Hasil untuk "Neoplasms. Tumors. Oncology. Including cancer and carcinogens"

Chenggeng Pan, Ruokuo Han, Senzhe Xia et al.

Abstract Objective Malignant phyllodes tumor of the breast (MPTB) represents a distinct breast tumor subtype associated with a poor prognosis. The objective of this research was to create and verify a nomogram to predict both overall survival (OS) and breast cancer-specific survival (BCSS) for individuals with a diagnosis of MPTB. Methods From the Surveillance, Epidemiology, and End Results (SEER) database, clinicopathological data of MPTB patients diagnosed between 2000 and 2020 were collected. The study cohort was randomly divided into training (70%) and validation (30%) sets using computer-generated random numbers. We performed Cox regression analyses to determine the independent factors that predict both OS and BCSS in the training cohort and internal validation cohort. Subsequently, a nomogram was developed integrating these significant predictors. Model performance was assessed using metrics such as the calibration curve, area under receiver operating characteristic curve (ROC), and decision curve analysis (DCA). Based on the nomogram scores, patients were categorized into low-risk and high-risk groups using restricted cubic spline (RCS), and survival differences were then assessed through the log-rank test and Kaplan-Meier curves. Results The study encompassed 1692 MPTB patients, randomly allocated into a training cohort (N = 1188, 70%,) and a validation cohort (N = 504, 30%). Eight independent predictors for OS were identified through univariate and multivariate analyses: age, marital status, income, stage, tumor stage, nodal stage, surgery, and chemotherapy. Additionally, six independent predictors for BCSS were identified through the same analytical approach: age, stage, tumor stage, nodal stage, surgery, and chemotherapy. Nomograms were constructed based on these variables to forecast OS and BCSS rates for patients with MPTB. Evaluation of the model’s discriminative ability using ROC demonstrated satisfactory predictive performance for OS and BCSS in both cohorts. Strong concordance between the probabilities observed and those predicted was indicated by the calibration curve. Furthermore, DCA underscored the clinical utility of the nomogram. High-risk patients (scores ≥ 85/118) had significantly reduced OS/BCSS vs. low-risk counterparts. Conclusions In this investigation, a nomogram was effectively constructed and internally confirmed to forecast OS and BCSS among individuals with MPTB. This predictive tool provides clinicians with essential prognostic information to guide their clinical decision-making processes.

Alicja Rajtak, Ilona Skrabalak, Natalia Ćwilichowska-Puślecka et al.

Abstract Ovarian cancer comprises a highly complex ecosystem of malignant cells and their surrounding tumor microenvironment (TME), where intricate interactions shape therapeutic responses. Most current predictive models fail to capture the full extent of these interactions. Here, we performed a comprehensive multi-omic analysis of pre-treatment ovarian tumor tissues, integrating clinical, genomic, transcriptomic, and immune features to correlate with pathological therapy response. Our results show that integrating genetic and immune parameters—particularly the interplay between NK cells and TP53 status in high grade serous ovarian cancer (HGSOC), and diverse genetic alterations in non-HGSOC—markedly improves therapy response prediction. We demonstrate that tumor TP53 status governs the persistence of early NK cells in HGSOC, and this persistent NK phenotype is associated with favorable clinical outcomes. Machine learning models harnessing these multi-omic features significantly outperform those based on any single information type alone. These findings highlight the central role of the baseline tumor ecosystem and support a precision oncology framework leveraging integrated multi-omic profiling and advanced analytics to improve prediction and guide treatment strategies.

Rui Zhu MD, Ziyuan Shen PhD, Haijuan Zhu MD et al.

Introduction: Soft Tissue and Other Extraosseous Sarcomas (STOES) represent a rare and heterogeneous group of malignancies with significant clinical challenges due to their complexity and aggressiveness. Despite their low prevalence, the global impact of STOES is substantial, necessitating a detailed examination of their epidemiology and disease burden. Methods: This comprehensive analysis utilized data from the Global Health Data Exchange (GHDx) covering the years 1990 to 2021. We assessed the incidence, prevalence, mortality, and Disability-Adjusted Life Years (DALYs) for STOES, categorized by location, sex, and socio-demographic indices. Statistical methods included Estimated Annual Percentage Change (EAPC), Spearman correlation analysis, and Bayesian age-period-cohort modeling. Findings: In 2021, STOES cases reached a global prevalence of 480,473, a significant increase from 1990. High Socio-Demographic Index (SDI) regions exhibited the highest age-standardized incidence and prevalence rates (ASIR and ASPR) at 2.05 and 10.61 per 100,000 population, respectively. Notably, significant increases were also observed in Central Asia, Central Europe, and Southern Sub-Saharan Africa. Males consistently showed higher disease rates than females. The decomposition analysis highlighted population growth and aging as primary drivers of the observed trends. Forecasting suggests a decline in the global STOES burden by 2030, though disparities will persist, particularly among males. Conclusion: The study reveals critical geographic and demographic disparities in the burden of STOES, underscoring the ongoing higher risk among males and in certain global regions. Despite projected declines in overall disease burden by 2030, substantial disparities are expected to persist, necessitating targeted public health interventions and robust policies to effectively mitigate these differences and enhance global health outcomes.

Moinak Bhattacharya, Angelica P. Kurtz, Fabio M. Iwamoto et al.

Neuro-oncology poses unique challenges for machine learning due to heterogeneous data and tumor complexity, limiting the ability of foundation models (FMs) to generalize across cohorts. Existing FMs also perform poorly in predicting uncommon molecular markers, which are essential for treatment response and risk stratification. To address these gaps, we developed a neuro-oncology specific FM with a distributionally robust loss function, enabling accurate estimation of tumor phenotypes while maintaining cross-institution generalization. We pretrained self-supervised backbones (BYOL, DINO, MAE, MoCo) on multi-institutional brain tumor MRI and applied distributionally robust optimization (DRO) to mitigate site and class imbalance. Downstream tasks included molecular classification of common markers (MGMT, IDH1, 1p/19q, EGFR), uncommon alterations (ATRX, TP53, CDKN2A/2B, TERT), continuous markers (Ki-67, TP53), and overall survival prediction in IDH1 wild-type glioblastoma at UCSF, UPenn, and CUIMC. Our method improved molecular prediction and reduced site-specific embedding differences. At CUIMC, mean balanced accuracy rose from 0.744 to 0.785 and AUC from 0.656 to 0.676, with the largest gains for underrepresented endpoints (CDKN2A/2B accuracy 0.86 to 0.92, AUC 0.73 to 0.92; ATRX AUC 0.69 to 0.82; Ki-67 accuracy 0.60 to 0.69). For survival, c-index improved at all sites: CUIMC 0.592 to 0.597, UPenn 0.647 to 0.672, UCSF 0.600 to 0.627. Grad-CAM highlighted tumor and peri-tumoral regions, confirming interpretability. Overall, coupling FMs with DRO yields more site-invariant representations, improves prediction of common and uncommon markers, and enhances survival discrimination, underscoring the need for prospective validation and integration of longitudinal and interventional signals to advance precision neuro-oncology.

Xijin Chen, Pavel Mozgunov, Richard D. Baird et al.

Dose-finding trials for oncology studies are traditionally designed to assess safety in the early stages of drug development. With the rise of molecularly targeted therapies and immuno-oncology compounds, biomarker-driven approaches have gained significant importance. In this paper, we propose a novel approach that incorporates multiple values of a predictive biomarker to assist in evaluating binary toxicity outcomes using the factorization of a joint model in phase I dose-finding oncology trials. The proposed joint model framework, which utilizes additional repeated biomarker values as an early predictive marker for potential toxicity, is compared to the likelihood-based continual reassessment method (CRM) using only binary toxicity data, across various dose-toxicity relationship scenarios. Our findings highlight a critical limitation of likelihood-based approaches in early-phase dose-finding studies with small sample sizes: estimation challenges that have been previously overlooked in the phase I dose-escalation setting. We explore potential remedies to address these challenges and emphasize the appropriate use of likelihood-based methods. Simulation results demonstrate that the proposed joint model framework, by integrating biomarker information, can alleviate estimation problems in the the likelihood-based continual reassessment method (CRM) and improve the proportion of correct selection. However, we highlight that the inherent data limitations in early-phase dose-finding studies remain a significant challenge that cannot fully be overcomed in the frequentist framework.

Moinak Bhattacharya, Gagandeep Singh, Prateek Prasanna

Accurate prediction of tumor trajectories under standard-of-care (SoC) therapies remains a major unmet need in oncology. This capability is essential for optimizing treatment planning and anticipating disease progression. Conventional reaction-diffusion models are limited in scope, as they fail to capture tumor dynamics under heterogeneous therapeutic paradigms. There is hence a critical need for computational frameworks that can realistically simulate SoC interventions while accounting for inter-patient variability in genomics, demographics, and treatment regimens. We introduce Standard-of-Care Digital Twin (SoC-DT), a differentiable framework that unifies reaction-diffusion tumor growth models, discrete SoC interventions (surgery, chemotherapy, radiotherapy) along with genomic and demographic personalization to predict post-treatment tumor structure on imaging. An implicit-explicit exponential time-differencing solver, IMEX-SoC, is also proposed, which ensures stability, positivity, and scalability in SoC treatment situations. Evaluated on both synthetic data and real world glioma data, SoC-DT consistently outperforms classical PDE baselines and purely data-driven neural models in predicting tumor dynamics. By bridging mechanistic interpretability with modern differentiable solvers, SoC-DT establishes a principled foundation for patient-specific digital twins in oncology, enabling biologically consistent tumor dynamics estimation. Code will be made available upon acceptance.

H. Feng, Q. Mei

Stephen V. Liu, Anandaroop Dasgupta, Dominick Latremouille-Viau et al.

PURPOSEAnti–PD-1/PD(L)1-based combination therapy is the standard of care in first line (1L) for metastatic nonsquamous non–small cell lung cancer (mnsqNSCLC) without driver alterations. This study aimed to evaluate real-world clinical outcomes in this population.METHODSEligible physicians in the United States, Europe, and Japan abstracted information from medical charts of eligible adult patients with mnsqNSCLC (without EGFR/ALK, no known ROS1 alterations) who initiated 1L anti–PD(L)1-based combination therapy for mnsqNSCLC between 2017 and 2021. Kaplan-Meier analyses were used to assess overall survival (OS), time-to-treatment discontinuation (TTD), and real-world progression-free survival (rwPFS) after 1L initiation.RESULTSOverall, 142 physicians contributed deidentified data from 430 patients' medical charts. The distribution of PD-L1 expression levels was 31.2% with tumor proportion score (TPS) <1%, 42.3% with TPS 1%-49%, and 26.5% with TPS ≥50%. In 1L, patients received anti-PD(L)1 + chemotherapy (84.6%), anti-PD(L)1 + anti-CTLA4 with or without chemotherapy (11.9%), and anti-PD(L)1 + chemotherapy + anti-vascular endothelial growth factor receptor (3.5%). The median OS was 21.7 months (TPS <1%: 18.3 months; TPS 1%-49%: 21.6 months; TPS ≥50%: 24.0 months). The median TTD was 11.0 months (TPS <1%: 9.1 months; TPS 1%-49%: 10.9 months; TPS ≥50%: 12.2 months). The median rwPFS was 11.2 months (TPS <1%: 9.3 months; TPS 1%-49%: 11.1 months; TPS ≥50%: 13.2 months).CONCLUSIONThis study assessed the real-world clinical effectiveness of 1L anti–PD(L)1-based combination therapy for mnsqNSCLC. Results from this study were generally consistent with previous clinical trials and published real-world evidence in 1L mnsqNSCLC.

Elio Ibrahim, Ernest Diab, Rony Hayek et al.

Triple-negative breast cancer (TNBC) is a breast malignancy with a poor prognosis and limited therapeutic options. Many studies show that TNBC exhibits heterogeneity across clinical, histopathological, and molecular levels. In this review, we discuss the immunogenic features of TNBC with a focus on immunotherapy and the current standard of care in the neoadjuvant, adjuvant, and metastatic setting. In addition, we address the ongoing research on immunotherapy, antibody-drug conjugates (ADCs), poly ADP-ribose polymerase (PARP) inhibitors, and future challenges in the treatment of this entity.

WU Xiaoyan, WANG Sujie, WANG Fang et al.

Objective To analyze the clinicopathological characteristics and prognosis of oligodendroglioma with IDH mutation and 1p/19q codeletion. Methods We collected the data of 54 oligodendroglioma patients with IDH mutation and 1p/19q codeletion.The patients'clinicopathological data, including age, histological grade, and tumor site, were analyzed for the effects on progression-free and overall survival. Results Among the 54 patients, 46 cases were with tumor sites in one lobe, and eight cases involved tumor sites in more than two lobes.A total of 12 and 42 cases had WHO grades 2 and 3 oligodendroglioma, respectively.Detection by fluorescence in situ hybridization showed 1p/19q co-deletion in all cases.Immunohistochemical tests revealed diffuse and strong positive results for Olig2.All glial fibrillary acidic proteins were positive.p53 was strongly positive in six cases.ATRX was expressed in all 48 cases.Ki-67 proliferation index ranged from 5% to 60%.Sanger sequencing showed that all 54 cases had IDH gene mutations (40 cases were IDH1 mutations, and 14 were IDH2 mutations), and 33 cases had telomerase reverse transcriptase promoter mutations.Relapse and metastasis occurred in 16 patients during treatment.Univariate analysis indicated that the postoperative recurrence and metastasis interval of more than two years can prolong the progression-free and overall survival of patients.All 54 patients had a mean progression-free survival of 33.5 months and the mean overall survival of 40.7 months. Conclusion For oligodendroglioma with IDH mutation and 1p/19q codeletion, precision chemoradiotherapy after surgery can reduce the risk of progression, and the postoperative recurrence and metastasis interval is associated with the prognosis.

Fuquan Wang, Fuquan Wang, Lei Chen et al.

PurposeThe purpose of this retrospective study was to compare the therapeutic efficacy and safety of drug-eluting bead transarterial chemoembolization (DEB-TACE) combined with systemic therapy to systemic therapy alone as first-line treatment for unresectable patients with colorectal liver metastases (CRLM).MethodsFrom December 2017 to December 2022, patients with unresectable CRLM who received systemic therapy with or without DEB-TACE as first-line treatment were included in the study. The primary endpoint was progression-free survival (PFS). Secondary endpoints were tumor response, conversion rate and adverse events.ResultsNinety-eight patients were enrolled in this study, including 46 patients who received systemic therapy combined with DEB-TACE (DEB-TACE group) and 52 patients who received systemic therapy alone (control group). The median PFS was elevated in the DEB-TACE group compared with the control group (12.1 months vs 8.4 months, p = 0.008). The disease control rate was increased in the DEB-TACE group compared with the control group (87.0% vs 67.3%, p = 0.022). Overall response rates (39.1% vs 25.0%; p = 0.133) and conversion rate to liver resection (33.8% vs 25.0%; p = 0.290) were no different between the two groups. The multivariate analysis showed that treatment options, size of liver metastasis, number of liver metastasis, synchronous metastases, and extrahepatic metastases were independent prognostic factor of PFS. Further subgroup analyses illustrated that PFS was beneficial with the DEB-TACE group in patients with age ≥ 60, male, left colon, synchronous metastases, bilobar, number of liver metastasis &gt; 5, extrahepatic metastases, non-extrahepatic metastases, CEA level &lt; 5 (ng/ml), and KRAS wild-type. No grade 4 or 5 toxicities related to DEB-TACE procedures were observed.ConclusionIn patients with unresectable CRLM, systemic chemotherapy with DEB-TACE as first-line treatment may improve progression-free survival and disease control rate outcomes over systemic chemotherapy alone with manageable safety profile.

Ziwei Yang, Rikuto Kotoge, Xihao Piao et al.

Framing the investigation of diverse cancers as a machine learning problem has recently shown significant potential in multi-omics analysis and cancer research. Empowering these successful machine learning models are the high-quality training datasets with sufficient data volume and adequate preprocessing. However, while there exist several public data portals, including The Cancer Genome Atlas (TCGA) multi-omics initiative or open-bases such as the LinkedOmics, these databases are not off-the-shelf for existing machine learning models. In this paper, we introduce MLOmics, an open cancer multi-omics database aiming at serving better the development and evaluation of bioinformatics and machine learning models. MLOmics contains 8,314 patient samples covering all 32 cancer types with four omics types, stratified features, and extensive baselines. Complementary support for downstream analysis and bio-knowledge linking are also included to support interdisciplinary analysis.

Guy Heger, Mirta Dumančić, Ishai Luz et al.

Alpha-DaRT is a new method for treating solid tumors with alpha particles, relying on the release of the alpha-emitting daughter atoms of radium-224 from sources inserted into the tumor. The most important model parameters for Alpha-DaRT dosimetry are the diffusion lengths of radon-220 and lead-212, and their estimation is essential for treatment planning. The aim of this work is to provide first experimental estimates for the diffusion length of radon-220. The diffusion length of radon-220 was estimated from autoradiography measurements of histological sections taken from 24 mice-borne subcutaneous tumors of five different types. Experiments were done in two sets: fourteen in-vivo tumors, where during the treatment the tumors were still carried by the mice with active blood supply, and ten ex-vivo tumors, where the tumors were excised before source insertion and kept in a medium at 37 degrees C with the source inside. The measured diffusion lengths of radon-220 lie in the range 0.25-0.6 mm, with no significant difference between the average values measured in in-vivo and ex-vivo tumors: 0.40 $\pm$ 0.08 mm for in-vivo vs. 0.39 $\pm$ 0.07 mm for ex-vivo. However, in-vivo tumors display an enhanced spread of activity 2-3 mm away from the source. This effect is not explained by the current model and is much less pronounced in ex-vivo tumors. The average measured radon-220 diffusion lengths in both in-vivo and ex-vivo tumors lie close to the upper limit of the previously estimated range of 0.2-0.4 mm. The observation that close to the source there is no apparent difference between in-vivo and ex-vivo tumors, and the good agreement with the theoretical model in this region suggest that the spread of radon-220 is predominantly diffusive in this region. The departure from the model prediction in in-vivo tumors at large radial distances may hint at potential vascular contribution.

Aydah Al-Awadhi, Humaid O. Al-Shamsi

AbstractNeuroendocrine tumors (NETs), despite their increasing incidence, are considered to be rare tumors. There is no published data about the incidence or prevalence of NETs in the UAE. In a survey in 2021 for oncologists in the UAE, 43 respondents completed the survey. Thirty-one respondents (72.1%) had active patients with neuroendocrine tumors at the time of the survey. Thirty-one respondents (73.8%) indicated that GI NET was the most common NET in their practice. Six respondents (14.3%) selected lung and two (4.8%) selected gynecological NETs as the most common NETs in their practices. This is the first study to address the potential burden of NETs in the UAE. More education for family physicians, endocrinologists, and gastroenterologists in the UAE is needed to facilitate early diagnosis.

Kirsten Rose Carlaw, Ahmer Hameed, Anthony Shakeshaft

Adrenocorticotropic (ACTH)-producing neuroendocrine tumours (NETs) are rarely found in the small bowel, and primary mesenteric NETs have only been reported in a few cases globally. We report the case of a 68-year-old female with ectopic Cushing’s syndrome due to excessive ACTH secretion from small bowel primary lesions and mesenteric metastasis. Initially, only the mesenteric mass was detected on imaging and endoscopy/colonoscopy, and it was only with surgical exploration that the small bowel lesions were found. This highlights the importance of high clinical suspicion and robust investigation when locating NETs. Surgical resection of the affected small bowel and mesentery was the definitive treatment for this patient. Initial hydrocortisone replacement therapy was needed, and subsequent biochemical tests and clinical reviews demonstrated no recurrence.

Zhen Li, Binglei Zhang, Xinxin Fan et al.

BackgroundHematopoietic stem cell transplantation (HSCT) is an important treatment for T-cell lymphoblastic lymphoma/leukemia (T-LBL). To compare the efficacy and influencing factors of autologous hematopoietic stem cell transplantation (auto-HSCT) with those of allogeneic hematopoietic stem cell transplantation (allo-HSCT) from different donors for the treatment of T-cell lymphoblastic lymphoma/leukemia (T-LBL) and provide a basis for selection of appropriate transplant methods and donors.MethodsTo provide evidence of appropriate transplant methods for these patients, we retrospectively summarized the clinical characteristics of 75 T-LBL patients receiving HSCT at Henan Cancer Hospital between March 2012 and October 2021. Overall survival (OS), progression-free survival (PFS), cumulative incidence of relapse (CIR), non-relapse mortality (NRM), and related factors affecting efficacy were analyzed.ResultsThe 3-year CIR (39.9% vs 31.1%, P=0.745), 3-year PFS (60.1% vs 49.6%, P=0.434), and 3-year OS (62.8% vs 53.0%, P=0.450) were not significantly different between the auto-HSCT and allo-HSCT groups. However, the 3-year NRM was significantly higher in the allo-HSCT group (0% vs 27.2%, P=0.033). Multivariate analysis showed that the first complete remission (CR1) after HSCT was an independent influencing factor of higher OS (HR=2.498, P=0.029) and PFS (HR=2.576, P=0.016). The absence of mediastinal invasion in patients receiving HSCT was an independent influencing factor of better PFS (HR=2.977, P=0.029) and lower CIR (HR=4.040, P=0.027). With respect to the impact of donor source, the NRM in the unrelated donor (URD) and haploid donor (HPD) groups was significantly higher than that in the auto-HSCT group (P=0.021 and P=0.003, respectively), while there was no significant difference between matched sibling donors (MSD) and auto-HSCT. Compared with the MSD-HSCT group, the auto-HSCT group showed an increasing trend in 3-year CIR (39.9 ± 11.1% vs 32.6 ± 11.2%, P=0.697) and a lower trend in 3-year OS (62.8 ± 11.4% vs 64.4 ± 12.2%, P=0.929).ConclusionsHSCT is an effective consolidation treatment option for patients with T-LBL without mediastinal invasion and with CR1 before transplantation. For CR1 patients, auto-HSCT and MSD-HSCT are effective modalities for improving survival. In non-CR1 patients without an MSD, matched unrelated donors and haploidentical donor transplantations are the best treatment options to reduce relapse and improve prognosis.

Guoliang Sun, Hui Zhou, Ke Chen et al.

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1186/s13046-020-01606-x.

Erik Blom, Stefan Engblom

The landscape of computational modeling in cancer systems biology is diverse, offering a spectrum of models and frameworks, each with its own trade-offs and advantages. Ideally, models are meant to be useful in refining hypotheses, to sharpen experimental procedures and, in the longer run, even for applications in personalized medicine. One of the greatest challenges is to balance model realism and detail with experimental data to eventually produce useful data-driven models. We contribute to this quest by developing a transparent, highly parsimonious, first principles silico model of a growing avascular tumor. We initially formulate the physiological considerations and the specific model within a stochastic cell-based framework. We next formulate a corresponding mean-field model using partial differential equations which is amenable to mathematical analysis. Despite a few notable differences between the two models, we are in this way able to successfully detail the impact of all parameters in the stability of the growth process and on the eventual tumor fate of the stochastic model. This facilitates the deduction of Bayesian priors for a given situation, but also provides important insights into the underlying mechanism of tumor growth and progression. Although the resulting model framework is relatively simple and transparent, it can still reproduce the full range of known emergent behavior. We identify a novel model instability arising from nutrient starvation and we also discuss additional insight concerning possible model additions and the effects of those. Thanks to the framework's flexibility, such additions can be readily included whenever the relevant data become available.

Ali Firooz, Avery T. Funkhouser, Julie C. Martin et al.

Nuanced cancer patient care is needed, as the development and clinical course of cancer is multifactorial with influences from the general health status of the patient, germline and neoplastic mutations, co-morbidities, and environment. To effectively tailor an individualized treatment to each patient, such multifactorial data must be presented to providers in an easy-to-access and easy-to-analyze fashion. To address the need, a relational database has been developed integrating status of cancer-critical gene mutations, serum galectin profiles, serum and tumor glycomic profiles, with clinical, demographic, and lifestyle data points of individual cancer patients. The database, as a backend, provides physicians and researchers with a single, easily accessible repository of cancer profiling data to aid-in and enhance individualized treatment. Our interactive database allows care providers to amalgamate cohorts from these groups to find correlations between different data types with the possibility of finding "molecular signatures" based upon a combination of genetic mutations, galectin serum levels, glycan compositions, and patient clinical data and lifestyle choices. Our project provides a framework for an integrated, interactive, and growing database to analyze molecular and clinical patterns across cancer stages and subtypes and provides opportunities for increased diagnostic and prognostic power.

Beata Smolarz, Anna Zadrożna-Nowak, Hanna Romanowicz

Long noncoding RNAs (lncRNAs) are the largest groups of ribonucleic acids, but, despite the increasing amount of literature data, the least understood. Given the involvement of lncRNA in basic cellular processes, especially in the regulation of transcription, the role of these noncoding molecules seems to be of great importance for the proper functioning of the organism. Studies have shown a relationship between disturbed lncRNA expression and the pathogenesis of many diseases, including cancer. The present article presents a detailed review of the latest reports and data regarding the importance of lncRNA in the development of cancers, including breast carcinoma.