Depiction of tumor stemlike features and underlying relationships with hazard immune infiltrations based on large prostate cancer cohorts

Integration of single-cell RNA-seq and bulk RNA-seq to construct liver hepatocellular carcinoma stem cell signatures to explore their impact on patient prognosis and treatment

BACKGROUND

Liver hepatocellular carcinoma (LIHC) is a prevalent form of primary liver cancer. Research has demonstrated the contribution of tumor stem cells in facilitating tumor recurrence, metastasis, and treatment resistance. Despite this, there remains a lack of established cancer stem cells (CSCs)-associated genes signatures for effectively predicting the prognosis and guiding the treatment strategies for patients diagnosed with LIHC.

METHODS

The single-cell RNA sequencing (scRNA-seq) and bulk RNA transcriptome data were obtained based on public datasets and computerized firstly using CytoTRACE package and One Class Linear Regression (OCLR) algorithm to evaluate stemness level, respectively. Then, we explored the association of stemness indicators (CytoTRACE score and stemness index, mRNAsi) with survival outcomes and clinical characteristics by combining clinical information and survival analyses. Subsequently, weighted co-expression network analysis (WGCNA) and Cox were applied to assess mRNAsi-related genes in bulk LIHC data and construct a prognostic model for LIHC patients. Single-sample gene-set enrichment analysis (ssGSEA), Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and Tumor Immune Estimation Resource (TIMER) analysis were employed for immune infiltration assessment. Finally, the potential immunotherapeutic response was predicted by the Tumor Immune Dysfunction and Exclusion (TIDE), and the tumor mutation burden (TMB). Additionally, pRRophetic package was applied to evaluate the sensitivity of high and low-risk groups to common chemotherapeutic drugs.

RESULTS

A total of four genes (including STIP1, H2AFZ, BRIX1, and TUBB) associated with stemness score (CytoTRACE score and mRNAsi) were identified and constructed a risk model that could predict prognosis in LIHC patients. It was observed that high stemness cells occurred predominantly in the late stages of LIHC and that poor overall survival in LIHC patients was also associated with high mRNAsi scores. In addition, pathway analysis confirmed the biological uniqueness of the two risk groups. Personalized treatment predictions suggest that patients with a low risk benefited more from immunotherapy, while those with a high risk group may be conducive to chemotherapeutic drugs.

CONCLUSION

The current study developed a novel prognostic risk signature with genes related to CSCs, which provides novel ideas for the diagnosis, prognosis and treatment of LIHC.

Integrative analysis of metabolism subtypes and identification of prognostic metabolism-related genes for glioblastoma

Increasing evidence has demonstrated that cancer cell metabolism is a critical factor in tumor development and progression; however, its role in glioblastoma (GBM) remains limited. In the present study, we classified GBM into three metabolism subtypes (MC1, MC2, and MC3) through cluster analysis of 153 GBM samples from the RNA-sequencing data of The Cancer Genome Atlas (TCGA) based on 2752 metabolism-related genes (MRGs). We further explored the prognostic value, metabolic signatures, immune infiltration, and immunotherapy sensitivity of the three metabolism subtypes. Moreover, the metabolism scoring model was established to quantify the different metabolic characteristics of the patients. Results showed that MC3, which is associated with a favorable survival outcome, had higher proportions of isocitrate dehydrogenase (IDH) mutations and lower tumor purity and proliferation. The MC1 subtype, which is associated with the worst prognosis, shows a higher number of segments and homologous recombination defects and significantly lower mRNA expression-based stemness index (mRNAsi) and epigenetic-regulation-based mRNAsi. The MC2 subtype has the highest T-cell exclusion score, indicating a high likelihood of immune escape. The results were validated using an independent dataset. Five MRGs (ACSL1, NDUFA2, CYP1B1, SLC11A1, and COX6B1) correlated with survival outcomes were identified based on metabolism-related co-expression module analysis. Laboratory-based validation tests further showed the expression of these MRGs in GBM tissues and how their expression influences cell function. The results provide a reference for developing clinical management approaches and treatments for GBM.

Development of a stemness-related prognostic index to provide therapeutic strategies for bladder cancer

Bladder cancer (BC) is a heterogeneous disease with varying clinical outcomes. Recent evidence suggests that cancer progression involves the acquisition of stem-like signatures, and assessing stemness indices help uncover patterns of intra-tumor molecular heterogeneity. We used the one-class logistic regression algorithm to compute the mRNAsi for each sample in BLCA cohort. We subsequently classified BC patients into two subtypes based on 189 mRNAsi-related genes, using the unsupervised consensus clustering. Then, we identified nine hub genes to construct a stemness-related prognostic index (SRPI) using Cox regression, LASSO regression and Random Forest methods. We further validated SRPI using two independent datasets. Afterwards, we examined the molecular and immune characterized of SRPI. Finally, we conducted multiply drug screening and experimental approaches to identify and confirm the most proper agents for patients with high SRPI. Based on the mRNAsi-related genes, BC patients were classified into two stemness subtypes with distinct prognosis, functional annotations, genomic variations and immune profiles. Using the SRPI, we identified a specific subgroup of BC patients with high SRPI, who had a poor response to immunotherapy, and were less sensitive to commonly used chemotherapeutic agents, FGFR inhibitors, and EGFR inhibitors. We further identified that dasatinib was the most promising therapeutic agent for this subgroup of patients. This study provides further insights into the stemness classification of BC, and demonstrates that SRPI is a promising tool for predicting prognosis and therapeutic opportunities for BC patients.

Systematic analysis of the prognostic value and immunological function of LTBR in human cancer

Lymphotoxin beta receptor (LTBR) is a positive T cell proliferation regulator gene. It is closely associated with the tumor immune microenvironment. However, its role in cancer and immunotherapy is unclear. Firstly, the expression level and prognostic value of LTBR were analyzed. Secondly, the expression of LTBR in clinical stages, immune subtypes, and molecular subtypes was analyzed. The correlation between LTBR and immune regulatory genes, immune checkpoint genes, and RNA modification genes was then analyzed. Correlations between LTBR and immune cells, scores, cancer-related functional status, tumor stemness index, mismatch repair (MMR) genes, and DNA methyltransferase were also analyzed. In addition, we analyzed the role of LTBR in DNA methylation, mutational status, tumor mutation burden (TMB), and microsatellite instability (MSI). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were used to explore the role of LTBR in pan-cancer. Finally, the drugs associated with LTBR were analyzed. The expression of LTBR was confirmed using quantitative real-time PCR and Western blot. LTBR is significantly overexpressed in most cancers and is associated with low patient survival. In addition, LTBR expression was strongly correlated with immune cells, score, cancer-related functional status, tumor stemness index, MMR genes, DNA methyltransferase, DNA methylation, mutational status, TMB, and MSI. Enrichment analysis revealed that LTBR was associated with apoptosis, necroptosis, and immune-related pathways. Finally, multiple drugs targeting LTBR were identified. LTBR is overexpressed in several tumors and is associated with a poor prognosis. It is related to immune-related genes and immune cell infiltration.

Identification of Novel Stemness-based Subtypes and Construction of a Prognostic Risk Model for Patients with Lung Squamous Cell Carcinoma

BACKGROUND

Although cancer stem cells (CSCs) contribute to tumorigenesis, progression, and drug resistance, stemness-based classification and prognostic signatures of lung squamous cell carcinoma (LUSC) remain unclarified. This study attempted to identify stemness-based subtypes and develop a prognostic risk model for LUSC.

METHODS

Based on RNA-seq data from The Cancer Genome Atlas (TCGA), Gene-Expression Omnibus (GEO) and Progenitor Cell Biology Consortium (PCBC), mRNA expression-based stemness index (mRNAsi) was calculated by one-class logistic regression (OCLR) algorithm. A weighted gene coexpression network (WGCNA) was employed to identify stemness subtypes. Differences in mutation, clinical characteristics, immune cell infiltration, and antitumor therapy responses were determined. We constructed a prognostic risk model, followed by validations in GEO cohort, pan-cancer and immunotherapy datasets.

RESULTS

LUSC patients with subtype C2 had a better prognosis, manifested by higher mRNAsi, higher tumor protein 53 (TP53) and Titin (TTN) mutation frequencies, lower immune scores and decreased immune checkpoints. Patients with subtype C2 were more sensitive to Imatinib, Pyrimethamine, and Paclitaxel therapy, whereas those with subtype C1 were more sensitive to Sunitinib, Saracatinib, and Dasatinib. Moreover, we constructed stemness-based signatures using seven genes (BMI1, CCDC51, CTNS, EIF1AX, FAM43A, THBD, and TRIM68) and found high-risk patients had a poorer prognosis in the TCGA cohort. Similar results were found in the GEO cohort. We verified the good performance of risk scores in prognosis prediction and therapy responses.

CONCLUSION

The stemness-based subtypes shed novel insights into the potential roles of LUSC-stemness in tumor heterogeneity, and our prognostic signatures offer a promising tool for prognosis prediction and guide therapeutic decisions in LUSC.

Deep dissection of stemness-related hierarchies in hepatocellular carcinoma

BACKGROUND

Increasing evidence suggests that hepatocellular carcinoma (HCC) stem cells (LCSCs) play an essential part in HCC recurrence, metastasis, and chemotherapy and radiotherapy resistance. Multiple studies have demonstrated that stemness-related genes facilitate the progression of tumors. However, the mechanism by which stemness-related genes contribute to HCC is not well understood. Here, we aim to construct a stemness-related score (SRscores) model for deeper analysis of stemness-related genes, assisting with the prognosis and individualized treatment of HCC patients.Further, we found that the gene LPCAT1 was highly expressed in tumor tissues by immunohistochemistry, and sphere-forming assay revealed that knockdown of LPCAT1 inhibited the sphere-forming ability of hepatocellular carcinoma cells.

METHODS

We used the TCGA-LIHC dataset to screen stemness-related genes of HCC from the MSigDB database. Prognosis, tumor microenvironment, immunological checkpoints, tumor immune dysfunction, rejection, treatment sensitivity, and putative biological pathways were examined. Random forest created the SRscores model. The anti-PD-1/anti-CTLA4 immunotherapy, tumor mutational burden, medication sensitivity, and cancer stem cell index were compared between the high- and low-risk score groups. We also examined risk scores for different cell types using single-cell RNA sequencing data and correlated transcription factor activity in cancer stem cells with SRscores genes. Finally, we tested core marker expression and biological functions.

RESULTS

Patients can be divided into two subtypes (Cluster1 and Cluster2) based on the TCGA-LIHC dataset's identification of 11 stemness-related genes. Additionally, a SRscores was developed based on subtypes. Cluster2 and the group with the lowest SRscores had superior survival and immunotherapy response than Cluster1 and the group with the highest SRscores. The group with a high SRscores was significantly more enriched in classical tumor pathways than the group with a low SRscores. Multiple transcription factors and SRscores genes are correlated. The core gene LPCAT1 is highly expressed in rat liver cancer tissues and promotes tumor cell sphere formation.

CONCLUSION

A SRscores model can be utilized to predict the prognosis of HCC patients as well as their response to immunotherapy.

Identification of neoantigens and immunological subtypes in clear cell renal cell carcinoma for mRNA vaccine development and patient selection

Clear cell renal cell carcinoma (ccRCC) is a common urological malignancy with diverse histological types. This study aimed to detect neoantigens in ccRCC to develop mRNA vaccines and distinguish between ccRCC immunological subtypes for construction of an immune landscape to select patients suitable for vaccination. Using The Cancer Genome Atlas SpliceSeq database, The Cancer Genome Atlas, and the International Cancer Genome Consortium cohorts, we comprehensively analysed potential tumour antigens of ccRCC associated with aberrant alternative splicing, somatic mutation, nonsense-mediated mRNA decay factors, antigen-presenting cells, and overall survival. Immune subtypes (C1/C2) and nine immune gene modules of ccRCC were identified by consistency clustering and weighted correlation network analysis. The immune landscape as well as molecular and cellular characteristics of immunotypes were assessed. Rho-guanine nucleotide exchange factor 3 (ARHGEF3) was identified as a new ccRCC antigen for development of an mRNA vaccine. A higher tumour mutation burden, differential expression of immune checkpoints, and immunogenic cell death were observed in cases with the C2 immunotype. Cellular characteristics increased the complexity of the immune environment, and worse outcomes were observed in ccRCC cases with the C2 immunotype. We constructed the immune landscape for selecting patients with the C2 immunotype suitable for vaccination.

Systematic analysis of the aberrances and functional implications of cuproptosis in cancer

Cuproptosis is a novel form of cell death driven by a copper-dependent proteotoxic stress response whose comprehensive landscape in tumors remains unclear. Here, we comprehensively characterized cuproptosis-related genes (CRGs) across 33 cancers using multi-omic data from The Cancer Genome Atlas (TCGA), showing complicated and diverse results in different cancers. We also explored the relationships between CRGs and cancer metabolic patterns, pathway activity, and tumor microenvironment (TME), suggesting that they played critical roles in tumor progression and TME cell infiltration. We further established the cuproptosis potential index (CPI) to reveal the functional roles of cuproptosis, and characterized multi-omic molecular features associated with cuproptosis. In clinical applications, we performed a combined analysis of the sensitivity of CRGs and CPI to drug response and immunotherapy. This study provides a rich resource for understanding cuproptosis, offering a broad molecular perspective for future functional and therapeutic studies of multiple cancer pathways mediated by cuproptosis.

LINC01589 serves as a potential tumor-suppressor and immune-related biomarker in endometrial cancer: A review

Currently, increasing attention is being paid to biomarkers in endometrial cancer. Immune infiltration of the tumor microenvironment has been shown to significantly affect the overall survival (OS) of uterine corpus endometrial carcinoma (UCEC) patients. LINC01589 is a long non-coding RNA (lncRNA) that is rarely reported in cancer and is assumed to play a role in immune regulation. We therefore evaluated the role of LINC01589 in UCEC using the Cancer Genome Atlas (TCGA) database. We analyzed the expression of LINC01589 using the gene expression profiles of LINC01589 in the UCEC projects in TCGA. Comparisons between the differentially expressed genes (DEGs) of the cancer and adjacent normal tissues of the UCEC projects revealed that LINC01589 expression was decreased in UCEC tissues. A multivariate cox regression analysis indicated that LINC01589 upregulation could serve as an independent prognostic factor for survival. Furthermore, there was a positive correlation between LINC01589 expression and B cell, T cell, NK cell, monocytic lineage, and myeloid dendritic cell infiltration in UCEC patients. In addition, 5 clusters of hub genes were detected by comparison of different expression levels of LINC01589 in the UCEC groups. The analysis of the reactome pathway using gene set enrichment analysis (GSEA) revealed immune-related pathways, including CD22-mediated B cell receptor (BCR) regulation and antigen-activated BCRs, leading to the generation of second messengers and complement cascade pathways that were significantly enriched in the high LINC01589 expression group. Thus, LINC01589 may serve as a prognostic biomarker, as it is associated with immune infiltration in UCEC.

Tumor immune contexture predicts recurrence after prostatectomy and efficacy of androgen deprivation and immunotherapy in prostate cancer

BACKGROUND

Prostate cancer is one of the most common cancers in men with notable interpatient heterogeneity. Implications of the immune microenvironment in predicting the biochemical recurrence-free survival (BCRFS) after radical prostatectomy and the efficacy of systemic therapies in prostate cancer remain ambiguous.

METHODS

The tumor immune contexture score (TICS) involving eight immune contexture-related signatures was developed using seven cohorts of 1120 patients treated with radical prostatectomy (training: GSE46602, GSE54460, GSE70769, and GSE94767; validation: GSE70768, DKFZ2018, and TCGA). The association between the TICS and treatment efficacy was investigated in GSE111177 (androgen deprivation therapy [ADT]) and EGAS00001004050 (ipilimumab).

RESULTS

A high TICS was associated with prolonged BCRFS after radical prostatectomy in the training (HR = 0.32, 95% CI 0.24-0.45, P < 0.001) and the validation cohorts (HR = 0.45, 95% CI 0.32-0.62, P < 0.001). The TICS showed stable prognostic power independent of tumor stage, surgical margin, pre-treatment prostatic specific antigen (PSA), and Gleason score (multivariable HR = 0.50, 95% CI 0.39-0.63, P < 0.001). Adding the TICS into the prognostic model constructed using clinicopathological features significantly improved its 1/2/3/4/5-year area under curve (P < 0.05). A low TICS was associated with high homologous recombination deficiency scores, abnormally activated pathways concerning DNA replication, cell cycle, steroid hormone biosynthesis, and drug metabolism, and fewer tumor-infiltrating immune cells (P < 0.05). The patients with a high TICS had favorable BCRFS with ADT (HR = 0.25, 95% CI 0.06-0.99, P = 0.034) or ipilimumab monotherapy (HR = 0.23, 95% CI 0.06-0.81, P = 0.012).

CONCLUSIONS

Our study delineates the associations of tumor immune contexture with molecular features, recurrence after radical prostatectomy, and the efficacy of ADT and immunotherapy. The TICS may improve the existing risk stratification systems and serve as a patient-selection tool for ADT and immunotherapy in prostate cancer.

Prognostic value of TMEM59L and its genomic and immunological characteristics in cancer

Background

TMEM59L is a newly discovered transmembrane protein; its functions in cancer remain unknown. This study was designed to reveal the prognostic value and the functional role of TMEM59L in cancer.

Methods

The gene expression profiles, methylation data, and corresponding clinical data of TMEM59L were retrieved from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression database. Survival analysis was employed to calculate the pan-cancer prognostic value of TMEM59L. The correlation between TMEM59L expression and tumor immune microenvironment, as well as DNA methylation dynamics and genomic heterogeneity across cancers were assessed based on data from TCGA.

Results

Our findings revealed that distinct differences of TMEM59L mRNA expression were observed in different cancer types and that higher TMEM59L expression was observed in the advanced pathological stage and associated with worse prognosis in kidney renal papillary cell carcinoma, bladder urothelial carcinoma, colon adenocarcinoma, and kidney renal clear cell carcinoma. Pathway analysis indicated that TMEM59L exerted a key influence in cancer development and in immune- and cancer-associated pathways such as epithelial-mesenchymal transition and TGF-β signaling. Moreover, correlation analysis hinted at a negative correlation of TMEM59L expression with CD8 T cells, activated CD4 T cells, and several immunomodulators, including IDO1, TIGIT, PD-L1, CTLA-4, and BTLA in various cancers. Survival analysis indicated that the hypermethylation of TMEM59L gene was associated with longer survival times. A significant correlation was also observed between TMEM59L expression and immunophenoscore, homologous recombination deficiency, loss of heterozygosity, tumor stemness score, and neoantigens in various cancers. Importantly, we also identified numerous potential agents that may target TMEM59L.

Conclusion

Our study revealed the prognostic value as well as the genomic and immunological characteristics of TMEM59L in cancers, highlighting the promising potential for TMEM59L as a prognostic cancer biomarker and a therapeutic target.

KIF11 As a Potential Pan-Cancer Immunological Biomarker Encompassing the Disease Staging, Prognoses, Tumor Microenvironment, and Therapeutic Responses

KIF11 is one of the 45 family members of kinesin superfamily proteins that functions as a motor protein in mitosis. Emerging evidence revealed that KIF11 plays pivotal roles in cancer initiation, development, and progression. However, the prognostic, oncological, and immunological values of KIF11 have not been comprehensively explored in pan-cancer. In present study, we comprehensively interrogated the role of KIF11 in tumor progression, tumor stemness, genomic heterogeneity, tumor immune infiltration, immune evasion, therapy response, and prognosis of cohorts from various cancer types. In general, KIF11 was significantly upregulated in tumors compared with paired normal tissues. KIF11 showed strong relationships with pathological stage, prognosis, tumor stemness, genomic heterogeneity, neoantigens, ESTIMATE, immune checkpoint, and drug sensitivity. The methylation level of KIF11 decreased in most cancers and was correlated with the survival probability in different human cancers. The expression of KIF11 was diverse in different molecular and immune subtypes and remarkably correlated with immune cell infiltration in the tumor microenvironment. Comparative study revealed that KIF11 was a powerful biomarker and associated with immune, targeted, and chemotherapeutic outcomes in various cancers. In addition, KIF11 interaction and coexpression networks mainly participated in the regulation of cell cycle, cell division, p53 signaling pathway, DNA repair and recombination, chromatin organization, antigen processing and presentation, and drug resistance. Our pan-cancer analysis provides a comprehensive understanding of the functions of KIF11 in oncogenesis, progression, and therapy in different cancers. KIF11 may serve as a potential prognostic and immunological pan-cancer biomarker. Moreover, KIF11 could be a novel target for tumor immunotherapy.

Identification and validation of an E2F-related gene signature for predicting recurrence-free survival in human prostate cancer

BACKGROUND

It is well-established that biochemical recurrence is detrimental to prostate cancer (PCa). In the present study, we explored the mechanisms underlying PCa progression.

METHODS

Five cohorts from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases were used to perform gene set variation analysis (GSVA) between nonrecurrent and recurrent PCa patients. We obtained the intersection of pathway enrichment results and extracted the corresponding gene list. LASSO Cox regression analysis was used to identify recurrence-free survival (RFS)-related significant genes and establish an RFS prediction gene signature and nomogram. MTT and colony formation assays were conducted to validate our findings.

RESULTS

The E2F signaling pathway was activated in recurrent PCa patients compared to nonrecurrent patients. We established an E2F-related gene signature for RFS prediction based on the four identified E2F-related genes (CDKN2C, CDKN3, RACGAP1, and RRM2) using LASSO Cox regression in the Memorial Sloan Kettering Cancer Center (MSKCC) cohort. The risk score of each patient in MSKCC was calculated based on the expression levels of CDKN2C, CDKN3, RACGAP1, and RRM2. PCa patients with low-risk scores exhibited higher RFS than those with high-risk scores. Receiver operating characteristic (ROC) curve analysis validated the good performance and prognostic accuracy of the E2F-related gene signature, which was validated in the TCGA-prostate adenocarcinoma (TCGA-PRAD) cohort. Compared to patients with low Gleason scores and early T stages, PCa patients with high Gleason scores and advanced T stages had high-risk scores. Moreover, the E2F-related gene signature-based nomogram yielded good performance in RFS prediction. Functional experiments further confirmed these results.

CONCLUSIONS

The E2F signaling pathway is associated with biochemical recurrence in PCa. Our established E2F-related gene signature and nomogram yielded good accuracy in predicting the biochemical recurrence in PCa.

A novel prognostic model for cutaneous melanoma based on an immune-related gene signature and clinical variables

Abundant evidence has indicated that the prognosis of cutaneous melanoma (CM) patients is highly complicated by the tumour immune microenvironment. We retrieved the clinical data and gene expression data of CM patients in The Cancer Genome Atlas (TCGA) database for modelling and validation analysis. Based on single-sample gene set enrichment analysis (ssGSEA) and consensus clustering analysis, CM patients were classified into three immune level groups, and the differences in the tumour immune microenvironment and clinical characteristics were evaluated. Seven immune-related CM prognostic molecules, including three mRNAs (SUCO, BTN3A1 and TBC1D2), three lncRNAs (HLA-DQB1-AS1, C9orf139 and C22orf34) and one miRNA (hsa-miR-17-5p), were screened by differential expression analysis, ceRNA network analysis, LASSO Cox regression analysis and univariate Cox regression analysis. Their biological functions were mainly concentrated in the phospholipid metabolic process, transcription regulator complex, protein serine/threonine kinase activity and MAPK signalling pathway. We established a novel prognostic model for CM integrating clinical variables and immune molecules that showed promising predictive performance demonstrated by receiver operating characteristic curves (AUC ≥ 0.74), providing a scientific basis for predicting the prognosis and improving the clinical outcomes of CM patients.

mRNAsi-related metabolic risk score model identifies poor prognosis, immunoevasive contexture, and low chemotherapy response in colorectal cancer patients through machine learning

Colorectal cancer (CRC) is one of the most fatal cancers of the digestive system. Although cancer stem cells and metabolic reprogramming have an important effect on tumor progression and drug resistance, their combined effect on CRC prognosis remains unclear. Therefore, we generated a 21-gene mRNA stemness index-related metabolic risk score model, which was examined in The Cancer Genome Atlas and Gene Expression Omnibus databases (1323 patients) and validated using the Zhongshan Hospital cohort (200 patients). The high-risk group showed more immune infiltrations; higher levels of immunosuppressive checkpoints, such as CD274, tumor mutation burden, and resistance to chemotherapeutics; potentially better response to immune therapy; worse prognosis; and advanced stage of tumor node metastasis than the low-risk group. The combination of risk score and clinical characteristics was effective in predicting overall survival. Zhongshan cohort validated that high-risk score group correlated with malignant progression, worse prognosis, inferior adjuvant chemotherapy responsiveness of CRC, and shaped an immunoevasive contexture. This tool may provide a more accurate risk stratification in CRC and screening of patients with CRC responsive to immunotherapy.

Signature for Prostate Cancer Based on Autophagy-Related Genes and a Nomogram for Quantitative Risk Stratification

Background. Prostate cancer (PCa) ranks as the most common malignancy and the second leading cause of cancer-related death among males worldwide. The essential role of autophagy in the progression of PCa and treatment resistance has been preliminarily revealed. However, comprehensive molecular elucidations of the correlation between PCa and autophagy are rare. Method. We obtained transcription information and corresponding clinicopathological profiles of PCa patients from TCGA, MSKCC, and GEO datasets. LAASO analysis was employed to select gene signatures and estimate the autophagy score for each patient. Correlations between the signature and prognosis of PCa were investigated by K-M and multivariate Cox regression analyses. A nomogram was established on the basis of the above results. Further validations relied on ROC, calibration analysis, decision curve analysis, and external cohorts. Variable activated signaling pathways were revealed using GSVA algorithms, and the genetic alteration landscape was elucidated via the oncodrive module from the “maftools” R package. In addition, we also examined the therapeutic role of the signature based on phenotype data from GDSC 2016. Result. Six autophagy-related genes were eventually selected to establish the signature, including ULK1, CAPN10, FKBP5, UBE2T, NLRC4, and BNIP3L. We used these genes and corresponding coefficients to calculate an autophagy score (AutS) for each patient in this study. A high AutS group and a low AutS group were divided on the mean AutS of the patients. Longer overall survival, higher Gleason score and PSA, and better response to ADT were observed in patients with high AutS. Meanwhile, we found that high AutS PCa was related to more proliferation-associated signaling activation and higher genetic mutation frequencies, manifesting a poor prognosis. A nomogram was constructed based on GS, T stage, PSA, and AutS as covariates. Its discriminative efficacy and clinical value were validated using robust statistical methods. Finally, we tested its prognostic value through two external cohorts and six published signatures. Conclusion. The autophagy-related gene signature is a highly discriminative model for risk stratification and drug therapy in PCa, and a nomogram incorporating AutS might be a promising tool for precision medicine.

Genome Instability-Associated Long Non-Coding RNAs Reveal Biomarkers for Glioma Immunotherapy and Prognosis

Genome instability is a hallmark of tumors and is involved in proliferation, invasion, migration, and treatment resistance of many tumors. However, the relationship of genome instability with gliomas remains unclear. Here, we constructed genome instability-derived long non-coding RNA (lncRNA)-based gene signatures (GILncSig) using genome instability-related lncRNAs derived from somatic mutations. Multiple platforms were used to confirm that the GILncSig were closely related to patient prognosis and clinical characteristics. We found that GILncSig, the glioma microenvironment, and glioma cell DNA methylation-based stemness index (mDNAsi) interacted with each other to form a complex regulatory network. In summary, this study confirmed that GILncSig was an independent prognostic indicator for patients, distinguished high-risk and low-risk groups, and affected immune-cell infiltration and tumor-cell stemness indicators (mDNAsi) in the tumor microenvironment, resulting in tumor heterogeneity and immunotherapy resistance. GILncSig are expected to provide new molecular targets for the clinical treatment of patients with gliomas.

FAM81A identified as a stemness-related gene by screening DNA methylation sites based on machine learning-accessed stemness in pancreatic cancer

Aim: We thoroughly discuss the interaction between the stemness index and DNA methylation in pancreatic cancer (PC). Materials & methods: First, the stemness indices of PC (denoted mRNAsi and mDNAsi) were calculated using a one-class logistic regression machine-learning algorithm. Second, we screened the central methylation sites associated with stemness and screened out the key genes. We investigated the DNA methylation regulators associated with the key genes. Finally, using CIBERSORT and TIMER, we assessed the influence of stemness indexes and key genes on PC microenvironment formation. Results: In this study we quantified the stemness indices for PC and screened 20 related central DNA methylation sites. Further analysis of the methylation site cg22687244, located in the 3' UTR, revealed that it promoted the expression of the key gene FAM81A. We show that FAM81A may be regulated by DNA methylation regulators. Furthermore, immune cells were found to be more abundant in PC microenvironments with high expression of FAM81A. Conclusion: We report for the first time that the 3' UTR methylation of FAM81A is closely related to PC stemness and contributes to tumor immune infiltration. Therefore FAM81A may serve as a potential marker to guide the treatment of PC.

Stemness Subtypes and Scoring System Predict Prognosis and Efficacy of Immunotherapy in Soft Tissue Sarcoma

Tumor stemness has been reported to play important roles in cancers. However, a comprehensive analysis of tumor stemness remains to be performed to investigate the specific mechanisms and practical values of stemness in soft tissue sarcomas (STS). Here, we applied machine learning to muti-omic data of patients from TCGA-SARC and GSE21050 cohorts to reveal important roles of stemness in STS. We demonstrated limited roles of existing mRNAsi in clinical application. Therefore, based on stemness-related signatures (SRSs), we identified three stemness subtypes with distinct stemness, immune, and metabolic characteristics using consensus clustering. The low-stemness subtype had better prognosis, activated innate and adaptive immunity (e.g., infiltrating B, DC, Th1, CD8+ T, activated NK, gamma delta T cells, and M1 macrophages), more enrichment of metabolic pathways, more sites with higher methylation level, higher gene mutations, CNA burdens, and immunogenicity indicators. Furthermore, the 16 SRS-based stemness prognostic index (SPi) was developed, and we found that low-SPi patients with low stemness had better prognosis and other characteristics similar to those in the low-stemness subtype. Besides, low-stemness subtype and low-SPi patients could benefit from immunotherapy. The predictive value of SPi in immunotherapy was more accurate after the addition of MSI into SPi. MSI^lowSPi^low patients might be more sensitive to immunotherapy. In conclusion, we highlighted mechanisms and practical values of the stemness in STS. We also recommended the combination of MSI and SPi which is a promising tool to predict prognosis and achieve precise treatments of immunotherapy in STS.

Upregulated of ANXA3, SORL1, and Neutrophils May Be Key Factors in the Progressionof Ankylosing Spondylitis

Introduction

The specific pathogenesis of ankylosing spondylitis (AS) remains unclear, and our study aimed to investigate the possible pathogenesis of AS.

Materials and Methods

Two datasets were downloaded from the GEO database to perform differentially expressed gene analysis, GO enrichment analysis, KEGG pathway analysis, DO enrichment analysis, GSEA analysis of differentially expressed genes, and construction of diagnostic genes using SVM and WGCNA along with Hypoxia-related genes. Also, drug sensitivity analysis was performed on diagnostic genes. To identify the differentially expressed immune genes in the AS and control groups, we analyzed the composition of immune cells between them. Then, we examined differentially expressed genes in three AS interspinous ligament specimens and three Degenerative lumbar spine specimens using high-throughput sequencing while the immune cells were examined using the neutrophil count data from routine blood tests of 1770 HLA-B27-positive samples and 7939 HLA-B27-negative samples. To assess the relationship between ANXA3 and SORL1 and disease activity, we took the neutrophil counts of the first 50 patients with above-average BASDAI scores and the last 50 patients with below-average BASDAI scores for statistical analysis. We used immunohistochemistry to verify the expression of ANXA3 and SORL1 in AS and in controls.

Results

ANXA3 and SORL1 were identified as new diagnostic genes for AS. These two genes showed a significant differential expression between AS and controls, along with showing a significant positive correlation with the neutrophil count. The results of high-throughput sequencing verified that these two gene deletions were indeed differentially expressed in AS versus controls. Data from a total of 9707 routine blood tests showed that the neutrophil count was significantly higher in AS patients than in controls (p < 0.001). Patients with AS with a high BASDAI score had a much higher neutrophil count than those with a low score, and the difference was statistically significant (p < 0.001). The results of immunohistochemistry showed that the expression of ANXA3 and SORL1 in AS was significantly higher than that in the control group.

Conclusion

Upregulated of ANXA3, SORL1, and neutrophils may be a key factor in the progression of Ankylosing spondylitis.

Risk subtyping and prognostic assessment of prostate cancer based on consensus genes

Prostate cancer (PCa) is the most frequent malignancy in male urogenital system around worldwide. We performed molecular subtyping and prognostic assessment based on consensus genes in patients with PCa. Five cohorts containing 1,046 PCa patients with RNA expression profiles and recorded clinical follow-up information were included. Univariate, multivariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression were used to select prognostic genes and establish the signature. Immunohistochemistry staining, cell proliferation, migration and invasion assays were used to assess the biological functions of key genes. Thirty-nine intersecting consensus prognostic genes from five independent cohorts were identified. Subsequently, an eleven-consensus-gene classifier was established. In addition, multivariate Cox regression analyses showed that the classifier served as an independent indicator of recurrence-free survival in three of the five cohorts. Combined receiver operating characteristic (ROC) analysis achieved synthesized effects by combining the classifier with clinicopathological features in four of five cohorts. SRD5A2 inhibits cell proliferation, while ITGA11 promotes cell migration and invasion, possibly through the PI3K/AKT signaling pathway. To conclude, we established and validated an eleven-consensus-gene classifier, which may add prognostic value to the currently available staging system.

By analysis of gene expression profiles of prostate cancer patients from multiple platforms, an eleven-consensus-gene classifier is constructed to provide a robust tool for the prediction of recurrence-free survival.

Implications of Stemness Features in 1059 Hepatocellular Carcinoma Patients from Five Cohorts: Prognosis, Treatment Response, and Identification of Potential Compounds

Simple Summary

Cancer stemness has been reported to drive hepatocellular carcinoma (HCC) tumorigenesis and treatment resistance. However, comprehensive interpretations of transcriptomic stemness features in HCC patients have not been conducted in multiple cohorts. Our aim was to interpret clinical and therapeutic implications of transcriptional stemness features and explore potential compounds for HCC treatment. We found that transcriptional stemness indexes (mRNAsi) were independently associated with worse HCC prognosis. The HCC stemness risk model (HSRM) developed in this study significantly predicted prognosis and treatment response in various HCC cohorts. Analysis of two stemness subtypes suggested several liver-specific metabolic pathways, and mutations of TP53 and RB1 were associated with HCC transcriptional stemness. Moreover, we also identified potential compounds that target HCC transcriptional stemness. Our findings comprehensively characterized transcriptional stemness as a risk factor in HCC progression and treatment.

Abstract

Cancer stemness has been reported to drive hepatocellular carcinoma (HCC) tumorigenesis and treatment resistance. In this study, five HCC cohorts with 1059 patients were collected to calculate transcriptional stemness indexes (mRNAsi) by the one-class logistic regression machine learning algorithm. In the TCGA-LIHC cohort, we found mRNAsi was an independent prognostic factor, and 626 mRNAsi-related genes were identified by Spearman correlation analysis. The HCC stemness risk model (HSRM) was trained in the TCGA-LIHC cohort and significantly discriminated overall survival in four independent cohorts. HSRM was also significantly associated with transarterial chemoembolization treatment response and rapid tumor growth in HCC patients. Consensus clustering was conducted based on mRNAsi-related genes to divide 1059 patients into two stemness subtypes. On gene set variation analysis, samples of subtype I were found enriched with pathways such as DNA replication and cell cycle, while several liver-specific metabolic pathways were inhibited in these samples. Somatic mutation analysis revealed more frequent mutations of TP53 and RB1 in the subtype I samples. In silico analysis suggested topoisomerase, cyclin-dependent kinase, and histone deacetylase as potential targets to inhibit HCC stemness. In vitro assay showed two predicted compounds, Aminopurvalanol-a and NCH-51, effectively suppressed oncosphere formation and impaired viability of HCC cell lines, which may shed new light on HCC treatment.

Prognostic Value of mRNAsi/Corrected mRNAsi Calculated by the One-Class Logistic Regression Machine-Learning Algorithm in Glioblastoma Within Multiple Datasets

Glioblastoma (GBM) is the most common glial tumour and has extremely poor prognosis. GBM stem-like cells drive tumorigenesis and progression. However, a systematic assessment of stemness indices and their association with immunological properties in GBM is lacking. We collected 874 GBM samples from four GBM cohorts (TCGA, CGGA, GSE4412, and GSE13041) and calculated the mRNA expression-based stemness indices (mRNAsi) and corrected mRNAsi (c_mRNAsi, mRNAsi/tumour purity) with OCLR algorithm. Then, mRNAsi/c_mRNAsi were used to quantify the stemness traits that correlated significantly with prognosis. Additionally, confounding variables were identified. We used discrimination, calibration, and model improvement capability to evaluate the established models. Finally, the CIBERSORTx algorithm and ssGSEA were implemented for functional analysis. Patients with high mRNAsi/c_mRNAsi GBM showed better prognosis among the four GBM cohorts. After identifying the confounding variables, c_mRNAsi still maintained its prognostic value. Model evaluation showed that the c_mRNAsi-based model performed well. Patients with high c_mRNAsi exhibited significant immune suppression. Moreover, c_mRNAsi correlated negatively with infiltrating levels of immune-related cells. In addition, ssGSEA revealed that immune-related pathways were generally activated in patients with high c_mRNAsi. We comprehensively evaluated GBM stemness indices based on large cohorts and established a c_mRNAsi-based classifier for prognosis prediction.

Construction and Clinical Translation of Causal Pan-Cancer Gene Score Across Cancer Types

Pan-cancer strategy, an integrative analysis of different cancer types, can be used to explain oncogenesis and identify biomarkers using a larger statistical power and robustness. Fine-mapping defines the casual loci, whereas genome-wide association studies (GWASs) typically identify thousands of cancer-related loci and not necessarily have a fine-mapping component. In this study, we develop a novel strategy to identify the causal loci using a pan-cancer and fine-mapping assumption, constructing the CAusal Pan-cancER gene (CAPER) score and validating its performance using internal and external validation on 1,287 individuals and 985 cell lines. Summary statistics of 15 cancer types were used to define 54 causal loci in 15 potential genes. Using the Cancer Genome Atlas (TCGA) training set, we constructed the CAPER score and divided cancer patients into two groups. Using the three validation sets, we found that 19 cancer-related variables were statistically significant between the two CAPER score groups and that 81 drugs had significantly different drug sensitivity between the two CAPER score groups. We hope that our strategies for selecting causal genes and for constructing CAPER score would provide valuable clues for guiding the management of different types of cancers.

Epigenetic Regulator KDM4D Restricts Tumorigenesis via Modulating SYVN1/HMGB1 Ubiquitination Axis in Esophageal Squamous Cell Carcinoma

Background

Increasing researches have been reported that epigenetic alterations play critical roles in ESCC development. However, the role of the histone demethylase KDM4D in ESCC tumorigenesis is poorly investigated. This study aims to discover the underlying mechanisms between KDM4D and ESCC progression.

Methods

CCK-8 assays, clone formation assay and soft-agar assays were performed to assess cell proliferation. Transwell assay was utilized to assess cell migration efficiency, while sphere formation assay was used to evaluate the cell self-renewal ability. Bioinformatic analysis was conducted to identify prognostic factors and predict the potential E3 ubiquitin ligases. In vitro ubiquitination assay was conducted to confirm the regulations between SYVN1 and HMGB1. The mRNA levels or protein levels of genes were detected by real-time PCR and western blot analysis. In vivo tumor xenograft models were used to determine whether the HMGB1 inhibition affected the malignant features of ESCC cells.

Result

Epigenome screening and low-throughput validations highlighted that KDM4D is a tumor suppressor in ESCC. KDM4D expressed lowly in tumors that predicts poor prognosis. KDM4D deficiency significantly enhanced tumor growth, migration and stemness. Mechanistically, KDM4D transcriptionally activates SYVN1 expressions via H3K9me3 demethylation at the promoter region, thereby triggering the ubiquitin-dependent degradation of HMGB1. Low KDM4D depended on accumulated HMGB1 to drive ESCC progression and aggressiveness. Targeting HMGB1 (Glycyrrhizin) could remarkably suppress ESCC tumor growth in vitro and in vivo, especially in KDM4D-deficient cells.

Conclusions

We systematically identified KDM4D/SYVN1/HMGB1 axis in ESCC progression, proving novel biomarkers and potential therapeutic targets.

Tumor stemness and immune infiltration synergistically predict response of radiotherapy or immunotherapy and relapse in lung adenocarcinoma

Cancer stem cells (CSCs) have been shown to accelerate tumor recurrence, radiotherapy, and chemotherapy resistance. Immunotherapy is a powerful anticancer treatment that can significantly prolong the overall survival of patients with lung adenocarcinoma (LUAD). However, little is known about the function of genes related to tumor stemness and immune infiltration in LUAD. After integrating the tumor stemness index based on mRNA expression (mRNAsi), immune score, mRNA expression, and clinical information from the TCGA database, we screened 380 tumor stemness and immune (TSI)-related genes and constructed a five TSI-specific-gene (CPS1, CCR2, NT5E, ANLN, and ABCC2) signature (TSISig) using a machine learning method. Survival analysis indicated that TSISig could stably predict the prognosis of patients with LUAD. Comparison of mRNAsi and immune score between high- and low-TSISig groups suggested that TSISig characterized tumor stemness and immune infiltration. In addition, enrichment of immune subpopulations showed that the low-TSISig group held more immune subpopulations. GSEA revealed that TSISig had a strong association with the cell cycle and human immune response. Further analysis revealed that TSISig not only had a good predictive ability for prognosis but could also serve as an excellent predictor of tumor recurrence and response to radiotherapy and immunotherapy in LUAD patients. TSISig might regulate the development of LUAD by coordinating tumor stemness and immune infiltration. Finally, a connectivity map (CMap) analysis demonstrated that the HDAC inhibitor could target TSISig.

Identification of novel key genes associated with the metastasis of prostate cancer based on bioinformatics prediction and validation

Background

Metastatic prostate cancer (PCa) is a lethal tumor. However, the molecular mechanisms underlying PCa progression have not been fully elucidated.

Methods

Transcriptome expression profiling and clinical information on primary and metastatic PCa samples were obtained from TCGA. R software was used to screen the DEGs, and LASSO logistical regression method was utilized to identify the pivotal PCa metastasis-related DEGs. The transcriptional expression levels of the key genes were analyzed using the UALCAN database, and the corresponding protein expression were validated by Immunohistochemistry (IHC). Survival analysis of the key genes was performed using the GEPIA database. Wound healing assay and Transwell assay were conducted to determine whether knockdown of the key genes influence the migration and invasion abilities of PCa cells (22Rv1 and PC3). GSEA was performed to predict key genes-mediated signaling pathways for the development of PCa. Western blotting was used to evaluate the expression changes of E-cadherin, Twist1, and Vimentin in PCa cells with the key genes silencing. An in vivo mouse metastatic model for PCa was also generated to verify the important role of ISG15 and CST2 in PCa metastasis.

Results

A comparison between primary and metastatic PCa tissues was conducted, and 19 DEGs were screened. Among these, three key genes were identified that might be closely associated with PCa progression according to the LASSO logistical analysis, namely ISG15, DNAH8, and CST2. Further functional experiments revealed that knockdown of ISG15 and CST2 suppressed wound healing, migration, and invasion of PCa cells. To explore the molecular mechanism of ISG15 and CST2 in the development of PCa, GSEA was performed, and it was found that both genes play crucial roles in cell adhesion molecules, extracellular matrix-receptor interaction, and focal adhesion. Western blotting results exhibited that inhibiting ISG15 and CST2 led to increase the expression of E-cadherin and decrease the expression of Twist1 and Vimentin. Additionally, the metastatic in vivo study demonstrated that both PC3 and 22Rv1 cells expressing with luciferase-shISG15 and luciferase-shCST2 had significantly lower detectable bioluminescence than that in the control PCa cells.

Conclusion

ISG15 and CST2 may participate in PCa metastasis by regulating the epithelial-mesenchymal transition (EMT) signaling pathway. These findings may help to better understand the pathogenetic mechanisms governing PCa and provide promising therapeutic targets for metastatic PCa therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02258-3.

Integrated Analysis of Stemness-Related LncRNAs Helps Predict the Immunotherapy Responsiveness of Gastric Cancer Patients

The immune microenvironment plays a critical role in tumor biology. As a critical feature of cancers, stemness is acknowledged as a contributor to the development of drug resistance in gastric cancers (GCs). Long non-coding RNAs (lncRNAs) have been revealed to participate in this process. In this study, we aimed to develop a stemness-related lncRNA signature (SRLncSig) with guiding significance for immunotherapy. Three cohorts (TCGA, Zhongshan, and IMvigor210) were enrolled for analysis. A list of stemness-related lncRNAs (SRlncRNAs) was collected by co-expression strategy under the threshold of coefficient value >0.35 and p-value < 0.05. Cox and Lasso regression analysis was further applied to find out the SRlncRNAs with prognosis-predictive value to establish the SRLncSig in the TCGA cohort. IPS and TIDE algorithms were further applied to predict the efficacy of SRLncSig in TCGA and Zhongshan cohorts. IMvigor210 was composed of patients with clinical outcomes of immunotherapy. The results indicated that SRLncSig not only was confirmed as an independent risk factor for GCs but also identified as a robust indicator for immunotherapy. The patient with a lower SRLncSig score was more likely to benefit from immunotherapy, and the results were highly consistent in three cohorts. In conclusion, our study not only could clarify the correlations between stemness and immunotherapy in GC patients but also provided a model to guide the applications of immunotherapy in clinical practice.

Stemness-related LncRNA pair signature for predicting therapy response in gastric cancer

Objective

As a critical feature of cancers, stemness is acknowledged as a contributor to the development of drug resistance in gastric cancer (GC). LncRNAs have been revealed to participate in this process. In this study, we tried to develop a stemness-related lncRNA pair signature as guidance for clinical decisions.

Methods

The analysis was initiated by collecting stemness-related lncRNAs in TCGA cohort. The differentially expressed stemness-related lncRNAs between normal and tumor tissues in GC patients from TCGA datasets were further collected to establish the signature based on Lasso and Cox regression analyses. The predictive efficacy of the signature for chemotherapy and immunotherapy was also tested. The practicality of this signature was also validated by Zhongshan cohort.

Results

A 13-DEsrlncRNA pair-based signature was established. The cutoff point acquired by the AIC algorithm divided the TCGA cohort into high and low risk groups. We found that the low-risk group presented with better survival (Kaplan-Meier analysis, p < 0.001). Cox regression analyse was also conducted to confirm the signature as an independent risk factor for GC {p < 0.001, HR = 1.300, 95% CI (1.231–1.373)]}. As for the practicality of this signature, the IC50 of cytotoxic chemotherapeutics was significantly higher in the high-risk group. The low-risk group also presented with higher immunophenoscore (IPS) in both the “CTLA4+ PD1+” (Mann-Whitney U test, p = 0.019) and “CTLA4- PD1+” (Mann-Whitney U test, p = 0.013) groups, indicating higher sensitivity to immunotherapy. The efficacy of the signature was also validated by Zhongshan cohort.

Conclusions

This study could not only provide a stemness-related lncRNA signature for survival prediction in GC patients but also established a model with predictive potentials for GC patients’ sensitivity to chemotherapy and immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08798-1.

Integrative Analysis of Gene Expression Through One-Class Logistic Regression Machine Learning Identifies Stemness Features in Multiple Myeloma

Tumor progression includes the obtainment of progenitor and stem cell-like features and the gradual loss of a differentiated phenotype. Stemness was defined as the potential for differentiation and self-renewal from the cell of origin. Previous studies have confirmed the effective application of stemness in a number of malignancies. However, the mechanisms underlying the growth and maintenance of multiple myeloma (MM) stem cells remain unclear. We calculated the stemness index for samples of MM by utilizing a novel one-class logistic regression (OCLR) machine learning algorithm and found that mRNA expression-based stemness index (mRNAsi) was an independent prognostic factor of MM. Based on the same cutoff value, mRNAsi could stratify MM patients into low and high groups with different outcomes. We identified 127 stemness-related signatures using weighted gene co-expression network analysis (WGCNA) and differential expression analysis. Functional annotation and pathway enrichment analysis indicated that these genes were mainly involved in the cell cycle, cell differentiation, and DNA replication and repair. Using the molecular complex detection (MCODE) algorithm, we identified 34 pivotal signatures. Meanwhile, we conducted unsupervised clustering and classified the MM cohorts into three MM stemness (MMS) clusters with distinct prognoses. Samples in MMS-cluster3 possessed the highest stemness fractions and the worst prognosis. Additionally, we applied the ESTIMATE algorithm to infer differential immune infiltration among the three MMS clusters. The immune core and stromal score were significantly lower in MMS-cluster3 than in the other clusters, supporting the negative relation between stemness and anticancer immunity. Finally, we proposed a prognostic nomogram that allows for individualized assessment of the 3- and 5-year overall survival (OS) probabilities among patients with MM. Our study comprehensively assessed the MM stemness index based on large cohorts and built a 34-gene based classifier for predicting prognosis and potential strategies for stemness treatment.

LPCAT1 overexpression promotes the progression of hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) remains one of the most common malignant neoplasms. Lysophosphatidylcholine acyltransferase 1 (LPCAT1) plays a key role in the lipid remodelling and is correlated with various neoplasms. Nonetheless, the biological functions and molecular mechanisms of LPCAT1 underlying HCC remain obscure.

METHODS

In the present study, we investigated the role of LPCAT1 in the progression of HCC. In-house RT-qPCR, tissue microarrays, and immunohistochemistry were performed to detect the expression levels and the clinical value of LPCAT1 in HCC. External datasets were downloaded to confirm the results. Proliferation, migration, invasiveness, cell cycle, and apoptosis assays were conducted to reveal the biological effects LPCAT1 has on SMMC-7721 and Huh7 cells. HCC differentially expressed genes and LPCAT1 co-expressed genes were identified to explore the molecular mechanisms underlying HCC progression.

RESULTS

LPCAT1 showed upregulated expression in 3715 HCC specimens as opposed to 3105 non-tumour specimens. Additionally, LPCAT1 might be an independent prognostic factor for HCC. LPCAT1-knockout hampered cellular proliferation, migration, and metastasis in SMMC-7721 and Huh7 cells. More importantly, the cell cycle and chemical carcinogenesis were the two most enriched signalling pathways.

CONCLUSIONS

The present study demonstrated that increased LPCAT1 correlated with poor prognosis in HCC patients and fuelled HCC progression by promoting cellular growth, migration, and metastasis.

Immune-Related Gene-Based Novel Subtypes to Establish a Model Predicting the Risk of Prostate Cancer

Background

There is significant heterogeneity in prostate cancer (PCa), but immune status can reflect its prognosis. This study aimed to explore immune-related gene-based novel subtypes and to use them to create a model predicting the risk of PCa.

Methods

We downloaded the data of 487 PCa patients from The Cancer Genome Atlas (TCGA) database. We used immunologically relevant genes as input for consensus clustering and applied survival analysis and principal component analysis to determine the properties of the subtypes. We also explored differences of somatic variations, copy number variations, TMPRSS2-ERG fusion, and androgen receptor (AR) scores among the subtypes. Then, we examined the infiltration of different immune cells into the tumor microenvironment in each subtype. We next performed Gene Set Enrichment Analysis (GSEA) to illustrate the characteristics of the subtypes. Finally, based on the subtypes, we constructed a risk predictive model and verified it in TCGA, Gene Expression Omnibus (GEO), cBioPortal, and International Cancer Genome Consortium (ICGC) databases.

Results

Four PCa subtypes (C1, C2, C3, and C4) were identified on immune status. Patients with the C3 subtype had the worst prognosis, while the other three groups did not differ significantly from each other in terms of their prognosis. Principal component analysis clearly distinguished high-risk (C3) and low-risk (C1 + 2 + 4) patients. Compared with the case in the low-risk subtype, the Speckle-type POZ Protein (SPOP) had a higher mutation frequency and lower transcriptional level in the high-risk subtype. In C3, there was also a higher frequency of copy number alterations (CNA) of Clusterin (CLU) and lower CLU expression. In addition, C3 had a higher frequency of TMPRSS2-ERG fusion and higher AR scores. M2 macrophages also showed significantly higher infiltration in the high-risk subtype, while CD8⁺ T cells and dendritic cells had significantly higher infiltration in the low-risk subtype. GSEA revealed that MYC, androgen, and KRAS were relatively activated and p53 was relatively suppressed in high-risk subtype, compared with the levels in the low-risk subtype. Finally, we trained a six-gene signature risk predictive model, which performed well in TCGA, GEO, cBioPortal, and ICGC databases.

Conclusion

PCa can be divided into four subtypes based on immune-related genes, among which the C3 subtype is associated with a poor prognosis. Based on these subtypes, a risk predictive model was developed, which could indicate patient prognosis.