Enhancing immuno-oncology investigations through multidimensional decoding of tumor microenvironment with IOBR 2.0

Immunotyping the Tumor Microenvironment Reveals Molecular Heterogeneity for Personalized Immunotherapy in Cancer

The tumor microenvironment (TME) significantly influences cancer prognosis and therapeutic outcomes, yet its composition remains highly heterogeneous, and currently, no highly accessible, high-throughput method exists to define it. To address this complexity, the TMEclassifier, a machine-learning tool that classifies cancers into three distinct subtypes: immune Exclusive (IE), immune Suppressive (IS), and immune Activated (IA), is developed. Bulk RNA sequencing categorizes patient samples by TME subtype, and in vivo mouse model validates TME subtype differences and differential responses to immunotherapy. The IE subtype is marked by high stromal cell abundance, associated with aggressive cancer phenotypes. The IS subtype features myeloid-derived suppressor cell infiltration, intensifying immunosuppression. In contrast, the IA subtype, often linked to EBV/MSI, exhibits robust T-cell presence and improved immunotherapy response. Single-cell RNA sequencing is applied to explore TME cellular heterogeneity, and in vivo experiments demonstrate that targeting IL-1 counteracts immunosuppression of IS subtype and markedly improves its responsiveness to immunotherapy. TMEclassifier predictions are validated in this prospective gastric cancer cohort (TIMES-001) and other diverse cohorts. This classifier could effectively stratify patients, guiding personalized immunotherapeutic strategies to enhance precision and overcome resistance.

Distinct characteristics of brain metastasis in lung adenocarcinoma: development of high-confidence cell lines

Lung cancer is a leading cause of cancer-related deaths worldwide, with brain metastasis occurring in approximately 30-55% of patients, particularly in lung adenocarcinoma. Due to the challenges in obtaining genuine brain metastasis tumor cells, researchers commonly use nude mouse models to establish brain metastasis cell lines, though traditional methods have limitations such as high costs, lengthy timeframes, and the need for specialized imaging equipment. To address these issues, we developed an improved approach by performing low cell number circulating intracranial injections (500-4000 cells) in nude mice, successfully establishing the H1975-BM1, BM2, and BM3 cell lines. Through RNA sequencing and bioinformatics analyses, we identified transcriptomic differences among these cell lines, revealing that H1975-BM1 cells primarily exhibit stem cell function and migration characteristics, while H1975-BM3 cells display enhanced chemotaxis, cell adhesion, and cytokine secretion associated with interactions. Experimental validation, including Transwell assays, CCK8, cell adhesion assays, and subcutaneous tumor implantation in nude mice, further confirmed these findings, with H1975-BM3 forming larger tumors and a more pronounced secretion cystic cavity. In conclusion, our improved methodology successfully established high-confidence brain metastasis lung adenocarcinoma cell lines, elucidating distinct transcriptomic and functional characteristics at different stages of brain metastasis progression.

DBI as a Novel Immunotherapeutic Candidate in Colorectal Cancer: Dissecting Genetic Risk and the Immune Landscape via GWAS, eQTL, and pQTL

Background: Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide. Identifying drug targets associated with CRC is crucial for developing targeted therapies. Methods: MR (IVW, Wald ratio, weighted median, and MR-Egger) and SMR analyses were used to screen candidate genes associated with CRC risk. Further validation was performed using The Cancer Genome Atlas (TCGA) to assess gene expression patterns and prognostic significance. Additionally, immune infiltration analysis was conducted to characterize the tumor immune microenvironment. Drug prediction was performed to explore potential therapeutic interventions. Results: Eight genes were identified associated with CRC. IGFBP3, CD72, SERPINH1, CHRDL2, LRP11, and SPARCL1 were linked to an increased risk of CRC, whereas DBI and HYAL1 were associated with a decreased risk of CRC. Notably, DBI exhibited a potentially favorable immune profile, negatively correlated with Tregs and MDSCs while positively associated with activated CD8+ and CD4+ T cells. Conclusions: Eight genes were identified as associated with CRC, among which DBI exhibited a potential protective role, correlating with improved patient survival, enhanced immune activation, and increased responsiveness to immunotherapy. The remaining proteins demonstrated diverse and complex functions within the tumor immune microenvironment, providing novel insights for the development of precision diagnostics and immunotherapeutic strategies.

Discovery and Validation of a New Biomarker Integrating Ferroptosis and Glycolysis-Related Genes in Bladder Cancer

Bladder cancer (BCa) is a highly invasive tumor with few successful therapies, and its unfavorable prognosis mainly stems from late diagnosis and resistance to treatment. Ferroptosis is a type of non-apoptotic cell death characterized by iron-dependent regulated necrosis due to extensive lipid peroxidation. Glycolysis is fundamental to cancer cell metabolism, with cancer cells developing various strategies to enhance this process. In this study, we combined ferroptosis and glycolysis gene sets, two biological processes closely related to tumorigenesis and development, and obtained ferroptosis and glycolysis-related gene sets (FGRGs). By leveraging both single-cell and bulk transcriptome data from BCa, we have investigated the presence and role of FGRGs in the onset and progression of BCa through various approaches. Using machine learning algorithms, we identified a feature gene set consisting of 13 genes in the TCGA data set to predict the prognosis of BCa and verified it in the GEO data set. After that, we explored FGRGs in depth using a variety of bioinformatics analyses, such as mutational landscape analysis, functional enrichment analysis, immune infiltration analysis, FGRGs-associated risk and clinical characterization, and drug susceptibility analysis. Finally, we validated the function of the core gene chondroitin polymerizing factor 2 (CHPF2) using CCK-8, clone formation, transwell, and wound healing assays. Our research innovatively combines ferroptosis with glycolytic genes and applies it as an independent prognostic factor in the study of BCa. It reveals new characteristic genes and therapeutic targets that can predict the prognosis of BCa patients and lays a foundation for the study of the occurrence and development mechanism of BCa and targeted data strategies.

E2F expression profiling-based subtypes in head and neck squamous cell carcinoma: clinical relevance, prognostic implications, and personalized therapy

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous malignancy with poor prognosis. Dysregulation of E2F transcription factors (E2Fs), which control cell proliferation and apoptosis, is implicated in HNSCC pathogenesis. This study explores HNSCC molecular heterogeneity via E2Fs expression, identifies distinct subtypes, and develops a prognostic model that integrates gene expression, immune infiltration, and drug sensitivity.

METHODS

We analyzed the TCGA-HNSC dataset (n = 494) and classified samples based on the expression of eight E2Fs using ConsensusClusterPlus. The optimal number of clusters (k = 2) was determined with the getOptK() function, which assesses cluster stability via internal consistency metrics. Differentially expressed genes between subtypes were identified with limma, and functional annotation was performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. A prognostic model was constructed using LASSO regression on genes significant in univariate Cox analysis and validated in an independent GSE41613 dataset (n = 97). Immune cell infiltration was estimated using CIBERSORT, and drug sensitivity predicted via pRRophetic. Confounding factors such as HPV and smoking status were not included due to incomplete data.

RESULTS

Two distinct E2F-based subtypes emerged. Cluster 1, characterized by lower E2Fs expression, exhibited poorer overall survival (log-rank, p = 0.035) and was enriched in genes related to epidermal development, keratinocyte differentiation, and IL-17 signaling. In contrast, Cluster 2 showed higher E2Fs expression, better survival, and enrichment in genes associated with DNA replication and repair. Notably, high-risk patients demonstrated increased infiltration of M0 and M2 macrophages (p < 0.05), suggesting an immunosuppressive tumor microenvironment that adversely affects prognosis. Our seven-gene prognostic model (AREG, CXCL14, FAM83E, FDCSP, ARHGAP4, EPHX3, and SPINK6) exhibited robust performance with AUCs of 0.692, 0.673, and 0.679 for 1-, 3-, and 5-year survival, a C-index of 0.66, and good calibration. High-risk patients also showed greater sensitivity to targeted agents such as pazopanib and imatinib.

CONCLUSIONS

These findings reveal two distinct E2F-based molecular subtypes of HNSCC that differ in prognosis, functional pathways, immune infiltration, and drug sensitivity. The prognostic model offers valuable risk stratification and identifies potential biomarkers and therapeutic targets, warranting further experimental and clinical validation.

TM9SF4 is a potential prognostic biomarker in hepatocellular carcinoma

BACKGROUND

The transmembrane 9 superfamily protein member 4 (TM9SF4) is a transmembrane protein upregulated in multiple cancers; however, its role in hepatocellular carcinoma (HCC) remains unknown.

METHODS

The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) and International Cancer Genome Consortium (ICGC) databases were utilized to investigate the differential expression of TM9SF4 in HCC and tumor tissues. The prognostic and value of TM9SF4 in HCC was evaluated using Kaplan-Meier analysis, Cox regression, and receiver operating characteristic (ROC) curve analyses. The expression pattern and prognostic value of TM9SF4 was further verified using immunohistochemical (IHC) examination of 87 pairs of HCC clinical specimens. A nomogram was constructed by combining TM9SF4 expression and clinicopathological parameters to predict prognosis for individual patient. Additionally, gene set enrichment analysis (GSEA) was performed to identify key pathways related to TM9SF4.

RESULTS

TM9SF4 expression was upregulated in the HCC tissues. High expression of TM9SF4 was significantly associated with advanced T stage, histological grade, and worse survival. Multivariable Cox analysis revealed that TM9SF4 expression was an independent factor for overall survival. The nomogram by incorporating the TM9SF4 and T stage showed good performance in predicting prognosis. Moreover, GSEA analysis revealed that TM9SF4 was functionally involved in pathways associated with the cell cycle.

CONCLUSIONS

These findings suggest that TM9SF4 is a promising biomarker with prognostic potential and functional significance in HCC.

Multi-omics analysis constructs a novel neuroendocrine prostate cancer classifier and classification system

Neuroendocrine prostate cancer (NEPC), a subtype of prostate cancer (PCa) with poor prognosis and high heterogeneity, currently lacks accurate markers. This study aims to identify a robust NEPC classifier and provide new perspectives for resolving intra- tumoral heterogeneity. Multi-omics analysis included 19 bulk transcriptomics, 14 single-cell transcriptomics, 1 spatial transcriptomics, 16 published NE signatures and 10 cellular experiments combined with multiple machine learning algorithms to construct a novel NEPC classifier and classification. A comprehensive single-cell atlas of prostate cancer was created from 70 samples, comprising 196,309 cells, among which 9% were identified as NE cells. Within this framework and in combination with bulk transcriptomics, a total of 100 high-quality NE-specific feature genes were identified and differentiated into NEPup sig and NEPdown sig. The random forest (RF) algorithm proved to be the most effective classifier for NEPC, leading to the establishment of the NEP100 model, which demonstrated robust validation across various datasets. In clinical settings, the use of the NEP100 model can greatly improve the diagnostic and prognostic prediction of NEPC. Hierarchical clustering based on NEP100 revealed four distinct NEPC subtypes, designated VR_O, Prol_N, Prol_P, and EMT_Y, each of which presented unique biological characteristics. This allows us to select different targeted therapeutic strategies for different subtypes of phenotypic pathways. Notably, NEP100 expression correlated positively with neuroendocrine differentiation and disease progression, while the VR-NE phenotype dominated by VR_O cells indicated a propensity for treatment resistance. Furthermore, AMIGO2, a component of the NEP100 signature, was associated with chemotherapy resistance and a poor prognosis, indicating that it is a pivotal target for future therapeutic strategies. This study used multi-omics analysis combined with machine learning to construct a novel NEPC classifier and classification system. NEP100 provides a clinically actionable framework for NEPC diagnosis and subtyping.

ZNF385A was identified as a novel colorectal cancer-related functional gene by analysis of the interaction and immune characteristics of oxidative stress and the inflammatory response

BACKGROUND

Recently, oxidative stress and inflammatory responses have been shown to directly impact tumor growth and the tumor microenvironment (TME). However, more research is necessary to fully understand the relationship between oxidative stress and inflammatory responses and colorectal cancer (CRC).

METHODS

The FindCluster algorithm was used to extract CRC Single-cell RNA sequencing (scRNA-seq) data and identify tumor cell groupings. From the MSigDB database, genes associated with oxidative stress and the inflammatory response were taken. We identified molecular subtypes and built a predictive risk model with the LASSO-Cox method using the ConsensusClusterPlus software suite. We incorporated the prognostic risk model and other clinicopathological parameters into a column-line chart. Finally, we used Quantitative Polymerase Chain Reaction (qPCR) and immunohistochemistry to check the expression of the unreported hub model genes. Cell proliferation was assessed using EDU and colony formation assays. Reactive Oxygen Species (ROS) tests were used to quantitatively determine the ROS content in CRC cells. The ability of CRC cells to invade and migrate was examined using transwell experiments. The regulatory functions of hub model genes were discovered in vivo using a xenograft model tumor assay.

RESULTS

Oxidative stress and inflammatory response factors in monocytic/macrophages of CRC were significantly upregulated, and their oxidative stress and inflammatory response functions were significantly higher than those of other cell subgroups, as indicated by the enrichment score. These factors showed significant synergistic overexpression and enrichment in this cell population. We constructed a prognostic risk model consisting of seven signatures. The good and stable prognostic evaluation efficacy of the model was confirmed, and risk scores were determined to be independent prognostic factors for CRC. We explored the relationship between the risk score model and malignant progression of tumor cells, tumor immune microenvironment, genomic variation, chemotherapy resistance, and immune response. Further qPCR and immunohistochemistry analysis showed that the expression of ZNF385A was high in CRC tissues. The functional experiment results indicated that interfering with the expression of ZNF385A could suppress the proliferation, ROS, migration and invasion of SW620 cells in vitro and the growth of xenograft tumors in vivo.

CONCLUSION

In this study, we investigated the critical expression patterns of oxidative stress- and inflammatory response-related genes in CRC, which may contribute to the prognosis and immunotherapy of CRC. Additionally, we discovered ZNF385A to be a novel oncogene in CRC. These findings imply that this model may be applied to assess prognostic risk and identify potential therapeutic targets for CRC patients.

RASGRF2 as a potential pathogenic gene mediating the progression of alcoholic hepatitis to alcohol-related cirrhosis and hepatocellular carcinoma

BACKGROUND AND AIMS

Alcoholic hepatitis (AH) and hepatocellular carcinoma (HCC) are common liver diseases. Chronic inflammation caused by AH can progress to alcoholic cirrhosis (AC) and eventually HCC.

METHODS

This study sought to ascertain potential shared genes between AH and HCC through the utilization of multiple transcriptome databases. Employing an immune infiltration analysis, and calculating the correlation between shared genes and immune infiltration results, in conjunction with independent bulk transcriptome validation sets, led to the identification of core shared genes. Subsequently, single-cell transcriptome data, clinical sample immunohistochemistry experiments, and overexpressed core shared genes in HepG2 cells were employed to validate the core shared genes of AH and HCC.

RESULTS

Through the bulk transcriptome discovery sets of AH and HCC, 206 potential shared genes were identified. After screening with two machine learning algorithms, five shared genes remained. Combining the results of the immune infiltration and bulk transcriptome results from an independent validation cohort, the core shared gene was determined to be RASGRF2. Single-cell data further demonstrated that RASGRF2 and its downstream genes were highly expressed in AH, AC, and HCC tissues. Spatial transcriptome data indicated that RASGRF2 was highly expressed in HCC tumor tissues. Compared with the paracancerous tissues, the RASGRF2 gene was significantly overexpressed in HCC tissues. Overexpression of RASGRF2 in HepG2 cells resulted in significantly enhanced migration, invasion, and proliferation abilities.

CONCLUSION

RASGRF2 serve as a pathogenic gene that mediates the progression of AH to AC and potentially to HCC.