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Tumour microenvironment harbours diverse stress factors that affect the progression of multiple myeloma (MM), and the survival of MM cells heavily relies on crucial stress pathways. However, the impact of cellular stress on clinical prognosis of MM patients remains largely unknown. This study aimed to provide a cell stress-related model for survival and treatment prediction in MM. We incorporated five cell stress patterns including heat, oxidative, hypoxic, genotoxic, and endoplasmic reticulum stresses, to develop a comprehensive cellular stress index (CSI). Then we systematically analysed the effects of CSI on survival outcomes, clinical characteristics, immune microenvironment, and treatment sensitivity in MM. Molecular subtypes were identified using consensus clustering analysis based on CSI gene profiles. Moreover, a prognostic nomogram incorporating CSI was constructed and validated to aid in personalised risk stratification. After screening from five stress models, a CSI signature containing nine genes was established by Cox regression analyses and validated in three independent datasets. High CSI was significantly correlated with cell division pathways and poor clinical prognosis. Two distinct MM subtypes were identified through unsupervised clustering, showing significant differences in prognostic outcomes. The nomogram that combined CSI with clinical features exhibited good predictive performances in both training and validation cohorts. Meanwhile, CSI was closely associated with immune cell infiltration level and immune checkpoint gene expression. Therapeutically, patients with high CSI were more sensitive to bortezomib and antimitotic agents, while their response to immunotherapy was less favourable. Furthermore, in vitro experiments using cell lines and clinical samples verified the expression and function of key genes from CSI. The CSI signature could be a clinically applicable indicator of disease evaluation, demonstrating potential in predicting prognosis and guiding therapy for patients with MM.

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Background: Gastric cancer (GC) can be anatomically categorized into two subtypes; that is, cardia gastric cancer (CGC) and non-cardia gastric cancer (NCGC), which have distinct molecular mechanisms and prognoses. At present, the majority of pharmacological interventions for GC adhere to non-specific treatment regimens. The stratification of GC based on molecular disparities between CGC and NCGC has important clinical guidance value and could help in the development of precision therapies tailored to individual patient needs. Nevertheless, research in this specialized field remains notably limited. This study aims to investigate the molecular differences between CGC and NCGC and to leverage these differences to develop a prognostic risk scoring model (PRSM). Methods: We used patient data from The Cancer Genome Atlas (TCGA) and performed a differentially expressed gene (DEG) analysis between CGC and NCGC. A PRSM was developed from the prognosis-associated DEGs identified through Cox regression analyses and was well validated using Gene Expression Omnibus (GEO) data. Results: A total of 339 DEGs were identified between CGC and NCGC, and four prognosis-associated genes were used to construct the PRSM. Using the risk coefficients and expression levels of signature genes, a median risk score (RS) was calculated to classify patients into high- and low-risk groups. The high-risk group had a significantly worse prognosis than the low-risk group. An in-depth analysis revealed that TP53 mutations were more prevalent in the high-risk group, and MUC16 mutations were more prevalent in the low-risk group. A gene set enrichment analysis (GSEA) and the CIBERSORT algorithm were used to assess the differences in the significantly enriched pathways and immune microenvironment in the high- and low-risk groups, respectively. The inhibitory concentration (IC50) values of the chemotherapy drugs for GC also varied between the two groups. Conclusions: This study elucidated the unique molecular characteristics of GC subtypes based on the anatomical site and provided a preliminary contribution for the development of precision medicine for GC.

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Background: Tumor microenvironment (TME) status is closely related to breast cancer (BC) prognosis and systemic therapeutic effects. However, to date studies have not considered the interactions of immune and stromal cells at the gene expression level in BC as a whole. Herein, we constructed a predictive model, for adjuvant decision-making, by mining TME molecular expression information related to BC patient prognosis and drug treatment sensitivity. Methods: Clinical information and gene expression profiles were extracted from The Cancer Genome Atlas (TCGA), with patients divided into high- and low-score groups according to immune/stromal scores. TME-related prognostic genes were identified using Kaplan-Meier analysis, functional enrichment analysis, and protein-protein interaction (PPI) networks, and validated in the Gene Expression Omnibus (GEO) database. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to construct and verify a prognostic model based on TME-related genes. In addition, the patients' response to chemotherapy and immunotherapy was assessed by survival outcome and immunohistochemistry (IPS). Immunohistochemistry (IHC) staining laid a solid foundation for exploring the value of novel therapeutic target genes. Results: By dividing patients into low- and high-risk groups, a significant distinction in overall survival was found (p < 0.05). The risk model was independent of multiple clinicopathological parameters and accurately predicted prognosis in BC patients (p < 0.05). The nomogram-integrated risk score had high prediction accuracy and applicability, when compared with simple clinicopathological features. As predicted by the risk model, regardless of the chemotherapy regimen, the survival advantage of the low-risk group was evident in those patients receiving chemotherapy (p < 0.05). However, in patients receiving anthracycline (A) therapy, outcomes were not significantly different when compared with those receiving no-A therapy (p = 0.24), suggesting these patients may omit from A-containing adjuvant chemotherapy. Our risk model also effectively predicted tumor mutation burden (TMB) and immunotherapy efficacy in BC patients (p < 0.05). Conclusion: The prognostic score model based on TME-related genes effectively predicted prognosis and chemotherapy effects in BC patients. The model provides a theoretical basis for novel driver-gene discover in BC and guides the decision-making for the adjuvant treatment of early breast cancer (eBC).

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BACKGROUND: Colorectal tumor differentially expressed (CRNDE) is specifically expressed in human brains and is the most highly expressed lncRNA in gliomas. Nevertheless, its implications in low grade glioma (LGG) are still indistinct. This study presented systematic analyses of CRNDE in LGG biology. METHODS: We retrospectively retrieved TCGA, CGGC and GSE16011 LGG cohorts. Survival analysis was conducted for evaluating the prognostic significance of CRNDE in LGG. A CRNDE-based nomogram was established, and its predictive performance was verified. Signaling pathways underlying CRNDE were analyzed through ssGSEA and GSEA approaches. The abundance of immune cells and activity of cancer-immunity cycle were estimated with ssGSEA approach. Immune checkpoints, HLAs, chemokines, and immunotherapeutic response indicators (TIDE, and TMB) was quantified. U251 and SW1088 cells were transfected with specific shRNAs of CRNDE, and flow cytometry (apoptosis) and western blot (ß-catenin and Wnt5a) assays were conducted. RESULTS: Up-regulated CRNDE was found in LGG, and was linked to unfavorable clinical outcomes. The CRNDE-based nomogram enabled to accurately predict patients' prognosis. High CRNDE expression was linked to more genomic variations, activity of tumorigenic pathways, tumor immunity (increase in infiltration of immune cells, expression of immune checkpoints, HLAs and chemokines, and cancer-immunity cycle), and therapeutic sensitivity. CRNDE knockdown mitigated malignant phenotypes of LGG cells. CONCLUSIONS: Our study determined CRNDE as a novel predictor for patient prognosis, tumor immunity and therapeutic response in LGG. Assessment of CRNDE expression is a promising approach for predicting the therapeutic benefits of LGG patients.

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Over the last three decades, p21-activated kinases (PAKs) have emerged as prominent intracellular nodular signaling molecules in cancer cells with a spectrum of cancer-promoting functions ranging from cell survival to anchorage-independent growth to cellular invasiveness. As PAK family members are widely overexpressed and/or hyperactivated in a variety of human tumors, over the years PAKs have also emerged as therapeutic targets, resulting in the development of clinically relevant PAK inhibitors. Over the last two decades, this has been a promising area of active investigation for several academic and pharmaceutical groups. Similar to other kinases, blocking the activity of one PAK family member leads to compensatory activity on the part of other family members. Because PAKs are also activated by stress-causing anticancer drugs, PAKs are components in the rewiring of survival pathways in the action of several therapeutic agents; in turn, they contribute to the development of therapeutic resistance. This, in turn, creates an opportunity to co-target the PAKs to achieve a superior anticancer cellular effect. Here we discuss the role of PAKs and their effector pathways in the modulation of cellular susceptibility to cancer therapeutic agents and therapeutic resistance.

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Prostate cancer (PCa) is a common malignancy that poses a major threat to the health of men. Prostate-specific antigen (PSA) and its derivatives, as FDA-approved detection assays, are insufficient to serve as optimal markers for patient prognosis and clinical decision-making. It is widely acknowledged that aberrant glycolytic metabolism in PCa is related to tumor progression and acidifies the tumor microenvironment (TME). Considering the non-negligible impacts of glycolysis and immune functions on PCa, we developed a combined classifier in prostate cancer. The Glycolysis Score containing 19 genes and TME Score including three immune cells were created, using the univariate and multivariate Cox proportional hazards model, log-rank test, least absolute shrinkage and selection operator (LASSO) regression analysis and the bootstrap approach. Combining the glycolysis and immunological landscape, the Glycolysis-TME Classifier was then constructed. It was observed that the classifier was more accurate in predicting the prognosis of patients than the current biomarkers. Notably, there were significant differences in metabolic activity, signaling pathways, mutational landscape, immunotherapeutic response, and drug sensitivity among the Glycolysishigh/TMElow, Mixed group and Glycolysislow/TMEhigh identified by this classifier. Overall, due to the significant prognostic value and potential therapeutic guidance of the Glycolysis-TME Classifier, we anticipate that this classifier will be clinically beneficial in the management of patients with PCa.

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DNA damage repair (DDR) is critical in maintaining normal cellular function and genome integrity and is associated with cancer risk, progression, and therapeutic response. However, there is still a lack of a thorough understanding of the effects of DDR genes' expression level in cancer progression and therapeutic resistance. Therefore, we defined a tumor-related DDR score (TR-DDR score), utilizing the expression levels of 20 genes, to quantify the tumor signature of DNA damage repair pathways in tumors and explore the possible function and mechanism for the score among different cancers. The TR-DDR score has remarkably predictive power for tumor tissues. It is a more accurate indicator for the response of chemotherapy or immunotherapy combined with the tumor-infiltrating lymphocyte (TIL) and G2M checkpoint score than the pre-existing predictors (CD8 or PD-L1). This study points out that the TR-DDR score generally has positive correlations with patients of advanced-stage, genome-instability, and cell proliferation signature, while negative correlations with inflammatory response, apoptosis, and p53 pathway signature. In the context of tumor immune response, the TR-DDR score strongly positively correlates with the number of T cells (CD4+ activated memory cells, CD8+ cells, T regs, Tfh) and macrophages M1 polarization. In addition, by difference analysis and correlation analysis, COL2A1, MAGEA4, FCRL4, and ZIC1 are screened out as the potential modulating factors for the TR-DDR score. In summary, we light on a new biomarker for DNA damage repair pathways and explore its possible mechanism to guide therapeutic strategies and drug response prediction.

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Although patients with lower-grade gliomas (LGGs; grades II and III) have a relatively favorable prognosis, patients frequently relapse and tend to progress to higher-grade gliomas, leading to treatment resistance, poor survival, and ultimately treatment failure. However, until now, thorough research has not yet been reported on the relationship between PD-L2 and immune infiltration and therapeutic sensitivity to immunotherapy and TMZ-based chemotherapy of LGGs. In this study, we found that the expression of PD-L2 is upregulated in glioma, with high PD-L2 expression predicting a worse prognosis. Univariate and multivariate Cox regression analysis both indicated that PD-L2 represented an independent prognostic factor with high accuracy in survival prediction for LGGs. A nomogram comprising of age, grade, IDH mutation, and PD-L2 was established for predicting OS. Additionally, PD-L2 was found to be remarkably correlated with immune infiltration and some anti-tumor immune functions. The degree of PD-L2 expression was also found to be strongly related to the prediction of therapeutic sensitivity to immunotherapy and TMZ-based chemotherapy. Furthermore, immunohistochemistry demonstrated that PD-L2 and the macrophage biomarker CD68 were both increased in glioma, with PD-L2 expression having a strong positive connection with CD68 expression. Taken together, PD-L2 is a prognostic biomarker for LGGs patients that may provide novel insights into glioma individualized therapeutic strategies and guide effective immunotherapy and chemotherapy.

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Acute myeloid leukemia (AML) is usually associated with poor prognosis and low complete remission (CR) rate due to individual biological heterogeneity. Pyroptosis is a special form of inflammatory programmed cell death related to the progression, treatment response, and prognosis of multiple tumors. However, the potential connection of pyroptosis-related genes (PRGs) and AML still remains unclear. We described the genetic and transcriptional alterations of PRGs in 151 AML samples and presented a consensus clustering of these patients into two subtypes with distinct immunological and prognostic characteristics. Cluster A, associated with better prognosis, was characterized by relatively lower PRG expression, activated immune cells, higher immune scores in the tumor microenvironment (TME), and downregulation of immunotherapy checkpoints. Subsequently, a PRG score was constructed to predict overall survival (OS) of AML patients by using univariate and multivariate Cox regression analysis, and its immunological characteristics and predictive capability were further validated by 1,054 AML samples in external datasets. Besides an immune-activated status, low-PRG score cohorts exhibited higher chemotherapeutic drug sensitivity and significant positive correlation with the cancer stem cell (CSC) index. Combined with age, clinical French-American-British (FAB) subtypes, and PRG score, we successfully constructed a nomogram to effectively predict the 1-/3-/5-year survival rate of AML patients, and the predictive capability was further validated in multiple external datasets with a high area under the curve (AUC) value. The various transcriptomic analysis helps us screen significant pyroptosis-related signatures of AML and provide a new clinical application of PRG scores in predicting the prognosis and benefits of treatment for AML patients.

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Cancer-associated fibroblasts (CAFs), a prominent population of stromal cells, play a crucial role in tumor progression, prognosis, and treatment response. However, the relationship among CAF-based molecular signatures, clinical outcomes, and tumor microenvironment infiltration remains largely elusive in pancreatic cancer (PC). Here, we collected multicenter PC data and performed integrated analysis to investigate the role of CAF-related genes (CRGs) in PC. Firstly, we demonstrated that α-SMA+ CAFs were the most prominent stromal components and correlated with the poor survival rates of PC patients in our tissue microarrays. Then, we discriminated two diverse molecular subtypes (CAF clusters A and B) and revealed the significant differences in the tumor immune microenvironment (TME), four reported CAF subpopulations, clinical characteristics, and prognosis in PC samples. Furthermore, we analyzed their association with the immunotherapy response of PC patients. Lastly, a CRG score was constructed to predict prognosis, immunotherapy responses, and chemosensitivity in pancreatic cancer patients. In summary, these findings provide insights into further research targeting CAFs and their TME, and they pave a new road for the prognosis evaluation and individualized treatment of PC patients.

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BACKGROUND: The left-sided and right-sided colon cancer (LCCs and RCCs, respectively) have unique molecular features and clinical heterogeneity. This study aimed to identify the characteristics of immune cell infiltration (ICI) subtypes for evaluating prognosis and therapeutic benefits. METHODS: The independent gene datasets, corresponding somatic mutation and clinical information were collected from The Cancer Genome Atlas and Gene Expression Omnibus. The ICI contents were evaluated by "ESTIMATE" and "CIBERSORT." We performed two computational algorithms to identify the ICI landscape related to prognosis and found the unique infiltration characteristics. Next, principal component analysis was conducted to construct ICI score based on three ICI patterns. We analyzed the correlation between ICI score and tumor mutation burden (TMB), and stratified patients into prognostic-related high- and low- ICI score groups (HSG and LSG, respectively). The role of ICI scores in the prediction of therapeutic benefits was investigated by "pRRophetic" and verified by Immunophenoscores (IPS) (TCIA database) and an independent immunotherapy cohort (IMvigor210). The key genes were preliminary screened by weighted gene co-expression network analysis based on ICI scores. And they were further identified at various levels, including single cell, protein and immunotherapy response. The predictive ability of ICI score for prognosis was also verified in IMvigor210 cohort. RESULTS: The ICI features with a better prognosis were marked by high plasma cells, dendritic cells and mast cells, low memory CD4+ T cells, M0 macrophages, M1 macrophages, as well as M2 macrophages. A high ICI score was characterized by an increased TMB and genomic instability related signaling pathways. The prognosis, sensitivities of targeted inhibitors and immunotherapy, IPS and expression of immune checkpoints were significantly different in HSG and LSG. The genes identified by ICI scores and various levels included CA2 and TSPAN1. CONCLUSION: The identification of ICI subtypes and ICI scores will help gain insights into the heterogeneity in LCC and RCC, and identify patients probably benefiting from treatments. ICI scores and the key genes could serve as an effective biomarker to predict prognosis and the sensitivity of immunotherapy.

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Background: The purpose of our study was to develop a prognostic risk model based on differential genomic instability-associated (DGIA) long non-coding RNAs (lncRNAs) of left-sided and right-sided colon cancers (LCCs and RCCs); therefore, the prognostic key lncRNAs could be identified. Methods: We adopted two independent gene datasets, corresponding somatic mutation and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Identification of differential DGIA lncRNAs from LCCs and RCCs was conducted with the appliance of "Limma" analysis. Then, we screened out key lncRNAs based on univariate and multivariate Cox proportional hazard regression analysis. Meanwhile, DGIA lncRNAs related prognostic model (DRPM) was established. We employed the DRPM in the model group and internal verification group from TCGA for the purpose of risk grouping and accuracy verification of DRPM. We also verified the accuracy of key lncRNAs with GEO data. Finally, the differences of immune infiltration, functional pathways, and therapeutic sensitivities were analyzed within different risk groups. Results: A total of 123 DGIA lncRNAs were screened out by differential expression analysis. We obtained six DGIA lncRNAs by the construction of DRPM, including AC004009.1, AP003555.2, BOLA3-AS1, NKILA, LINC00543, and UCA1. After the risk grouping by these DGIA lncRNAs, we found the prognosis of the high-risk group (HRG) was significantly worse than that in the low-risk group (LRG) (all p < 0.05). In all TCGA samples and model group, the expression of CD8+ T cells in HRG was lower than that in LRG (all p < 0.05). The functional analysis indicated that there was significant upregulation with regard to pathways related to both genetic instability and immunity in LRG, including cytosolic DNA sensing pathway, response to double-strand RNA, RIG-Ⅰ like receptor signaling pathway, and Toll-like receptor signaling pathway. Finally, we analyzed the difference and significance of key DGIA lncRNAs and risk groups in multiple therapeutic sensitivities. Conclusion: Through the analysis of the DGIA lncRNAs between LCCs and RCCs, we identified six key DGIA lncRNAs. They can not only predict the prognostic risk of patients but also serve as biomarkers for evaluating the differences of genetic instability, immune infiltration, and therapeutic sensitivity.

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MicroRNAs are a set of highly conserved non-coding RNAs that control gene expression at the post-transcriptional/translational levels by binding to the 3'-UTR of diverse target genes. Increasing evidence indicates that miRNAs not only play a vital role in many biological processes, but they are also frequently deregulated in pathological conditions, including cancer. The miR-320 family is one of many tumor suppressor families and is composed of five members, which has been demonstrated to be related to the repression of epithelial-mesenchymal transition (EMT) inhibition, cell proliferation, and apoptosis. Moreover, this family has been shown to regulate drug resistance, and act as a potential biomarker for the diagnosis, prognosis, and prediction of cancer. In this review, we summarized recent research with reference to the tumor suppressor function of miR-320 and the regulation mechanisms of miR-320 expression. The collected evidence shown here supports that miR-320 may act as a novel biomarker for cancer prognosis and therapeutic response to cancer treatment.

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MircoRNA (miRNA) are a group of small, non-coding, regulatory RNA with an average length of approximately 22 nucleotides, which mostly modulate gene expression post-transcriptionally through complementary binding to the 3'-untranslated region (3'-UTR) of multiple target genes. Emerging evidence has shown that miRNA are frequently dysregulated in a variety of human malignancies. Among them, microRNA-145 (miR-145) has been increasingly identified as a critical suppressor of carcinogenesis and therapeutic resistance. Resistance to tumor therapy is a challenge in cancer treatment due to the daunting range of resistance mechanisms. We reviewed the status quo of recent advancements in the knowledge of the functional role of miR-145 in therapeutic resistance and the tumor microenvironment. It may serve as an innovative biomarker for therapeutic response and cancer prognosis.

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Pyroptosis is a gasdermins mediated programmed cell death, which has been widely studied in inflammatory disease models. Recently, there are growing evidences that pyroptosis can be chemically induced in cancer cells without any bacterial or viral infection. Pyroptosis may affect all stages of carcinogenesis and has become a new topic in cancer research. In this review, we first briefly introduced pyroptosis. In the subsequent section, we discussed the induction of pyroptosis in cancer and its potential role as a promising target for cancer therapy. In addition, the biological characteristics of gasdermin D (GSDMD) and gasdermin E (GSDME), two important pyroptosis substrates, and their prognostic role in cancer management were reviewed. These results help us to understand the pathogenesis of cancer and develop new drugs, which based on pyroptosis modulation, for cancer patients.

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Intermediary metabolism in cancer cells is regulated by diverse cell-autonomous processes, including signal transduction and gene expression patterns, arising from specific oncogenotypes and cell lineages. Although it is well established that metabolic reprogramming is a hallmark of cancer, we lack a full view of the diversity of metabolic programs in cancer cells and an unbiased assessment of the associations between metabolic pathway preferences and other cell-autonomous processes. Here, we quantified metabolic features, mostly from the 13C enrichment of molecules from central carbon metabolism, in over 80 non-small cell lung cancer (NSCLC) cell lines cultured under identical conditions. Because these cell lines were extensively annotated for oncogenotype, gene expression, protein expression, and therapeutic sensitivity, the resulting database enables the user to uncover new relationships between metabolism and these orthogonal processes.

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Fanconi anemia (FA) is a DNA repair disorder associated with a high risk of cancer and bone marrow failure. Patients with FA may present with certain dysmorphic features, such as radial ray abnormalities, short stature, typical facies, bone marrow failure, or certain solid malignancies. Some patients may be recognized due to exquisite sensitivity after exposure to cancer therapy. FA is diagnosed by increased chromosomal breakage after exposure to clastogenic agents. It follows autosomal recessive and X-linked inheritance depending on the underlying genomic alterations. Recognizing patients with FA is important for therapeutic decisions, genetic counseling, and optimal clinical management.

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Cancer microenvironment is created not only by malignant epithelial cells, but also by several kinds of stromal cells. Since Paget proposed the "seed and soil" hypothesis, the biological importance of the cancer microenvironment has come to be widely accepted. The main compartment of host stromal cells are fibroblasts (Cancer-Associated Fibroblasts; CAFs), which are the main source of the collagen-producing cells. CAFs directly communicate with the cancer cells and other types of stromal cells to acquire a specific biological phenotype. CAFs play important roles in several aspects of the tumor progression process and the chemotherapeutic process. However, CAFs have heterogeneous origins, phenotypes, and functions under these conditions. A crucial challenge is to understand how much of this heterogeneity serves different biological responses to cancer cells. In this review, we highlight the issue of how diverse and heterogeneous functions given by CAFs can exert potent influences on tumor progression and therapeutic response. Furthermore, we also discuss the current advances in the development of novel therapeutic strategies against CAFs.

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The tumour microenvironment contributes greatly to the response of tumour cells. It consists of chemical gradients, for example of oxygen and nutrients. However, a physical environment is also present. Apart from chemical input, cells also receive physical signals. Tumours display unique mechanical properties: they are a lot stiffer than normal tissue. This may be either a cause or a consequence of cancer, but literature suggests it has a major impact on tumour cells as will be described in this review. The mechanical microenvironment may cause malignant transformation, possibly through activation of oncogenic pathways and inhibition of tumour suppressor genes. In addition, the mechanical microenvironment may promote tumour progression by influencing processes such as epithelial-to-mesenchymal transition, enhancing cell survival through autophagy, but also affects sensitivity of tumour cells to therapeutics. Furthermore, multiple intracellular signalling pathways prove sensitive to the mechanical properties of the microenvironment. It appears the increased stiffness is unlikely to be caused by increased stiffness of the tumour cells themselves. However, there are indications that tumours display a higher cell density, making them more rigid. In addition, increased matrix deposition in the tumour, as well as increased interstitial fluid pressure may account for the increased stiffness of tumours. Overall, tumour mechanics are significantly different from normal tissue. Therefore, this feature should be further explored for use in cancer prevention, detection and treatment.

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