MALAT1 rs619586 as a potential genetic marker of pituitary adenoma susceptibility and aggressiveness
Martyna Juskiene, Monika Duseikaite-Vidike, Alvita Vilkeviciute, Ieva Baikstiene, Jurgita Makstiene, Lina Poskiene, Arimantas Tamasauskas, Rasa Verkauskiene, Rasa Liutkeviciene, Birute Zilaitiene
TL;DR
This study identifies a genetic variant in MALAT1 that increases the risk and aggressiveness of pituitary adenomas.
Contribution
The MALAT1 rs619586 variant is newly identified as a potential genetic marker for pituitary adenoma susceptibility and aggressiveness.
Findings
The MALAT1 rs619586 G allele increases the odds of developing pituitary adenomas by 4.1-fold.
The rs619586 G allele is associated with more aggressive features like invasiveness and recurrence in pituitary adenomas.
Higher p53 expression is observed in pituitary adenoma tissues with the rs619586 AA genotype.
Abstract
Pituitary adenomas are slow-growing tumors that originate from the anterior part of the pituitary gland. These tumors are associated with dysregulation of a number of long non-coding RNAs (lncRNAs). Metastasis-associated lung adenocarcinoma transcript-1 (MALAT-1) is a long non-coding RNA (lncRNA) that has been implicated in the regulation of cell proliferation, gene expression, apoptosis, differentiation, and cell cycle transition in various tumors, including pituitary adenomas (PA). To evaluate the impact of MALAT1 gene variants (rs3200401, rs619586, and rs1194338) and immunohistochemical markers (Ki-67 and p53) on the susceptibility and clinical characteristics of PA. a case-control study included patients with PA and age- and gender-matched controls. PA diagnosis was confirmed through MRI/CT imaging and/or histopathological examination. DNA was extracted from peripheral blood…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Figure 1
Figure 2| Characteristics | Group | ||
|---|---|---|---|
| PA, n (%) (n = 145) | Control, n (%) (n = 245) | ||
| Age median (IQR) | 53 (20) | 55 (22) | 0.600* |
| Gender, n % | 0.989** | ||
| Tumor size, n (%) | – | – | |
| Hormonal activity, n (%) | – | – | |
| Invasiveness, n (%) | – | – | |
| Recurrence, n (%) | – | – | |
| Gene | Genotype/Allele | PA group n (%) (n=145) | Control group n (%) (n=245) | HWE | |
|---|---|---|---|---|---|
| CC | 91 (62.8) | 145 (59.2) | 0.629 | 0.592 | |
| CA | 48 (33.1) | 85 (34.7) | |||
| AA | 6 (4.1) | 15 (6.1) | |||
| In total: | 145 (100) | 245 (100) | |||
| Allele: | |||||
| AA | 135 (93.1) | 241 (98.4) | 0.017 | 0.897 | |
| AG | 8 (5.5) | 4 (1.6) | |||
| GG | 2 (1.4) | 0 (0) | |||
| In total: | 145 (100) | 245 (100) | |||
| Allele: |
| ||||
| CC | 101 (69.7) | 175 (71.4) | 0.770 | 0.213 | |
| CT | 40 (27.6) | 61 (24.9) | |||
| TT | 4 (2.8) | 9 (3.7) | |||
| In total: | 145 (100) | 245 (100) | |||
| Allele: |
| Model | Genotype/Allele | OR (95% CI) | AIC | |
|---|---|---|---|---|
| Codominant | CA vs. CC | 0.900 (0.579-1.398) | 0.638 | 517.774 |
| Dominant | CA+AA vs. CC | 0.860 (0.564-1.312) | 0.485 | 516.236 |
| Recessive | AA vs. CC+CA | 0.662 (0.251-1.746) | 0.404 | 515.995 |
| Overdominant | CA vs. CC+AA | 0.931 (0.603-1.438) | 0.749 | 516.622 |
| Additive | A | 0.853 (0.601-1.210) | 0.374 | 515.925 |
| Model | Genotype/Allele | OR (95% CI) | AIC | |
| Codominant | AG vs. AA | 3.570 (1.056-12.076) | 0.041 | 510.231 |
| Dominant | AG+GG vs. AA | 4.463 (1.373-14.503) | 0.013 | 509.706 |
| Recessive | GG vs. AA+AG | – | – | 512.750 |
| Overdominant | AG vs. AA+GG | 3.518 (1.040-11.898) | 0.043 | 512.309 |
| Additive | G | 4.139 (1.365-12.551) |
|
|
| Model | Genotype/Allele | OR (95% CI) | AIC | |
| Condominant | CT vs. CC | 1.136 (0.712-1.814) | 0.593 | 518.198 |
| Dominant | CT+TT vs. CC | 1.089 (0.695-1.707) | 0.710 | 516.587 |
| Recessive | TT vs. CC+CT | 0.744 (0.225-2.460) | 0.628 | 516.482 |
| Overdominant | CT vs. CC+TT | 1.149 (0.722-1.830) | 0.558 | 516.348 |
| Additive | T | 1.030 (0.703-1.511) | 0.878 | 516.702 |
| Gene | Genotype/Allele | PA group males (n=65) n (%) | Control group males (n=110) n (%) | |
|---|---|---|---|---|
| CC | 45 (69.2) | 66 (60) | 0.467 | |
| CA | 18 (27.7) | 39 (35.5) | ||
| AA | 2 (3.1) | 5 (4.5) | ||
| In total: | 65 (100) | 110 (100) | ||
| Allele: | ||||
| AA | 60 (92.3) | 108 (98.2) | 0.098 | |
| AG | 3 (4.6) | 2 (1.8) | ||
| GG | 2 (3.1) | 0 (0) | ||
| In total: | 65 (100) | 110 (100) | ||
| Allele: |
| |||
| CC | 49 (75.4) | 82 (74.5) | 0.399 | |
| CT | 16 (24.6) | 25 (22.7) | ||
| TT | 0 (0) | 3 (2.7) | ||
| In total: | 65 (100) | 110 (100) | ||
| Allele: |
| Gene | Genotype/Allele | Control group (n=245) n (%) | Micro PA (n=45) n (%) | Macro PA (n=100) n (%) | ||
|---|---|---|---|---|---|---|
| CC | 145 (59.2) | 33 (73.3) | 0.174 | 58 (58) | 0.870 | |
| CA | 85 (34.7) | 11 (24.4) | 37 (37) | |||
| AA | 15 (6.1) | 1 (2.2) | 5 (5) | |||
| In total: | 245 (100) | 45 (100) | 100 (100) | |||
| Allele: | | | | | | |
| AA | 241 (98.4) | 41 (91.1) |
| 94 (94) | 0.034 | |
| AG | 4 (1.6) | 4 (8.9) | 4 (4) | |||
| GG | 0 (0) | 0 (0) | 2 (2) | |||
| In total: | 245 (100) | 45 (100) | 100 (100) | |||
| Allele: | | | | | | |
| CC | 175 (71.4) | 33 (73.3) | 0.909 | 68 (68) | 0.486 | |
| CT | 61 (24.9) | 10 (22.2) | 30 (30) | |||
| TT | 9 (3.7) | 2 (4.4) | 2 (2) | |||
| In total: | 245 (100) | 45 (100) | 100 (100) | |||
| Allele: | | | | | |
| Model | Genotype/Allele | OR (95% CI) | AIC | |
|---|---|---|---|---|
| Micro PA | ||||
| Codominant | CA vs. CC | 0.569 (0.273-1.184) | 0.131 | 250.524 |
| Dominant | CA+AA vs. CC | 0.527 (0.260-1.070) | 0.076 | 248.965 |
| Recessive | AA vs. CC+CA | 0.348 (0.045-2.706) | 0.313 | 250.950 |
| Overdominant | CA vs. CC+AA | 0.609 (0.294-1.262) | 0.182 | 250.431 |
| Additive | A | 0.559 (0.301-1.039) | 0.066 | 248.531 |
| Macro PA | ||||
| Codominant | CA vs. CC AA vs. CC | 1. 088 (0.666-1.779) | 0.736 | 419.113 |
| Dominant | CA+AA vs. CC | 1.050 (0.655-1.683) | 0.839 | 417.354 |
| Recessive | AA vs. CC+CA | 0.807 (0.285-2.283) | 0.686 | 417.227 |
| Overdominant | CA vs. CC+AA | 1.106 (0.682-1.793) | 0.684 | 417.231 |
| Additive | A | 1.002 (0.682-1.472) | 0.993 | 417.395 |
| Model | Genotype/Allele | OR (95% CI) | AIC | |
| Micro PA | ||||
| Codominant | AG vs. AA | 5.878 (1.414-24.438) | 0.015 | 248.941 |
| Dominant | AG+GG vs. AA | 5.878 (1.414-24.438) | 0.015 | 246.941 |
| Recessive | GG vs. AA+AG | – | – | 252.315 |
| Overdominant | AG vs. AA+GG | 5.878 (1.414-24.438) | 0.015 | 246.941 |
| Additive | G | 5.878 (1.414-24.438) |
|
|
| Macro PA | ||||
| Codominant | AG vs. AA | 2.564 (0.628-10.462) | 0.189 | 412.746 |
| Dominant | AG+GG vs. AA | 3.846 (1.061-13.935) | 0.040 | 413.116 |
| Recessive | GG vs. AA+AG | – | – | 412.413 |
| Overdominant | AG vs. AA+GG | 2.510 (0.615-10.241) | 0.199 | 415.799 |
| Additive | G | 3.601 (1.153-11.245) | 0.027 | 411.696 |
| Micro PA | ||||
| Model | Genotype/Allele | OR (95% CI) | AIC | |
| Codominant | CT vs. CC TT vs. CC | 0.869 (0.404-1.869) | 0.720 | 254.125 |
| Dominant | CT+TT vs. CC | 0.909 (0.444-1.861) | 0.794 | 252.246 |
| Recessive | TT vs. CC+CT | 1.220 (0.255-5.840) | 0.804 | 252.255 |
| Overdominant | CT vs. CC+TT | 0.862 (0.403-1.843) | 0.701 | 252.165 |
| Additive | T | 0.962 (0.532-1.740) | 0.897 | 252.298 |
| Macro PA | ||||
| Codominant | CT vs. CC TT vs. CC | 1.266 (0.753-2.127) | 0.374 | 417.909 |
| Dominant | CT+TT vs. CC | 1.176 (0.711-1.946) | 0.527 | 416.998 |
| Recessive | TT vs. CC+CT | 0.535 (0.114-2.522) | 0.429 | 416.691 |
| Overdominant | CT vs. CC+TT | 1.293 (0.771-2.167) | 0.330 | 416.458 |
| Additive | T | 1.063 (0.690-1.639) | 0.781 | 417.319 |
| Gene | Genotype/Allele | Control group (n=245) n (%) | Non- invasive PA (n=70) n (%) | Invasive PA (n=75) n (%) | ||
|---|---|---|---|---|---|---|
| CC | 145 (59.2) | 46 (65.7) | 0.443 | 45 (60) | 0.967 | |
| CA | 85 (34.7) | 22 (31.4) | 26 (34.7) | |||
| AA | 15 (6.1) | 2 (2.9) | 4 (5.3) | |||
| In total: | 245 (100) | 70 (100) | 75 (100) | |||
| Allele: | | | | | | |
| AA | 241 (98.4) | 66 (94.3) | 0.070 | 69 (92) |
| |
| AG | 4 (1.6) | 3 (4.3) | 5 (6.7) | |||
| GG | 0 (0) | 1 (1.4) | 1 (1.3) | |||
| In total: | 245 (100) | 70 (100) | 75 (100) | |||
| Allele: | | |
| |
| |
| CC | 175 (71.4) | 49 (70) | 0.558 | 52 (69.3) | 0.940 | |
| CT | 61 (24.9) | 20 (28.6) | 20 (26.7) | |||
| TT | 9 (3.7) | 1 (1.4) | 3 (4) | |||
| In total: | 245 (100) | 70 (100) | 75 (100) | |||
| Allele: | | | | | |
| Non-invasive PA | ||||
|---|---|---|---|---|
| Model | Genotype/Allele | OR (95% CI) | AIC | |
| Codominant | CA vs. CC AA vs. CC | 0.816 (0.459-1.449) | 0.487 | 335.925 |
| Dominant | CA+AA vs. CC | 0.757 (0.434-1.318) | 0.325 | 334.730 |
| Recessive | AA vs. CC+CA | 0.451 (0.101-2.021) | 0.298 | 334.413 |
| Overdominant | CA vs. CC+AA | 0.863 (0.488-1.524) | 0.611 | 335.454 |
| Additive | A | 0.748 (0.467-1.198) | 0.227 | 334.194 |
| Invasive PA | ||||
| Codominant | CA vs. CC AA vs. CC | 0.986 (0.567-1.712) | 0.959 | 352.418 |
| Dominant | CA+AA vs. CC | 0.967 (0.570-1.638) | 0.900 | 350.470 |
| Recessive | AA vs. CC+CA | 0.864 (0.278-2.686) | 0.800 | 350.420 |
| Overdominant | CA vs. CC+AA | 0.999 (0.580-1.720) | 0.997 | 350.486 |
| Additive | A | 0.957 (0.623-1.470) | 0.841 | 350.445 |
| Gene | Genotype/Allele | Control group (n=245) n (%) | Non- active PA (n=66) n (%) | Active PA (n=79) n (%) | ||
|---|---|---|---|---|---|---|
| CC | 145 (59.2) | 44 (66.7) | 0.431 | 47 (59.5) | 0.939 | |
| CA | 85 (34.7) | 20 (30.3) | 28 (35.4) | |||
| AA | 15 (6.1) | 2 (3) | 4 (5.1) | |||
| In total: | 245 (100) | 66 (100) | 79 (100) | |||
| Allele: | | | | | | |
| AA | 241 (98.4) | 61 (92.4) | 0.020 | 74 (93.7) | 0.048 | |
| AG | 4 (1.6) | 4 (6.1) | 4 (5.1) | |||
| GG | 0 (0) | 1 (1.5) | 1 (1.3) | |||
| In total: | 245 (100) | 66 (100) | 79 (100) | |||
| Allele: | | |
| |
| |
| CC | 175 (71.4) | 52 (78.8) | 0.421 | 49 (62) | 0.264 | |
| CT | 61 (24.9) | 13 (19.7) | 27 (34.2) | |||
| TT | 9 (3.7) | 1 (1.5) | 3 (3.8) | |||
| In total: | 245 (100) | 66 (100) | 79 (100) | |||
| Allele: | | | | | |
| Gene | Genotype/Allele | Control group (n=245) n (%) | PA without recurrence (n=116) n (%) | PA with recurrence (n=29) n (%) | ||
|---|---|---|---|---|---|---|
| CC | 145 (59.2) | 76 (65.5) | 0.387 | 15 (51.7) | 0.741 | |
| CA | 85 (34.7) | 36 (31) | 12 (41.4) | |||
| AA | 15 (6.1) | 4 (3.4) | 2 (6.9) | |||
| In total: | 245 (100) | 116 (100) | 29 (100) | |||
| Allele: | ||||||
| AA | 241 (98.4) | 109 (94) | 0.036 | 26 (89.7) |
| |
| AG | 4 (1.6) | 5 (4.3) | 3 (10.3) | |||
| GG | 0 (0) | 2 (1.7) | 0 (0) | |||
| In total: | 245 (100) | 116 (100) | 29 (100) | |||
| Allele: |
|
| ||||
| CC | 175 (71.4) | 82 (70.7) | 0.823 | 19 (65.5) | 0.773 | |
| CT | 61 (24.9) | 31 (26.7) | 9 (31) | |||
| TT | 9 (3.7) | 3 (2.6) | 1 (3.4) | |||
| In total: | 245 (100) | 116 (100) | 29 (100) |
| PA without recurrence | ||||
|---|---|---|---|---|
| Model | Genotype/Allele | OR (95% CI) | AIC | |
| Codominant | CA vs. CC AA vs. CC | 0.808 (0.501-1.304) | 0.383 | 455.338 |
| Dominant | CA+AA vs. CC | 0.763 (0.482-1.209) | 0.249 | 453.979 |
| Recessive | AA vs. CC+CA | 0.548 (0.178-1.688) | 0.294 | 454.107 |
| Overdominant | CA vs. CC+AA | 0.847 (0.528-1.360) | 0.492 | 454.844 |
| Additive | A | 0.767 (0.522-1.129) | 0.179 | 453.466 |
| PA with recurrence | ||||
| Codominant | CA vs. CC AA vs. CC | 1.365 (0.610-3.052) | 0.449 | 188.482 |
| Dominant | CA+AA vs. CC | 1.353 (0.626-2.928) | 0.442 | 186.487 |
| Recessive | AA vs. CC+CA | 1.136 (0.246-5.237) | 0.870 | 187.049 |
| Overdominant | CA vs. CC+AA | 1.329 (0.606-2.911) | 0.478 | 186.578 |
| Additive | A | 1.235 (0.675-2.261) | 0.494 | 186.618 |
| PA without recurrence | ||||
| Model | Genotype/Allele | OR (95% CI) | AIC | |
| Codominant | AG vs. AA GG vs. AA | 2.764 (0.728-10.493) | 0.135 | 450.533 |
| Dominant | AG+GG vs. AA | 3.869 (1.110-13.493) | 0.034 | 450.588 |
| Recessive | GG vs. AA+AG | – | – | 450.756 |
| Overdominant | AG vs. AA+GG | 2.714 (0.715-10.301) | 0.142 | 453.174 |
| Additive | G | 3.620 (1.173-11.177) | 0.025 | 449.315 |
| PA with recurrence | ||||
| Codominant | AG vs. AA GG vs. AA | 6.952 (1.475-32.775) | 0.014 | 184.058 |
| Dominant | AG+GG vs. AA | 6.952 (1.475-32.775) | 0.014 | 182.058 |
| Recessive | GG vs. AA+AG | – | – | 182.058 |
| Overdominant | AG vs. AA+GG | 6.952 (1.475-32.775) | 0.014 | 182.058 |
| Additive | G | 6.952 (1.475-32.775) |
|
|
| PA without recurrence | ||||
| Model | Genotype/Allele | OR (95% CI) | AIC | |
| Codominant | CT vs. CC TT vs. CC | 1.085 (0.654-1.798) | 0.753 | 456.919 |
| Dominant | CT+TT vs. CC | 1.037 (0.637-1.686) | 0.885 | 455.300 |
| Recessive | TT vs. CC+CT | 0.696 (0.185-2.621) | 0.592 | 455.018 |
| Overdominant | CT vs. CC+TT | 1.100 (0.665-1.819) | 0.710 | 455.183 |
| Additive | T | 0.988 (0.652-1.496) | 0.954 | 455.317 |
| PA with recurrence | ||||
| Codominant | CT vs. CC TT vs. CC | 1.359 (0.584-3.163) | 0.477 | 188.580 |
| Dominant | CT+TT vs. CC | 1.316 (0.583-2.971) | 0.509 | 186.648 |
| Recessive | TT vs. CC+CT | 0.937 (0.114-7.669) | 0.951 | 187.071 |
| Overdominant | CT vs. CC+TT | 1.357 (0.587-3.139) | 0.475 | 186.580 |
| Additive | T | 1.202 (0.612-2.360) | 0.594 | 186.799 |
| PA subgroups | p53 H score median (IQR) | |
|---|---|---|
| Micro PA | 16 (18.33) |
|
| Macro PA | 27.34 (32.16) | |
| Non-invasive PA | 22.66 (18.42) | 0.490 |
| Invasive PA | 26.67 (31.72) | |
| Non-active PA | 18.34 (23.65) | 0.272 |
| Active PA | 27.99 (32.33) | |
| PA without recurrence | 26.67 (32.58) | 0.167 |
| PA with recurrence | 15.01 (17.41) |
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Taxonomy
TopicsCancer-related molecular mechanisms research · Pituitary Gland Disorders and Treatments · RNA regulation and disease
Introduction
1
Pituitary adenomas (PAs) are among the most prevalent benign intracranial tumors, originating from the adenohypophyseal cells of the anterior pituitary gland. They constitute approximately 10- 15% of all surgically resected intracranial tumors, making them a significant clinical entity within neuro-oncology and endocrinology (1, 2). Epidemiological studies indicate that PAs are common in the general population, with prevalence rates reaching up to 20% based on autopsy and radiological findings (3). The tumors present a broad spectrum of clinical manifestations, primarily determined by their size and hormonal activity. Microadenomas are defined as tumors smaller than 1cm, often identified incidentally or during evaluation for endocrine dysfunction, while macroadenomas exceed 1 cm and are more likely to cause compressive symptoms such as headaches, visual disturbances, and hypopituitarism. Moreover, PAs are classified based on their hormonal secretion into functioning and non-functioning adenomas. Functioning adenomas secrete excess hormones like prolactin, growth hormone (GH), adrenocorticotropic hormone (ACTH), and others, leading to clinical syndromes such as hyperprolactinemia, acromegaly, and Cushing’s disease. Conversely, non-functioning adenomas may remain asymptomatic until they grow sufficiently large to induce compressive effects (3–5). Despite their benign histology, PAs display a capacity for invasive growth, with approximately 45-55% invading local structures such as the cavernous sinus, sphenoid sinus, or dura mater. This local invasion complicates surgical resection and increases the likelihood of residual disease and recurrence. Residual disease and recurrence are clinically significant, as they often necessitate repeated surgical interventions, long-term medical management, and radiotherapy, thereby increasing patient morbidity, diminishing quality of life, and raising healthcare costs. Accurate assessment of invasiveness and tumor behavior remains challenging, as traditional histopathological markers, like Ki-67 proliferation index and p53 expression, lack sufficient predictive power. Hence, there is a pressing need to identify reliable biomarkers that can facilitate early diagnosis, predict tumor aggressiveness, and guide personalized treatment strategies (6).
Recent advances in molecular biology have revolutionized our understanding of tumorigenesis, emphasizing the pivotal roles of genetic and epigenetic factors beyond classical oncogenes and tumor suppressor genes. Notably, non-coding RNAs (ncRNAs), including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), have emerged as critical regulators of gene expression. These molecules, although not translating into proteins, influence cellular processes such as proliferation, apoptosis, differentiation, and metastasis. In particular, lncRNAs, which are transcripts longer than 200 nucleotides, have been implicated in tumor initiation and progression across various cancer types, including neuroendocrine tumors such as pituitary adenoma, papillary thyroid carcinoma and gastrointestinal neuroendocrine tumors (7–9).
Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) lncRNA, which was initially identified as an oncogene in a study of non-small cell lung cancer (NSCLC), is situated at 11q13 (9). Since its discovery, MALAT1 has contributed significantly to the progression, metastasis, drug resistance, and treatment of cancer, as well as its clinical importance in predicting the tumor metastasis of early-stage cancer, particularly lung cancer (9, 10). Subsequently, overexpression of MALAT1 was also found to be involved in tumor cell proliferation, migration, invasion, and apoptosis in various cancers. In addition, increased expression level of MALAT1 can also be used as a potential biomarker for tumor diagnosis and prognosis, including liver, colorectal, pancreatic, papillary thyroid, renal cancers, and gastrointestinal diffuse large B-cell lymphoma (11–16).
In addition to MALAT1, other molecular markers such as Ki-67, p53 are being explored to improve diagnostic accuracy and prognostic predictions. Ki-67, a nuclear protein associated with cellular proliferation, has been correlated with tumor aggressiveness, although its standalone utility remains limited. Mutations and expression patterns of p53, a tumor suppressor, are also under investigation, yet their roles in PA are not fully clarified. Given the complex molecular landscape of PAs, integrating genetic, epigenetic, and molecular profiles could enhance our ability to stratify tumors based on their invasive potential and risk of recurrence. Specifically, the study of lncRNA gene variants, such as those in MALAT1, may unearth novel insights into tumor biology and facilitate the development of targeted therapies in patients with PA.
Materials and methods
2
Study design
2.1
The research was carried out at the Institute and Department of Endocrinology, the Institute of Neuroscience, the Laboratory of Ophthalmology, and the Department of Pathology of the Lithuanian University of Health Sciences (LUHS) Hospital Kauno Klinikos. Ethical approval was granted by the Kaunas Regional Biomedical Research Ethics Committee (No. BE-2-47, issued on December 25, 2016). All participants were fully informed about the study’s purpose and procedures, and each provided written consent in compliance with ethical research standards.
Study population
2.2
The study group consisted of 145 patients diagnosed with pituitary adenoma (PA), while the control group included 245 healthy individuals. Patient selection was conducted according to predefined inclusion and exclusion criteria. The median age of PA patients was 53 years (IQR = 20), whereas the median age of the control group was 55 years (IQR = 22). Age and gender distributions did not differ significantly between the groups (p > 0.05), and the control group was therefore considered adequately matched to the PA group. The detailed inclusion and exclusion criteria have been described in our previous work (17). Although control subjects were not systematically screened with pituitary imaging, incidental microadenomas are common and usually clinically silent, while clinically significant PAs are rare. Any resulting non-differential misclassification would therefore be expected to attenuate, rather than inflate, the observed genetic associations.
At the available sample size (145 cases and 245 controls) and α=0.016, post-hoc power indicates adequate power (≥80%) only for detecting comparatively large effects (approximately OR ≥ 2 for rs1194338/rs3200401, and OR ≥ 9 for rs619586). Therefore, analyses involving rs619586, particularly subgroup stratifications, should be interpreted as exploratory and require replication in larger cohorts.
All participants were recruited from the same geographic region (Lithuania) and represent a relatively homogeneous Northern European population. Cases and controls were matched by age and sex and were enrolled at the same tertiary referral center. Although ancestry informative markers were not analyzed, all genotype distributions in the control group conformed to Hardy–Weinberg equilibrium, reducing the likelihood of major population stratification effects.
SNV selection criteria
2.3
The MALAT1 SNVs (rs1194338, rs619586, and rs3200401) were chosen because they are the most widely studied MALAT1 variants. Many studies across different cancer types have explored their effects on MALAT1 expression and tumor behavior.
In most malignancies, the rs619586 G allele confers protection: in papillary thyroid cancer it decreases susceptibility and down-regulates MALAT1, reducing proliferation and promoting apoptosis (18); in meningioma, the G allele reduces invasiveness by down-regulating COL5A1 (19); and in neuroblastoma, it is linked to reduced risk and increased NEAT1 expression (20). By contrast, in oral squamous cell carcinoma MALAT1 gene single nucleotide variant rs619586 (AG/GG genotype) is associated with a more advanced stage and larger tumour size (21).
Evidence for rs3200401 is inconsistent: meta-analyses report no overall effect on cancer risk, although a modest increase in colorectal cancer risk has been noted (22), and a Korean study found that CT, CT+TT genotypes increase gastric cancer risk in certain subgroups (23).
Regarding the MALAT1 single nucleotide variant rs1194338, the majority of published studies suggest that this variant confers a protective effect against cancer development and tumor aggressiveness, but not universally across all tumor types. In colorectal cancer, the A allele correlates with lower MALAT1 expression and less advanced disease (24). In hepatocellular carcinoma, carriers of the CA or AA genotype correlate with a lower risk of vascular invasion and severe disease (25).
To our knowledge, however, no previous work has examined these variants in PA. Our study fills this gap by demonstrating that, unlike in several other tumour types where rs619586 is protective, the rs619586 G allele is associated with increased risk and aggressiveness in PA.
Activeness, recurrence, and invasiveness evaluation
2.4
The analysis of all PAs was based on histopathological findings of the tumor and preoperative hormone levels in the blood serum. All 145 subjects were categorized into two groups: hormonally active and inactive PA. The hormonally active PA group was not further subdivided according to specific hormone secretion because the majority of tumors were prolactinomas (i.e., prolactin-secreting pituitary adenomas), and the remaining functioning subtypes were insufficient in number for meaningful subgroup analysis. Since some of the 145 subjects had previously undergone surgery, patients were additionally categorized by recurrence of PA into two groups: PA with and without recurrence. Recurrence was defined as enlargement of a residual tumor or the appearance of a new growth on follow-up magnetic resonance imaging (MRI) after surgical resection. The residual tumor was considered stable if there were no signs of tumor progression on follow-up MRI. Most prolactinomas were surgically treated because of the remaining pressure effects of surrounding structures or because of ineffective medical treatment.
Recurrence was defined radiologically as enlargement of residual tumor tissue or the appearance of new tumor growth on follow-up magnetic resonance imaging after surgical treatment. Due to the retrospective design and limited availability of detailed surgical extent-of-resection data, distinction between true biological recurrence and regrowth related to incomplete resection was not feasible.
Tumor invasiveness was assessed preoperatively using magnetic resonance imaging (MRI) and classified according to the Knosp grading system by an experienced neuroradiologist. Tumors graded as Knosp 3B–4 were considered invasive, whereas grades 0–3A were classified as non-invasive. Although Knosp grading is the standard radiological method for evaluating cavernous sinus invasion and guiding surgical planning, it does not fully capture microscopic or histologically confirmed invasion.
DNA extraction, Genotyping, and Immunohistochemistry
2.5
The genotyping of MALAT1 lncRNA (rs3200401, rs619586, and rs1194338) was performed at the Laboratory of Ophthalmology, Neuroscience Institute, Lithuanian University of Health Sciences (LUHS).
DNA extraction, single-nucleotide variant genotyping, and immunohistochemistry analysis were performed as previously described in our earlier publication (17). In accordance with World Health Organization and European Society of Endocrinology guidelines, immunohistochemical evaluation in our centre routinely includes Ki-67 and p53 as established prognostic markers. Other markers, such as PD-L1 and cyclin D1, are currently under investigation but are not part of our routine diagnostic panel and were therefore not included in the present study.
Genotyping quality control
2.5.1
Genotyping was performed using TaqMan^®^ allelic discrimination assays according to the manufacturer’s instructions. Samples with failed or ambiguous genotype calls were excluded prior to analysis, resulting in a complete call rate for all analyzed variants. Genotype clusters were visually inspected to confirm clear allele separation. Blind duplicate samples were included as an internal quality control measure, with 100% concordance observed between duplicates. Genotype distributions for all variants were consistent with Hardy–Weinberg equilibrium in the control group, supporting genotyping accuracy.
Statistical analysis
2.6
Statistical analysis was performed using the SPSS/W 31 software (Statistical Package for the Social Sciences for Windows, Inc., Chicago, IL, USA). Descriptive variables are presented as absolute numbers and percentages, while continuous data are expressed as medians with interquartile ranges (IQRs). Genotypic and allelic distributions between patients with PA and control subjects were compared using the chi-square test. The relationship between MALAT1 genotypes and the risk of PA development was evaluated through binary logistic regression, with results expressed as odds ratios (ORs) and 95% confidence intervals (CIs). The most suitable genetic model was determined according to the Akaike Information Criterion (AIC), with lower AIC values. For immunohistochemical markers, nonparametric statistical methods were applied. The Mann-Whitney U test was used to compare p53 H-scores across PA subgroups, and the correlation between the Ki-67 LI and p53 H-score was analyzed using Spearman’s rank-order correlation coefficient (ρ). Although analyses were restricted to predefined SNVs and markers based on specific hypotheses, Bonferroni correction was applied, and statistical significance was set at p < 0.016.
Results
3
A case-control study was conducted involving 390 subjects divided into two groups: the control group (n = 245) and a group of PA (n = 145). After forming the groups of subjects, MALAT1 rs1194338, rs619586, and rs3200401 variants were analyzed. The median age of PA patients was 53 years. The patients’ group was later divided into subgroups by the PA’s tumor size, hormonal activity, invasiveness, and recurrence. The median age of the control group was 55 years. The age and gender did not differ between study groups (p > 0.05). The demographic data of the subjects are presented in Table 1.
We examined the genotype and allele frequency distributions of MALAT1 rs1194338, rs619586, and rs3200401 in the PA and the control groups. The analysis showed that the MALAT1 rs619586 G allele is statistically significantly more frequent in PA patients than in the control group subjects (4.1% vs. 0.8%, p = 0.001) (Table 2).
To assess the impact of MALAT1 gene variants on the onset of PA, we conducted a binary logistic regression analysis (Table 3). The statistically significant findings revealed that the G allele of MALAT1 rs619586 increases the odds of developing PA by 4.1-fold under the additive model (OR= 4.139, 95% CI: 1.365-12.551, p = 0.012).
Gender-based analysis
3.1
When stratified by gender, no statistically significant differences in the distribution of MALAT1 genotypes or alleles were observed among female participants (Supplementary Table S1 and Supplementary Table S2). However, in the male subgroup, the analysis showed that the MALAT1 rs619586 G allele was found to be statistically significantly more frequent in patients with PA compared to male controls (5.4% vs. 0.9%, p = 0.010) (Table 4).
Binary logistic regression analysis revealed no statistically significant differences between males with PA and the control group males (all p > 0.016) (Supplementary Table S3).
The overall sex distribution did not differ significantly between PA patients and controls, indicating no clear gender predominance in pituitary adenoma occurrence in this cohort. Although a higher frequency of the rs619586 G allele was observed among male PA patients at the allele level, this finding did not translate into a statistically significant association in corrected regression analyses and should therefore be interpreted with caution.
Associations of MALAT1 (rs1194338, rs619586 and rs3200401) with pituitary adenoma’s tumor size
3.2
PA was divided into Micro PA and Macro PA groups. After evaluating the distribution of genotypes and alleles of MALAT1 rs1194338, rs619586, and rs3200401 variants in Micro/Macro PA and the control groups, statistically significant differences in the distribution of MALAT1 rs619586 genotypes (AA, AG, and GG) were found between Micro PA and controls: 91.1%, 8.9% and 0% vs. 98.4%, 1.6% and 0%, respectively (p=0.006). The same variant G allele was also more frequent in the Micro PA group than in the control group (4.4% vs. 0.8%, p = 0.006). Also, in the Macro PA group, it was found that the MALAT1 rs619586 G allele was more frequent in the Macro PA group than in the control group (4% vs. 0.8%, p = 0.003) (Table 5).
Binary logistic regression analysis revealed that the G allele of MALAT1 rs619586 increases the odds of developing Micro PA by 5.8-fold under the additive model (OR = 5.878, 95% CI: 1.414- 24.438, p = 0.015) (Table 6).
Associations of MALAT1 (rs1194338, rs619586 and rs3200401) with pituitary adenoma’s invasiveness
3.2
Distribution of the genotypes and alleles’ analysis was performed between the non-invasive and invasive PA groups and the control group. After evaluating the distribution of genotypes and alleles of MALAT1 rs1194338, rs619586, and rs3200401 variants in non-invasive/invasive PA and the control groups, the analysis revealed that the MALAT1 rs619586 G allele was statistically significantly more frequent in the non-invasive PA group than in the control group subjects (3.6% vs. 0.8%, p = 0.015). In the invasive PA group, our statistical analysis revealed that the same variant genotype distributions (AA, AG, and GG) differ between the invasive PA and control groups (92%, 6.7%, and 1.3% vs. 98.4%, 1.6%, and 0%, p = 0.013). Also, the analysis revealed that the MALAT1 rs619586 G allele was statistically significantly more frequent in the invasive PA group than in the control group subjects (4.7% vs. 0.8%, p = 0.001) (Table 7).
Binary logistic regression analysis revealed that the G allele of MALAT1 rs619586 increases the odds of developing invasive PA by 4.9-fold under the additive model (OR = 4.910, 95% CI: 1.430- 16.851, p = 0.011) (Table 8).
Associations of MALAT1 (rs1194338, rs619586 and rs320040) with Pituitary Adenomas’ activity
3.3
The frequencies of genotypes and alleles for the selected single nucleotide variants (SNVs) were analyzed within the study groups, stratified by PAs’ activeness. In both the non-active and active PA subgroups, the analysis showed that the MALAT1 rs619586 G allele was statistically significantly more frequent in PA subgroups than in the control group (4.5% vs. 0.8%, p = 0.002; 3.8% vs. 0.8%, p = 0.008, respectively) (Table 9).
Binary logistic regression analysis between the non-active/active PA group and the control group of MALAT1 rs1194338, rs619586, and rs3200401 did not show any statistically significant results (Supplementary Table S4).
Associations of MALAT1 (rs1194338, rs619586 and rs3200401) with Pituitary Adenomas’ recurrence
3.4
All patients with PA were also divided into PA without recurrence and PA with recurrence groups. After evaluating the distribution of genotypes and alleles of MALAT1 rs1194338, rs619586, and rs3200401 variants in PA without recurrence/PA with recurrence and the control group, the analysis revealed that the MALAT1 rs619586 G allele was statistically significantly more frequent in the non-invasive PA group than in the control group subjects (3.9% vs. 0.8%, p = 0.003). In the PA with recurrence group, it was found that the same variant genotype distributions (AA, AG, and GG) differ between the PA with recurrence group and control group (89.7%, 10.3%, and 0% vs. 98.4%, 1.6%, and 0%, p = 0.005). Also, the analysis revealed that the MALAT1 rs619586 G allele was statistically significantly more frequent in the PA with recurrence group than in the control group subjects (5.2% vs. 0.8%, p = 0.005) (Table 10).
Binary logistic regression analysis revealed that the G allele of MALAT1 rs619586 increases the odds of developing PA with recurrence by 6.9-fold under the additive model (OR = 6.952, 95% CI: 1.475-32.775, p = 0.014) (Table 11).
Ki-67 labeling index
3.5
76 PA tissue samples were analyzed. The Ki-67 Labeling Index (LI) was evaluated in 43 females (56.6%) and 33 males (43.4%). The results revealed no statistically significant differences in the Ki-67 LI between females and males (p = 0.079).
Immunohistochemistry for Ki-67 revealed an LI < 1% in 23.7% of patients with PA, a Ki-67 LI 1% in 7.9%, and a Ki-67 LI > 1% in 68.4% of patients. Further analyses revealed no statistical significance concerning tumor size (p=0.199), invasiveness (p=0.160), activity (p=0.207), or recurrence (p=0.853) (Supplementary Table S5). The analysis of the Ki-67 LI with the indicated genetic variations (MALAT1 rs1194338, rs619586, and rs3200401) also revealed no statistically significant results (SupplementaryTable S6).
p53 analysis in PA tissues
3.6
62 PA tissue samples were analyzed for p53. The p53 was evaluated in 32 women (51.6%) and 30 men (48.4%). The results revealed no statistically significant differences in the p53 H-score between women and men (p = 0.916). Immunohistochemistry for p53 revealed that macroadenomas had statistically significantly higher p53 H-score compared to the microadenomas (median (IQR): 27.34 (32.16) vs. 16 (18.33), p = 0.047). Further analyses revealed no statistical significance regarding the PA invasiveness, activity, or recurrence (Table 12).
To assess the association of the MALAT1 rs1194338, rs619586 and rs3200401 variants with p53, the p53 H-score was calculated in different genotype groups. We found no statistical significant results comparing MALAT1 rs1194338 and rs3200401 genotypes with p53 H-score (Supplementary Figure S1 and Supplementary Figure S2). Moreover, PA patients who had the MALAT1 rs619586 AA genotype had statistically significantly higher p53 H-score than patients with AG genotype (median (IQR): 26.33 (28.91) vs. 9.67 (5.84), p = 0.001, respectively) (Mann-Whitney U test was used) (Figure 1).
MALAT1 rs619586 genotype associations with p53 H-score.
Correlation between Ki-67 and p53.
3.7
A nonparametric Spearman’s rank-order correlation was performed to assess the relationship between Ki-67 LI and p53 H-score. The results demonstrated a moderate, statistically significant positive correlation between Ki-67 LI and p53 expression (Spearman’s ρ=0.268, p=0.035, n=62). The 95% CI for the correlation coefficient ranged from 0.012 to 0.491. These findings suggest that increased proliferative activity, as measured by Ki-67, is associated with higher p53 expression in the studied samples. The scatter plot categorizes Ki-67 LI into <1%, 1%, and >1%. It shows that higher Ki-67 levels are generally associated with higher p53 H-scores, particularly in the >1% group, where more variability and elevated H-scores are evident (Figure 2).
Correlation between Ki-67 LI and p53 H-score.
Discussion
4
This study aimed to investigate the involvement of lncRNA MALAT1 gene variants in the pathology of pituitary adenoma and their potential clinical relevance.
Long non-coding RNAs (lncRNAs) have emerged as important regulators of gene expression, chromatin structure, and cellular homeostasis. In pituitary adenomas, lncRNAs modulate fundamental biological processes such as cell proliferation, apoptosis, differentiation and tumour progression, thereby influencing tumour phenotype and aggressiveness (26, 27).
Also known as nuclear enrichment autosomal transcript 2 (NEAT2), MALAT1 was initially identified through subtractive hybridization as one of the transcripts most significantly overexpressed in metastatic non-small cell lung cancer tissues (26). Since then, MALAT1 has become one of the most extensively studied lncRNAs, with accumulating evidence suggesting its multifaceted roles in pituitary adenoma biology (28). The available literature supports the concept that MALAT1 may contribute to tumorogenesis and progression in pituitary adenomas through mechanisms similar to those described in other cancers, such as promoting proliferation, angiogenesis, apoptosis, and epithelial-mesenchymal transition (EMT) (26, 29, 30).
In recent years, studies investigating the role of lncRNA MALAT1 in the pathogenesis of pituitary adenoma development have shown inconsistent results. Li and colleagues evaluated MALAT1 alongside MEG3 and HOTAIR expression in non-functioning pituitary adenoma (NFPAs) and reported no significant difference in MALAT1 expression between tumor and normal pituitary tissue, nor did they correlate with clinicopathological parameters (31). It seems that MALAT1 may exert an anti-cancer effect in NFPAs (32). Lu et al. examined growth-hormone-secreting pituitary adenomas (GHPA) and reported that high H19 expression was associated with tumour invasion, whereas MALAT1 expression did not differ significantly between invasive and non-invasive GHPA (33). Recent work by Ghafouri-Fard et al. analysed lncRNAs in pituitary adenoma tissues and observed a strong positive correlation between PVT1 and MALAT1, suggesting that interactions between lncRNAs may contribute to tumour pathogenesis and highlight the complex regulatory networks in which MALAT1 participates (34).
While several MALAT1 SNVs, including rs619586, have been investigated in lung, colorectal, hepatocellular and other cancers, the evidence that these variants influence risk or progression, often through changes in MALAT1 expression and downstream oncogenic pathways, is inconsistent (35). To date, there is currently no established evidence of direct or consistent association between MALAT1 SNVs and neuroendocrine tumors, particularly PAs, susceptibility or behavior (29, 36).
Our study is the first to demonstrate that the MALAT1 rs6198586 G allele is significantly more frequent in pituitary adenoma patients compared to controls and is associated with tumour aggressiveness, including invasiveness and recurrence. These results contrast with most other reports in other diseases, where the rs619586 allele tends to be protective. It is important to note that rs619586 is located within a non-coding region of the MALAT1 gene, and direct functional evidence demonstrating its impact on MALAT1 expression, splicing, or RNA-binding properties in pituitary cells is currently lacking (28).
Although functional studies in other tumor types have linked rs619586 to altered MALAT1 expression and tumor-related phenotypes, these effects appear to be tissue-specific (18, 21). Therefore, the present findings should be interpreted as evidence of genetic association rather than direct mechanistic causality. Functional studies in pituitary-derived models will be required to elucidate the molecular mechanisms underlying the observed associations.
For example, a meta-analysis of nine case-control studies involving several cancer types found that the rs619586 G allele was associated with reduced overall cancer risk in Asians (odds ratio ≈ 0.87 for the G vs. A allele) (37), and a case-control study of papillary thyroid cancer (PTC) demonstrated that the rs619586 G allele was a protective factor (OR ≈ 0.76) while decreasing.
MALAT1 expression, suppressed cell proliferation and increased apoptosis (18). Similarly, in studies of cerebral tumors, for example meningioma, the rs619586 A>G variant lowers MALAT1 expression and reduces tumor invasiveness (19). In contrast, in oral squamous cell carcinoma (OSCC), the rs619586 AG/GG genotype was associated with higher tumour stage and larger tumours, especially in patients with a betel-nut chewing habit (21). Together, these findings illustrate the tissue-specific nature of rs619586, it is protective or neutral in most cancers, yet linked to aggressiveness in OSCC and, as our data show, in pituitary adenoma.
The current literature suggests that rs3200401 and rs1194338 have cancer type-specific effects, in some cases, and in others are associated with aggressive disease. In advanced lung adenocarcinoma patients, the rs3200401 T allele was associated with better survival (38). Qu Y et al. reported that the T allele increases the risk of esophageal squamous cell carcinoma (39). A Taiwanese prostate cancer cohort showed that male patients with at least one rs1194338 A allele had more than a threefold increased risk of lymph-node metastasis (40). In our study, we did not observe any significant associations between these MALAT1 variants and pituitary adenoma characteristics.
A high Ki-67 LI is generally interpreted as evidence of rapid proliferation and potential invasiveness into surrounding structures (41). Several Ki-67 cut-off values have been proposed to stratify aggressiveness in pituitary adenomas, ranging from 1,5% to 4% (42). Some studies have linked higher Ki−67 indices to invasiveness or recurrence, whereas others have not.
A large retrospective analysis of pituitary adenomas found no significant differences in Ki-67 LI with respect to sex, tumor type, diameter, or invasiveness, although the same study reported an association between higher Ki-67 LI and recurrence (43). In a review involving 28 studies on Ki- 67, 18 studies reported high Ki-67 expression in recurrent adenomas, while the other 10 studies showed no correlation (44). A subsequent meta−analysis concluded that a Ki−67 ≥ 3% warrants closer postoperative surveillance, as these tumors were more likely to recur; however, Ki-67 is not an independent predictor of tumor recurrence. Moreover, factors such as tumor subtype, extent of surgical resection, cavernous sinus invasion and hormonal activity often exert a stronger influence on recurrence than Ki−67 alone (45).
Our findings that Ki-67 LI did not correlate with tumor size, invasiveness, hormonal activity or recurrence align with those studies showing limited prognostic utility of Ki-67. Also there were no significant results between Ki-67 Li and MALAT1 genetic variations.
Normal pituitary tissue expresses little p53, and most pituitary adenomas show only minimal or focal p53 staining. Earlier studies have suggested that tumors with high p53 expression exhibit more frequent tumor progression and cavernous sinus invasion, but they did not assess recurrence (46, 47). Oliveira et al. subsequently reported no correlation between p53 expression and PA recurrence. In their study of 148 pituitary adenomas, only 1.3% were p53-positive, indicating that p53 is insufficient as a routine marker of recurrence (48). Our study demonstrated that macroadenomas had significantly higher p53 H-scores than microadenomas, however, p53 expression was not associated with invasiveness, hormonal activity or recurrence. Also, we found that patients carrying the MALAT1 rs619586 AA genotype had higher p53 H-scores than those with AG genotype (median 26.33 vs. 9.67, p = 0,001). Moreover, p53-positive adenomas generally display a higher Ki-67 compared with p53-negative tumors, which is consistent with previous observations (47). Although p53 H-scores were higher in tumors from patients with the rs619586 AA genotype, this does not contradict the association of the rs619586 G allele with more aggressive clinical features. In pituitary adenomas, increased p53 immunoreactivity often reflects accumulation of dysfunctional or stabilized p53 protein and does not consistently correlate with invasion, recurrence, or proliferative activity (47). Therefore, p53 expression should be interpreted as a context-dependent cellular response rather than a direct indicator of tumor aggressiveness (46, 49). The discordant directions observed between p53 expression and clinical aggressiveness further support the notion that rs619586-associated tumor behavior is unlikely to be mediated directly through p53 signaling.
Despite the valuable insights gained through this exploratory study, limitations undoubtedly exist. Future studies could evaluate these variants in larger PA cohorts, in diverse ethnic groups and functional assays to determine whether they influence tumour susceptibility or behaviour, and functional assays should clarify how these variants modulate MALAT1 expression or its interactions with downstream targets.
Although rs619586 was consistently associated with PA risk and several aggressive clinical features in the present cohort, independent replication in an external population has not yet been performed. Therefore, rs619586 should be regarded as a potential or candidate genetic marker rather than a validated biomarker. Replication in independent, geographically distinct cohorts will be required to confirm these findings and to determine their generalizability.
Limitations
5
Although age and sex were matched between cases and controls, multivariable logistic regression models incorporating clinical factors such as hormone excess, medication use, and treatment history were not applied in the present analysis. This study was designed primarily to evaluate genetic associations, and many clinical variables are intrinsically related to tumor phenotype and post-diagnostic management rather than baseline genetic risk. In addition, the low frequency of certain variants limited the feasibility of stable multivariable modeling.
Given the low number of rs619586 G-allele carriers, odds ratio estimates derived from standard logistic regression, particularly in subgroup analyses, may be affected by sparse data bias and should therefore be interpreted with caution. Future studies in larger cohorts will be required to obtain more stable effect estimates and to delineate the independent and combined effects of genetic and clinical factors on pituitary adenoma risk and behavior.
In addition, no statistically significant associations were observed for p53 H-score or Ki-67 labeling index in several analyses. This may be attributable to the heterogeneous and context-dependent expression of these markers in PAs, as well as limited statistical power after subgroup stratification. Moreover, p53 and Ki-67 reflect downstream cellular responses and proliferative activity, which may not directly mirror underlying genetic susceptibility. Larger studies integrating comprehensive clinicopathological data will be required to better define their relationship with MALAT1 genetic variants.
Another limitation of this study is the lack of ancestry informative markers, which precludes formal testing for subtle population stratification. However, the relatively homogeneous ethnic background of the cohort and Hardy–Weinberg equilibrium in the control group mitigate, though do not eliminate, this concern.
The relatively small recurrence subgroup and the inability to fully distinguish true biological recurrence from regrowth of residual tumor tissue represent additional limitations of the recurrence-related analyses.
Finally, tumor invasiveness was assessed using radiological Knosp grading, which, although clinically standard, has limited sensitivity for detecting microscopic invasion. Integration of surgical and histopathological validation in future studies may therefore improve predictive accuracy.
Conclusions
6
This study highlights the potential role of MALAT1 genetic variants, particularly rs619586, in the susceptibility and clinical behavior of PAs. The rs619586 G allele was found to be significantly more frequent among PA patients compared to controls and remained associated with several aggressive tumor features, including micro- and macroadenoma formation, invasiveness, and recurrence, even after Bonferroni correction. These findings suggest that rs619586 may serve as a molecular biomarker linked to PA development and progression. In addition, a significant association between MALAT1 rs619586 genotypes and p53 expression, along with a positive correlation between p53 and Ki-67, further supports the interplay between genetic and proliferative.
factors in PA pathophysiology. Although other investigated variants (rs1194338 and rs3200401) did not show significant associations, the overall results underscore the importance of lncRNA MALAT1 in pituitary tumorigenesis. Future studies involving larger and more diverse cohorts, as well as functional analyses, are warranted to validate these associations and elucidate the molecular mechanisms linking MALAT1 dysregulation to PA aggressiveness and recurrence.
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