Endothelial cells (ECs) play an important role in tumor progression. Currently, the main target of anti-angiogenic therapy is the vascular endothelial growth factor (VEGF) pathway. Some patients do benefit from anti-VEGF/VEGFR therapy; however, a large number of patients do not have response or acquire drug resistance after treatment. Moreover, anti-VEGF/VEGFR therapy may lead to nephrotoxicity and cardiovascular-related side effects due to its action on normal ECs. Therefore, it is necessary to identify targets that are specific to tumor ECs and could be applied to various cancer types. We integrated single-cell RNA sequencing data from six cancer types and constructed a multi-cancer EC atlas to decode the characteristic of tumor ECs. We found that tip-like ECs mainly exist in tumor tissues but barely exist in normal tissues. Tip-like ECs are involved in the promotion of tumor angiogenesis and inhibition on anti-tumor immune responses. Moreover, tumor cells, myeloid cells, and pericytes are the main sources of pro-angiogenic factors. High proportion of tip-like ECs is associated with poor prognosis in multiple cancer types. We also identified that prostate-specific membrane antigen (PSMA) is a specific marker for tip-like ECs in all the cancer types we studied. In summary, we demonstrate that tip-like ECs are the main differential EC subcluster between tumors and normal tissues. Tip-like ECs may promote tumor progression through promoting angiogenesis while inhibiting anti-tumor immune responses. PSMA was a specific marker for tip-like ECs, which could be used as a potential target for the diagnosis and treatment of non-prostate cancers.

Accumulating evidence has recognized that cancer-associated fibroblasts (CAFs) are major players in the desmoplastic stroma of ovarian cancer, modulating tumor progression and therapeutic response. However, it is unclear regarding the signatures of CAFs could be utilized to predict the clinical outcomes of ovarian cancer patients. To fill in this gap, we explored the intratumoral compartment of ovarian cancer by analyzing the single-cell RNA-sequencing (scRNA-seq) datasets of ovarian carcinoma samples, and identified two distinct CAFs (tumor-promoting CAF_c1 subtype and myofibroblasts-like CAF_c2 subtype). The clinical significance of CAF subtypes was further validated in The Cancer Genomics Atlas (TCGA) database and other independent immunotherapy response datasets, and the results revealed that the patients with a higher expression of CAF_c1 signatures had a worse prognosis and showed a tendency of resistance to immunotherapy. This work uncovered the signatures of the CAF_c1 subtype that could serve as a novel prognostic indicator and predictive marker for immunotherapy.

Tumor necrosis factor-α-inducible protein 8-like 2 (TIPE2) is encoded by TNFAIP8L2 and is a newly identified negative regulator of natural and acquired immunity that plays a critical function in maintaining immune homeostasis. Recently, CAR-NK immune cell therapy has been a focus of major research efforts as a novel cancer therapeutic strategy. TIPE2 is a potential checkpoint molecule for immune cell maturation and antitumor immunity that could be used as a novel NK cell-based immunotherapeutic approach. In this study, we explored the expression of TNFAIP8L2 across various tumor types and found that TNFAIP8L2 was highly expressed in most tumor types and correlated with prognosis. Survival analysis showed that TNFAIP8L2 expression was predictive of improved survival in cervical-squamous-cell-carcinoma (CESC), sarcoma (SARC) and skin-cutaneous-melanoma (SKCM). Conversely, TNFAIP8L2 expression predicted poorer survival in acute myeloid leukemia (LAML), lower-grade-glioma (LGG), kidney-renal-clear-cell-carcinoma (KIRC) and uveal-melanoma (UVM). Analysis of stemness features and immune cell infiltration indicated that TNFAIP8L2 was significantly associated with cancer stem cell index and increased macrophage and dendritic cell infiltration. Our data suggest that TNFAIP8L2 may be a novel immune checkpoint biomarker across different tumor types, particularly in LAML, LGG, KIRC and UVM, and may have further utility as a potential target for immunotherapy.

Centrosome and spindle pole-associated protein (CSPP1) is a centrosome and microtubule-binding protein that plays a role in cell cycle-dependent cytoskeleton organization and cilia formation. Previous studies have suggested that CSPP1 plays a role in tumorigenesis; however, no pan-cancer analysis has been performed. This study systematically investigates the expression of CSPP1 and its potential clinical outcomes associated with diagnosis, prognosis, and therapy. CSPP1 is widely present in tissues and cells and its aberrant expression serves as a diagnostic biomarker for cancer. CSPP1 dysregulation is driven by multi-dimensional mechanisms involving genetic alterations, DNA methylation, and miRNAs. Phosphorylation of CSPP1 at specific sites may play a role in tumorigenesis. In addition, CSPP1 correlates with clinical features and outcomes in multiple cancers. Take brain low-grade gliomas (LGG) with a poor prognosis as an example, functional enrichment analysis implies that CSPP1 may play a role in ferroptosis and tumor microenvironment (TME), including regulating epithelial-mesenchymal transition, stromal response, and immune response. Further analysis confirms that CSPP1 dysregulates ferroptosis in LGG and other cancers, making it possible for ferroptosis-based drugs to be used in the treatment of these cancers. Importantly, CSPP1-associated tumors are infiltrated in different TMEs, rendering immune checkpoint blockade therapy beneficial for these cancer patients. Our study is the first to demonstrate that CSPP1 is a potential diagnostic and prognostic biomarker associated with ferroptosis and TME, providing a new target for drug therapy and immunotherapy in specific cancers.

The SARS-CoV-2 is constantly mutating, and the new coronavirus such as Omicron has spread to many countries around the world. Anexelekto (AXL) is a transmembrane protein with biological functions such as promoting cell growth, migration, aggregation, metastasis and adhesion, and plays an important role in cancers and coronavirus disease 2019 (COVID-19). Unlike angiotensin-converting enzyme 2 (ACE2), AXL was highly expressed in respiratory system cells. In this study, we verified the AXL expression in cancer and normal tissues and found AXL expression was strongly correlated with cancer prognosis, tumor mutation burden (TMB), the microsatellite instability (MSI) in most tumor types. Immune infiltration analysis also demonstrated that there was an inextricable link between AXL expression and immune scores in cancer patients, especially in BLCA, BRCA and CESC. The NK-cells, plasmacytoid dendritic cells, myeloid dendritic cells, as one of the important components of the tumor microenvironment, were highly expressed AXL. In addition, AXL-related tumor neoantigens were identified and might provide the novel potential targets for tumor vaccines or SARS-Cov-2 vaccines research in cancer patients.

KDM6A is the disease causative gene of type 2 Kabuki Syndrome, a rare multisystem disease; it is also a known cancer driver gene, with multiple somatic mutations found in a few cancer types. In this study, we looked at eleven missense variants in lung squamous cell carcinoma, one of the most common lung cancer subtypes, to see how they affect the KDM6A catalytic mechanisms. We found that they influence the interaction with histone H3 and the exposure of the trimethylated Lys27, which is critical for wild-type physiological function to varying degrees, by altering the conformational transition.

Shc SH2-domain binding protein 1 (SHCBP1), a protein specific binding to SH2 domain of Src homolog and collagen homolog (Shc), takes part in the regulation of various signal transduction pathways, which has been reported to be associated with tumorigenesis and progression. However, the pathological mechanisms are not completely investigated. Thus, this study aimed to comprehensively elucidate the potential functions of SHCBP1 in multiple cancer types. The comprehensive analyses for SHCBP1 in various tumors, including gene expression, diagnosis, prognosis, immune-related features, genetic alteration, and function enrichment, were conducted based on multiple databases and analysis tools. SHCBP1 was upregulated in most types of cancers. The results of qRT-PCR had confirmed that SHCBP1 mRNA was significantly upregulated in lung adenocarcinoma (LUAD) and liver hepatocellular carcinoma (LIHC) cell lines. Based on the receiver operating characteristic (ROC) and survival analysis, SHCBP1 was considered as a potential diagnostic and prognostic biomarker. Furthermore, SHCBP1 expression was linked with tumor immunity and immunosuppressive microenvironment according to the correlation analysis of SHCBP1 expression with immune cells infiltration, immune checkpoint genes, and immune-related genes (MHC genes, chemokines, and chemokines receptors). Moreover, SHCBP1 expression correlated with tumor mutational burden (TMB), microsatellite instability (MSI), and neoantigens. The feature of SHCBP1 mutational landscape in pan-cancer was identified. Finally, we focused on investigating the clinical significance and the potential biological role of SHCBP1 in LUAD. Our study comprehensively uncovered that SHCBP1 could be identified as an immune-related biomarker for cancer diagnosis and prognosis, and a potential therapeutic target for tumor immunotherapy.

Machine learning is an important artificial intelligence technique that is widely applied in cancer diagnosis and detection. More recently, with the rise of personalised and precision medicine, there is a growing trend towards machine learning applications for prognosis prediction. However, to date, building reliable prediction models of cancer outcomes in everyday clinical practice is still a hurdle. In this work, we integrate genomic, clinical and demographic data of lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) patients from The Cancer Genome Atlas (TCGA) and introduce copy number variation (CNV) and mutation information of 15 selected genes to generate predictive models for recurrence and survivability. We compare the accuracy and benefits of three well-established machine learning algorithms: decision tree methods, neural networks and support vector machines. Although the accuracy of predictive models using the decision tree method has no significant advantage, the tree models reveal the most important predictors among genomic information (e.g. KRAS, EGFR, TP53), clinical status (e.g. TNM stage and radiotherapy) and demographics (e.g. age and gender) and how they influence the prediction of recurrence and survivability for both early stage LUAD and LUSC. The machine learning models have the potential to help clinicians to make personalised decisions on aspects such as follow-up timeline and to assist with personalised planning of future social care needs.

Acidosis, regardless of hypoxia involvement, is recognized as a chronic and harsh tumor microenvironment (TME) that educates malignant cells to thrive and metastasize. Although overwhelming evidence supports an acidic environment as a driver or ubiquitous hallmark of cancer progression, the unrevealed core mechanisms underlying the direct effect of acidification on tumorigenesis have hindered the discovery of novel therapeutic targets and clinical therapy. Here, chemical-induced and transgenic mouse models for colon, liver and lung cancer were established, respectively. miR-7 and TGF-β2 expressions were examined in clinical tissues (n = 184). RNA-seq, miRNA-seq, proteomics, biosynthesis analyses and functional studies were performed to validate the mechanisms involved in the acidic TME-induced lung cancer metastasis. Our data show that lung cancer is sensitive to the increased acidification of TME, and acidic TME-induced lung cancer metastasis via inhibition of miR-7-5p. TGF-β2 is a direct target of miR-7-5p. The reduced expression of miR-7-5p subsequently increases the expression of TGF-β2 which enhances the metastatic potential of the lung cancer. Indeed, overexpression of miR-7-5p reduces the acidic pH-enhanced lung cancer metastasis. Furthermore, the human lung tumor samples also show a reduced miR-7-5p expression but an elevated level of activated TGF-β2; the expressions of both miR-7-5p and TGF-β2 are correlated with patients\' survival. We are the first to identify the role of the miR-7/TGF-β2 axis in acidic pH-enhanced lung cancer metastasis. Our study not only delineates how acidification directly affects tumorigenesis, but also suggests miR-7 is a novel reliable biomarker for acidic TME and a novel therapeutic target for non-small cell lung cancer (NSCLC) treatment. Our study opens an avenue to explore the pH-sensitive subcellular components as novel therapeutic targets for cancer treatment.

BACKGROUND: Cox proportional hazard regression (CPH) model relies on the proportional hazard (PH) assumption: the hazard of variables is independent of time. CPH has been widely used to identify prognostic markers of the transcriptome. However, the comprehensive investigation on PH assumption in transcriptomic data has lacked.
RESULTS: The whole transcriptomic data of the 9,056 patients from 32 cohorts of The Cancer Genome Atlas and the 3 lung cancer cohorts from Gene Expression Omnibus were collected to construct CPH model for each gene separately for fitting the overall survival. An average of 8.5% gene CPH models violated the PH assumption in TCGA pan-cancer cohorts. In the gene interaction networks, both hub and non-hub genes in CPH models were likely to have non-proportional hazards. Violations of PH assumption for the same gene models were not consistent in 5 non-small cell lung cancer datasets (all kappa coefficients < 0.2), indicating that the non-proportionality of gene CPH models depended on the datasets. Furthermore, the introduction of log(t) or sqrt(t) time-functions into CPH improved the performance of gene models on overall survival fitting in most tumors. The time-dependent CPH changed the significance of log hazard ratio of the 31.9% gene variables.
CONCLUSIONS: Our analysis resulted that non-proportional hazards should not be ignored in transcriptomic data. Introducing time interaction term ameliorated performance and interpretability of non-proportional hazards of transcriptome data in CPH.