Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer

A lactate-responsive gene signature predicts the prognosis and immunotherapeutic response of patients with triple-negative breast cancer

Background

Increased glycolytic activity and lactate production are characteristic features of triple-negative breast cancer (TNBC). The aim of this study was to determine whether a subset of lactate-responsive genes (LRGs) could be used to classify TNBC subtypes and predict patient outcomes.

Methods

Lactate levels were initially measured in different breast cancer (BC) cell types. Subsequently, MDA-MB-231 cells treated with 2-Deoxy-d-glucose or l-lactate were subjected to RNA sequencing (RNA-seq). The gene set variation analysis algorithm was utilized to calculate the lactate-responsive score, conduct a differential analysis, and establish an association with the extent of immune infiltration. Consensus clustering was then employed to classify TNBC patients. Tumor immune dysfunction and exclusion, cibersort, single-sample gene set enrichment analysis, and EPIC, were used to compare the tumor-infiltrating immune cells between TNBC subtypes and predict the response to immunotherapy. Furthermore, a prognostic model was developed by combining 98 machine learning algorithms, to assess the predictive significance of the LRG signature. The predictive value of immune infiltration and the immunotherapy response was also assessed. Finally, the association between lactate and various anticancer drugs was examined based on expression profile similarity principles.

Results

We found that the lactate levels of TNBC cells were significantly higher than those of other BC cell lines. Through RNA-seq, we identified 14 differentially expressed LRGs in TNBC cells under varying lactate levels. Notably, this LRG signature was associated with interleukin-17 signaling pathway dysregulation, suggesting a link between lactate metabolism and immune impairment. Furthermore, the LRG signature was used to categorize TNBC into two distinct subtypes, whereby Subtype A was characterized by immunosuppression, whereas Subtype B was characterized by immune activation.

Conclusion

We identified an LRG signature in TNBC, which could be used to predict the prognosis of patients with TNBC and gauge their response to immunotherapy. Our findings may help guide the precision treatment of patients with TNBC.

Comprehensive investigation of malignant epithelial cell-related genes in clear cell renal cell carcinoma: development of a prognostic signature and exploration of tumor microenvironment interactions

Clear cell renal cell carcinoma (ccRCC) is a prevalent malignancy with complex heterogeneity within epithelial cells, which plays a crucial role in tumor progression and immune regulation. Yet, the clinical importance of the malignant epithelial cell-related genes (MECRGs) in ccRCC remains insufficiently understood. This research aims to undertake a comprehensive investigation into the functions and clinical relevance of malignant epithelial cell-related genes in ccRCC, providing valuable understanding of the molecular mechanisms and offering potential targets for treatment strategies. Using data from single-cell sequencing, we successfully identified 219 MECRGs and established a prognostic model MECRGS (MECRGs' signature) by synergistically analyzing 101 machine-learning models using 10 different algorithms. Remarkably, the MECRGS demonstrated superior predictive performance compared to traditional clinical features and 92 previously published signatures across six cohorts, showcasing its independence and accuracy. Upon stratifying patients into high- and low-MECRGS subgroups using the specified cut-off threshold, we noted that patients with elevated MECRGS scores displayed characteristics of an immune suppressive tumor microenvironment (TME) and showed worse outcomes after immunotherapy. Additionally, we discovered a distinct ccRCC tumor cell subtype characterized by the high expressions of PLOD2 (procollagen-lysine,2-oxoglutarate 5-dioxygenase 2) and SAA1 (Serum Amyloid A1), which we further validated in the Renji tissue microarray (TMA) cohort. Lastly, 'Cellchat' revealed potential crosstalk patterns between these cells and other cell types, indicating their potential role in recruiting CD163 + macrophages and regulatory T cells (Tregs), thereby establishing an immunosuppressive TME. PLOD2 + SAA1 + cancer cells with intricate crosstalk patterns indeed show promise for potential therapeutic interventions.

Application of machine learning for high-throughput tumor marker screening

High-throughput sequencing and multiomics technologies have allowed increasing numbers of biomarkers to be mined and used for disease diagnosis, risk stratification, efficacy assessment, and prognosis prediction. However, the large number and complexity of tumor markers make screening them a substantial challenge. Machine learning (ML) offers new and effective ways to solve the screening problem. ML goes beyond mere data processing and is instrumental in recognizing intricate patterns within data. ML also has a crucial role in modeling dynamic changes associated with diseases. Used together, ML techniques have been included in automatic pipelines for tumor marker screening, thereby enhancing the efficiency and accuracy of the screening process. In this review, we discuss the general processes and common ML algorithms, and highlight recent applications of ML in tumor marker screening of genomic, transcriptomic, proteomic, and metabolomic data of patients with various types of cancers. Finally, the challenges and future prospects of the application of ML in tumor therapy are discussed.

Integration analysis of cell division cycle-associated family genes revealed potential mechanisms of gliomagenesis and constructed an artificial intelligence-driven prognostic signature

Cell division cycle-associated (CDCA) gene family members are essential cell proliferation regulators and play critical roles in various cancers. However, the function of the CDCA family genes in gliomas remains unclear. This study aims to elucidate the role of CDCA family members in gliomas using in vitro and in vivo experiments and bioinformatic analyses. We included eight glioma cohorts in this study. An unsupervised clustering algorithm was used to identify novel CDCA gene family clusters. Then, we utilized multi-omics data to elucidate the prognostic disparities, biological functionalities, genomic alterations, and immune microenvironment among glioma patients. Subsequently, the scRNA-seq analysis and spatial transcriptomic sequencing analysis were carried out to explore the expression distribution of CDCA2 in glioma samples. In vivo and in vitro experiments were used to investigate the effects of CDCA2 on the viability, migration, and invasion of glioma cells. Finally, based on ten machine-learning algorithms, we constructed an artificial intelligence-driven CDCA gene family signature called the machine learning-based CDCA gene family score (MLCS). Our results suggested that patients with the higher expression levels of CDCA family genes had a worse prognosis, more activated RAS signaling pathways, and more activated immunosuppressive microenvironments. CDCA2 knockdown inhibited the proliferation, migration, and invasion of glioma cells. In addition, the MLCS had robust and favorable prognostic predictive ability and could predict the response to immunotherapy and chemotherapy drug sensitivity.

RAC1high NK cell-based immunotherapy in hepatocellular carcinoma via STAT3-NKG2D axis

Natural killer (NK) cells exert an indispensable role in innate immune responses against cancer progression, however NK cell dysfunction has been rarely reported in hepatocellular carcinoma (HCC). This study sought to uncover the immunoregulatory mechanisms of tumor-infiltrating NK cells in HCC. A consensus NK cell-based signature (NKS) was constructed using integrative machine learning algorithms based on multi-omics data of HCC patients. HCC tumors had lower numbers of infiltrating NK cells than para-tumor normal liver tissues. Based on the NK cell-associated genes, the NKS was built for HCC prognostic prediction and clinical utilities. Drug targets and novel compounds were then identified for high-NKS groups. RAC1 was confirmed as the hub gene in the NKS genes. RAC1 was upregulated in HCC tumors and positively correlated with shorter survival time. RAC1 overexpression in NK-92 cells facilitated the cancer-killing capacity by the anticancer cytotoxic effectors and the upregulated NKG2D. The survival time of PDX-bearing mice was also prolonged upon NK-92RAC1 cells. Mechanistically, RAC1 interacted with STAT3 and facilitated its activation, thereby enabling its binding to the promoter region of NKG2D and functioning as a transcriptional regulator in NK-92 via molecular docking, Co-IP assay, CHIP and luciferase experiments. Collectively, our study describes a novel function of RAC1 in potentiating NK cell-mediated cytotoxicity against HCC, highlighting the clinical utilities of NKS score and RAC1high NK cell subset in HCC immunotherapy.

FPR1, as a Potential Biomarker of Diagnosis and Infliximab Therapy Responses for Crohn's Disease, is Related to Disease Activity, Inflammation and Macrophage Polarization

Purpose

Crohn's disease (CD) represents a multifaceted inflammatory gastrointestinal condition, with a profound significance placed on unraveling its molecular pathways to enhance both diagnostic capabilities and therapeutic interventions. This study focused on identifying a robust macrophage-related signatures (MacroSig) for diagnosing CD, emphasizing the role of FPR1 in macrophage polarization and its implications in CD.

Patients and Methods

Expression profiles from intestinal biopsies and macrophages of 1804 CD patients were retrieved from the Gene Expression Omnibus (GEO). Utilizing CIBERSORTx, differential expression analysis, and weighted correlation network analysis to to identify macrophage-related genes (MRGs). By unsupervised clustering, distinct clusters of CD were identified. Potential biomarkers were identified via using four machine learning algorithms, leading to the establishment of MacroSig which combines insights from 12 machine learning algorithms. Furthermore, the expression of FPR1 was verified in intestinal biopsies of CD patients and two murine experimental colitis models. Finally, we further explored the role of FPR1 in macrophage polarization through single-cell analysis as well as through the study of RAW264.7 cells and peritoneal macrophages.

Results

Two distinct clusters with differential levels of macrophage infiltration and inflammation were identified. The MacroSig, which included FPR1 and LILRB2, exhibited high diagnostic accuracy and outperformed existing biomarkers and signatures. Clinical analysis demonstrated a strong correlation of FPR1 with disease activity, endoscopic inflammation status, and response to infliximab treatment. The expression levels of FPR1 were validated in our CD cohort by immunohistochemistry and confirmed in two colitis mouse models. Single-cell analysis indicated that FPR1 is predominantly expressed in macrophages and monocytes. In vitro studies demonstrated that FPR1 was upregulated in M1 macrophages, and its activation promoted M1 polarization.

Conclusion

We developed a promising diagnostic signature for CD, and targeting FPR1 to modulate macrophage polarization may represent a novel therapeutic strategy.

Molecular subtypes of ischemic heart disease based on circadian rhythm

Coronary atherosclerotic heart disease (CAD) is among the most prevalent chronic diseases globally. Circadian rhythm disruption (CRD) is closely associated with the progression of various diseases. However, the precise role of CRD in the development of CAD remains to be elucidated. The Circadian rhythm disruption score (CRDscore) was employed to quantitatively assess the level of CRD in CAD samples. Our investigation revealed a significant association between high CRDscore and adverse prognosis in CAD patients, along with a substantial correlation with CAD progression. Remarkably distinct CRDscore distributions were also identified among various subtypes. In summary, we have pioneered the revelation of the relationship between CRD and CAD at the single-cell level and established reliable markers for the development, treatment, and prognosis of CAD. A deeper understanding of these mechanisms may offer new possibilities for incorporating "the therapy of coronary heart disease based circadian rhythm" into personalized medical treatment regimens.

Modulating the Expression of Exercise-induced lncRNAs: Implications for Cardiovascular Disease Progression

Recent research shows exercise is good for heart health, emphasizing the importance of physical activity. Sedentary behavior increases the risk of cardiovascular disease, while exercise can help prevent and treat it. Additionally, physical exercise can modulate the expression of lncRNAs, influencing cardiovascular disease progression. Therefore, understanding this relationship could help identify prospective biomarkers and therapeutic targets pertaining to cardiovascular ailments. This review has underscored recent advancements concerning the potential biomarkers of lncRNAs in cardiovascular diseases, while also summarizing existing knowledge regarding dysregulated lncRNAs and their plausible molecular mechanisms. Additionally, we have contributed novel perspectives on the underlying mechanisms of lncRNAs, which hold promise as potential biomarkers and therapeutic targets for cardiovascular conditions. The knowledge imparted in this review may prove valuable in guiding the design of future investigations and furthering the understanding of lncRNAs as diagnostic, prognostic, and therapeutic biomarkers for cardiovascular diseases.

Circular RNA regulatory role in pathological cardiac remodelling

Cardiac remodelling involves structural, cellular and molecular alterations in the heart after injury, resulting in progressive loss of heart function and ultimately leading to heart failure. Circular RNAs (circRNAs) are a recently rediscovered class of non-coding RNAs that play regulatory roles in the pathogenesis of cardiovascular diseases, including heart failure. Thus, a more comprehensive understanding of the role of circRNAs in the processes governing cardiac remodelling may set the ground for the development of circRNA-based diagnostic and therapeutic strategies. In this review, the current knowledge about circRNA origin, conservation, characteristics and function is summarized. Bioinformatics and wet-lab methods used in circRNA research are discussed. The regulatory function of circRNAs in cardiac remodelling mechanisms such as cell death, cardiomyocyte hypertrophy, inflammation, fibrosis and metabolism is highlighted. Finally, key challenges and opportunities in circRNA research are discussed, and orientations for future work to address the pharmacological potential of circRNAs in heart failure are proposed.

Machine learning-based identification of a cell death-related signature associated with prognosis and immune infiltration in glioma

Accumulating evidence suggests that a wide variety of cell deaths are deeply involved in cancer immunity. However, their roles in glioma have not been explored. We employed a logistic regression model with the shrinkage regularization operator (LASSO) Cox combined with seven machine learning algorithms to analyse the patterns of cell death (including cuproptosis, ferroptosis, pyroptosis, apoptosis and necrosis) in The Cancer Genome Atlas (TCGA) cohort. The performance of the nomogram was assessed through the use of receiver operating characteristic (ROC) curves and calibration curves. Cell-type identification was estimated by using the cell-type identification by estimating relative subsets of known RNA transcripts (CIBERSORT) and single sample gene set enrichment analysis methods. Hub genes associated with the prognostic model were screened through machine learning techniques. The expression pattern and clinical significance of MYD88 were investigated via immunohistochemistry (IHC). The cell death score represents an independent prognostic factor for poor outcomes in glioma patients and has a distinctly superior accuracy to that of 10 published signatures. The nomogram performed well in predicting outcomes according to time-dependent ROC and calibration plots. In addition, a high-risk score was significantly related to high expression of immune checkpoint molecules and dense infiltration of protumor cells, these findings were associated with a cell death-based prognostic model. Upregulated MYD88 expression was associated with malignant phenotypes and undesirable prognoses according to the IHC. Furthermore, high MYD88 expression was associated with poor clinical outcomes and was positively related to CD163, PD-L1 and vimentin expression in the in-horse cohort. The cell death score provides a precise stratification and immune status for glioma. MYD88 was found to be an outstanding representative that might play an important role in glioma.

Construction of Immune Infiltration-Related LncRNA Signatures Based on Machine Learning for the Prognosis in Colon Cancer

Colon cancer is one of the malignant tumors with high morbidity, lethality, and prevalence across global human health. Molecular biomarkers play key roles in its prognosis. In particular, immune-related lncRNAs (IRL) have attracted enormous interest in diagnosis and treatment, but less is known about their potential functions. We aimed to investigate dysfunctional IRL and construct a risk model for improving the outcomes of patients. Nineteen immune cell types were collected for identifying house-keeping lncRNAs (HKLncRNA). GSE39582 and TCGA-COAD were treated as the discovery and validation datasets, respectively. Four machine learning algorithms (LASSO, Random Forest, Boruta, and Xgboost) and a Gaussian mixture model were utilized to mine the optimal combination of lncRNAs. Univariate and multivariate Cox regression was utilized to construct the risk score model. We distinguished the functional difference in an immune perspective between low- and high-risk cohorts calculated by this scoring system. Finally, we provided a nomogram. By leveraging the microarray, sequencing, and clinical data for immune cells and colon cancer patients, we identified the 221 HKLncRNAs with a low cell type-specificity index. Eighty-seven lncRNAs were up-regulated in the immune compared to cancer cells. Twelve lncRNAs were beneficial in improving performance. A risk score model with three lncRNAs (CYB561D2, LINC00638, and DANCR) was proposed with robust ROC performance on an independent dataset. According to immune-related analysis, the risk score is strongly associated with the tumor immune microenvironment. Our results emphasized IRL has the potential to be a powerful and effective therapy for enhancing the prognostic of colon cancer.

Machine learning developed an intratumor heterogeneity signature for predicting prognosis and immunotherapy benefits in skin cutaneous melanoma

Intratumor heterogeneity (ITH) is defined as differences in molecular and phenotypic profiles between different tumor cells and immune cells within a tumor. ITH was involved in the cancer progression, aggressiveness, therapy resistance and cancer recurrence. Integrative machine learning procedure including 10 methods was conducted to develop an ITH-related signature (IRS) in The Cancer Genome Atlas (TCGA), GSE54467, GSE59455 and GSE65904 cohort. Several scores, including tumor immune dysfunction and exclusion (TIDE) score, tumor mutation burden (TMB) score and immunophenoscore (IPS), were used to evaluate the role of IRS in predicting immunotherapy benefits. Two immunotherapy datasets (GSE91061 and GSE78220) were utilized to the role of IRS in predicting immunotherapy benefits of skin cutaneous melanoma (SKCM) patients. The optimal prognostic IRS constructed by Lasso method acted as an independent risk factor and had a stable and powerful performance in predicting the overall survival rate in SKCM, with the area under the curve of 2-, 3- and 4-year receiver operating characteristic curve being 0.722, 0.722 and 0.737 in TCGA cohort. We also constructed a nomogram and the actual 1-, 3- and 5-year survival times were highly consistent with the predicted survival times. SKCM patients with low IRS scores had a lower TIDE score, lower immune escape score and higher TMB score, higher PD1&CTLA4 IPS. Moreover, SKCM patients with low IRS scores had a lower gene sets score involved in DNA repair, angiogenesis, glycolysis, hypoxia, IL2-STAT5 signaling, MTORC1 signaling, NOTCH signaling and P53 pathway. The current study constructed a novel IRS in SKCM using 10 machine learning methods. This IRS acted as an indicator for predicting the prognosis and immunotherapy benefits of SKCM patients.

Multi-omic profiling reveals potential biomarkers of hepatocellular carcinoma prognosis and therapy response among mitochondria-associated cell death genes in the context of 3P medicine

Background

Cancer cell growth, metastasis, and drug resistance are major challenges in treating liver hepatocellular carcinoma (LIHC). However, the lack of comprehensive and reliable models hamper the effectiveness of the predictive, preventive, and personalized medicine (PPPM/3PM) strategy in managing LIHC.

Methods

Leveraging seven distinct patterns of mitochondrial cell death (MCD), we conducted a multi-omic screening of MCD-related genes. A novel machine learning framework was developed, integrating 10 machine learning algorithms with 67 different combinations to establish a consensus mitochondrial cell death index (MCDI). This index underwent rigorous evaluation across training, validation, and in-house clinical cohorts. A comprehensive multi-omics analysis encompassing bulk, single-cell, and spatial transcriptomics was employed to achieve a deeper insight into the constructed signature. The response of risk subgroups to immunotherapy and targeted therapy was evaluated and validated. RT-qPCR, western blotting, and immunohistochemical staining were utilized for findings validation.

Results

Nine critical differentially expressed MCD-related genes were identified in LIHC. A consensus MCDI was constructed based on a 67-combination machine learning computational framework, demonstrating outstanding performance in predicting prognosis and clinical translation. MCDI correlated with immune infiltration, Tumor Immune Dysfunction and Exclusion (TIDE) score and sorafenib sensitivity. Findings were validated experimentally. Moreover, we identified PAK1IP1 as the most important gene for predicting LIHC prognosis and validated its potential as an indicator of prognosis and sorafenib response in our in-house clinical cohorts.

Conclusion

This study developed a novel predictive model for LIHC, namely MCDI. Incorporating MCDI into the PPPM framework will enhance clinical decision-making processes and optimize individualized treatment strategies for LIHC patients.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-024-00362-8.

Omics-based molecular classifications empowering in precision oncology

BACKGROUND

In the past decades, cancer enigmatical heterogeneity at distinct expression levels could interpret disparities in therapeutic response and prognosis. It built hindrances to precision medicine, a tactic to tailor customized treatment informed by the tumors' molecular profile. Single-omics analysis dissected the biological features associated with carcinogenesis to some extent but still failed to revolutionize cancer treatment as expected. Integrated omics analysis incorporated tumor biological networks from diverse layers and deciphered a holistic overview of cancer behaviors, yielding precise molecular classification to facilitate the evolution and refinement of precision medicine.

CONCLUSION

This review outlined the biomarkers at multiple expression layers to tutor molecular classification and pinpoint tumor diagnosis, and explored the paradigm shift in precision therapy: from single- to multi-omics-based subtyping to optimize therapeutic regimens. Ultimately, we firmly believe that by parsing molecular characteristics, omics-based typing will be a powerful assistant for precision oncology.

Molecular Signatures for Microbe-Associated Colorectal Cancers

Background

Genetic factors and microbial imbalances play crucial roles in colorectal cancers (CRCs), yet the impact of infections on cancer initiation remains poorly understood. While bioinformatic approaches offer valuable insights, the rising incidence of CRCs creates a pressing need to precisely identify early CRC events. We constructed a network model to identify continuum states during CRC initiation spanning normal colonic tissue to pre-cancer lesions (adenomatous polyps) and examined the influence of microbes and host genetics.

Methods

A Boolean network was built using a publicly available transcriptomic dataset from healthy and adenoma affected patients to identify an invariant Microbe-Associated Colorectal Cancer Signature (MACS). We focused on Fusobacterium nucleatum ( Fn ), a CRC-associated microbe, as a model bacterium. MACS-associated genes and proteins were validated by RT-qPCR, RNA seq, ELISA, IF and IHCs in tissues and colon-derived organoids from genetically predisposed mice ( CPC-APC Min+/- ) and patients (FAP, Lynch Syndrome, PJS, and JPS).

Results

The MACS that is upregulated in adenomas consists of four core genes/proteins: CLDN2/Claudin-2 (leakiness), LGR5/leucine-rich repeat-containing receptor (stemness), CEMIP/cell migration-inducing and hyaluronan-binding protein (epithelial-mesenchymal transition) and IL8/Interleukin-8 (inflammation). MACS was induced upon Fn infection, but not in response to infection with other enteric bacteria or probiotics. MACS induction upon Fn infection was higher in CPC-APC Min+/- organoids compared to WT controls. The degree of MACS expression in the patient-derived organoids (PDOs) generally corresponded with the known lifetime risk of CRCs.

Conclusions

Computational prediction followed by validation in the organoid-based disease model identified the early events in CRC initiation. MACS reveals that the CRC-associated microbes induce a greater risk in the genetically predisposed hosts, suggesting its potential use for risk prediction and targeted cancer prevention.

Construction and significance of a breast cancer prognostic model based on cuproptosis-related genotyping and lncRNAs

BACKGROUND

/Purpose: Cuproptosis may play a significant role in breast cancer (BC). We aimed to investigate the prognostic impact of cuproptosis-related lncRNAs in BC.

METHODS

Consensus clustering analysis categorized TCGA-BRCA samples into 3 clusters, followed by survival and immune analyses of the 3 clusters. LASSO-COX analysis was performed on cuproptosis-related lncRNAs differentially expressed in BC to construct a BC prognostic model. Gene Ontology/Kyoto Encyclopedia of Genes and Genomes (GO/KEGG) enrichment, immune, and drug prediction analyses were performed on the high-risk and low-risk groups. Cell experiments were conducted to analyze the results of drug prediction and two cuproptosis-related lncRNAs (AC104211.1 and LINC01863).

RESULTS

Significant differences were observed in survival outcomes and immune infiltration levels among the three clusters (p < 0.05). The validation of the model showed significant differences in survival outcomes between the high-risk and low-risk groups in both the training and validation sets (p < 0.05). Differential mRNAs between the two groups were significantly enriched in the Neuroactive ligand-receptor interaction and cAMP signaling pathway. Additionally, significant differences were found in immune infiltration levels, human leukocyte antigen (HLA) expression, Immunophenoscore (IPS) scores, and Tumor Immune Dysfunction and Exclusion (TIDE) scores between the two groups (p < 0.05). Drug prediction and corresponding cell experimental results showed that Trametinib, 5-fluorouracil, and AICAR significantly inhibited the viability of MCF-7 cells (p < 0.05). AC104211.1 and LINC01863 were found to impact the proliferation of BC cells.

CONCLUSION

The risk-scoring model obtained in this study may serve as a robust prognostic biomarker, potentially aiding in clinical decision-making for BC patients.

Long non-coding RNA RP11-197K6.1 as ceRNA promotes colorectal cancer progression via miR-135a-5p/DLX5 axis

BACKGROUND

Colorectal cancer (CRC) remains a major global health challenge, with high incidence and mortality rates. The role of long noncoding RNAs (lncRNAs) in cancer progression has received considerable attention. The present study aimed to investigate the function and mechanisms underlying the role of lncRNA RP11-197K6.1, microRNA-135a-5p (hsa-miR-135a-5p), and DLX5 in CRC development.

METHODS

We analyzed RNA sequencing data from The Cancer Genome Atlas Colorectal Cancer dataset to identify the association between lncRNA RP11-197K6.1 and CRC progression. The expression levels of lncRNA RP11-197K6.1 and DLX5 in CRC samples and cell lines were determined by real-time quantitative PCR and western blotting assays. Fluorescence in situ hybridization was used to confirm the cellular localization of lncRNA RP11-197K6.1. Cell migration capabilities were assessed by Transwell and wound healing assays, and flow cytometry was performed to analyze apoptosis. The interaction between lncRNA RP11-197K6.1 and miR-135a-5p and its effect on DLX5 expression were investigated by the dual-luciferase reporter assay. Additionally, a xenograft mouse model was used to study the in vivo effects of lncRNA RP11-197K6.1 on tumor growth, and an immunohistochemical assay was performed to assess DLX5 expression in tumor tissues.

RESULTS

lncRNA RP11-197K6.1 was significantly upregulated in CRC tissues and cell lines as compared to that in normal tissues, and its expression was inversely correlated with patient survival. It promoted the migration and metastasis of CRC cells by interacting with miR-135a-5p, alleviated suppression of DLX5 expression, and facilitated tumor growth.

CONCLUSION

This study demonstrated the regulatory network and mechanism of action of the lncRNA RP11-197K6.1/miR-135a-5p/DLX5 axis in CRC development. These findings provided insights into the molecular pathology of CRC and suggested potential therapeutic targets for more effective treatment of patients with CRC.

Comprehensive analysis of immune-related lncRNAs in AML patients uncovers potential therapeutic targets and prognostic biomarkers

Purpose

The objective of this study was to provide theoretically feasible strategies by understanding the relationship between the immune microenvironment and the diagnosis and prognosis of AML patients. To this end, we built a ceRNA network with lncRNAs as the core and analyzed the related lncRNAs in the immune microenvironment by bioinformatics analysis.

Methods

AML transcriptome expression data and immune-related gene sets were obtained from TCGA and ImmPort. Utilizing Pearson correlation analysis, differentially expressed immune-related lncRNAs were identified. Then, the LASSO-Cox regression analysis was performed to generate a risk signature consisting immune-related lncRNAs. Accuracy of signature in predicting patient survival was evaluated using univariate and multivariate analysis. Next, GO and KEGG gene enrichment and ssGSEA were carried out for pathway enrichment analysis of 183 differentially expressed genes, followed by drug sensitivity and immune infiltration analysis with pRRophetic and CIBERSORT, respectively. Cytoscape was used to construct the ceRNA network for these lncRNAs.

Results

816 common lncRNAs were selected to acquire the components related to prognosis. The final risk signature established by multivariate Cox and stepwise regression analysis contained 12 lncRNAs engaged in tumor apoptotic and metastatic processes: LINC02595, HCP5, AC020934.2, AC008770.3, LINC01770, AC092718.4, AL589863.1, AC131097.4, AC012368.1, C1RL-AS1, STARD4-AS1, and AC243960.1. Based on this predictive model, high-risk patients exhibited lower overall survival rates than low-risk patients. Signature lncRNAs showed significant correlation with tumor-infiltrating immune cells. In addition, significant differences in PD-1/PD-L1 expression and bleomycin/paclitaxel sensitivity were observed between risk groups.

Conclusion

LncRNAs related to immune microenvironment were prospective prognostic and therapeutic options for AML.

Machine learning-based integration develops a stress response stated T cell (Tstr)-related score for predicting outcomes in clear cell renal cell carcinoma

BACKGROUND

Establishment of a reliable prognostic model and identification of novel biomarkers are urgently needed to develop precise therapy strategies for clear cell renal cell carcinoma (ccRCC). Stress response stated T cells (Tstr) are a new T-cell subtype, which are related to poor disease stage and immunotherapy response in various cancers.

METHODS

10 machine-learning algorithms and their combinations were applied in this work. A stable Tstr-related score (TCs) was constructed to predict the outcomes and PD-1 blockade treatment response in ccRCC patients. A nomogram based on TCs for personalized prediction of patient prognosis was constructed. Functional enrichment analysis and TimiGP algorithm were used to explore the underlying role of Tstr in ccRCC. The key TCs-related gene was identified by comprehensive analysis, and the bioinformatics results were verified by immunohistochemistry using a tissue microarray.

RESULTS

A robust TCs was constructed and validated in four independent cohorts. TCs accurately predicted the prognosis and PD-1 blockade treatment response in ccRCC patients. The novel nomogram was able to precisely predict the outcomes of ccRCC patients. The underlying biological process of Tstr was related to acute inflammatory response and acute-phase response. Mast cells were identified to be involved in the role of Tstr as a protective factor in ccRCC. TNFS13B was shown to be the key TCs-related gene, which was an independent predictor of unfavorable prognosis. The protein expression analysis of TNFSF13B was consistent with the mRNA analysis results. High expression of TNFSF13B was associated with poor response to PD-1 blockade treatment.

CONCLUSIONS

This study provides a Tstr cell-related score for predicting outcomes and PD-1 blockade therapy response in ccRCC. Tstr cells may exert their pro-tumoral role in ccRCC, acting against mast cells, in the acute inflammatory tumor microenvironment. TNFSF13B could serve as a key biomarker related to TCs.

Glycometabolism and lipid metabolism related genes predict the prognosis of endometrial carcinoma and their effects on tumor cells

BACKGROUND

Glycometabolism and lipid metabolism are critical in cancer metabolic reprogramming. The primary aim of this study was to develop a prognostic model incorporating glycometabolism and lipid metabolism-related genes (GLRGs) for accurate prognosis assessment in patients with endometrial carcinoma (EC).

METHODS

Data on gene expression and clinical details were obtained from publicly accessible databases. GLRGs were obtained from the Genecards database. Through nonnegative matrix factorization (NMF) clustering, molecular groupings with various GLRG expression patterns were identified. LASSO Cox regression analysis was employed to create a prognostic model. Use rich algorithms such as GSEA, GSVA, xCELL ssGSEA, EPIC,CIBERSORT, MCPcounter, ESTIMATE, TIMER, TIDE, and Oncoppredict to analyze functional pathway characteristics of the forecast signal, immune status, anti-tumor therapy, etc. The expression was assessed using Western blot and quantitative real-time PCR techniques. A total of 113 algorithm combinations were combined to screen out the most significant GLRGs in the signature for in vitro experimental verification, such as colony formation, EdU cell proliferation, wound healing, apoptosis, and Transwell assays.

RESULTS

A total of 714 GLRGs were found, and 227 of them were identified as prognostic-related genes. And ten GLRGs (AUP1, ESR1, ERLIN2, ASS1, OGDH, BCKDHB, SLC16A1, HK2, LPCAT1 and PGR-AS1) were identified to construct the prognostic model of patients with EC. Based on GLRGs, the risk model's prognosis and independent prognostic value were established. The signature of GLRGs exhibited a robust correlation with the infiltration of immune cells and the sensitivity to drugs. In cytological experiments, we selected HK2 as candidate gene to verify its value in the occurrence and development of EC. Western blot and qRT-PCR revealed that HK2 was substantially expressed in EC cells. According to in vitro experiments, HK2 knockdown can increase EC cell apoptosis while suppressing EC cell migration, invasion, and proliferation.

CONCLUSION

The GLRGs signature constructed in this study demonstrated significant prognostic value for patients with endometrial carcinoma, thereby providing valuable guidance for treatment decisions.

Machine learning developed an intratumor heterogeneity signature for predicting prognosis and immunotherapy benefits in cholangiocarcinoma

BACKGROUND

Cholangiocarcinoma is a kind of epithelial cell malignancy with high mortality. Intratumor heterogeneity (ITH) is involved in tumor progression, aggressiveness, treatment resistance, and disease recurrence.

METHODS

Integrative machine learning procedure including 10 methods (random survival forest, elastic network, Lasso, Ridge, stepwise Cox, CoxBoost, partial least squares regression for Cox, supervised principal components, generalized boosted regression modeling, and survival support vector machine) was performed to construct an ITH-related signature (IRS) for cholangiocarcinoma. Single cell analysis was performed to clarify the communication between immune cell subtypes. Cellular experiment was used to verify the biological function of hub gene.

RESULTS

The optimal prognostic IRS developed by Lasso method served as an independent risk factor and had a stable and powerful performance in predicting the overall survival rate in cholangiocarcinoma, with the AUC of 2-, 3-, and 4-year ROC curve being 0.955, 0.950 and 1.000 in TCGA cohort. low IRS score indicated with a lower tumor immune dysfunction and exclusion score, lower tumor microsatellite instability, lower immune escape score, lower MATH score, and higher mutation burden score in cholangiocarcinoma. Single cell analysis revealed a strong communication between fibroblasts, microphage and epithelial cells by specific ligand-receptor pairs, including COL4A1-(ITGAV+ITGB8) and COL1A2-(ITGAV+ITGB8). Down-regulation of BET1L inhibited the proliferation, migration and invasion as well as promoted apoptosis of cholangiocarcinoma cell.

CONCLUSION

Integrative machine learning analysis was performed to construct a novel IRS in cholangiocarcinoma. This IRS acted as an indicator for predicting the prognosis and immunotherapy benefits of cholangiocarcinoma patients.

Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) presents a significant global health burden, characterized by a heterogeneous molecular landscape and various genetic and epigenetic alterations. Programmed cell death (PCD) plays a critical role in CRC, offering potential targets for therapy by regulating cell elimination processes that can suppress tumor growth or trigger cancer cell resistance. Understanding the complex interplay between PCD mechanisms and CRC pathogenesis is crucial. This study aims to construct a PCD-related prognostic signature in CRC using machine learning integration, enhancing the precision of CRC prognosis prediction.

METHOD

We retrieved expression data and clinical information from the Cancer Genome Atlas and Gene Expression Omnibus (GEO) datasets. Fifteen forms of PCD were identified, and corresponding gene sets were compiled. Machine learning algorithms, including Lasso, Ridge, Enet, StepCox, survivalSVM, CoxBoost, SuperPC, plsRcox, random survival forest (RSF), and gradient boosting machine, were integrated for model construction. The models were validated using six GEO datasets, and the programmed cell death score (PCDS) was established. Further, the model's effectiveness was compared with 109 transcriptome-based CRC prognostic models.

RESULT

Our integrated model successfully identified differentially expressed PCD-related genes and stratified CRC samples into four subtypes with distinct prognostic implications. The optimal combination of machine learning models, RSF + Ridge, showed superior performance compared with traditional methods. The PCDS effectively stratified patients into high-risk and low-risk groups, with significant survival differences. Further analysis revealed the prognostic relevance of immune cell types and pathways associated with CRC subtypes. The model also identified hub genes and drug sensitivities relevant to CRC prognosis.

CONCLUSION

The current study highlights the potential of integrating machine learning models to enhance the prediction of CRC prognosis. The developed prognostic signature, which is related to PCD, holds promise for personalized and effective therapeutic interventions in CRC.

Comprehensive analysis of bulk and single-cell transcriptomic data reveals a novel signature associated with endoplasmic reticulum stress, lipid metabolism, and liver metastasis in pancreatic cancer

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with high probability of recurrence and distant metastasis. Liver metastasis is the predominant metastatic mode developed in most pancreatic cancer cases, which seriously affects the overall survival rate of patients. Abnormally activated endoplasmic reticulum stress and lipid metabolism reprogramming are closely related to tumor growth and metastasis. This study aims to construct a prognostic model based on endoplasmic reticulum stress and lipid metabolism for pancreatic cancer, and further explore its correlation with tumor immunity and the possibility of immunotherapy.

METHODS

Transcriptomic and clinical data are acquired from TCGA, ICGC, and GEO databases. Potential prognostic genes were screened by consistent clustering and WGCNA methods, and the whole cohort was randomly divided into training and testing groups. The prognostic model was constructed by machine learning method in the training cohort and verified in the test, TCGA and ICGC cohorts. The clinical application of this model and its relationship with tumor immunity were analyzed, and the relationship between endoplasmic reticulum stress and intercellular communication was further explored.

RESULTS

A total of 92 characteristic genes related to endoplasmic reticulum stress, lipid metabolism and liver metastasis were identified in pancreatic cancer. We established and validated a prognostic model for pancreatic cancer with 7 signatures, including ADH1C, APOE, RAP1GAP, NPC1L1, P4HB, SOD2, and TNFSF10. This model is considered to be an independent prognosticator and is a more accurate predictor of overall survival than age, gender, and stage. TIDE score was increased in high-risk group, while the infiltration levels of CD8+ T cells and M1 macrophages were decreased. The number and intensity of intercellular communication were increased in the high ER stress group.

CONCLUSIONS

We constructed and validated a novel prognostic model for pancreatic cancer, which can also be used as an instrumental variable to predict the prognosis and immune microenvironment. In addition, this study revealed the effect of ER stress on cell-cell communication in the tumor microenvironment.

Identification of a 9-gene signature to enhance biochemical recurrence prediction in primary prostate cancer: A benchmarking study using ten machine learning methods and twelve patient cohorts

Prostate cancer (PCa) is a prevalent malignancy among men worldwide, and biochemical recurrence (BCR) after radical prostatectomy (RP) is a critical turning point commonly used to guide the development of treatment strategies for primary PCa. However, the clinical parameters currently in use are inadequate for precise risk stratification and informing treatment choice. To address this issue, we conducted a study that collected transcriptomic data and clinical information from 1662 primary PCa patients across 12 multicenter cohorts globally. We leveraged 101 algorithm combinations that consisted of 10 machine learning methods to develop and validate a 9-gene signature, named BCR SCR, for predicting the risk of BCR after RP. Our results demonstrated that BCR SCR generally outperformed 102 published prognostic signatures. We further established the clinical significance of these nine genes in PCa progression at the protein level through immunohistochemistry on Tissue Microarray (TMA). Moreover, our data showed that patients with higher BCR SCR tended to have higher rates of BCR and distant metastasis after radical radiotherapy. Through drug target prediction analysis, we identified nine potential therapeutic agents for patients with high BCR SCR. In conclusion, the newly developed BCR SCR has significant translational potential in accurately stratifying the risk of patients who undergo RP, monitoring treatment courses, and developing new therapies for the disease.

Integrated multi-omics analysis and machine learning developed a prognostic model based on mitochondrial function in a large multicenter cohort for Gastric Cancer

BACKGROUND

Gastric cancer (GC) is a common and aggressive type of cancer worldwide. Despite recent advancements in its treatment, the prognosis for patients with GC remains poor. Understanding the mechanisms of cell death in GC, particularly those related to mitochondrial function, is crucial for its development and progression. However, more research is needed to investigate the significance of the interaction between mitochondrial function and GC cell death.

METHODS

We employed a robust computational framework to investigate the role of mitochondria-associated proteins in the progression of GC in a cohort of 1,199 GC patients. Ten machine learning algorithms were utilized and combined into 101 unique combinations. Ultimately, we developed a Mitochondrial-related-Score (MitoScore) using the machine learning model that exhibited the best performance. We observed the upregulation of LEMT2 and further explored its function in tumor progression. Mitochondrial functions were assessed by measuring mitochondrial ATP, mitochondrial membrane potential, and levels of lactate, pyruvate, and glucose.

RESULTS

MitoScore showed significant correlations with GC immune and metabolic functions. The higher MitoScore subgroup exhibited enriched metabolic pathways and higher immune activity. Overexpression of LETM2 (leucine zipper and EF-hand containing transmembrane protein 2) significantly enhanced tumor proliferation and metastasis. LETM2 plays a role in promoting GC cell proliferation by activating the mTOR pathway, maintaining mitochondrial homeostasis, and promoting glycolysis.

CONCLUSION

The powerful machine learning framework highlights the significant potential of MitoScore in providing valuable insights and accurate assessments for individuals with GC. This study also enhances our understanding of LETM2 as an oncogene signature in GC. LETM2 may promote tumor progression by maintaining mitochondrial health and activating glycolysis, offering potential targets for diagnosis, treatment, and prognosis of GC.

Identification of BGN positive fibroblasts as a driving factor for colorectal cancer and development of its related prognostic model combined with machine learning

BACKGROUND

Numerous studies have indicated that cancer-associated fibroblasts (CAFs) play a crucial role in the progression of colorectal cancer (CRC). However, there are still many unknowns regarding the exact role of CAF subtypes in CRC.

METHODS

The data for this study were obtained from bulk, single-cell, and spatial transcriptomic sequencing data. Bioinformatics analysis, in vitro experiments, and machine learning methods were employed to investigate the functional characteristics of CAF subtypes and construct prognostic models.

RESULTS

Our study demonstrates that Biglycan (BGN) positive cancer-associated fibroblasts (BGN + Fib) serve as a driver in colorectal cancer (CRC). The proportion of BGN + Fib increases gradually with the progression of CRC, and high infiltration of BGN + Fib is associated with poor prognosis in terms of overall survival (OS) and recurrence-free survival (RFS) in CRC. Downregulation of BGN expression in cancer-associated fibroblasts (CAFs) significantly reduces migration and proliferation of CRC cells. Among 101 combinations of 10 machine learning algorithms, the StepCox[both] + plsRcox combination was utilized to develop a BGN + Fib derived risk signature (BGNFRS). BGNFRS was identified as an independent adverse prognostic factor for CRC OS and RFS, outperforming 92 previously published risk signatures. A Nomogram model constructed based on BGNFRS and clinical-pathological features proved to be a valuable tool for predicting CRC prognosis.

CONCLUSION

In summary, our study identified BGN + Fib as drivers of CRC, and the derived BGNFRS was effective in predicting the OS and RFS of CRC patients.

Bulk and single-cell RNA sequencing analysis with 101 machine learning combinations reveal neutrophil extracellular trap involvement in hepatic ischemia-reperfusion injury and early allograft dysfunction

BACKGROUND

Hepatic ischaemia-reperfusion injury (HIRI) is a major clinical concern during the perioperative period and is closely associated with early allograft dysfunction (EAD), acute rejection (AR) and long-term graft survival. Neutrophil extracellular traps (NETs) are extracellular structures formed by the release of decondensed chromatin and granular proteins following neutrophil stimulation. There is growing evidence that NETs are involved in the progression of various liver transplantation complications, including ischaemia-reperfusion injury (IRI). This study aimed to comprehensively analyse the expression patterns of NET-related genes (NRGs) in HIRI, identify HIRI subtypes with distinct characteristics, and develop a reliable EAD prediction model.

METHODS

Microarray, bulk RNA-seq, and single-cell sequencing datasets were obtained from the GEO database. Initially, differentially expressed NRGs (DE-NRGs) were identified using differential gene expression analyses. We then utilised a non-negative matrix factorisation (NMF) algorithm to classify HIRI samples. Subsequently, we employed machine learning algorithms to screen the hub NRGs related to EAD and developed an EAD prediction model based on these hub NRGs. Concurrently, we assessed the expression patterns of hub NRGs at the single-cell level using the HIRI. Additionally, we validated C5AR1 expression and its effect on HIRI and NETs formation in a rat orthotopic liver transplantation (OLT) model.

RESULTS

In this study, we identified 11 DE-NRGs in the HIRI context. Based on these 11 DE-NRGs, HIRI samples were classified into two distinct clusters. Cluster1 exhibited a low expression of DE-NRGs, minimal neutrophil infiltration, mild inflammation, and a low incidence of EAD. Conversely, Cluster2 displayed the opposite phenotype, with an activated inflammatory subtype and a higher incidence of EAD. Furthermore, an EAD prediction model was developed using the four hub NRGs associated with EAD. Based on risk scores, HIRI samples were classified into high- and low-risk groups. The OLT model confirmed substantial upregulation of C5AR1 expression in the liver tissue, accompanied by increased formation of NETs. Treatment with a C5AR1 antagonist improved liver function, reduced tissue inflammation, and decreased NETs formation.

CONCLUSIONS

This study distinguished two apparent HIRI subtypes, established a predictive model for EAD, and validated the effect of C5AR1 on HIRI. These findings provide novel perspectives for the development of advanced clinical strategies to enhance the outcomes of liver transplant recipients.

Machine learning developed a fibroblast-related signature for predicting clinical outcome and drug sensitivity in ovarian cancer

Ovarian cancer (OC) is the leading cause of gynecological cancer death. Cancer-associated fibroblasts (CAF) is involved in wound healing and inflammatory processes, tumor occurrence and progression, and chemotherapy resistance in OC. GSE184880 dataset was used to identify CAF-related genes in OC. CAF-related signature (CRS) was constructed using integrative 10 machine learning methods with the datasets from the Cancer Genome Atlas, GSE14764, GSE26193, GSE26712, GSE63885, and GSE140082. The performance of CRS in predicting immunotherapy benefits was verified using 3 immunotherapy datasets (GSE91061, GSE78220, and IMvigor210) and several immune calculating scores. The Lasso + StepCox[forward] method-based predicting model having a highest average C index of 0.69 was referred as the optimal CRS and it had a stable and powerful performance in predicting clinical outcome of OC patients, with the 1-, 3-, and 5-year area under curves were 0.699, 0.708, and 0.767 in the Cancer Genome Atlas cohort. The C index of CRS was higher than that of tumor grade, clinical stage, and many developed signatures. Low CRS score demonstrated lower tumor immune dysfunction and exclusion score, lower immune escape score, higher PD1&CTLA4 immunophenoscore, higher tumor mutation burden score, higher response rate and better prognosis in OC, suggesting a better immunotherapy response. OC patients with low CRS score had a lower half maximal inhibitory concentration value of some drugs (Gemcitabine, Tamoxifen, and Nilotinib, etc) and lower score of some cancer-related hallmarks (Notch signaling, hypoxia, and glycolysis, etc). The current study developed an optimal CRS in OC, which acted as an indicator for the prognosis, stratifying risk and guiding treatment for OC patients.

Pan-cancer dissection of vasculogenic mimicry characteristic to provide potential therapeutic targets

Introduction

Vasculogenic mimicry (VM) represents a novel form of tumor angiogenesis that is associated with tumor invasiveness and drug resistance. However, the VM landscape across cancer types remains poorly understood. In this study, we elucidate the characterizations of VM across cancers based on multi-omics data and provide potential targeted therapeutic strategies.

Methods

Multi-omics data from The Cancer Genome Atlas was used to conduct comprehensive analyses of the characteristics of VM related genes (VRGs) across cancer types. Pan-cancer vasculogenic mimicry score was established to provide a depiction of the VM landscape across cancer types. The correlation between VM and cancer phenotypes was conducted to explore potential regulatory mechanisms of VM. We further systematically examined the relationship between VM and both tumor immunity and tumor microenvironment (TME). In addition, cell communication analysis based on single-cell transcriptome data was used to investigate the interactions between VM cells and TME. Finally, transcriptional and drug response data from the Genomics of Drug Sensitivity in Cancer database were utilized to identify potential therapeutic targets and drugs. The impact of VM on immunotherapy was also further clarified.

Results

Our study revealed that VRGs were dysregulated in tumor and regulated by multiple mechanisms. Then, VM level was found to be heterogeneous among different tumors and correlated with tumor invasiveness, metastatic potential, malignancy, and prognosis. VM was found to be strongly associated with epithelial-mesenchymal transition (EMT). Further analyses revealed cancer-associated fibroblasts can promote EMT and VM formation. Furthermore, the immune-suppressive state is associated with a microenvironment characterized by high levels of VM. VM score can be used as an indicator to predict the effect of immunotherapy. Finally, seven potential drugs targeting VM were identified.

Conclusion

In conclusion, we elucidate the characteristics and key regulatory mechanisms of VM across various cancer types, underscoring the pivotal role of CAFs in VM. VM was further found to be associated with the immunosuppressive TME. We also provide clues for the research of drugs targeting VM. Our study provides an initial overview and reference point for future research on VM, opening up new avenues for therapeutic intervention.

Differential whole-genome doubling based signatures for improvement on clinical outcomes and drug response in patients with breast cancer

Whole genome doublings (WGD), a hallmark of human cancer, is pervasive in breast cancer patients. However, the molecular mechanism of the complete impact of WGD on survival and treatment response in breast cancer remains unclear. To address this, we performed a comprehensive and systematic analysis of WGD, aiming to identify distinct genetic alterations linked to WGD and highlight its improvement on clinical outcomes and treatment response for breast cancer. A linear regression model along with weighted gene co-expression network analysis (WGCNA) was applied on The Cancer Genome Atlas (TCGA) dataset to identify critical genes related to WGD. Further Cox regression models with random selection were used to optimize the most useful prognostic markers in the TCGA dataset. The clinical implication of the risk model was further assessed through prognostic impact evaluation, tumor stratification, functional analysis, genomic feature difference analysis, drug response analysis, and multiple independent datasets for validation. Our findings revealed a high aneuploidy burden, chromosomal instability (CIN), copy number variation (CNV), and mutation burden in breast tumors exhibiting WGD events. Moreover, 247 key genes associated with WGD were identified from the distinct genomic patterns in the TCGA dataset. A risk model consisting of 22 genes was optimized from the key genes. High-risk breast cancer patients were more prone to WGD and exhibited greater genomic diversity compared to low-risk patients. Some oncogenic signaling pathways were enriched in the high-risk group, while primary immune deficiency pathways were enriched in the low-risk group. We also identified a risk gene, ANLN (anillin), which displayed a strong positive correlation with two crucial WGD genes, KIF18A and CCNE2. Tumors with high expression of ANLN were more prone to WGD events and displayed worse clinical survival outcomes. Furthermore, the expression levels of these risk genes were significantly associated with the sensitivities of BRCA cell lines to multiple drugs, providing valuable insights for targeted therapies. These findings will be helpful for further improvement on clinical outcomes and contribution to drug development in breast cancer.

Architecting the metabolic reprogramming survival risk framework in LUAD through single-cell landscape analysis: three-stage ensemble learning with genetic algorithm optimization

Recent studies have increasingly revealed the connection between metabolic reprogramming and tumor progression. However, the specific impact of metabolic reprogramming on inter-patient heterogeneity and prognosis in lung adenocarcinoma (LUAD) still requires further exploration. Here, we introduced a cellular hierarchy framework according to a malignant and metabolic gene set, named malignant & metabolism reprogramming (MMR), to reanalyze 178,739 single-cell reference profiles. Furthermore, we proposed a three-stage ensemble learning pipeline, aided by genetic algorithm (GA), for survival prediction across 9 LUAD cohorts (n = 2066). Throughout the pipeline of developing the three stage-MMR (3 S-MMR) score, double training sets were implemented to avoid over-fitting; the gene-pairing method was utilized to remove batch effect; GA was harnessed to pinpoint the optimal basic learner combination. The novel 3 S-MMR score reflects various aspects of LUAD biology, provides new insights into precision medicine for patients, and may serve as a generalizable predictor of prognosis and immunotherapy response. To facilitate the clinical adoption of the 3 S-MMR score, we developed an easy-to-use web tool for risk scoring as well as therapy stratification in LUAD patients. In summary, we have proposed and validated an ensemble learning model pipeline within the framework of metabolic reprogramming, offering potential insights for LUAD treatment and an effective approach for developing prognostic models for other diseases.

Exploring a specialized programmed-cell death patterns to predict the prognosis and sensitivity of immunotherapy in cutaneous melanoma via machine learning

The mortality and therapeutic failure in cutaneous melanoma (CM) are mainly caused by wide metastasis and chemotherapy resistance. Meanwhile, immunotherapy is considered a crucial therapy strategy for CM patients. However, the efficiency of currently available methods and biomarkers in predicting the response of immunotherapy and prognosis of CM is limited. Programmed cell death (PCD) plays a significant role in the occurrence, development, and therapy of various malignant tumors. In this research, we integrated fourteen types of PCD, multi-omics data from TCGA-SKCM and other cohorts in GEO, and clinical CM patients to develop our analysis. Based on significant PCD patterns, two PCD-related CM clusters with different prognosis, tumor microenvironment (TME), and response to immunotherapy were identified. Subsequently, seven PCD-related features, especially CD28, CYP1B1, JAK3, LAMP3, SFN, STAT4, and TRAF1, were utilized to establish the prognostic signature, namely cell death index (CDI). CDI accurately predicted the response to immunotherapy in both CM and other cancers. A nomogram with potential superior predictive ability was constructed, and potential drugs targeting CM patients with specific CDI have also been identified. Given all the above, a novel CDI gene signature was indicated to predict the prognosis and exploit precision therapeutic strategies of CM patients, providing unique opportunities for clinical intelligence and new management methods for the therapy of CM.

A degradome-related signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma based on machine learning procedure

Stomach adenocarcinoma (STAD) is one of the subtype of gastric cancer with high invasiveness, extreme heterogeneity, high morbidity, and high mortality. The degradome is the most abundant class of cellular enzymes that play an essential role in regulating cellular activity and carcinogenesis. An integrative machine learning procedure including 10 methods was performed to develop a prognostic degradome-based prognostic signature (DPS) in TCGA, GSE15459, GSE26253, and GSE62254 datasets. Investigations of the DPS concerning immune infiltration, immunotherapy benefits, and drug priority were orchestrated. The DPS developed by Enet [alpha = 0.3] method was regarded as the optimal prognostic model. The DPS had a stable and powerful performance in predicting the clinical outcome of STAD and served as an independent risk factor in training and testing cohorts. The C-index of DPS was higher than that of age, sex, and clinical stage. STAD patients with low DPS scores had a higher abundance of B cells, CD8+ T cells, higher cytolytic scores, and T cell co-stimulation scores. Moreover, low DPS score indicated a lower tumor immune dysfunction and exclusion score, lower T cell dysfunction and exclusion score, higher PD1&CTLA4 immunophenoscore, and higher tumor mutation burden score in STAD, demonstrating a better immunotherapy response. STAD patients with a high DPS score had a lower IC50 value of common chemotherapy and targeted therapy regimens (Cisplatin, Docetaxel, Gefitinib, etc). Our study developed an optimal DPS for STAD. The DPS could predict the prognosis, risk stratification and guide treatment for STAD patients.

Machine learning-based identification of a consensus immune-derived gene signature to improve head and neck squamous cell carcinoma therapy and outcome

Background

Head and neck squamous cell carcinoma (HNSCC), an extremely aggressive tumor, is often associated with poor outcomes. The standard anatomy-based tumor-node-metastasis staging system does not satisfy the requirements for screening treatment-sensitive patients. Thus, an ideal biomarker leading to precise screening and treatment of HNSCC is urgently needed.

Methods

Ten machine learning algorithms-Lasso, Ridge, stepwise Cox, CoxBoost, elastic network (Enet), partial least squares regression for Cox (plsRcox), random survival forest (RSF), generalized boosted regression modelling (GBM), supervised principal components (SuperPC), and survival support vector machine (survival-SVM)-as well as 85 algorithm combinations were applied to construct and identify a consensus immune-derived gene signature (CIDGS).

Results

Based on the expression profiles of three cohorts comprising 719 patients with HNSCC, we identified 236 consensus prognostic genes, which were then filtered into a CIDGS, using the 10 machine learning algorithms and 85 algorithm combinations. The results of a study involving a training cohort, two testing cohorts, and a meta-cohort consistently demonstrated that CIDGS was capable of accurately predicting prognoses for HNSCC. Incorporation of several core clinical features and 51 previously reported signatures, enhanced the predictive capacity of the CIDGS to a level which was markedly superior to that of other signatures. Notably, patients with low CIDGS displayed fewer genomic alterations and higher immune cell infiltrate levels, as well as increased sensitivity to immunotherapy and other therapeutic agents, in addition to receiving better prognoses. The survival times of HNSCC patients with high CIDGS, in particular, were shorter. Moreover, CIDGS enabled accurate stratification of the response to immunotherapy and prognoses for bladder cancer. Niclosamide and ruxolitinib showed potential as therapeutic agents in HNSCC patients with high CIDGS.

Conclusion

CIDGS may be used for stratifying risks as well as for predicting the outcome of patients with HNSCC in a clinical setting.

Development of gene panel for predicting recurrence in early-stage cervical cancer patients

Cervical cancer (CC) is a common malignancy affecting women worldwide. Our objective was to develop a consensus-based gene panel using multi-omics data that could effectively predict recurrence in early-stage cervical cancer patients. We utilized the "Multi-Omics Consensus Integration Analysis (MOVICS)" package for consensus clustering design to integrate multiple omics datasets and improve the molecular classification landscape of early-stage CC. We identified the "resting and naive" tumor microenvironment (TME) as cancer subtype (CS) 2. Leveraging the feature genes from the CS classifier, we employed machine learning algorithms to identify a gene panel, including ALDH1A1, CLDN10, MUC13, and C10orf99, which could generate a consensus machine learning-driven score (CMLS) for each patient. Stratifying patients into high and low CMLS groups resulted in Kaplan-Meier curves demonstrating a significant difference in recurrence rates between the two groups. This difference remained significant even after adjusting for clinical features in multivariate Cox regression analysis, with the risk ratio of CMLS surpassing that of clinical characteristics. Furthermore, the TME exhibited notable differences between the different CMLS groups, suggesting that patients with low CMLS may exhibit a better response to immunotherapy. This study highlights the potential of the CMLS approach in predicting recurrence in early-stage cervical cancer patients and provides a screening model for selecting patients suitable for immunotherapy.

Prognostic and immunotherapeutic potential of regulatory T cell-associated signature in ovarian cancer

Tumour-induced immunosuppressive microenvironments facilitate oncogenesis, with regulatory T cells (Tregs) serving as a crucial component. The significance of Treg-associated genes within the context of ovarian cancer (OC) remains elucidated insufficiently. Utilizing single-cell RNA sequencing (scRNA-Seq) for the identification of Treg-specific biomarkers, this investigation employed single-sample gene set enrichment analysis (ssGSEA) for the derivation of a Treg signature score. Weighted gene co-expression network analysis (WGCNA) facilitated the identification of Treg-correlated genes. Machine learning algorithms were employed to determine an optimal prognostic model, subsequently exploring disparities across risk strata in terms of survival outcomes, immunological infiltration, pathway activation and responsiveness to immunotherapy. Through WGCNA, a cohort of 365 Treg-associated genes was discerned, with 70 implicated in the prognostication of OC. A Tregs-associated signature (TAS), synthesized from random survival forest (RSF) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms, exhibited robust predictive validity across both internal and external cohorts. Low TAS OC patients demonstrated superior survival outcomes, augmented by increased immunological cell infiltration, upregulated immune checkpoint expression, distinct pathway enrichment and differential response to immunotherapeutic interventions. The devised TAS proficiently prognosticates patient outcomes and delineates the immunological milieu within OC, offering a strategic instrument for the clinical stratification and selection of patients.

Comprehensive analysis of the function of helicobacter-associated ferroptosis gene YWHAE in gastric cancer through multi-omics integration, molecular docking, and machine learning

Gastric cancer is strongly associated with Helicobacter pylori (H. pylori) infection. However, the molecular mechanisms underlying the development of gastric cancer in the context of H. pylori infection, particularly in relation to ferroptosis, remain poorly understood. In this study, we investigated the role of the Helicobacter-associated ferroptosis gene YWHAE in gastric cancer. We analyzed multi-omics data, performed molecular docking, and employed machine learning to comprehensively evaluate the expression, function, and potential implications in gastric cancer, including its influence on drug sensitivity, mutation, immune microenvironment, immunotherapy, and prognosis. Our findings demonstrated that the YWHAE gene exhibits high expression in both H. pylori-associated gastritis and gastric cancer. Pan-cancer analysis revealed elevated expression of YWHAE in several cancer types compared to normal tissues. We also examined the methylation, single nucleotide variations (SNVs), and copy number variations (CNVs) associated with YWHAE. Single-cell analysis indicated that the YWHAE gene is expressed in various cell types, with its expression level potentially influenced by H. pylori infection. Functionally, we observed a positive correlation between YWHAE gene expression and ferroptosis in gastric cancer and associated with multiple cancer-related signaling pathways, including MAPK, NF-κB, and PI3K. Furthermore, we predicted five small molecule compounds that show promise for treating gastric cancer patients and screened five drugs with the highest correlation with YWHAE and validated them by molecular docking. Additionally, significant differences were observed in various immune cell types and immunotherapeutic response between the high and low YWHAE gene expression groups. Moreover, we found a positive correlation between YWHAE gene expression and the tumour mutation burden (TMB). By applying 10 machine learning algorithms and 101 integration combinations, we developed a prognostic model for YWHAE-related genes. Finally, qRT-PCR and immunohistochemistry (IHC) consistently demonstrated the upregulation of YWHAE in gastric cancer. In conclusion, we conducted a comprehensive analysis of YWHAE gene in gastric cancer. Our findings provided novel insights into the role of YWHAE as a gene associated with H. pylori infection and ferroptosis in gastric cancer and expanded our understanding of the molecular mechanisms underlying gastric carcinogenesis.

Exploring the translational landscape of the long noncoding RNA transcriptome in acute respiratory distress syndrome: it is a long way to the top

Acute respiratory distress syndrome (ARDS) poses a significant and widespread public health challenge. Extensive research conducted in recent decades has considerably improved our understanding of the disease pathophysiology. Nevertheless, ARDS continues to rank among the leading causes of mortality in intensive care units and its management remains a formidable task, primarily due to its remarkable heterogeneity. As a consequence, the syndrome is underdiagnosed, prognostication has important gaps and selection of the appropriate therapeutic approach is laborious. In recent years, the noncoding transcriptome has emerged as a new area of attention for researchers interested in biomarker development. Numerous studies have confirmed the potential of long noncoding RNAs (lncRNAs), transcripts with little or no coding information, as noninvasive tools for diagnosis, prognosis and prediction of the therapeutic response across a broad spectrum of ailments, including respiratory conditions. This article aims to provide a comprehensive overview of lncRNAs with specific emphasis on their role as biomarkers. We review current knowledge on the circulating lncRNAs as potential markers that can be used to enhance decision making in ARDS management. Additionally, we address the primary limitations and outline the steps that will be essential for integration of the use of lncRNAs in clinical laboratories. Our ultimate objective is to provide a framework for the implementation of lncRNAs in the management of ARDS.

MOCS, a novel classifier system integrated multimoics analysis refining molecular subtypes and prognosis for skin melanoma

PURPOSE

The present investigation focuses on Skin Cutaneous Melanoma (SKCM), a melanocytic carcinoma characterized by marked aggression, significant heterogeneity, and a complex etiological background, factors which collectively contribute to the challenge in prognostic determinations. We defined a novel classifier system specifically tailored for SKCM based on multiomics.

METHODS

We collected 423 SKCM samples with multi omics datasets to perform a consensus cluster analysis using 10 machine learning algorithms and verified in 2 independent cohorts. Clinical features, biological characteristics, immune infiltration pattern, therapeutic response and mutation landscape were compared between subtypes.

RESULTS

Based on consensus clustering algorithms, we identified two Multi-Omics-Based-Cancer-Subtypes (MOCS) in SKCM in TCGA project and validated in GSE19234 and GSE65904 cohorts. MOCS2 emerged as a subtype with poor prognosis, characterized by a complex immune microenvironment, dysfunctional anti-tumor immune state, high cancer stemness index, and genomic instability. MOCS2 exhibited resistance to chemotherapy agents like erlotinib and sunitinib while sensitive to rapamycin, NSC87877, MG132, and FH355. Additionally, ELSPBP1 was identified as the target involving in glycolysis and M2 macrophage infiltration in SKCM.

CONCLUSIONS

MOCS classification could stably predict prognosis of SKCM; patients with a high cancer stemness index combined with genomic instability may be predisposed to an immune exhaustion state.Communicated by Ramaswamy H. Sarma.

Cuproptosis in glioblastoma: unveiling a novel prognostic model and therapeutic potential

Glioblastoma, a notably aggressive brain tumor, is characterized by a brief survival period and resistance to conventional therapeutic approaches. With the recent identification of "Cuproptosis," a copper-dependent apoptosis mechanism, this study aimed to explore its role in glioblastoma prognosis and potential therapeutic implications. A comprehensive methodology was employed, starting with the identification and analysis of 65 cuproptosis-related genes. These genes were subjected to differential expression analyses between glioblastoma tissues and normal counterparts. A novel metric, the "CP-score," was devised to quantify the cuproptosis response in glioblastoma patients. Building on this, a prognostic model, the CP-model, was developed using Cox regression techniques, designed to operate on both bulk and single-cell data. The differential expression analysis revealed 31 genes with distinct expression patterns in glioblastoma. The CP-score was markedly elevated in glioblastoma patients, suggesting an intensified cuproptosis response. The CP-model adeptly stratified patients into distinct risk categories, unveiling intricate associations between glioblastoma prognosis, immune response pathways, and the tumor's immunological environment. Further analyses indicated that high-risk patients, as per the CP-model, exhibited heightened expression of certain immune checkpoints, suggesting potential therapeutic targets. Additionally, the model hinted at the possibility of personalized therapeutic strategies, with certain drugs showing increased efficacy in high-risk patients. The CP-model offers a promising tool for glioblastoma prognosis and therapeutic strategy development, emphasizing the potential of Cuproptosis in cancer treatment.

Computational identification and experimental verification of a novel signature based on SARS-CoV-2-related genes for predicting prognosis, immune microenvironment and therapeutic strategies in lung adenocarcinoma patients

Background

Early research indicates that cancer patients are more vulnerable to adverse outcomes and mortality when infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Nonetheless, the specific attributes of SARS-CoV-2 in lung Adenocarcinoma (LUAD) have not been extensively and methodically examined.

Methods

We acquired 322 SARS-CoV-2 infection-related genes (CRGs) from the Human Protein Atlas database. Using an integrative machine learning approach with 10 algorithms, we developed a SARS-CoV-2 score (Cov-2S) signature across The Cancer Genome Atlas and datasets GSE72094, GSE68465, and GSE31210. Comprehensive multi-omics analysis, including assessments of genetic mutations and copy number variations, was conducted to deepen our understanding of the prognosis signature. We also analyzed the response of different Cov-2S subgroups to immunotherapy and identified targeted drugs for these subgroups, advancing personalized medicine strategies. The expression of Cov-2S genes was confirmed through qRT-PCR, with GGH emerging as a critical gene for further functional studies to elucidate its role in LUAD.

Results

Out of 34 differentially expressed CRGs identified, 16 correlated with overall survival. We utilized 10 machine learning algorithms, creating 101 combinations, and selected the RFS as the optimal algorithm for constructing a Cov-2S based on the average C-index across four cohorts. This was achieved after integrating several essential clinicopathological features and 58 established signatures. We observed significant differences in biological functions and immune cell statuses within the tumor microenvironments of high and low Cov-2S groups. Notably, patients with a lower Cov-2S showed enhanced sensitivity to immunotherapy. We also identified five potential drugs targeting Cov-2S. In vitro experiments revealed a significant upregulation of GGH in LUAD, and its knockdown markedly inhibited tumor cell proliferation, migration, and invasion.

Conclusion

Our research has pioneered the development of a consensus Cov-2S signature by employing an innovative approach with 10 machine learning algorithms for LUAD. Cov-2S reliably forecasts the prognosis, mirrors the tumor's local immune condition, and supports clinical decision-making in tumor therapies.

Single-cell RNA sequencing integrated with bulk RNA sequencing analysis identifies a tumor immune microenvironment-related lncRNA signature in lung adenocarcinoma

BACKGROUND

Recently, long non-coding RNAs (lncRNAs) have been demonstrated as essential roles in tumor immune microenvironments (TIME). Nevertheless, researches on the clinical significance of TIME-related lncRNAs are limited in lung adenocarcinoma (LUAD).

METHODS

Single-cell RNA sequencing and bulk RNA sequencing data are integrated to identify TIME-related lncRNAs. A total of 1368 LUAD patients are enrolled from 6 independent datasets. An integrative machine learning framework is introduced to develop a TIME-related lncRNA signature (TRLS).

RESULTS

This study identified TIME-related lncRNAs from integrated analysis of single‑cell and bulk RNA sequencing data. According to these lncRNAs, a TIME-related lncRNA signature was developed and validated from an integrative procedure in six independent cohorts. TRLS exhibited a robust and reliable performance in predicting overall survival. Superior prediction performance barged TRLS to the forefront from comparison with general clinical features, molecular characters, and published signatures. Moreover, patients with low TRLS displayed abundant immune cell infiltration and active lipid metabolism, while patients with high TRLS harbored significant genomic alterations, high PD-L1 expression, and elevated DNA damage repair (DDR) relevance. Notably, subclass mapping analysis of nine immunotherapeutic cohorts demonstrated that patients with high TRLS were more sensitive to immunotherapy.

CONCLUSIONS

This study developed a promising tool based on TIME-related lncRNAs, which might contribute to tailored treatment and prognosis management of LUAD patients.

Integrative analysis with machine learning identifies diagnostic and prognostic signatures in neuroblastoma based on differentially DNA methylated enhancers between INSS stage 4 and 4S neuroblastoma

INTRODUCTION

Accumulating evidence demonstrates that aberrant methylation of enhancers is crucial in gene expression profiles across several cancers. However, the latent effect of differently expressed enhancers between INSS stage 4S and 4 neuroblastoma (NB) remains elusive.

METHODS

We utilized the transcriptome and methylation data of stage 4S and 4 NB patients to perform Enhancer Linking by Methylation/Expression Relationships (ELMER) analysis, discovering a differently expressed motif within 67 enhancers between stage 4S and 4 NB. Harnessing the 67 motif genes, we established the INSS stage related signature (ISRS) by amalgamating 12 and 10 distinct machine learning (ML) algorithms across 113 and 101 ML combinations to precisely diagnose stage 4 NB among all NB patients and to predict the prognosis of NB patients. Based on risk scores calculated by prognostic ISRS, patients were categorized into high and low-risk groups according to median risk score. We conducted comprehensive comparisons between two risk groups, in terms of clinical applications, immune microenvironment, somatic mutations, immunotherapy, chemotherapy and single-cell analysis. Ultimately, we empirically validated the differential expressions of two ISRS model genes, CAMTA2 and FOXD1, through immunochemistry staining.

RESULTS

Through leave-one-out cross-validation, in both feature selection and model construction, we selected the random forest algorithm to diagnose stage 4 NB, and Enet algorithm to develop prognostic ISRS, due to their highest average C-index across five NB cohorts. After validations, the ISRS demonstrated a stable predictive capability, outperforming the previously published NB signatures and several clinic variables. We stratified NB patients into high and low-risk group based on median risk score, which showed the low-risk group with a superior survival outcome, an abundant immune infiltration, a decreased mutation landscape, and an enhanced sensitivity to immunotherapy. Single-cell analysis between two risk groups reveals biologically cellular variations underlying ISRS. Finally, we verified the significantly higher protein levels of CAMTA2 and FOXD1 in stage 4S NB, as well as their protective prognosis value in NB.

CONCLUSION

Based on multi-omics data and ML algorithms, we successfully developed the ISRS to enable accurate diagnosis and prognostic stratification in NB, which shed light on molecular mechanisms of spontaneous regression and clinical utilization of ISRS.

Pyroptosis-related gene signature for predicting gastric cancer prognosis

Gastric cancer (GC) is a prevalent form of malignancy characterized by significant heterogeneity. The development of a specific prediction model is of utmost importance to improve therapy alternatives. The presence of H. pylori can elicit pyroptosis, a notable carcinogenic process. Furthermore, the administration of chemotherapeutic drugs is often employed as a therapeutic approach to addressing this condition. In the present investigation, it was observed that there were variations in the production of 17 pyroptosis-regulating proteins between stomach tissue with tumor development and GC cells. The predictive relevance of each gene associated with pyroptosis was assessed using the cohort from the cancer genome atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) was utilized to enhance the outcomes of the regression approach. Patients with gastric cancer GC in the cohort from the TCGA were categorized into low-risk or high-risk groups based on their gene expression profiles. Patients with a low risk of gastric cancer had a higher likelihood of survival compared to persons classified as high risk (P<0.0001). A subset of patients diagnosed with GC from a Genes Expression Omnibus (GEO) cohort was stratified according to their overall survival (OS) duration. The statistical analysis revealed a higher significance level (P=0.0063) regarding OS time among low-risk individuals. The study revealed that the GC risk score emerged as a significant independent prognostic factor for OS in patients diagnosed with GC. The results of Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) research revealed that genes associated with a high-risk group had significantly elevated levels of immune system-related activity. Furthermore, it was found that the state of immunity was diminished within this particular group. The relationship between the immune response to cancer and pyroptosis genes is highly interconnected, suggesting that these genes have the potential to serve as prognostic indicators for GC.

Cancer-associated fibroblast-secreted FGF7 as an ovarian cancer progression promoter

BACKGROUND

Ovarian cancer (OC) is distinguished by its aggressive nature and the limited efficacy of current treatment strategies. Recent studies have emphasized the significant role of cancer-associated fibroblasts (CAFs) in OC development and progression.

METHODS

Employing sophisticated machine learning techniques on bulk transcriptomic datasets, we identified fibroblast growth factor 7 (FGF7), derived from CAFs, as a potential oncogenic factor. We investigated the relationship between FGF7 expression and various clinical parameters. A series of in vitro experiments were undertaken to evaluate the effect of CAFs-derived FGF7 on OC cell activities, such as proliferation, migration, and invasion. Single-cell transcriptomic analysis was also conducted to elucidate the interaction between FGF7 and its receptor. Detailed mechanistic investigations sought to clarify the pathways through which FGF7 fosters OC progression.

RESULTS

Our findings indicate that higher FGF7 levels correlate with advanced tumor stages, increased vascular invasion, and poorer prognosis. CAFs-derived FGF7 significantly enhanced OC cell proliferation, migration, and invasion. Single-cell analysis and in vitro studies revealed that CAFs-derived FGF7 inhibits the ubiquitination and degradation of hypoxia-inducible factor 1 alpha (HIF-1α) via FGFR2 interaction. Activation of the FGF7/HIF-1α pathway resulted in the upregulation of mesenchymal markers and downregulation of epithelial markers. Importantly, in vivo treatment with neutralizing antibodies targeting CAFs-derived FGF7 substantially reduced tumor growth.

CONCLUSION

Neutralizing FGF7 in the medium or inhibiting HIF-1α signaling reversed the effects of FGF7-mediated EMT, emphasizing the dependence of FGF7-mediated EMT on HIF-1α activation. These findings suggest that targeting the FGF7/HIF-1α/EMT axis may offer new therapeutic opportunities to intervene in OC progression.

A Hypoxia-Related miRNA-mRNA Signature for Predicting the Response and Prognosis of Transcatheter Arterial Chemoembolization in Hepatocellular Carcinoma

Purpose

Transcatheter arterial chemoembolization (TACE) is commonly used in the treatment of hepatocellular carcinoma (HCC). However, not all patients respond to this treatment. TACE typically leads to hypoxia in the tumor microenvironment. Therefore, we aimed to construct a prognostic model based on hypoxia-related differentially expressed microRNA (miRNAs) in hepatocellular carcinoma (HCC) and to investigate the potential target mRNAs for predicting TACE response.

Methods

The hypoxia-related miRNAs (HRMs) were identified in liver cancer cells, then global test was performed to further select the miRNAs which were associated with recurrence and vascular invasion. A prognostic model was constructed based on multivariate Cox regression analysis; qRT-PCR analysis was used to validate the differentially expressed miRNAs in HCC cell lines under hypoxic condition. We further identified the putative target genes of the miRNAs and investigate the relationship between the target genes and TACE response, immune cells infiltration.

Results

We established a HRMs prognostic model for HCC patients, containing two miRNAs (miR-638, miR-501-5p), the patients with high-HRMs score showed worse survival in discovery and validation cohort; qRT-PCR analysis confirmed that these two miRNAs are up-regulated in hepatoma cells under hypoxic condition. Furthermore, four putative target genes of these two miRNAs were identified (ADH1B, CTH, FTCD, RCL1), which were significantly associated with TACE response, immune score, immunosuppressive immune cells infiltration, PDCD1 and CTLA4.

Conclusion

The HCC-HRMs signature may be utilized as a promising prognostic factor and may have implications for guiding TACE and immune therapy.

PIK3CA mutation-driven immune signature as a prognostic marker for evaluating the tumor immune microenvironment and therapeutic response in breast cancer

PURPOSE

Gene mutations drive tumor immune microenvironment (TIME) heterogeneity, in turn affecting prognosis and immunotherapy efficacy. PIK3CA is the most frequently mutated gene in breast cancer (BC), yet its relevance to BC prognosis remains controversial. Herein, we sought to determine the impact of PIK3CA mutation-driven immune genes (PDIGs) on BC prognosis in relation to TIME heterogeneity.

METHODS

PIK3CA mutation characteristics were compared and verified between the TCGA-BRCA dataset and a patient cohort from our hospital. PIK3CA mutation-driven differentially expressed genes were identified for consensus clustering and weighted gene co-expression network analysis to select the modules most relevant to the immune subtype. Thereafter, the two were intersected to obtain PDIGs. Univariate Cox, LASSO, and multivariate Cox regression analyses were sequentially performed on PDIGs to obtain a PIK3CA mutation-driven immune signature (PDIS), which was then validated using the Gene Expression Omnibus (GEO) database. Differences in functional enrichment, mutation landscape, immune infiltration, checkpoint gene expression, and drug response were compared between different risk groups.

RESULTS

PIK3CA mutation frequencies in the TCGA and validation cohorts were 34.49% and 40.83%, respectively. PIK3CA mutants were significantly associated with ER, PR, and molecular BC subtypes in our hospital cohort. The PDIS allowed for effective risk stratification and exhibited prognostic power in TCGA and GEO sets. The low-risk patients exhibited greater immune infiltration, higher expression of common immune checkpoint factors, and lower scores for tumor immune dysfunction and exclusion.

CONCLUSION

The PDIS can be used as an effective prognostic model for predicting immunotherapy response to guide clinical decision-making.

Crosstalk of cell death pathways unveils an autophagy-related gene AOC3 as a critical prognostic marker in colorectal cancer

The intricate crosstalk of various cell death forms was recently implicated in cancers, laying a foundation for exploring the association between cell death and cancers. Recent evidence has demonstrated that biological networks outperform snapshot gene expression profiles at discovering promising biomarkers or heterogenous molecular subtypes across different cancer types. In order to investigate the behavioral patterns of cell death-related interaction perturbation in colorectal cancer (CRC), this study constructed the interaction-perturbation network with 11 cell death pathways and delineated four cell death network (CDN) derived heterogeneous subtypes (CDN1-4) with distinct molecular characteristics and clinical outcomes. Specifically, we identified a subtype (CDN4) endowed with high autophagy activity and the worst prognosis. Furthermore, AOC3 was identified as a potential autophagy-related biomarker, which demonstrated exceptional predictive performance for CDN4 and significant prognostic value. Overall, this study sheds light on the complex interplay of various cell death forms and reveals an autophagy-related gene AOC3 as a critical prognostic marker in CRC.

Machine learning-based cell death signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma

Stomach adenocarcinoma (STAD) is a one of most common malignancies with high mortality-to-incidence ratio. Programmed cell death (PCD) exerts vital functions in the progression of cancer. The role of PCD-related genes (PRGs) in STAD are not fully clarified. Using TCGA, GSE15459, GSE26253, GSE62254 and GSE84437 datasets, PCD-related signature (PRS) was constructed with an integrative procedure including 10 machine learning methods. The role of PRS in predicting the immunotherapy benefits was evaluated by several predicting score and 3 immunotherapy datasets (GSE91061, GSE78220, and IMvigor210). The model developed by Lasso + CoxBoost algorithm having a highest average C-index of 0.66 was considered as the optimal PRS. As an independent risk factor for STAD patients, PRS had a good performance in predicting the overall survival rate of patients, with an AUC of 1-, 3-, and 5-year ROC curve being 0.771, 0.751 and 0.827 in TCGA cohort. High PRS score demonstrated a lower gene set score of some immune-activated cells and immune-activated activities. Patient with high PRS score had a higher TIDE score, higher immune escape score, lower PD1&CTLA4 immunophenoscore, lower TMB score, lower response rate and poor prognosis, indicating a less immunotherapy response. The IC50 value of some drugs correlated with chemotherapy and targeted therapy was higher in high PRS score group. Our investigation developed an optimal PRS in STAD and it acted as an indicator for predicting the prognosis, stratifying risk and guiding treatment for STAD patients.

Multi-omics comprehensive analyses of programmed cell death patterns to regulate the immune characteristics of head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous cancer with high morbidity and mortality. Triggering the programmed cell death (PCD) to enhance the anti-tumor therapies is being applied in multiple cancers. However, the limited understanding of genetic heterogeneity in HNSCC severely hampers the clinical efficacy. We systematically analyzed 14 types of PCD in HNSCC from The Cancer Genome Atlas (TCGA). We utilized ssGSEA to calculate the PCD scores and classify patients into two clusters. Subsequently, we displayed the genomic alteration landscape to unravel the significant differences in copy number alterations and gene mutations. Furthermore, we calculated the IC50 values of targeted drugs to predict the differences in sensitivity. To identify the immune-related prognostic types, we comprehensively estimated the relationship between immune indicators and all prognostic PCD in three datasets (TCGA, GSE65858, GSE41613). Finally, 7 regulators were filtered. Subsequently, we integrated 10 machine learning algorithms and 101 algorithm combinations to test the clinical predictive efficacy. Using WGCNA as a basis, we built a weighted co-expression network to identify modules involved in the immune landscape with different colors. Meanwhile, our results indicated that blue and red modules containing crucial regulators closely related to the CD4+, CD8+ T cells, TMB or PD-L1. FCGR2A from blue module, CSF2, INHBA, and THBS1 from the red module were determined. After verifying in vivo experiments, FCGR2A was identified as hub gene. In conclusion, our findings suggest a potential role of PCD in HNSCC, offering new insights into effective immunotherapy and anti-tumor therapies in HNSCC.