IGFL1 promotes CRC pathogenesis and progression through the suppression of lipophagy. CONCLUSIONS: IGFL1 exhibits oncogenic properties in colorectal cancer and may represent a potential therapeutic target.
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An ATXN2-linked stress granule transcript program marks proliferative and CD8-low states in colorectal cancer.
Immune checkpoint blockade benefits only a minority of colorectal cancer patients, and many tumors show immune-cold phenotypes at the transcript level, especially in microsatellite-stable (MSS) disease. Stress granules are conserved ribonucleoprotein assemblies that form under metabolic and proteotoxic stress and include the RNA-binding protein ataxin-2 (ATXN2). However, it remains unclear whether stress-granule-related tumor programs consistently track immune-cold colorectal cancer (CRC) states across independent cohorts. Here, we tested whether a compact stress-granule-related transcript program anchored on ATXN2 (CS_ATXN2) is associated with reduced CD8 cytotoxic transcript signal in bulk CRC beyond proliferation, hypoxia, and tumor purity. We analyzed three independent bulk CRC cohorts (total n = 1220) using multivariable models adjusting for proliferation, hypoxia, and tumor purity, pooled by random-effects meta-analysis. Program-level robustness and relative specificity were assessed using a P_BODY control, leave-one-gene-out testing, and expression-matched random gene sets. Four orthogonal immune deconvolution frameworks were applied, and patient-level pseudobulk analysis was performed in a CRC single-cell atlas. CS_ATXN2 was consistently associated with lower CD8 cytotoxic transcript signal across all three cohorts (pooled standardized beta = -0.159, 95% CI -0.215 to -0.103, p < 0.001, I² = 21.2%), surviving stromal adjustment and not reproduced by a P_BODY control. Immune deconvolution analyses provided convergent but not uniform support for reduced inferred lymphocyte signals in CS_ATXN2-high tumors. Patient-level single-cell analysis identified CXCL8 and LIF as candidate extracellular transcripts elevated in CS_ATXN2-high malignant cells (FDR < 0.05). In summary, CS_ATXN2 is a reproducible transcriptomic marker of proliferative and CD8-low CRC states.