A five-gene signature is a prognostic biomarker in pan-cancer and related with immunologically associated extracellular matrix

P4HA3 promotes head and neck squamous cell carcinoma progression via the WNT/β-catenin signaling pathway

Here, we explored the role of Prolyl 4-Hydroxylase Subunit Alpha 3 (P4HA3), the most recently identified member of the prolyl-4-hydroxylase (P4H) family, in head and neck squamous cell carcinoma (HNSCC) progression. P4HA3 is upregulated during cancer progression; however, its specific role in HNSCC progression remains elusive. Thus, this study aimed to elucidate the regulatory function of P4HA3 in HNSCC development and progression and to describe the underlying mechanisms. Initially, we analyzed the correlation between the expression of P4HA3 and the WNT pathway genes and clinicopathologic features in HNSCC based on microarray data from The Cancer Genome Atlas (TCGA). Next, we used Gene Oncology (GO) functional data to describe several potentially associated pathways in HNSCC. Then, we knocked down P4HA3 in SCC15 and SCC25 cells, two classic HNSCC cell lines, and assessed the resulting changes using RT-qPCR. Furthermore, we used Western blot to evaluate the regulatory role of P4HA3 in the epithelial-to-mesenchymal transition (EMT) and the WNT/β-catenin signaling pathway. To explore the effect of P4HA3 knockdown on tumor progression, in vivo experiments were conducted using a murine model. Immunohistochemistry assays were then employed to identify proteins associated with EMT and the WNT/β-catenin signaling pathway in tumor tissues. Upregulated P4HA3 in HNSCC patient tumor tissues was positively correlated with poor prognosis. Notably, P4HA3 knockdown significantly inhibited the proliferative and invasive abilities of HNSCC. The levels of genes and proteins associated with EMT and the WNT/β-catenin signaling pathway were also markedly reduced by P4HA3 knockdown. Importantly, the in vivo experiments demonstrated that P4HA3 can promote subcutaneous tumorigenesis in nude mice and knockdown of P4HA3 induce a significant ihibitation of EMT and WNT/β-catenin pathway detected by immunohistochemistry assay in tumor tissues. In summary, we demonstrated that P4HA3 is a promising diagnostic and therapeutic biomarker for HNSCC. As an oncogene, P4HA3 increases HNSCC proliferation by inducing the EMT and activating the WNT/β-catenin signaling pathway.

Pan-Cancer, Genome-Scale Metabolic Network Analysis of over 10,000 Patients Elucidates Relationship between Metabolism and Survival

Despite the high variability in cancer biology, cancers nevertheless exhibit cohesive hallmarks across multiple cancer types, notably dysregulated metabolism. Metabolism plays a central role in cancer biology, and shifts in metabolic pathways have been linked to tumor aggressiveness and likelihood of response to therapy. We therefore sought to interrogate metabolism across cancer types and understand how intrinsic modes of metabolism vary within and across indications and how they relate to patient prognosis. We used context specific genome-scale metabolic modeling to simulate metabolism across 10,915 patients from 34 cancer types from The Cancer Genome Atlas and the MMRF-COMMPASS study. We found that cancer metabolism clustered into modes characterized by differential glycolysis, oxidative phosphorylation, and growth rate. We also found that the simulated activities of metabolic pathways are intrinsically prognostic across cancer types, especially tumor growth rate, fatty acid biosynthesis, folate metabolism, oxidative phosphorylation, steroid metabolism, and glutathione metabolism. This work shows the prognostic power of individual patient metabolic modeling across multiple cancer types. Additionally, it shows that analyzing large-scale models of cancer metabolism with survival information provides unique insights into underlying relationships across cancer types and suggests how therapies designed for one cancer type may be repurposed for use in others.

Prognostic Value and Immunological Role of P4HA3 in Colon Adenocarcinoma

Purpose

Prolyl 4-hydroxylase subunit alpha 3 (P4HA3) has been proven to participate in the occurrence and development of multiple cancers. However, the functional role of P4HA3 in the tumor immune microenvironment (TIME) of colon adenocarcinoma (COAD) and the prognosis of COAD patients has not been clarified. This study aimed to elucidate the immunological role and prognostic value of P4HA3 in COAD.

Methods

P4HA3 expression in COAD tissues was analyzed via experiments and a bioinformatics algorithm. Based on the COAD patients in The Cancer Genome Atlas database, we comprehensively evaluated whether the expression levels of P4HA3 affected clinical prognosis, TIME, and immunotherapy of COAD using the R platforms and several public databases, including GEPIA, TIMER, TISIDB, and TCIA.

Results

The results of the pan-cancer analysis indicated that P4HA3 expression was significantly different in most tumor tissues compared with normal tissues. P4HA3 was overexpressed in COAD tissues, and overexpression of P4HA3 was associated with a worse overall survival and a shorted progression-free interval in COAD patients. The expression of P4HA3 was positively correlated with pathological stage, T stage, N stage, perineural infiltration, and lymphatic infiltration. There were significant correlations of P4HA3 expression levels with immune cell infiltration and their makers, as well as immunomodulators, chemokines, and microsatellite status. Moreover, overexpression of P4HA3 was associated with a lower response rate to immunotherapy in the IMvigor210 cohort.

Conclusion

Overexpression of P4HA3 is closely related to the poor prognosis of COAD patients, and P4HA3 is a potential target for immunotherapy in COAD patients.

Immune-related pan-cancer gene expression signatures of patient survival revealed by NanoString-based analyses

The immune system plays a central role in the onset and progression of cancer. A better understanding of transcriptional changes in immune cell-related genes associated with cancer progression, and their significance in disease prognosis, is therefore needed. NanoString-based targeted gene expression profiling has advantages for deployment in a clinical setting over RNA-seq technologies. We analysed NanoString PanCancer Immune Profiling panel gene expression data encompassing 770 genes, and overall survival data, from multiple previous studies covering 10 different cancer types, including solid and blood malignancies, across 515 patients. This analysis revealed an immune gene signature comprising 39 genes that were upregulated in those patients with shorter overall survival; of these 39 genes, three (MAGEC2, SSX1 and ULBP2) were common to both solid and blood malignancies. Most of the genes identified have previously been reported as relevant in one or more cancer types. Using Cibersort, we investigated immune cell levels within individual cancer types and across groups of cancers, as well as in shorter and longer overall survival groups. Patients with shorter survival had a higher proportion of M2 macrophages and γδ T cells. Patients with longer overall survival had a higher proportion of CD8+ T cells, CD4+ T memory cells, NK cells and, unexpectedly, T regulatory cells. Using a transcriptomics platform with certain advantages for deployment in a clinical setting, our multi-cancer meta-analysis of immune gene expression and overall survival data has identified a specific transcriptional profile associated with poor overall survival.

Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning

Cancer is one of the leading causes of death worldwide and can be caused by environmental aspects (for example, exposure to asbestos), by human behavior (such as smoking), or by genetic factors. To understand which genes might be involved in patients’ survival, researchers have invented prognostic genetic signatures: lists of genes that can be used in scientific analyses to predict if a patient will survive or not. In this study, we joined together five different prognostic signatures, each of them related to a specific cancer type, to generate a unique pan-cancer prognostic signature, that contains 207 unique probesets related to 187 unique gene symbols, with one particular probeset present in two cancer type-specific signatures (203072_at related to the MYO1E gene). We applied our proposed pan-cancer signature with the Random Forests machine learning method to 57 microarray gene expression datasets of 12 different cancer types, and analyzed the results. We also compared the performance of our pan-cancer signature with the performances of two alternative prognostic signatures, and with the performances of each cancer type-specific signature on their corresponding cancer type-specific datasets. Our results confirmed the effectiveness of our prognostic pan-cancer signature. Moreover, we performed a pathway enrichment analysis, which indicated an association between the signature genes and a protein-protein interaction analysis, that highlighted PIK3R2 and FN1 as key genes having a fundamental relevance in our signature, suggesting an important role in pan-cancer prognosis for both of them.

ADAR2 Protein Is Associated with Overall Survival in GBM Patients and Its Decrease Triggers the Anchorage-Independent Cell Growth Signature

Background: Epitranscriptomic mechanisms, such as A-to-I RNA editing mediated by ADAR deaminases, contribute to cancer heterogeneity and patients’ stratification. ADAR enzymes can change the sequence, structure, and expression of several RNAs, affecting cancer cell behavior. In glioblastoma, an overall decrease in ADAR2 RNA level/activity has been reported. However, no data on ADAR2 protein levels in GBM patient tissues are available; and most data are based on ADARs overexpression experiments. Methods: We performed IHC analysis on GBM tissues and correlated ADAR2 levels and patients’ overall survival. We silenced ADAR2 in GBM cells, studied cell behavior, and performed a gene expression/editing analysis. Results: GBM tissues do not all show a low/no ADAR2 level, as expected by previous studies. Although, different amounts of ADAR2 protein were observed in different patients, with a low level correlating with a poor patient outcome. Indeed, reducing the endogenous ADAR2 protein in GBM cells promotes cell proliferation and migration and changes the cell’s program to an anchorage-independent growth mode. In addition, deep-seq data and bioinformatics analysis indicated multiple RNAs are differently expressed/edited upon siADAR2. Conclusion: ADAR2 protein is an important deaminase in GBM and its amount correlates with patient prognosis.

Tumor mutational burden presents limiting effects on predicting the efficacy of immune checkpoint inhibitors and prognostic assessment in adrenocortical carcinoma

BACKGROUND

Adrenocortical carcinoma (ACC) is a highly malignant urologic cancer and tends to metastasize. Although immune checkpoint inhibitors (ICIs) bring a glimmer of light to conquer ACC, only a fraction of patients have benefit from ICIs treatment. It is well known that tumor mutational burden (TMB) is closely associated with the efficacy and response rate of immunotherapy. However, its roles in ACC were not investigated.

METHODS

Using somatic mutations data of 92 ACC samples in TCGA database, we calculated their TMB values by the 'maftools' package in R software (Ver 3.6.3). To explore the roles of TMB in ICIs therapy, we have addressed this issue from three perspectives. First, the effects of TMB levels on tumor immune microenvironment (TIM) were analyzed through CIBERSORT algorithm, ssGSEA method and TIMER web server. Second, we investigated the expressive correlations between TMB level and five pivotal immune checkpoints based on Pearson coefficient. Third, the difference in TIDE score between high- and low-TMB groups was compared. The prognostic value of TMB was also evaluated. Besides, GSEA was performed to determine the changes in the activities of signaling pathways caused by TMB.

RESULTS

TMB values in ACC samples were not high. The average of total mutation counts in each sample was only 21.5. High TMB could lead metabolic reprogramming and poor survival outcomes. However, it was unable to affect the infiltration levels of lymphocytes, and failed to facilitate the activities of immune-related pathways. Regarding immune checkpoints (ICs), only PD-L1 upregulation could result in a good prognosis, and TMB level did not correlate with the expressions of other ICs except for LAG3. There was no significant difference in TIDE score between high- and low-TMB groups. Combining the present results and previous study, we speculated that inadequate stimulation for neoantigens formation, intrinsic immune-resistance and special genomic alterations were three possible reasons for TMB limiting functions in TIM and ICIs. Besides, TMB was toughly applied in clinical practice due to its high cost of determination and non-universal definition of high TMB.

CONCLUSIONS

TMB presents limiting effects on prediction for ICIs efficacy and prognostic assessment for ACC patients.

Extracellular Matrix-Based Gene Expression Signature Defines Two Prognostic Subtypes of Hepatocellular Carcinoma With Different Immune Microenvironment Characteristics

Background: Accumulating evidence has suggested that the extracellular matrix (ECM) plays a vital role in the development and progression of cancer, and could be recognized as a biomarker of the response to immunotherapy. However, the effect of the ECM signature in hepatocellular carcinoma (HCC) is not well understood.

Methods: HCC patients derived from the TCGA-LIHC dataset were clustered according to the ECM signature. The differences in prognosis, functional enrichment, immune infiltration, and mutation characteristics between distinct molecular clusters were examined, and its predictive value on the sensitivities to chemotherapy and immunotherapy was further analyzed. Then, a prognostic model was built based on the ECM-related gene expression pattern.

Results: HCC patients were assigned into two molecular subtypes. Approximately 80% of HCC patients were classified into cluster A with poor prognosis, more frequent TP53 mutation, and lower response rate to immunotherapy. In contrast, patients in cluster B had better survival outcomes and higher infiltration levels of dendritic cells, macrophages, and regulatory T cells. The prognostic risk score model based on the expression profiles of six ECM-related genes (SPP1, ADAMTS5, MMP1, BSG, LAMA2, and CDH1) demonstrated a significant association with higher histologic grade and advanced TNM stage. Moreover, the prognostic risk score showed good performance in both the training dataset and validation dataset, as well as improved prognostic capacity compared with TNM stage.

Conclusions: We characterized two HCC subtypes with distinct clinical outcomes, immune infiltration, and mutation characteristics. A novel prognostic model based on the ECM signature was further developed, which may contribute to individualized prognostic prediction and aid in clinical decision-making.

A five-gene signature is a prognostic biomarker in pan-cancer and related with immunologically associated extracellular matrix

The tumor microenvironment (TME) is related to extracellular matrix (ECM) dynamics and has a broad fundamental and mechanistic role in tumorigenesis and cancer progression. We hypothesized that ECM regulators might play an essential role in pan‐cancer attribution by causing a generic effect through its regulation of the dynamics of ECM alteration. By analyzing data from TCGA using GSEA and univariate Cox regression analysis, we found that ECM regulator genes were significantly enriched and contributed to mortality in various cancer types. Notably, UMAP analysis revealed that ECM regulator genes dominated the differences between tumor and adjacent normal tissues based on 59 or 31 pan‐survival‐related ECM gene sets. Subsequently, a five‐gene signature consisting of the predominant ECM regulators ADAM12, MMP1, SERPINE1, PLOD3, and P4HA3 was identified. We found that this five‐gene signature was pro‐mortality in 18 types of cancer in TCGA, and validated eleven other cancer types in TCGA and seven types in the TARGET and CoMMpass databases using overall survival analysis. KEGG pathway enrichment and Pearson correlation analysis indicated that these five component genes that were correlated with specific ECM proteins involved in tumorigenesis from the ECM receptor interaction gene set. Additionally, the fitted results of a linear model were applied to strengthen the discovery, demonstrating that the five genes were correlated with immune infiltration score and especially associated with typically immunologically “cold” tumors. We thus conclude that the ADAM12, MMP1, SERPINE1, PLOD3, and P4HA3 signature showed a close association with a pan‐cancer effect on prognosis and is related to ECM proteins in the TME which corresponding with immunologically “cold” cancer types.