Visualizing and interpreting cancer genomics data via the Xena platform

Identification of endoplasmic reticulum stress-related genes as prognostic markers in colon cancer

Endoplasmic reticulum stress (ERS) has been implicated in the pathogenesis of various cancers, including colon cancer, by regulating tumor cell survival, growth, and immune response. However, the specific genes involved in ERS that could serve as prognostic markers in colon cancer remain underexplored. This study aims to identify and validate endoplasmic reticulum stress related genes (ERSRGs) in colon cancer that correlate with patient prognosis, thereby enhancing the understanding of ERS in oncological outcomes and potential therapeutic targeting. We utilized bioinformatics analyses to identify ERSRGs from publicly available colon cancer datasets. Differential expression analysis and survival analysis were performed to assess the prognostic significance of these genes. Validation was conducted through quantitative real-time PCR (RT-qPCR) on selected colon cancer cell lines. Our study identified nine ERS related genes (ASNS, ATF4, ATF6B, BOK, CLU, DDIT3, MANF, SLC39A14, TRAF2) involved in critical pathways including IL-12, PI3K-AKT, IL-7, and IL-23 signaling, and linked to 1-, 3-, and 5-year survival of patients with colon cancer. A multivariate Cox model based on these ERS related genes demonstrated significant prognostic power. Further, TRAF2 strong correlated with immune cells infiltration, suggesting its potential roles in modulating immune responses in the tumor microenvironment. The RT-qPCR validation confirmed the differential expression of these genes in human colon cancer cell lines versus human normal colonic epithelial cell line. The identified ERSRGs could serve as valuable prognostic markers and may offer new insights into the therapeutic targeting of ERS in colon cancer.

Identification of TTLL8, POTEE, and PKMYT1 as immunogenic cancer-associated antigens and potential immunotherapy targets in ovarian cancer

Most high-grade serous ovarian cancers (OC) do not respond to current immunotherapies. To identify potential new actionable tumor antigens in OC, we performed immunopeptidomics on a human OC cell line expressing the HLA-A02:01 haplotype, which is commonly expressed across many racial and ethnic groups. From this dataset, we identified TTLL8, POTEE, and PKMYT1 peptides as candidate tumor antigens with low expression in normal tissues and upregulated expression in OC. Using tissue microarrays, we assessed the protein expression of TTLL8 and POTEE and their association with patient outcomes in a large cohort of OC patients. TTLL8 was found to be expressed in 56.7% of OC and was associated with a worse overall prognosis. POTEE was expressed in 97.2% of OC patients and had no significant association with survival. In patient TILs, increases in cytokine production and tetramer-positive populations identified antigen-specific CD8 T cell responses, which were dependent on antigen presentation by HLA class I. Antigen-specific T cells triggered cancer cell killing of antigen-pulsed OC cells. These findings suggest that TTLL8, POTEE, and PKMYT1 are potential targets for the development of antigen-targeted immunotherapy in OC.

Phosphoribosyl transferase domain containing 1: A prognostic biomarker in testicular germ cell tumors

Due to the heterogeneity and complex classification of testicular germ cell tumors (TGCTs), prognostic evaluation and therapeutic targets remain unclear. Therefore, identifying a novel biomarker to comprehensively assess TGCT prognosis and immunotherapy response is crucial. We collected data from 457 TGCT patient samples and 12 normal testicular samples across six cohorts. Differential expression analysis combined with univariate Cox regression identified prognostic markers for TGCT. Multivariate Cox regression and survival analysis further evaluated the prognostic value of phosphoribosyl transferase domain containing 1 (PRTFDC1). Immunohistochemistry on tissue microarrays validated PRTFDC1's predictive value in clinical samples. We then investigated the relationship between PRTFDC1 and somatic mutations, copy number variations, immune cell infiltration, and immunotherapy response. Through these analyses, we identified PRTFDC1 as an independent risk factor indicating poor prognosis in TGCT. Immunohistochemistry demonstrated high PRTFDC1 expression in TGCT tissues. Gene set enrichment analysis revealed that PRTFDC1 suppresses immune-related pathways. Immune infiltration showed that high PRTFDC1 expression is associated with low CD8+ T cell infiltration. Immunotherapy response analysis indicated that low PRTFDC1 expression predicts better immunotherapy response and favorable prognosis. In conclusion, this study elucidates the biological and clinical significance of PRTFDC1, suggesting it as an effective and reliable biomarker for predicting TGCT prognosis and immunotherapy response.

CD301b+ monocyte-derived dendritic cells mediate resistance to radiotherapy

Monocytes infiltrating tumors acquire various states that distinctly impact cancer treatment. Here, we show that resistance of tumors to radiotherapy (RT) is controlled by the accumulation of monocyte-derived dendritic cells (moDCs). These moDCs are characterized by the expression of CD301b and have a superior capacity to generate regulatory T cells (Tregs). Accordingly, moDC depletion limits Treg generation and improves the therapeutic outcome of RT. Mechanistically, we demonstrate that granulocyte-macrophage colony-stimulating factor (GM-CSF) derived from radioresistant tumor cells following RT is necessary for the accumulation of moDCs. Our results unravel the immunosuppressive function of moDCs and identify GM-CSF as an immunotherapeutic target during RT.

Pan-Cancer Analysis and Experimental Validation of CEND1 as a Prognostic and Immune Infiltration-Associated Biomarker for Gliomas

Cell cycle exit and neuronal differentiation 1 (CEND1), highly expressed in the brain, is a specific transmembrane protein which plays a tumor suppressor role. This study is performed to investigate the role of CEND1 in various cancers through pan-cancer analysis, and further investigate its functions in gliomas by cell experiments. The expression and subcellular localization of CEND1 in different cancer types were analyzed utilizing the data from the GEPIA, UCSC, UALCAN and HPA databases. Relationships of CEND1 expression with prognosis, immunomodulation-related genes, immune checkpoint genes, microsatellite instability (MSI), tumor mutation burden (TMB) and RNA modifications were analyzed based on the TCGA database. The ESTIMATE algorithm was utilized to evaluate tumors' StromalScore, Immune Score, and ESTIMATES Score. The cBioPortal database was employed to analyze the categories and frequencies of CEND1 gene alterations. Biological functions and co-expression patterns of CEND1 in gliomas were explored using the LinkedOmics database, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. The interactions between CEND1 and drugs were explored employing the Comparative Toxicogenomics Database and molecular docking technology. Cell experiments were conducted to analyze triptonide's effects on glioma cells through CCK-8, flow cytometry and qRT-PCR. CEND1 was lowly expressed in gliomas, and high CEND1 expression was correlated to better overall survival of glioma patients (HR = 0.65, P = 0.02). Deep deletion was the main type of hereditary change of CEND1 mutation. CEND1 expression was markedly associated with immune infiltration, TMB, MSI, and RNA modification in various tumors (r > 0.3, P < 0.05). CEND1 co-expressed genes in gliomas were markedly correlated with immune responses and cell cycle (FDR < 0.05). Triptonide could bind well to CEND1 (-5.0 kcal/mol), and triptonide could facilitate CEND1 expression in glioma cells and cell apoptosis, and block the cell cycle progression (P < 0.05). CEND1 serves as a potential biomarker for pan-cancer. Particularly in gliomas, CEND1 is a key regulator of cell apoptosis and cell cycle, and a potential target for glioma treatment.

SRSF2 overexpression induces transcription-/replication-dependent DNA double-strand breaks and interferes with DNA repair pathways to promote lung tumor progression

SRSF2 (serine/arginine-rich splicing factor 2) is a critical regulator of pre-messenger RNA splicing, which also plays noncanonical functions in transcription initiation and elongation. Although elevated levels of SRSF2 are associated with advanced stages of lung adenocarcinoma (LUAD), the mechanisms connecting SRSF2 to lung tumor progression remain unknown. We show that SRSF2 overexpression increases global transcription and replicative stress in LUAD cells, which correlates with the production of DNA damage, notably double-strand breaks (DSBs), likely resulting from conflicts between transcription and replication. Moreover, SRSF2 regulates DNA repair pathways by promoting homologous recombination and inhibiting nonhomologous end joining. Mechanistically, SRSF2 interacts with and enhances MRE11 (meiotic recombination 11) recruitment to chromatin, while downregulating 53BP1 messenger RNA and protein levels. Both events are likely contributing to SRSF2-mediated DNA repair process rerouting. Lastly, we show that SRSF2 and MRE11 expression is commonly elevated in LUAD and predicts poor outcome of patients. Altogether, our results identify a mechanism by which SRSF2 overexpression promotes lung cancer progression through a fine control of both DSB production and repair. Finally, we show that SRSF2 knockdown impairs late repair of ionizing radiation-induced DSBs, suggesting a more global function of SRSF2 in DSB repair by homologous recombination.

Landscape of lncRNAs expressed in Mexican patients with triple‑negative breast cancer

Long non‑coding RNAs (lncRNAs) are key regulators of gene expression, that can regulate a range of carcinogenic processes. Moreover, they exhibit stability in biological fluids, with some displaying tissue specificity. As their expression depends on specific conditions or is linked to the regulation of particular signaling pathways, lncRNAs are promising candidates for providing insights into the likely progression of the disease. This allows for the stratification of patients based on their risk of progression, making them potential prognostic biomarkers in various types of cancer. In addition, the tissue‑specific expression profile of lncRNAs renders them ideal candidates for detection, prognosis and monitoring of cancer progression. The present study aims to provide an overview of differentially expressed lncRNAs in Mexican patients with triple‑negative breast cancer (TNBC), a subtype of breast cancer. The aim was to identify potential prognostic biomarkers that can be applied to improve the clinical management of Mexican patients with TNBC. Human Transcriptome Array 2.0 microarrays were used to analyze the transcriptome of TNBC and luminal tumors, which are reported to have a good prognosis amongst aggressive tumor types. Subsequently, results from these microarrays were validated in a cohort from The Cancer Genome Atlas, an independent cohort of Mexican patients and in breast cancer cell lines (MCF7, ZR75, T47D, MDA‑MB‑231, MDA‑MB‑468 and BT20). A total of 746 differentially expressed transcripts were identified, including 102 lncRNAs in TNBC compared with luminal tumors. Among the lncRNAs with the most significant changes in expression levels, SOX9‑AS was highly expressed in TNBC, whereas the expression of Lnc‑peroxidasin‑3:1 (Lnc‑PXDN‑3:1), Lnc‑RNA Synapse Defective Rho GTPase Homolog (Lnc‑SYDE) and long intergenic non‑coding RNA (LINC)01087 were decreased. In addition, the low expression of lncRNA LINC01087, LINC02568, ACO22196, and lncRNA eosinophil granule ontogeny transcript (Lnc‑EGOT) was associated with poor overall survival (OS). Further analysis revealed that the high expression levels of Lnc‑PXDN‑3:1, Lnc RNA fibrous sheath interacting protein 1‑6:3 and (LINC)00182 were associated with reduced survival in patients with the luminal subtype of breast cancer. Similarly, low expression levels of lncRNAs such as GATA binding protein 3‑1 (Lnc‑GATA‑3‑1), LINC01087, and BX679671.1 in luminal subtypes of breast cancer, as well as LINC00504 and LncRNA rho guanine nucleotide exchange factor 38 intronic transcript 1 (Lnc‑ARHGEF38‑IT1) in basal subtypes have been linked to poorer survival. The interactions and functions of LINC01087 were then investigated, revealing the interaction of LINC01087 with RNAs and transcription factors, highlighting their potential involvement in the estrogen receptor pathway. The present study provided a detailed analysis of the expression of lncRNAs in TNBC, which highlights the role of lncRNAs as a biomarker in the survival outcomes of patients with breast cancer to improve the understanding of transcriptional regulation in TNBC.

Predicting the Prognosis of Bladder Cancer Patients Through Integrated Multi-omics Exploration of Chemotherapy-Related Hypoxia Genes

Bladder cancer is a prevalent malignancy with high mortality rates worldwide. Hypoxia is a critical factor in the development and progression of cancers. However, whether and how hypoxia-related genes (HRGs) could affect the development and the chemotherapy response of bladder cancer is still largely unexplored. This study comprehensively explored the complex molecular landscape associated with hypoxia in bladder cancer by analyzing 260 hypoxia genes based on transcriptomic and genomic data in 411 samples. Employing the 109 dysregulated hypoxia genes for consensus clustering, we delineated two distinct bladder cancer clusters characterized by disparate survival outcomes and distinct oncogenic roles. We defined a HPscore that was correlated with a variety of clinical features, including TNM stages and pathologic grades. Tumor immune landscape analysis identified three immune clusters and close interactions between hypoxia genes and the various immune cells. Utilizing a network-based method, we defined 129 HRGs exerting influence on apoptotic processes and critical signaling pathways in cancer. Further analysis of chemotherapy drug sensitivity identified potential drug-target HRGs. We developed a Risk Score model that was related to the overall survival of bladder cancer patients based on doxorubicin-target HRGs: ACTG2, MYC, PDGFRB, DHRS2, and KLRD1. This study not only enhanced our understanding of bladder cancer at the molecular level but also provided promising avenues for the development of targeted therapies, representing a significant step toward the identification of effective treatments and addressing the urgent need for advancements in bladder cancer management.

A comprehensive transcriptome based meta-analysis to unveil the aggression nexus of oral squamous cell carcinoma

Lymph node metastasis in oral cancer (OC) complicates management due to its aggressive nature and high risk of recurrence, underscoring the need for biomarkers for early detection and targeted therapies. However, the drivers of this aggressive phenotype remain unclear due to the variability in gene expression patterns. To address this, an integrative meta-analysis of six publicly available transcriptomic profiles, categorized by lymph nodal status, is conducted. Key determinants of disease progression are identified through functional characterization and the TopConfects ranking approach of nodal associated differentially expressed genes (DEGs). To explore the critical nexus between lymph node metastasis and OC recurrence, significant metastatic genes were cross-analysed with literature-derived genes exhibiting aberrant methylation patterns in OC recurrence. Their clinical relevance and expression patterns were then validated in an external dataset from the TCGA head and neck cancer cohort. The analysis identified elevated expression of genes involved in extracellular matrix remodelling and immune response, while the expression of genes related to cellular differentiation and barrier functions was reduced, driving the transition to nodal positivity. The highest-ranked gene, MMP1, showed a log-fold change (LFC) of 4.946 (95 % CI: 3.71, 6.18) in nodal-negative samples, which increased to 5.899 (95 % CI: 4.80, 6.99) in nodal-positive samples, indicating consistent elevation across disease stages. In contrast, TMPRSS11B was significantly downregulated, with an LFC of -5.512 (95 % CI: -6.63, -4.38) in nodal-negative samples and -5.898 (95 % CI: -7.15, -4.64) in nodal-positive samples. Furthermore, MEIS1, down-regulated in nodal-positive status, was found to exhibit hypermethylation at CpG sites associated with OC recurrence. This study represents the first transcriptomic meta-analysis to explore the intersection of lymph node metastasis and OC recurrence, identifying MEIS1 as a potential key contributor. These comprehensive insights into disease trajectories offer potential biomarkers and therapeutic targets for future treatment strategies.

TRanscriptome ANalysis of StratifiEd CohorTs (TRANSECT) enables automated assessment of global gene regulation linked to disparate expression in user defined genes and gene sets

Publicly accessible expression data produced by large consortium projects like TCGA and GTEx are increasing in number and size at an unprecedented rate. Their utility cannot be underestimated given the diversity of valuable tools widely used to interrogate these data and the many discoveries of biological and clinical significance already garnered from these datasets. However, there remain undiscovered ways to mine these rich resources and a continuing need to provide researchers with easily accessible and user-friendly applications for complex or bespoke analyses. We introduce TRanscriptome ANalysis of StratifiEd CohorTs (TRANSECT), a bioinformatics application automating the stratification and subsequent differential expression analysis of cohort data to provide further insights into gene regulation. TRANSECT works by defining two groups within a cohort based on disparate expression of a gene or a gene set and subsequently compares the groups for differences in global expression. Akin to reverse genetics minus the inherent requirement of in vitro or in vivo perturbations, cell lines or model organisms and all the while working within natural physiological limits of expression, TRANSECT compiles information about global transcriptomic change and functional outcomes. TRANSECT is freely available as a command line application or online at https://transect.au.

Nano-polymeric platinum activates PAR2 gene editing to suppress tumor metastasis

Metastasis as the hallmark of cancer preferentially contributes to tumor recurrence and therapy resistance, aggrandizing the lethality of patients with cancer. Despite their robust suppressions of tumor progression, chemotherapeutics failed to attenuate cancer cell migration and even triggered pro-metastatic effects. In parallel, protease-activated receptor 2 (PAR2), a member of the G protein-coupled receptor subfamily, actively participates in cancer metastasis via multiple signal transduction pathways. CRISPR/Cas9 that is a dominating genome editing tool can evoke PAR2 knockout to inhibit cancer metastasis. However, the absence of valid delivery systems largely limits its efficacy. Herein, we nanosized polymeric platinum (NanoPt) as therapeutical drug carries to deliver CRISPR/Cas9 to elicit genome editing of PAR2, which drastically augmented anti-metastatic effects and alleviated systematic toxicity of platinum-based treatment in vitro and in vivo. More importantly, the NanoPt@Cas9-PAR2 initiated PAR2 deficiency to mechanistically attenuate EMT process and ferroptosis via RAGE/ERK signalling, consequently preventing cancer cell migration. Our findings indicate that NanoPt@Cas9-PAR2 that mitigated PAR2 signalling and cytotoxic effects of platinum could be a safe and powerful all-in-one combinatorial strategy for cancer treatment.

Deciphering aging-associated prognosis and heterogeneity in gastric cancer through a machine learning-driven approach

Gastric cancer (GC) is a prevalent malignancy with a high mortality rate and limited treatment options. Aging significantly contributes to tumor progression, and GC was confirmed as an aging-related heterogeneous disease. This study established an aging-associated index (AAI) using a machine learning-derived gene panel to stratify GC patients. High AAI scores associated with poor prognosis and indicated potential benefits from adjuvant chemotherapy, while showing resistance to immunotherapy. Single-cell transcriptome analysis revealed that AAI was enriched in monocyte cells within the tumor microenvironment. Two distinct molecular subtypes of GC were identified through unsupervised clustering, leading to the development of a subtype-specific regulatory network highlighting SOX7 and ELK3 as potential therapeutic targets. Drug sensitivity analyses indicated that patients with high ELK3 expression may respond to FDA-approved drugs (axitinib, dacarbazine, crizotinib, and vincristine). Finally, a user-friendly Shiny application was created to facilitate access to the prognostic model and molecular subtype classifier for GC.

Deficiency of DDI2 suppresses liver cancer progression by worsening cell survival conditions

The levels of reactive oxygen species (ROS) and the extent of ensuing DNA damage significantly influence cancer initiation and progression. Of crucial importance, the aspartate protease DDI2 has been proposed to play a pivotal role in monitoring intracellular ROS levels (to trigger oxidative eustress or distress), as well as in the oxidative DNA damage repair, through redox homeostasis-determining factor Nrf1 (encoded by NFE2L1). However, the specific role of DDI2 in the multi-step process resulting in the development and progression of liver cancer remains elusive to date. In the present study, we employed the CRISPR/Cas9 gene editing system to create two nuanced lines of DDI2 knockout (i.e., DDI2-/- and DDI2insG/-) from liver cancer cells. Subsequent experiments indicate that the knockout of DDI2 leads to increased ROS levels in hepatoma cells by downregulating two major antioxidant transcription factors Nrf1 and Nrf2 (encoded by NFE2L2), exacerbating endogenous DNA damages caused by ROS and not-yet-identified factors, thereby inhibiting cell proliferation and promoting apoptosis, and ultimately hindering in vivo malignant growth of xenograft tumor cells. Conversely, the restoration of DDI2 expression reverses the accumulation of ROS and associated DNA damage caused by DDI2 knockout, eliminating the subsequent inhibitory effects of DDI2 deficiency on both in vitro and in vivo growth of liver cancer cells. Collectively, these findings demonstrate that DDI2 deficiency impedes liver tumor growth by disrupting its survival environment, suggesting that DDI2 may serve as a novel therapeutic target for anti-cancer strategies aimed at modulating ROS or DNA damage processes.

BAP1 Loss Affords Lipotoxicity Resistance in Uveal Melanoma

Uveal melanoma (UM) is an aggressive intraocular malignancy. Despite effective control of primary tumors, ~50% of UM patients develop metastases, with the liver being the predominant secondary site. BAP1 deficiency, present in ~80% of metastatic UM cases, is strongly associated with increased metastatic risk and poor prognosis. In silico analysis of UM patient samples suggests that reduced BAP1 is linked to enhanced expression of genes involved in fatty acid processing; therefore, we hypothesize that BAP1 deficiency primes UM cells for survival in the hepatic microenvironment by enhancing lipid tolerance and oxidative stress responses. Our findings demonstrate BAP1-mutant UM resist lipotoxicity, whereas BAP1-competent UM exhibit sensitivity due to lipid peroxide accumulation-a hallmark of ferroptotic-like stress, and a response that can be mitigated by ferroptosis inhibition. Using an ex vivo liver slice model, we found that disrupting lipid metabolism with atorvastatin, an HMG-CoA reductase inhibitor, reduced tumor burden of BAP1-mutant UM. Moreover, we demonstrate a positive correlation between BAP1 and an epigenetic regulator of lipid homeostasis, ASXL2. Notably, ASXL2 depletion in BAP1-competent UM phenocopies the lipotoxicity resistance observed in BAP1-mutant UM-an effect that may be mediated by altered PPAR expression. This study reveals a novel mechanism linking BAP1 expression to lipid sensitivity via ASXL2, providing insights into liver tropism and potential therapeutic avenues for metastatic uveal melanoma.

A Novel Neutrophil Extracellular Trap Signature Predicts Patient Chemotherapy Resistance and Prognosis in Lung Adenocarcinoma

Chemoresistance is a key obstacle in the long-term survival of patients with locally and advanced lung adenocarcinoma (LUAD). This study used bioinformatic analysis to reveal the chemoresistance of gene-neutrophil extracellular traps (NETs) associated with LUAD. RNA sequencing data and LUAD expression patterns were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively. The GeneCards database was used to identify NETosis-related genes (NRGs). To identify hub genes with significant and consistent expression, differential analysis was performed using the TCGA-LUAD and GEO datasets. LUAD subtypes were determined based on these hub genes, followed by prognostic analysis. Immunological scoring and infiltration analysis were conducted using NETosis scores (N-scores) derived from the TCGA-LUAD dataset. A clinical prognostic model was established and analyzed, and its clinical applications explored. Twenty-two hub genes were identified, and consensus clustering was used to identify two subgroups based on their expression levels. The Kaplan-Meier (KM) curves demonstrated statistically significant differences in prognosis between the two LUAD subtypes. Based on the median score, patients were further divided into high and low N-score groups, and KM curves showed that the N-scores were more precise at predicting the prognosis of patients with LUAD for overall survival (OS). Immunological infiltration analysis revealed significant differences in the abundances of 10 immune cell infiltrates between the high and low N-score groups. Risk scores indicated significant differences in prognosis between the two extreme score groups. The risk scores for the prognostic model also indicated significant differences between the two groups. The results provide new insights into NETosis-related differentially expressed genes (NRDEGs) associated with chemotherapy resistance in patients with LUAD. The established prognostic model is promising and could help with clinical applications to evaluate patient survival and therapeutic efficiency.

m6A-induced DEAD-box RNA helicase 21 enhances lipid metabolism via 3‑hydroxy-3-methylglutaryl-CoA synthases 1 in colorectal cancer

BACKGROUND

Altered lipid metabolism is a well-known hallmark of cancer. However, the underlying mechanisms of altered lipid metabolism in colorectal cancer (CRC) progression requires further investigation. Previously we have revealed that DEAD-box RNA helicase 21 (DDX21) promotes CRC metastasis via liquid-liquid phase separation. In this study, we identify DDX21 as a novel regulator of lipid metabolism in CRC.

METHODS

In vitro and in vivo assays illustrated the biological role of DDX21 and YTH N6-methyladenosine RNA binding protein 1 (YTHDF1) in CRC lipid metabolism and progression. Bioinformatics analysis, ChIP, meRIP, RIP, RNA stability assay and dual-luciferase reporter assay were applied to explore the underlying molecular mechanisms. The expression levels and prognostic role of YTHDF1/DDX21/HMGCS1 axis in CRC patients were analyzed by immunohistochemical staining and Kaplan-Meier plotter.

RESULTS

DDX21 enhanced CRC progression via promoting lipid metabolism. Mechanistically, YTHDF1 enhanced DDX21 mRNA stability by recognizing its m6A-modified sites. DDX21 further binded to 3‑hydroxy-3-methylglutaryl-CoA synthase 1 (HMGCS1) promoter region and directly activated HMGCS1 transcription. Moreover, our clinical data showed that a simultaneously high expression of YTHDF1, DDX21 and HMGCS1 predicted an unfavorable overall survival in CRC patients.

CONCLUSIONS

Our study demonstrates that the YTHDF1/DDX21/HMGCS1 axis promotes CRC progression via regulating lipid metabolism and DDX21 might be a promising target for CRC therapy.

Proteomic insights into lymph node metastasis in breast cancer subtypes: Key biomarkers and pathways

BACKGROUND

Breast cancer (BC) is a significant global cause of death in women, primarily due to its diversity and metastatic potential.

METHODS

BC, healthy lymph node (HL), and metastatic lymph node (ML) tissues were collected from 19 patients diagnosed with infiltrating ductal carcinoma. Protein isolation was performed, followed by two-dimensional gel electrophoresis (2DE) and mass spectrometry (MALDI-TOF/TOF) to identify differentially expressed proteins. Bioinformatic analyses, including protein-protein interaction networks and molecular pathways, were conducted using STRING. Kaplan-Meier analysis was performed with KM plotter to evaluate the prognostic significance of identified proteins. Receiver operating characteristic (ROC) curves were generated using TCGA and GTEx data from UCSC Xena and easyROC to assess diagnostic relevance.

RESULTS

Distinct pathways related to cytoskeletal regulation, immune modulation, and oxidative stress response were enriched in each subtype. Key proteins such as TUBA1C, CCT6A, and Vimentin (LNA), CAPZB and ENO1 (LNB), GSTO1 (HER2 OE), and CORO1A and LAP3 (TNBC) were identified as significant in driving metastatic behavior. KM survival analysis showed that CAPZB (LNB) and CORO1A (TNBC) were associated with patient outcomes, while GSTO1 was linked to improved distant metastasis-free survival in HER2 OE. ROC analysis highlighted CAPZB as a strong diagnostic marker.

CONCLUSIONS

These findings form a basis for comprehending the molecular mechanisms underlying metastasis in different subtypes of breast cancer. They may lead to the identification of new therapeutic targets for customized interventions against invasion and metastasis. Further validation is required to confirm their clinical utility in larger cohorts.

Epigenetic regulation of HOXA3 and its impact on oral squamous cell carcinoma progression

OBJECTIVE

The role of homeobox A3 (HOXA3) in cancer progression is gaining prominence, however, to date, no studies have investigated its regulatory function in oral cancer. In this study, we explored the role of HOXA3 through epigenetic mechanisms.

METHODS

Clinical samples were collected from 25 potentially malignant oral lesions and 50 oral squamous cell carcinoma (OSCC) patients, categorized into low-stage and high-stage tumors. The promoter activity of HOXA3 was determined through cloning and luciferase assays. CpG methylation patterns across the gene were identified using methyl-capture sequencing. Gene expression was analyzed using RT‒qPCR. The Survminer R package was used to assess the clinical significance of 3' UTR methylation associated with overall survival. RNA‒RNA interactions were analyzed using RNAInter and TargetScan v8.0.

RESULTS

HOXA3 expression was upregulated in dysplasia and downregulated in advanced cancer stages, showing an inverse correlation with promoter methylation, suggesting epigenetic regulation by DNA methylation. Hypermethylation of the 3' UTR was associated with poor overall survival in advanced stages. Long noncoding RNAs and microRNAs may post-transcriptionally modulate HOXA3 in oral carcinogenesis.

CONCLUSION

CpG-specific hypermethylation in the 3' UTR may serve as a potential biomarker in OSCC.

CD44v, S1PR1, HER3, MET and cancer-associated amino acid transporters are promising targets for the pancreatic cancers characterized using mAb

Effective therapies have yet to be established for pancreatic ductal adenocarcinomas (PDAC) even though it is the most aggressive cancer. In the present study, PDAC was analyzed using novel rat mAbs against membrane proteins in conjunction with flow cytometry and immunohistochemistry. Human epidermal growth receptor (HER)1-4, mesenchymal to epithelial transition factor (MET), sphingosine-1-phospahate receptor 1 (S1PR1), l-type amino acid transporter 1 (LAT1), system x- c transporter (xCT), alanine-serine-cysteine transporter (ASCT2), cationic amino acid transporter 1 (CAT1) and variant CD44 (CD44v) were expressed at high frequencies in both in vitro and in vivo PDAC. Internalization of membrane proteins by mAbs and growth inhibition by toxin-linked mAbs were demonstrated in many PDAC cell lines, and mAbs against S1PR1, ASCT2, HER3 and CD44v inhibited the growth of xenografted MIA PaCa-2 PDAC cells. Furthermore, CD44v-high PDAC showed high mRNA expression of HER1-3, MET and CD44v, and was correlated with poor prognosis. Taken together, our results suggest that CD44v, S1PR1, HER3, MET and the above-mentioned cancer-associated amino acid transporters might be promising targets for the diagnosis and treatment of PDAC.

SIRT5-mediated desuccinylation of PPA2 enhances HIF-1alpha-dependent adaptation to hypoxic stress and colorectal cancer metastasis

Metastasis is the primary cause of death in patients with colorectal cancer (CRC). Hypoxia is a hallmark of solid tumors that promotes cellular metabolic adaptation and dissemination. However, the mechanisms linking hypoxia-regulated metabolic adaptation to CRC metastasis remain unclear. Here, we found that inorganic pyrophosphatase 2 (PPA2) suppresses metastatic progression of CRC via its phosphatase function. PPA2 expression levels are reduced in CRC specimen and correlate with enhanced response to hypoxia by promoting hypoxia-inducible factor-1 (HIF-1) signaling to promote CRC cell glycolysis and dissemination. Mechanistically, PPA2 decreases HIF-1alpha stability through non-canonical ubiquitin-mediated proteasomal degradation via recruitment of E3 ligase NEDD4. Furthermore, PPA2 directly dephosphorylates NEDD4 at threonine 758 residue, resulting in its activation. Under hypoxic stress, NAD-dependent protein deacetylase sirtuin-5 promotes the dissociation of PPA2 and NEDD4 by inducing PPA2 desuccinylation at lysine 176, contributing to the improved stability of HIF-1alpha under hypoxic conditions. Our findings reveal a tumor-suppressive role of PPA2 in HIF-1alpha-dependent colorectal cancer, providing a potential therapeutic target and prognostic strategy.

ALDH1A1 in breast cancer: A prospective target to overcome therapy resistance (Review)

The expression of cytosolic aldehyde dehydrogenases (ALDHs), which mediate the last step in the pathway of the synthesis of all-trans retinoic acid, is dysregulated in various types of human cancer, and has been associated with the development of cancer stem cells (CSCs) in solid tumors and hematological malignancies. CSCs are considered a minor fraction of cancer cells with the capacity to initiate neoplastic tumors. ALDH1A1 serves a crucial role in the emergence of the CSC phenotype, induces the malignant behavior of cancer cells and promotes treatment resistance. Notably, ALDH1A1-induced therapy resistance is not exclusive to just one group of drugs, but affects diverse types of drugs that use different mechanisms to kill cells. This diversity of drug resistance-inducing effects is associated with the stemness-supporting functions of ALDH1A1. The inhibition of ALDH1A1 activity using chemicals or the depletion of ALDH1A1 via genetic approaches, such as the use of small interfering RNA, can overcome diverse pathways of therapy resistance. In the context of breast cancer, it is critical that only a fraction of malignant cells are expected to manifest stem-like features, which include increased expression of ALDH1A1. From the angle of disease prognosis, the extent of the association of ALDH1A1 with increased malignant behavior and drug resistance remains to be determined through the application of cutting-edge methods that detect the expression of tracked biomarkers within tumors.

A bioinformatics analysis and experimental validation of PDGFD as a promising diagnostic biomarker for acute myeloid leukemia

Acute myeloid leukemia (AML) is a malignant blood cancer resulting from leukemia stem cells (LSCs) supplanting normal stem cells. Platelet-derived growth factors (PDGFs) are important for LSCs but have not been studied in the development of AML. In this study, transcriptome data of PDGFs were sourced from The Cancer Genome Atlas (TCGA) and GTEx databases, and relevant differential expression and prognosis analysis were performed using R software packages and online tools (UCSC-Xena Shiny tools, GEPIA2, Kaplan-Meier Plotter databases, etc.). Then, we focused on PDGFD expression in AML, along with its clinical and diagnostic importance, drug resistance studies, and association with immunotherapy. The real-time quantitative polymerase chain reaction (RT-qPCR) was performed to verify the expression and clinical characteristics of PDGFD. Analyses of public data and clinical samples revealed that PDGFD expression was upregulated compared with other PDGF genes, and only this upregulation was associated with poor prognosis in AML. High expression of PDGFD showed a significant positive correlation with intermediate-high cytogenetic risk, NPM1 mutation, FLT3-ITD mutation, and unfavorable prognosis. ROC curve analysis indicated that PDGFD holds substantial diagnostic potential for AML patients. Functional enrichment analysis revealed the role of PDGFD in calcium and Rap1 signaling pathways. Additionally, PDGFD expression exhibited a significant positive correlation with natural killer cells and dendritic cells. Furthermore, we propose that MiR-203-3p targeting PDGFD has potential anti-leukemic effects in AML. In conclusion, PDGFD serves as a possible diagnostic and prognostic biomarker, as well as a target for cellular immunotherapy in AML.

TFDP1 drives triple-negative breast Cancer development through senescence suppression and serves as a therapeutic target for topotecan

Triple-negative breast cancer (TNBC) constitutes the molecular subtype exhibiting the poorest prognosis. Targeted therapy emerges as a pivotal strategy to enhance the clinical outcomes of individuals with TNBC. Identifying targets and corresponding therapeutic agents is essential for reducing TNBC-related mortality. Topotecan, a chemotherapeutic agent approved for treating metastatic breast cancer, remains under investigation regarding its specific targets and molecular mechanisms in TNBC. Data procured from CRISPR/Cas9 library screenings showed that TFDP1 may be a therapeutic target in TNBC, and the L1000FWD database suggested that TFDP1 serves as a potential target of topotecan. The overexpression of TFDP1 was observed in TNBC tissues, correlating with poorer prognosis. Knockdown of TFDP1 inhibited the cell growth, clonal expansion, and tumorigenicity of TNBC cells. Mechanistically, TFDP1 inhibited cellular senescence in TNBC cells. In vitro experiments demonstrated that topotecan inhibited TNBC cell growth and promoted cellular senescence, counteracting the effects of TFDP1 overexpression on TNBC cells. These findings suggest that topotecan impedes TNBC cell growth by targeting TFDP1. This interaction provides valuable insights into the molecular mechanisms governing TNBC cell senescence, presenting TFDP1 as a potential therapeutic target. Combining topotecan with senolytic therapies may offer a promising strategy for TNBC treatment.

Comprehensive pan-cancer analysis of CHRDL1 and experimental validation of its role in lung adenocarcinoma

Chordin-like 1 (CHRDL1) is a secreted antagonist of bone morphogenetic proteins, and has been implicated in various biological processes and cancer prognosis. This study offered a detailed examination of CHRDL1 expression across 33 diverse cancer types, leveraging data from The Cancer Genome Atlas (TCGA) and supplementary public datasets. We demonstrated that, for the majority of cancer types, CHRDL1 expression was reduced in tumor tissues compared to normal adjacent tissues. Notably, lower CHRDL1 expression led to negative prognosis in malignancies such as lung adenocarcinoma (LUAD), melanoma (SKCM), and mesothelioma (MESO). Furthermore, CHRDL1 expression was positively correlated with the infiltration of CD4⁺ T cells, CD8⁺ T cells, B cells, neutrophils, macrophages, and dendritic cells in most tumors. Higher CHRDL1 expression correlated with more favorable immune profiles and a reduction in tumor stemness. To assess the effect of CHRDL1 overexpression on LUAD progression, we conducted CCK-8, wound healing, and invasion assays in vitro, along with subcutaneous tumor formation experiments in nude mice. The results showed that the proliferation, migration, and invasion abilities of A549 and H1299 cells with high CHRDL1 expression were reduced, and the growth of A549 cells was also significantly inhibited in nude mice. These findings underscored CHRDL1's potential as a prognostic biomarker and its influence on tumor immunology and cellular dynamics.

ZMYND8 drives HER2 antibody resistance in breast cancer via lipid control of IL-27

Anti-HER2 antibodies are effective but often lead to resistance in patients with HER2+ breast cancer. Here, we report an epigenetic crosstalk with aberrant glycerophospholipid metabolism and inflammation as a key resistance mechanism of anti-HER2 therapies in HER2+ breast cancer. Histone reader ZMYND8 specifically confers resistance to cancer cells against trastuzumab and/or pertuzumab. Mechanistically, ZMYND8 enhances cPLA2α expression in resistant tumor cells through inducing c-Myc. cPLA2α inactivates phosphatidylcholine-specific phospholipase C to inhibit phosphatidylcholine breakdown into diacylglycerol, which diminishes protein kinase C activity leading to interleukin-27 secretion. Supplementation with interleukin-27 protein counteracts cPLA2α loss to reinforce trastuzumab resistance in HER2+ tumor cells and patient-derived organoids. Upregulation of ZMYND8, c-Myc, cPLA2α, and IL-27 is prevalent in HER2+ breast cancer patients following HER2-targeted therapies. Targeting c-Myc or cPLA2α effectively overcomes anti-HER2 therapy resistance in patient-derived xenografts. Collectively, this study uncovers a druggable signaling cascade that drives resistance to HER2-targeted therapies in HER2+ breast cancer.

Clinicopathological correlation of PTPN3 expression in breast cancer and in silico drug screening against PTPN3 for therapeutics

PTPN3 regulates cellular signaling and is dysregulated in cancer. There has been less research about the oncogenic impact of PTPN3 in breast cancer patients. This study analyzed PTPN3 mRNA levels and their prognostic significance in breast cancer using TCGA datasets. qRT-PCR was used to assess PTPN3 expression in formalin-fixed, paraffin-embedded Indian breast cancer patient samples (tumor-74, control-36). PTPN3 protein levels (ER-positive 15; ER-negative: 15; distant normal breast tissues: 20) were also immunohistochemically assessed using the H-score method. The biomarker potential was examined using a receiver operating characteristic (ROC) analysis. Docking and molecular dynamics (MD) simulations were used to find PTPN3 inhibitors (PDB ID: 2B49) from 892 FDA-approved natural chemicals in the ZINC database. PTPN3 mRNA and protein expression were significantly higher in breast cancers and associated with clinicopathological variables such as age, ER status, tumor stage, grade, Ki-67 index, menopause, and lymph node metastasis (p < 0.05). ROC analysis revealed an AUC of 0.7654, indicating PTPN3's biomarker potential. Docking yielded three high-affinity inhibitors: Cyclocort (ZINC000003977777), Toposar (ZINC000003938684), and Tetracycline (ZINC000084441937), with binding energies of -9.3, -8.73, and -8.66 kcal/mol, respectively. MD simulations confirmed stable connections via hydrogen bonds and hydrophobic interactions under minimal constraints. In conclusion, PTPN3 overexpression supports its role as a prognostic biomarker, and Cyclocort, Toposar, and Tetracycline need further confirmation as potential PTPN3 inhibitors.

A Deep Learning Survival Model for Evaluating the Survival Prognosis of Papillary Thyroid Cancer: A Population-Based Cohort Study

BACKGROUND

Deep learning can assess the individual survival prognosis in sizeable datasets with intricate underlying processes. However, studies exploring the performance of deep learning survival in papillary thyroid cancer (PTC) are lacking. This study aimed to construct a deep learning model based on clinical risk factors for survival prediction in patients with PTC.

METHODS

A Cox proportional hazards deep neural network (DeepSurv) was developed and validated by using consecutive patients with PTC from 17 US Surveillance, Epidemiology, and End Results Program (SEER) cancer registries (2000-2020). The performance of the DeepSurv model was further validated on two external test datasets from the University of Texas MD Anderson Cancer Center (MDACC) and The Cancer Genome Atlas (TCGA). Using the survival risk scores at 10 years predicted by the DeepSurv model, we classified patients with PTC into low-risk and high-risk groups and explored their overall survival (OS).

RESULTS

The concordance index of the DeepSurv model for predicting OS was 0.798 in the SEER test dataset, 0.893 in the MDACC dataset, and 0.848 in the TCGA dataset. The DeepSurv model was capable of classifying patients with PTC into low-risk and high-risk groups according to the survival risk scores at 10 years. Patients in the high-risk group had significantly worse OS than patients in the low-risk group in all three test datasets (all P < 0.001).

CONCLUSION

The DeepSurv model was capable of classifying patients with PTC into low-risk and high-risk groups, which may provide important prognostic information for personalized treatment in patients with PTC.

LPCAT3 regulates the immune infiltration and prognosis of ccRCC patients by mediating ferroptosis and endoplasmic reticulum stress

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) accounts for 70% of renal cell carcinoma (RCC) cases. Although surgery remains the mainstay treatment, renal injury and high metastasis rates after nephrectomy dramatically reduce patient quality of life. Drugs that stimulate the immune system by targeting checkpoint pathways improve overall survival in patients with RCC. Here, we investigated the applicability of lysophosphatidylcholine acyltransferase 3 (LPCAT3) as a target for immunotherapy.

METHODS

In the present study, high LPCAT3 expression in ccRCC was identified using The Cancer Genome Atlas (TCGA) data and validated in two external cohorts from the Gene Expression Omnibus (GEO) database. qRT-PCR was performed to identify the mRNA level of LPCAT3 in tumors and adjacent normal tissues. And immunohistochemistry was used to evaluate the protein level of LPCAT3 between two groups of samples. Furthermore, gene set enrichment analysis was performed to explore the biological processes and pathways related to LPCAT3 expression. Key gene expression and correlation analyses were performed to determine the crosstalk among LPCAT3 expression, ferroptosis, and endoplasmic reticulum stress (ERS). Subsequently, CIBERSORT was used to analyze the immune infiltration status of patients with high and low LPCAT3 expression.

RESULTS

TCGA and GEO data revealed that LPCAT3 expression in ccRCC tumor tissues was higher than that in adjacent normal tissues; moreover, patients with high LPCAT3 expression had better survival outcomes. qRT-PCR and immunohistochemistry verified the high LPCAT3 expression in tumor tissue. Pathways related to ferroptosis and ERS were upregulated in patients with high LPCAT3 expression. Univariate and multivariate regression analyses revealed that low LPCAT3 levels represent an independent risk factor for ccRCC. LPCAT3 expression was positively correlated with M2 macrophage infiltration levels but negatively correlated with the memory B cell, CD8+ T cell, follicular helper T cell, regulatory T cell, activated natural killer cell, and activated memory CD4+ T cell infiltration levels.

CONCLUSIONS

LPCAT3was identified as a ccRCC biomarker and may regulate immune infiltration and prognosis in ccRCC by mediating ferroptosis and ERS. Thus, it has potential for exploitation as a prognostic and immune therapeutic target for patients with ccRCC.

ADAMTSL2 is an independent predictor for the prognosis of gastric cancer

AIMS

To explore novel biomarkers capable of predicting the prognosis of gastric cancer (GC) and investigate the mechanisms underlying the development of GC.

METHODS

Firstly, differentially expressed genes (DEGs) in GC tumors and adjacent tissues were analyzed using transcriptome sequencing data. Then, the DEGs significantly associated with the prognosis of GC were selected. From this subset, genes with high protein expression levels in tumor tissues were focused. Multivariate hazard analysis was performed to further identify DEGs with independent prognostic value for GC patients. Eventually, the potential mechanisms involving DEGs that underlie the development of GC were investigated.

RESULTS

Altogether, 25 previously DEGs that have not been reported before were discovered in the context of GC. Among these genes, ADAMTSL2, DSCC1, COL5A3, F2RL2, GRIN2D, IGSF6, IER5L, PLA2G7, PODNL1, RCN3 and RTN4RL2 were significantly associated with the overall survival, first progression and post progression survival of GC patients. Moreover, protein levels of ADAMTSL2, COL5A3, DSCC1, GRIN2D, PODNL1 and RCN3 were consistently highly expressed in clinical GC specimens. Furthermore, multivariate hazard analysis identified ADAMTSL2 as an independent predictor of GC prognosis. Further exploration revealed a potential regulatory connection between ADAMTSL2 and hsa-miR-7-2-3p. hsa-miR-7-2-3p was significantly down-regulated in GC and GC patients with low expression of hsa-miR-7 had a poor overall survival. Additionally, ADAMTSL2 was significantly co-expressed with key molecules (NOTCH1, NOTCH3, NOTCH4 and HEY1) in Notch signaling pathway.

CONCLUSIONS

ADAMTSL2 stands out as an independent predictor for the prognosis of GC and may play a crucial pathological role in the development of GC.

MammOnc-DB, an integrative breast cancer data analysis platform for target discovery

Breast cancer (BCa), a leading malignancy among women, is characterized by morphological and molecular heterogeneity. While early-stage, hormone receptor, and HER2-positive BCa are treatable, triple-negative BCa and metastatic BCa remains largely untreatable. Advances in sequencing and proteomic technologies have improved our understanding of the molecular alterations that occur during BCa initiation and progression and enabled identification of subclass-specific biomarkers and therapeutic targets. Despite the availability of abundant omics data in public repositories, user-friendly tools for multi-omics data analysis and integration are scarce. To address this, we developed a comprehensive BCa data analysis platform called MammOnc-DB ( http://resource.path.uab.edu/MammOnc-Home.html ), comprising data from more than 20,000 BCa samples. MammOnc-DB facilitates hypothesis generation and testing, biomarker discovery, and therapeutic targets identification. The platform also includes pre- and post-treatment data, which can help users identify treatment resistance markers and support combination therapy strategies, offering researchers and clinicians a comprehensive tool for BCa data analysis and visualization.

Construction of a prognostic model and identification of key genes in liver hepatocellular carcinoma based on multi-omics data

Liver hepatocellular carcinoma (LIHC) strongly contributes to global cancer mortality, highlighting the need for a deeper understanding of its molecular mechanisms to enhance patient prognosis and treatment approaches. We aimed to investigate the differential expression of immunogenic cell death-related genes (ICDRGs) and cellular senescence-related genes (CSRGs) in LIHC and their effects on patient prognosis. We combined the GSE25097, GSE46408, and GSE121248 datasets by eliminating batch effects and standardizing the data. After processing, 16 genes were identified as ICDR&CSR differentially expressed genes (ICDR&CSRDEGs), including UBE2T, HJURP, PTTG1, CENPA, and FOXM1. Gene set enrichment analysis indicated a strong enrichment of these genes in pre-Notch expression and processing. Gene set variation analysis revealed 20 pathways with significant differences between the LIHC and control groups. Mutation analysis identified TP53 as the most commonly mutated gene in LIHC samples. A prognostic risk model integrating 12 ICDR&CSRDEGs was developed, showing high precision at 1 year but diminished accuracy at 2 and 3 years. Our constructed prognostic risk model provides valuable insights for predicting patient outcomes and may guide future therapeutic interventions targeting these specific genes. Further research is needed to explore the mechanistic roles of these genes in LIHC progression and treatment response.

Purinosomes as a therapeutic target in hepatocellular carcinoma: insights and opportunities

The formation of purinosomes, dynamic complexes involved in de novo purine biosynthesis, has been recognized as a critical process for cell growth. Although their upregulation in cancer cells suggests their potential as a therapeutic target, the specific role of purinosomes in hepatocellular carcinoma (HCC) remains uncertain. The purinosome score was found to have prognostic value. Enrichment analyses indicated a connection between purinosome-related genes and cell cycle regulation. Moreover, our research has demonstrated a correlation between the upregulation of genes associated with purinosomes and the enhanced formation of purinosomes in Huh-7 cells. Pyrimethamine has been identified as a promising therapeutic option for targeting purinosome to exert anti-cancer effects. Furthermore, the purinosome score exhibited an positive relationship with the response to immunotherapy. It may guide the stratification of liver cancer patients and screen for populations that may benefit from immunotherapy. This study examines the prognostic and predictive value of purinosome in liver cancer, suggesting that targeting purinosome formation with pyrimethamine or immunotherapy could benefit patients with high purinosome scores.

PRC1 as an independent adverse prognostic factor in Wilms tumor via integrated bioinformatics and experimental validation

Wilms Tumor (WT), a prevalent pediatric renal malignancy, exhibits marked heterogeneity and variable clinical outcomes. Epithelial-mesenchymal transition (EMT), a biological process enabling epithelial cells to acquire mesenchymal traits associated with enhanced migratory and invasive capacities, plays a crucial role in cancer progression. Protein Regulator of Cytokinesis 1 (PRC1) is a critical protein in cell division, whose overexpression is linked to poor prognosis in various cancers. This study investigates the role of PRC1 as a key prognostic factor in WT and explore the mechanism through comprehensive bioinformatic and experimental approaches. Through bulk RNA-seq data from the TARGET database, we identified PRC1 as significantly up-regulated in WT and associated with poor overall survival. Functional enrichment analyses (GO, KEGG, GSEA) demonstrated PRC1's involvement in cell division, chromatin dynamics, and activation of oncogenic pathways including Wnt/β-catenin, PI3K/AKT/mTOR, and Hedgehog signaling. Immunological analysis showed that elevated PRC1 expression correlates with diminished immune cell activity, particularly in NK cells, suggesting potential immune evasion mechanisms. Single-cell RNA-seq analysis (GSE200256) confirmed PRC1's elevated expression in anaplastic Wilms tumor (AWT) compared to favorable Wilms tumor (FWT), and highlighted its involvement in intercellular communication and metastasis via the EMT process. Genomic analyses identified copy number variations (CNVs) and downregulated PRC1-targeting microRNAs as drivers of its overexpression. In vitro, PRC1 knockdown in WIT-49 cells significantly impaired migratory capacity, invasive potential, EMT progression, and glycolytic metabolism. These findings collectively position PRC1 as a promising therapeutic target and prognostic biomarker in WT.

6-Methoxyflavone inhibits glycolytic energy metabolism in HeLa cells

BACKGROUND

Enhanced glycolytic levels in cancer cells are a common characteristic of many cancer types. Modulation of glycolytic metabolism is crucial for enhancing the efficacy of cancer therapy. The specific role of 6-methoxyflavone in regulating glycolytic metabolism in cancer cells remains unclear. This study aimed to elucidate the impact of 6-methoxyflavone on glycolytic metabolism in cervical cancer cells and its clinical relevance.

METHODS

The tandem mass tag (TMT) proteomic analysis was used to identify significantly enriched biological processes and pathways in HeLa cells after treatment with 6-methoxyflavone. Additionally, the differential expression of glycolysis-related proteins was validated using parallel reaction monitoring (PRM) proteomics. Untargeted and targeted metabolomics analyses were used to identify differentially expressed glycolysis-related metabolites. Furthermore, alternative splicing, new transcripts, and domain analyses were used to detect the effects of 6-methoxyflavone on the structures of glycolysis-related genes and proteins. Subcellular localization, molecular docking, and non-covalent interaction analyses were used to detect the subcellular localization, affinity of 6-methoxyflavone for glycolysis-related proteins, and sites of non-covalent interactions. Clinical characteristics and immunological correlation analyses were used to elucidate the relationships between glycolysis-related genes and clinicopathological characteristics, survival, prognosis, and immune-related indicators of patients with cervical cancer. Finally, glycolysis stress tests and enzyme activity assays were used to verify the effect of 6-methoxyflavone on glycolysis in HeLa cells.

RESULTS

TMT and PRM proteomics, as well as untargeted and targeted metabolomics results, showed that 6-methoxyflavone downregulated the expression levels of glycolysis-related proteins and metabolites in HeLa cells, and that the structures and functions of glycolysis-related genes and proteins in the cytoplasm underwent changes. 6-Methoxyflavone had a good affinity for nine glycolysis-related proteins, all of which had non-covalent interaction sites. Clinical characteristics and immune correlation analyses showed relationships between 6-methoxyflavone and five clinical characteristics, survival prognosis, and four immune-related indicators in patients with cervical cancer. After treatment with 6-methoxyflavone, the basal glycolytic level, maximum glycolytic capacity, and glycolytic reserve of HeLa cells were downregulated. Additionally, 6-methoxyflavone inhibited the activity of pyruvate kinase.

CONCLUSION

6-Methoxyflavone inhibited energy metabolism in HeLa cells through the glycolysis pathway. 6-Methoxyflavone may be related to five clinical characteristics, prognosis, tumor microenvironment, immune cells, immune checkpoints, and immunotherapy efficacy in patients with cervical cancer.

IRF4 as a molecular biomarker in pan-cancer through multiple omics integrative analysis

IRF4, characterized by its unique helix-turn-helix DNA-binding motif, is a member of the interferon regulatory factor (IRF) family. It plays a critical role in regulating host defense mechanisms, including innate and adaptive immune responses, as well as oncogenesis. However, the precise role of IRF4 in malignant tumors remains poorly understood. In this study, we first investigated IRF4 gene expression across various cancer types and its distribution within different molecular and immunological subtypes, providing a comprehensive understanding of its expression patterns in pan-cancer. We further explored the interacting proteins, diagnostic significance, molecular characteristics, prognostic relevance, and biological functions of IRF4 in diverse cancers. Focusing on colorectal cancer (CRC), we conducted a detailed analysis of IRF4, examining its associations with clinical features and outcomes across multiple clinical subgroups and databases. Additionally, we assessed IRF4 expression at both transcriptional and translational levels in CRC tumor specimens using tissue microarrays. Our findings revealed that IRF4 expression varies significantly not only across cancer types but also among molecular and immunological subtypes. In CRC, elevated IRF4 expression was associated with poorer overall survival. Notably, IRF4 was predominantly expressed in immune cells and showed a strong correlation with tumor immune regulation. Given its high predictive accuracy for cancer outcomes and robust prognostic associations, IRF4 may serve as a valuable prognostic biomarker for CRC. In conclusion, IRF4 represents a unique molecular biomarker for pan-cancer prognosis and an independent prognostic risk factor for CRC. Its critical role in immune regulation also positions IRF4 as a promising target for immunotherapeutic strategies in CRC.

An ultrasensitive method for detection of cell-free RNA

Sensitive methods for detection of cell-free RNA (cfRNA) could facilitate non-invasive gene expression profiling and monitoring of diseases1-6. Here we describe RARE-seq (random priming and affinity capture of cfRNA fragments for enrichment analysis by sequencing), a method optimized for cfRNA analysis. We demonstrate that platelet contamination can substantially confound cfRNA analyses and develop an approach to overcome it. In analytical validations, we find RARE-seq to be approximately 50-fold more sensitive for detecting tumour-derived cfRNA than whole-transcriptome RNA sequencing (RNA-seq), with a limit of detection of 0.05%. To explore clinical utility, we profiled 437 plasma samples from 369 individuals with cancer or non-malignant conditions and controls. Detection of non-small-cell lung cancer expression signatures in cfRNA increased with stage (6 out of 20 (30%) in stage I; 5 out of 8 (63%) in stage II; 10 out of 15 (67%) in stage III; 80 out of 96 (83% sensitivity) in stage IV at 95% specificity) and RARE-seq was more sensitive than tumour-naive circulating tumour DNA (ctDNA) analysis. In patients with EGFR-mutant non-small-cell lung cancer who developed resistance to tyrosine kinase inhibitors, we detected both histological transformation and mutation-based resistance mechanisms. Finally, we demonstrate the potential utility of RARE-seq for determination of tissue of origin, assessing benign pulmonary conditions and tracking response to mRNA vaccines. These results highlight the potential value of ultrasensitive cfRNA analysis and provide proof of concept for diverse clinical applications.

Comprehensive pan-cancer single-cell analysis reveals glycolysis-related signatures as predictive biomarkers for immunotherapy response and their role in bladder cancer

Glycolysis is a vital metabolic biological process in tumor progression and immune modulation. This study comprehensively investigated the roles of glycolysis in pan-cancer, especially in bladder cancer. Exploration of 34 single-cell RNA sequencing (scRNA-seq) cohorts, eight ICI-treated bulk RNA-seq cohorts, and TCGA bulk pan-cancer RNA-seq cohorts uncovered a Glycolysis.Sig which strongly correlated with immunotherapy response and demonstrated excellent predictive performance in prognosis and immune response. Hub-Glycolysis.Sig exhibited varying interactions with the immune microenvironment based on cancer type. In bladder cancer, higher glycolysis risk scores correlated with poorer prognosis, with distinct immune infiltration characteristics between subtypes. scRNA-seq revealed high glycolysis levels in bladder epithelial cells. COPB2 was highly expressed in bladder cancer, promoting cell proliferation, migration, and glycolytic activity in vitro and in vivo. Our large-scale data analysis confirmed the negative correlation between glycolysis and immunotherapy outcomes, identifying Glycolysis.Sig as a novel predictive biomarker. Hub-Glycolysis.Sig provides clinical insights for bladder cancer therapy strategies, while COPB2 and other potential therapeutic targets facilitate personalized cancer treatment.

Impact of Non-SMC Condensin I Complex Subunit D2 Upregulation on Oral Squamous Cell Carcinoma Prognosis

OBJECTIVE

To explore the influence of non-SMC condensin I complex subunit D2 (NCAPD2) on the prognosis of oral squamous cell carcinoma (OSCC) and the correlation between NCAPD2 and OSCC.

METHODS

In this study, NCAPD2 gene expression profiles of OSCC and normal tissues were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The real-time quantitative polymerase chain reaction (RT-qPCR) was employed to preliminarily validate OSCC cell strains and normal epithelial cell strains. Besides EdU, cell scratch, and transwell assays were performed to assess the proliferation, migration, and invasion of OSCC cell strains with the silence of NCAPD2. Moreover, immunohistochemistry (IHC) staining was utilised to measure the expression of NCAPD2 and tumour-related markers in 74 OSCC specimens. Finally, the Kaplan-Meier analysis was performed to evaluate the influence of NCAPD2 in the prognosis of OSCC.

RESULTS

The expression of NCAPD2 in OSCC tissues was higher than that in normal tissues. Inhibiting NCAPD2 can reduce the proliferation and migration of OSCC cell lines and inhibit the invasion of these cells. The IHC staining results indicated that the high expression of NCAPD2 in OSCC tissues was positively correlated with T stages, Ki67 expression, and affected sites. The Kaplan-Meier analysis results validated that the up-regulated expression of NCAPD2 was significantly correlated with the poor overall survival (OS) of OSCC patients.

CONCLUSION

NCAPD2 is a potential molecular marker for the poor prognosis of OSCC, and it is expected to become a target for the treatment of this carcinoma.

Potential role of hypobaric hypoxia environment in treating pan-cancer

Cancer incidence and mortality are lower among high-altitude residents, suggesting that hypobaric hypoxia (HH) might protect against cancer. Our study aimed to develop a pan-cancer prognosis risk model using ADME genes, which are influenced by low oxygen, to explore HH's impact on overall survival (OS) across various cancers. We constructed and validated the model with gene expression and survival data from 8628 samples, using three gene expression databases. AltitudeOmics confirmed HH's significant effects. We employed single-gene prognostic analysis, weighted gene co-expression network analysis, and stepwise Cox regression to identify biomarkers and refine the model. Drugs interacting with the model were explored using LINCS L1000, AutoDockTools, and STITCH. Eight ADME genes significantly altered by HH were identified, revealing their prognostic value across cancers. The model showed lower risk scores linked to better prognosis in 25 cancers, with reduced overall gene expression and decreased tumor mortality risk. Higher T cell infiltration was observed in the low-risk group. Additionally, three potential drugs to modulate our model were identified. This study presents a novel pan-cancer survival prognosis model based on ADME genes influenced by HH, offering new insights into cancer prevention and treatment.

Nicotinamide metabolism affects the prognosis of hepatocellular carcinoma by influencing the tumor microenvironment

In this study, we utilized the public database along with single-cell genomics techniques to systematically analyze the expression patterns and clinical significance of key genes in the nicotinamide metabolism pathway in liver cancer samples. The findings indicate that differential nicotinamide metabolism-related key genes are expressed in liver cancer samples. The liver cancer samples were put into separate subgroups using consistency clustering analysis based on differential gene expression levels observed. Additionally, immune infiltration and drug sensitivity analysis also revealed differences between the two subgroups. Survival analysis suggested that the key genes were associated with prognosis. Finally, a prognostic model was established using the key genes, offering a fresh viewpoint on the molecular mechanism investigating liver cancer. This study demonstrated the significant correlation between key genes in the nicotinamide metabolism pathway and the occurrence and progression of liver cancer and indicated that these key genes could serve as prognostic markers and tailored treatment targets for liver cancer.

A spatial transcriptomics study of MES-like and mono/macro cells in gliomas

Gliomas, including both glioblastoma multiforme (GBM) and lower-grade glioma (LGG), present a substantial challenge in neuro-oncology because of genetic heterogeneity and unsatisfactory prognosis. This study aimed to conduct a comprehensive multi-omics analysis of gliomas using various bioinformatics approaches to identify potential therapeutic targets and prognostic markers. A comprehensive analysis was conducted on 1327 sequencing data samples alongside their relevant clinical information sourced from The Cancer Genome Atlas (TCGA) pertaining to glioblastoma (GBM), low-grade glioma (LGG), the Chinese Glioma Genome Atlas (CCGA) and University of California Santa Cruz Xena (UCSC Xena) datasets. These tools were employed for gene expression profiling, survival analysis, and cell communication mapping. Spatial transcriptomics revealed the localization of mesenchymal (MES)-like malignant tumors, and drug sensitivity analysis was performed to evaluate responses to quinpirole and meropenem. Additionally, the Tumor Immune Dysfunction and Exclusion (TIDE) framework was utilized to gauge the responsiveness to immunotherapy. The MES-like malignant and monocyte/macrophage (mono/macro) cell subsets showed high hallmark scores, playing key roles in the tumor microenvironment. MES-like malignant marker gene scores correlated with overall survival across datasets, whereas mono/macro marker gene scores were significant in the TCGA-LGG and CCGA datasets. Key interactions between these cell types were found, especially with CD14-ITGB2, LGALS1-CD69, and APOE-TREM2. The mono/macro cell subset demonstrated better immune therapy responsiveness, as indicated by lower TIDE scores. Spatial transcriptomics revealed that MES-like malignant tumors are predominantly localized in four distinct regions, with the marker genes CHI3L1 and ADM confirming these locations. Drug sensitivity analysis revealed differential responses of the MES-like malignant cell subset to quinpirole and meropenem. Our results offer fresh perspectives on the differential roles of MES-like malignant and monocyte/macrophage cell subsets in tumor progression and immune modulation, providing novel insights into glioma biology.

Construction of a Prognostic Model based on CSC-related Genes in Patients with Colorectal Cancer

Colorectal cancer (CRC) is one of the most common and deadly malignancies. Lack of efficient biomarkers for prognosis has limited the improvement of survival outcome in patients with CRC. Numerous studies have demonstrated the important roles of cancer stem cells (CSCs) in both treatment resistance and disease recurrence of CRC. Thus, the current study aims to construct a prognostic model based on expression level of CSC-related genes for precise molecular subtyping of CRC patients with different prognoses, TME infiltration patterns and therapeutic responses. The RNA sequencing data and clinical information were obtained from UCSC Xena database, followed by identification of differential expressed genes, univariate Cox regression, and LASSO regression to identify prognostic CSC-related genes and construct a novel prognostic risk scoring model consisting of 21 CSC-related genes. The patients in high-risk group suffered poor survival outcome (P<0.0001). Moreover, the performance of CSC-related prognostic model was validated in individual GEO datasets including GSE41258 and GSE39582 (P<0.05). Furthermore, patients with high-risk score exhibited lower response rate to immune checkpoint inhibitors as compared to those in low-risk group (17.4% vs. 28.2%), indicating the potential of CSC-related prognostic model to predict the immunotherapy response. Collectively, our findings provide an effective model to predict the immunotherapy response and survival outcome in patients with CRC.

Identification of the role of MED6 in the development and prognosis of lung adenocarcinoma based on multi-omics profiling

Background: Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. Recent studies have highlighted the importance of Mediator complex subunits in cancer, but their specific roles in LUAD are still unclear. Methods: The CRISPR-Cas9 loss-of-function data was used to assess gene dependency in cell growth. RNA-seq data were analyzed to evaluate the prognostic value of Mediator subunits and explore their downstream pathways. Single-cell sequencing data were utilized to examine the tumor microenvironment in LUAD. A drug sensitivity analysis was performed to identify potential therapeutic options. Results: Mediator complex subunit 6 (MED6) was found to influence tumor cell growth in LUAD. Additionally, MED6 expression levels were associated with patient prognosis. MED6-positive tumor cells showed more active interactions with other cells in the LUAD microenvironment, promoting tumor progression. Based on MED6 expression, drugs such as paclitaxel, afatinib, and brivanib were identified as potential treatments. Conclusions: This study revealed the role of MED6 in LUAD and its potential as a biomarker. Our findings suggest that MED6 has an effect on LUAD progression and provide valuable insights for patient stratification and personalized treatment strategies.

Exploring the tumor microenvironment of breast cancer to develop a prognostic model and predict immunotherapy responses

Breast cancer is the most prevalent malignancy in women and exhibits significant heterogeneity. The tumor microenvironment (TME) plays a critical role in tumorigenesis, progression, and response to therapy. However, its impact on the prognosis and immunotherapy responses is incompletely understood. Using public databases, we conducted a comprehensive investigation of transcriptome and single-cell sequencing data. After performing immune infiltration analysis, we conducted consensus clustering, weighted gene co-expression network analysis (WGCNA), Cox regression, and least absolute shrinkage and selection operator (Lasso) regression to identify independent prognostic genes in breast cancer. Subsequently, we developed a prognostic model for patients with breast cancer. Tumor Immune Dysfunction and Exclusion (TIDE) values were used to assess patient's responsiveness to breast cancer. Based on single-cell RNA-sequencing data, we identified various cell types through cluster analysis and investigated the expression of prognostic model genes in each cell type. The drug sensitivity of targeted therapeutic agents for breast cancer treatment was analyzed in different cell types. We identified 12 independent prognostic genes associated with breast cancer and used these genes to construct a prognostic model. The prognostic model accurately discriminated between patients classified as high- and low-risk, providing precise prognostic predictions for individual patients. Additionally, our model exhibited a robust capacity to predict the immunotherapeutic response in breast cancer patients. Our investigation revealed a notable association between the proportion of endothelial cells (ECs) and patient prognosis in breast cancer. A prognostic model for breast cancer was formulated that showed close associations between prognosis and response to immunotherapy. For patients predicted by our model to not respond effectively to immunotherapeutic agents, it may be considered to combine immunotherapeutic agents with targeted therapeutic agents identified through our drug sensitivity analysis, which could potentially enhance treatment efficacy.

TPM4 influences the initiation and progression of gastric cancer by modulating ferroptosis via SCD1

Gastric cancer (GC) is a deadly disease with poor prognosis and few treatment options. Tropomyosin 4 (TPM4) is an actin-binding protein that stabilizes the cytoskeleton of cells and has an unclear role in GC. This study aimed to elucidate the role and underlying mechanisms of TPM4 in GC pathogenesis. The expression and diagnostic and prognostic value of TPM4 in GC were analyzed using bioinformatics. A nomogram based on TPM4 expression was created and validated with an external cohort. TPM4-knockdown GC cells and xenograft models in nude mice were used to study the function of TPM4 in vitro and in vivo. Proteomic and rescue experiments confirmed the regulatory effect of TPM4 on stearoyl-CoA desaturase 1 (SCD1) in GC. Immunohistochemistry verified the expression and correlation of the TPM4 and SCD1 proteins in GC tissues. Our study identified TPM4 as an oncogene in GC, suggesting its potential diagnostic and prognostic value. The TPM4-based nomogram showed potential prognostic value for clinical use. TPM4 knockdown inhibited GC cell proliferation, induced ferroptosis, and slowed tumor growth in vivo, which is achieved by inhibiting SCD1 expression. Immunohistochemical analysis of GC tissues revealed elevated expression levels of both TPM4 and SCD1 proteins, with a positive correlation observed between their expression. TPM4 is a promising target for new diagnostic, prognostic, and therapeutic strategies for GC. Downregulation of TPM4 inhibits GC cell growth and induces ferroptosis by suppressing SCD1 expression.

Spatial mapping of gene signatures in H&E-stained images: a proof of concept for interpretable predictions using additive multiple instance learning

The relative abundance of cancer-associated fibroblast (CAF) subtypes influences a tumor's response to treatment, especially immunotherapy. However, the gene expression signatures associated with these CAF subtypes have yet to realize their potential as clinical biomarkers. Here, we describe an interpretable machine learning (ML) approach, additive multiple instance learning (aMIL), to predict bulk gene expression signatures from hematoxylin and eosin (H&E)-stained whole slide images (WSI), focusing on an immunosuppressive LRRC15+ CAF-enriched TGFβ-CAF signature. aMIL models accurately predicted TGFβ-CAF across various cancer types. Tissue regions contributing most highly to slide-level predictions of TGFβ-CAF were evaluated by ML models characterizing spatial distributions of diverse cell and tissue types, stromal subtypes, and nuclear morphology. In breast cancer, regions contributing most to TGFβ-CAF-high predictions ("excitatory") were localized to cancer stroma with high fibroblast density and mature collagen fibers. Regions contributing most to TGFβ-CAF-low predictions ("inhibitory") were localized to cancer epithelium and densely inflamed stroma. Fibroblast and lymphocyte nuclear morphology also differed between excitatory and inhibitory regions. Thus, aMIL enables a data-driven link between histologic features and transcription, offering biological interpretability beyond typical black-box models.

Regulatory T Cell Infiltration-Driven Single-Cell Transcriptomic Analysis Identifies SAP18 as a Prognostic Marker for Esophageal Squamous Cell Carcinoma

BACKGROUND

Advanced esophageal squamous cell carcinoma (ESCC) is characterized by molecular heterogeneity and distinct patterns of immune cell infiltration. Regulatory T cells (Tregs), in particular, play a critical role in shaping an immunosuppressive tumor microenvironment (TME), which is associated with poor clinical outcomes.

METHODS

We developed a prognostic model by integrating GEO-derived bulk RNA sequencing data and single-cell transcriptome. Model predictions were confirmed through RT-qPCR, Western blot, and immunohistochemistry on clinical specimens, while in vitro assays (CCK8, transwell invasion, scratch, colony formation, and immunofluorescence) validated the function of SAP18 in cell proliferation, invasion, and ECM remodeling.

RESULTS

Expression patterns of the 5 Tregs-associated genes in clinical specimens aligned with model predictions, underscoring the model's robustness. The high-risk subgroup was associated with upregulated extracellular matrix (ECM) remodeling, an abundance of immune-suppressive cells, higher TP53 mutation rate, and limited benefit from immunotherapy. In contrast, the low-risk subgroup exhibited anti-tumor immunity. Cell-cell communication analysis also implicated the collagen pathway in Tregs-mediated immune evasion in ESCC. Functional assays indicated that SAP18 in the prognostic model significantly promotes proliferation, invasion, and ECM reconstruction, further highlighting its potential as a therapeutic target.

CONCLUSION

Our findings elucidate the role of Tregs in the TME, underscoring significant potential of SAP18, which is essential for assessing patient prognosis and may facilitate the development of personalized therapies for ESCC.

Hyperinflammatory repolarisation of ovarian cancer patient macrophages by anti-tumour IgE antibody, MOv18, restricts an immunosuppressive macrophage:Treg cell interaction

Ovarian cancer is the most lethal gynaecological cancer and treatment options remain limited. In a recent first-in-class Phase I trial, the monoclonal IgE antibody MOv18, specific for the tumour-associated antigen Folate Receptor-α, was well-tolerated and preliminary anti-tumoural activity observed. Pre-clinical studies identified macrophages as mediators of tumour restriction and pro-inflammatory activation by IgE. However, the mechanisms of IgE-mediated modulation of macrophages and downstream tumour immunity in human cancer remain unclear. Here we study macrophages from patients with epithelial ovarian cancers naive to IgE therapy. High-dimensional flow cytometry and RNA-seq demonstrate immunosuppressive, FcεR-expressing macrophage phenotypes. Ex vivo co-cultures and RNA-seq interaction analyses reveal immunosuppressive associations between patient-derived macrophages and regulatory T (Treg) cells. MOv18 IgE-engaged patient-derived macrophages undergo pro-inflammatory repolarisation ex vivo and display induction of a hyperinflammatory, T cell-stimulatory subset. IgE reverses macrophage-promoted Treg cell induction to increase CD8+ T cell expansion, a signature associated with improved patient prognosis. On-treatment tumours from the MOv18 IgE Phase I trial show evidence of this IgE-driven immune signature, with increased CD68+ and CD3+ cell infiltration. We demonstrate that IgE induces hyperinflammatory repolarised states of patient-derived macrophages to inhibit Treg cell immunosuppression. These processes may collectively promote immune activation in ovarian cancer patients receiving IgE therapy.

HOXA10 and HOXA11 in Human Endometrial Benign Disorders: Unraveling Molecular Pathways and Their Impact on Reproduction

HOX genes, a family of conserved transcription factors, are critical for reproductive tract development and endometrial functionality. This review highlights the molecular underpinnings of HOXA10/HOXA11 in reproductive health and their dysregulation in benign pathologies associated with infertility, such as endometriosis, adenomyosis, and endometrial polyps. These genes are dynamically regulated by estrogen and progesterone, with peak expression during the secretory phase of the menstrual cycle when implantation takes place. The molecular mechanisms underlying their action include the modulation of extracellular matrix (ECM) remodeling via metalloproteinases, cytokines like leukemia inhibitory factor, and cell adhesion molecules such as β3-integrin, all of which are essential for the differentiation of epithelial and stromal cells, as well as for trophoblast invasion. Aberrant HOX gene expression, driven by DNA hypermethylation or altered histone acetylation, compromises endometrial receptivity and implantation. For instance, reduced HOXA10 expression in endometriosis stems from hypermethylation and chronic inflammation, disrupting immune modulation and cytokine signaling. Similarly, adenomyosis alters HOXA11-regulated ECM remodeling and β3-integrin expression, impairing embryo attachment. Furthermore, regulatory pathways involving vitamin D and retinoic acid offer promising therapeutic avenues pathways, as they enhance HOXA10/HOXA11 expression and endometrial receptivity. This review underscores the critical molecular roles of HOXA10/HOXA11 genes as biomarkers and therapeutic targets to optimize fertility outcomes and address reproductive pathologies.

Peripheral positioning of lysosomes supports melanoma aggressiveness

Emerging evidence suggests that the function and position of organelles are pivotal for tumor cell dissemination. Among them, lysosomes stand out as they integrate metabolic sensing with gene regulation and secretion of proteases. Yet, how their function is linked to their position and how this controls metastasis remains elusive. Here, we analyze lysosome subcellular distribution in patient-derived melanoma cells and patient biopsies and show that lysosome spreading scales with melanoma aggressiveness. Peripheral lysosomes promote matrix degradation and cell invasion which is directly linked to the lysosomal and cell transcriptional programs. Using chemo-genetical control of lysosome positioning, we demonstrate that perinuclear clustering impairs lysosome secretion, matrix degradation and invasion. Impairing lysosome spreading significantly reduces invasive outgrowth in two in vivo models, mouse and zebrafish. Our study provides a direct demonstration that lysosome positioning controls cell invasion, illustrating the importance of organelle adaptation in carcinogenesis and suggesting its potential utility for diagnosis of metastatic melanoma.